diff --git a/Contributors.md b/Contributors.md
index 48ddf42050..fd29ebd69b 100644
--- a/Contributors.md
+++ b/Contributors.md
@@ -22,6 +22,8 @@ This is a list of current and past contributors to the EODASH project, including
 * [Dorothyrono](https://github.com/Dorothyrono) - **Dorothy Rono** - [Sentinel Hub](https://www.sentinel-hub.at/)
 * [maximlamare](https://github.com/maximlamare) - **Maxim Lamare** - [Sentinel Hub](https://www.sentinel-hub.at/)
 * [Kampen1988](https://github.com/Kampen1988) - **Max Kampen** - [Sentinel Hub](https://www.sentinel-hub.at/)
+* [TonioF](https://github.com/TonioF) - **Tonio Fincke** - [BC](www.brockmann-consult.de)
+* [thomasstorm](https://github.com/thomasstorm) - **Thomas Storm** - [BC](www.brockmann-consult.de)
 
 ESA Data Applications Team leading the scientific content production:
 
@@ -35,5 +37,3 @@ ESA Data Applications Team leading the scientific content production:
 * [aapopescu](https://github.com/aapopescu) - **Anca Anghelea** - [ESA](https://www.esa.int/)
 
 The data populating the RACE Dashboard are a collective effort of a number of industrial and academic partners. A full list of the industrial and institutional contributors can be accessed on the About section of this website: https://race.esa.int 
-
-*in progress*
diff --git a/app/public/css/esa.scss b/app/public/css/esa.scss
index 2a4a9478c8..04570ff903 100644
--- a/app/public/css/esa.scss
+++ b/app/public/css/esa.scss
@@ -1,4 +1,4 @@
-@import url('https://fonts.googleapis.com/css2?family=Open+Sans&display=swap');
+@import url('./opensans.css');
 
 @font-face{
   font-family: 'NotesESABold';
diff --git a/app/public/css/fonts/materialdesignicons/v6/LICENSE b/app/public/css/fonts/materialdesignicons/v6/LICENSE
new file mode 100644
index 0000000000..7a4a3ea242
--- /dev/null
+++ b/app/public/css/fonts/materialdesignicons/v6/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/app/public/css/fonts/materialdesignicons/v6/materialdesignicons-webfont.woff2 b/app/public/css/fonts/materialdesignicons/v6/materialdesignicons-webfont.woff2
new file mode 100644
index 0000000000..207bcdf712
Binary files /dev/null and b/app/public/css/fonts/materialdesignicons/v6/materialdesignicons-webfont.woff2 differ
diff --git a/app/public/css/fonts/opensans/v28/LICENSE b/app/public/css/fonts/opensans/v28/LICENSE
new file mode 100644
index 0000000000..75b52484ea
--- /dev/null
+++ b/app/public/css/fonts/opensans/v28/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQUwaEQbjA.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQUwaEQbjA.woff
new file mode 100644
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diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4iaVQUwaEQbjB_mQ.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4iaVQUwaEQbjB_mQ.woff
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diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4jaVQUwaEQbjB_mQ.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4jaVQUwaEQbjB_mQ.woff
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diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4kaVQUwaEQbjB_mQ.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4kaVQUwaEQbjB_mQ.woff
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diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4saVQUwaEQbjB_mQ.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4saVQUwaEQbjB_mQ.woff
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diff --git a/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4taVQUwaEQbjB_mQ.woff b/app/public/css/fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4taVQUwaEQbjB_mQ.woff
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--- /dev/null
+++ b/app/public/css/fonts/roboto/v29/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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diff --git a/app/public/css/materialdesignicons.min.css b/app/public/css/materialdesignicons.min.css
new file mode 100644
index 0000000000..ec9e237cf9
--- /dev/null
+++ b/app/public/css/materialdesignicons.min.css
@@ -0,0 +1,3 @@
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tent:"\F092E"}.mdi-wifi-strength-outline::before{content:"\F092F"}.mdi-wifi-sync::before{content:"\F16C7"}.mdi-wikipedia::before{content:"\F05AC"}.mdi-wind-turbine::before{content:"\F0DA5"}.mdi-wind-turbine-alert::before{content:"\F19AB"}.mdi-wind-turbine-check::before{content:"\F19AC"}.mdi-window-close::before{content:"\F05AD"}.mdi-window-closed::before{content:"\F05AE"}.mdi-window-closed-variant::before{content:"\F11DB"}.mdi-window-maximize::before{content:"\F05AF"}.mdi-window-minimize::before{content:"\F05B0"}.mdi-window-open::before{content:"\F05B1"}.mdi-window-open-variant::before{content:"\F11DC"}.mdi-window-restore::before{content:"\F05B2"}.mdi-window-shutter::before{content:"\F111C"}.mdi-window-shutter-alert::before{content:"\F111D"}.mdi-window-shutter-open::before{content:"\F111E"}.mdi-windsock::before{content:"\F15FA"}.mdi-wiper::before{content:"\F0AE9"}.mdi-wiper-wash::before{content:"\F0DA6"}.mdi-wiper-wash-alert::before{content:"\F18DF"}.mdi-wizard-hat::before{content:"\F1477"}.mdi-wordpress::before{content:"\F05B4"}.mdi-wrap::before{content:"\F05B6"}.mdi-wrap-disabled::before{content:"\F0BDF"}.mdi-wrench::before{content:"\F05B7"}.mdi-wrench-clock::before{content:"\F19A3"}.mdi-wrench-outline::before{content:"\F0BE0"}.mdi-xamarin::before{content:"\F0845"}.mdi-xml::before{content:"\F05C0"}.mdi-xmpp::before{content:"\F07FF"}.mdi-yahoo::before{content:"\F0B4F"}.mdi-yeast::before{content:"\F05C1"}.mdi-yin-yang::before{content:"\F0680"}.mdi-yoga::before{content:"\F117C"}.mdi-youtube::before{content:"\F05C3"}.mdi-youtube-gaming::before{content:"\F0848"}.mdi-youtube-studio::before{content:"\F0847"}.mdi-youtube-subscription::before{content:"\F0D40"}.mdi-youtube-tv::before{content:"\F0448"}.mdi-yurt::before{content:"\F1516"}.mdi-z-wave::before{content:"\F0AEA"}.mdi-zend::before{content:"\F0AEB"}.mdi-zigbee::before{content:"\F0D41"}.mdi-zip-box::before{content:"\F05C4"}.mdi-zip-box-outline::before{content:"\F0FFA"}.mdi-zip-disk::before{content:"\F0A23"}.mdi-zodiac-aquarius::before{content:"\F0A7D"}.mdi-zodiac-aries::before{content:"\F0A7E"}.mdi-zodiac-cancer::before{content:"\F0A7F"}.mdi-zodiac-capricorn::before{content:"\F0A80"}.mdi-zodiac-gemini::before{content:"\F0A81"}.mdi-zodiac-leo::before{content:"\F0A82"}.mdi-zodiac-libra::before{content:"\F0A83"}.mdi-zodiac-pisces::before{content:"\F0A84"}.mdi-zodiac-sagittarius::before{content:"\F0A85"}.mdi-zodiac-scorpio::before{content:"\F0A86"}.mdi-zodiac-taurus::before{content:"\F0A87"}.mdi-zodiac-virgo::before{content:"\F0A88"}.mdi-blank::before{content:"\F68C";visibility:hidden}.mdi-18px.mdi-set,.mdi-18px.mdi:before{font-size:18px}.mdi-24px.mdi-set,.mdi-24px.mdi:before{font-size:24px}.mdi-36px.mdi-set,.mdi-36px.mdi:before{font-size:36px}.mdi-48px.mdi-set,.mdi-48px.mdi:before{font-size:48px}.mdi-dark:before{color:rgba(0,0,0,0.54)}.mdi-dark.mdi-inactive:before{color:rgba(0,0,0,0.26)}.mdi-light:before{color:#fff}.mdi-light.mdi-inactive:before{color:rgba(255,255,255,0.3)}.mdi-rotate-45:before{-webkit-transform:rotate(45deg);-ms-transform:rotate(45deg);transform:rotate(45deg)}.mdi-rotate-90:before{-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.mdi-rotate-135:before{-webkit-transform:rotate(135deg);-ms-transform:rotate(135deg);transform:rotate(135deg)}.mdi-rotate-180:before{-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.mdi-rotate-225:before{-webkit-transform:rotate(225deg);-ms-transform:rotate(225deg);transform:rotate(225deg)}.mdi-rotate-270:before{-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.mdi-rotate-315:before{-webkit-transform:rotate(315deg);-ms-transform:rotate(315deg);transform:rotate(315deg)}.mdi-flip-h:before{-webkit-transform:scaleX(-1);transform:scaleX(-1);filter:FlipH;-ms-filter:"FlipH"}.mdi-flip-v:before{-webkit-transform:scaleY(-1);transform:scaleY(-1);filter:FlipV;-ms-filter:"FlipV"}.mdi-spin:before{-webkit-animation:mdi-spin 2s infinite linear;animation:mdi-spin 2s infinite linear}@-webkit-keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}
+
+/*# sourceMappingURL=materialdesignicons.css.map */
\ No newline at end of file
diff --git a/app/public/css/opensans.css b/app/public/css/opensans.css
new file mode 100644
index 0000000000..7de49af271
--- /dev/null
+++ b/app/public/css/opensans.css
@@ -0,0 +1,72 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4taVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4kaVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4saVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4jaVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0370-03FF;
+}
+/* hebrew */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4iaVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0590-05FF, U+200C-2010, U+20AA, U+25CC, U+FB1D-FB4F;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4vaVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4uaVQUwaEQbjB_mQ.woff) format('woff');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Open Sans';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  src: url(fonts/opensans/v28/memSYaGs126MiZpBA-UvWbX2vVnXBbObj2OVZyOOSr4dVJWUgsjZ0B4gaVQUwaEQbjA.woff) format('woff');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
diff --git a/app/public/css/roboto.css b/app/public/css/roboto.css
new file mode 100644
index 0000000000..6f53b24091
--- /dev/null
+++ b/app/public/css/roboto.css
@@ -0,0 +1,336 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxFIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxMIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxEIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxLIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxHIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxGIzIXKMnyrYk.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 100;
+  src: url(fonts/roboto/v29/KFOkCnqEu92Fr1MmgVxIIzIXKMny.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fCRc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fABc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fCBc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fBxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fCxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fChc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 300;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmSU5fBBc4AMP6lQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu72xKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu5mxKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu7mxKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu4WxKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu7WxKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu7GxKKTU1Kvnz.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  src: url(fonts/roboto/v29/KFOmCnqEu92Fr1Mu4mxKKTU1Kg.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fCRc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fABc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fCBc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fBxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fCxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fChc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 500;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmEU9fBBc4AMP6lQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfCRc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfABc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfCBc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfBxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfCxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfChc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 700;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmWUlfBBc4AMP6lQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfCRc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfABc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfCBc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfBxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfCxc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfChc4AMP6lbBP.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 900;
+  src: url(fonts/roboto/v29/KFOlCnqEu92Fr1MmYUtfBBc4AMP6lQ.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
diff --git a/app/public/data/dashboards/2b11a69e94e47a4a.json b/app/public/data/dashboards/2b11a69e94e47a4a.json
index fb42e36e12..a8d0861726 100644
--- a/app/public/data/dashboards/2b11a69e94e47a4a.json
+++ b/app/public/data/dashboards/2b11a69e94e47a4a.json
@@ -8,17 +8,17 @@
   "features": [
     {
       "width": 2,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/f5823e24a0ef418b9534acfca19716b8f53f8aa0/app/public/data/story-images/1-Atmosphere_Overview_Picture.jpg<--IMG-->Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion",
+      "text": "<--IMG-->/data/story-images/1-Atmosphere_Overview_Picture.jpg<--IMG-->Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion",
       "id": "Air quality-1650227222655",
       "title": "Air quality",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://raw.githubusercontent.com/eurodatacube/eodash/f5823e24a0ef418b9534acfca19716b8f53f8aa0/app/public/data/story-images/1-Atmosphere_Overview_Picture.jpg\">https://raw.githubusercontent.com/eurodatacube/eodash/f5823e24a0ef418b9534acfca19716b8f53f8aa0/app/public/data/story-images/1-Atmosphere_Overview_Picture.jpg</a>&lt;--IMG--&gt;Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-Atmosphere_Overview_Picture.jpg&lt;--IMG--&gt;Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion</p>\n"
     },
     {
       "width": 2,
-      "text": "# NASA, ESA and JAXA, Working Together to Monitor our Atmosphere\n\n&nbsp;  \nTraffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion. Humans have known for more than a century that burning fossil fuels and other materials generates smoke that contains a mixture of potentially hazardous particles and trace gases. Breathing air with a high concentration of gases from combustion can irritate airways in the human respiratory system and contribute to respiratory diseases and other health problems.\n\nUsing long-term global observations from satellites and ground-based instruments, scientists have kept a watchful eye on several gases emitted by combustion for more than a decade.  These Earth observations help predict and track the pollutants that affect human health and contribute to global climate change.\n\n**NASA, the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA)** present separate but complementary stories here, that show how data from satellites are used to enhance the scientific understanding of combustion-related trace gases. Several of these combustion-related gases negatively impact human health and some contribute to global climate change. The data also provide fundamental information for improving climate change prediction and investigating solutions to reduce air pollution in major cities around the world.\n\n* NASA - Data from the Ozone Monitoring Instrument (OMI) show atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels for various regions in the United States, India, and China. This instrument has been tracking the annual average abundance of these gases since 2005. These charts highlight different trends in these combustion gases; in some countries the levels of these gases have decreased dramatically, while other countries show a long-term increase. \nIn the eastern US, for example, both NO2 and SO2 levels decreased dramatically from 2005 to 2015. SO2 concentrations over the Ohio River valley and western Pennsylvania fell by 80%, and NO2 levels in the region fell by more than 40%, in response to clean air legislation.\n\nThe annual OMI data can be explored to generate maps and trends in these two combustion-related gases at both the regional and city scales. [Click here to learn more about this research]().\n\n* ESA - Carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) reveal the global and regional air quality changes of the 2021 mega-wildfires in Australia. Smoldering wood from wildfires produces CO that stays in the atmosphere for about a month. Satellite observations of CO are used to track wildfire smoke as it is transported around the world.  [Click here to learn more about this research](). \n\n* JAXA \u2013 JAXA uses data from the Greenhouse Gases Observing Satellites (GOSAT and GOSAT-2) to observe and study changes in carbon dioxide (CO2) in Cairo and major cities around the globe. To better understand the changes in CO2 from these selected major cities, this investigation also leverages complementary satellite observations of NO2, CO2, as well as an indicator of photosynthesis (i.e., solar-induced fluorescence [SIF]) from other partner agency satellites (e.g., ESA\u2019s TROPOMI and NASA\u2019s Orbiting Carbon Observatory-2 [OCO-2]). [Click here to learn more about this research]().",
+      "text": "# NASA, ESA and JAXA, Working Together to Monitor our Atmosphere\n\n&nbsp;  \nTraffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion. Humans have known for more than a century that burning fossil fuels and other materials generates smoke that contains a mixture of potentially hazardous particles and trace gases. Breathing air with a high concentration of gases from combustion can irritate airways in the human respiratory system and contribute to respiratory diseases and other health problems.\n\nUsing long-term global observations from satellites and ground-based instruments, scientists have kept a watchful eye on several gases emitted by combustion for more than a decade.  These Earth observations help predict and track the pollutants that affect human health and contribute to global climate change.\n\n**NASA, the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA)** present separate but complementary stories here, that show how data from satellites are used to enhance the scientific understanding of combustion-related trace gases. Several of these combustion-related gases negatively impact human health and some contribute to global climate change. The data also provide fundamental information for improving climate change prediction and investigating solutions to reduce air pollution in major cities around the world.\n\n* NASA - Data from the Ozone Monitoring Instrument (OMI) show atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels for various regions in the United States, India, and China. This instrument has been tracking the annual average abundance of these gases since 2005. These charts highlight different trends in these combustion gases; in some countries the levels of these gases have decreased dramatically, while other countries show a long-term increase. \nIn the eastern US, for example, both NO2 and SO2 levels decreased dramatically from 2005 to 2015. SO2 concentrations over the Ohio River valley and western Pennsylvania fell by 80%, and NO2 levels in the region fell by more than 40%, in response to clean air legislation.\n\nThe annual OMI data can be explored to generate maps and trends in these two combustion-related gases at both the regional and city scales. [Click here to learn more about this research](/story?id=air-pollution-us-india-china).\n\n* ESA - Carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) reveal the global and regional air quality changes of the 2021 mega-wildfires in Australia. Smoldering wood from wildfires produces CO that stays in the atmosphere for about a month. Satellite observations of CO are used to track wildfire smoke as it is transported around the world.  [Click here to learn more about this research](/story?id=australian-bushfires). \n\n* JAXA \u2013 JAXA uses data from the Greenhouse Gases Observing Satellites (GOSAT and GOSAT-2) to observe and study changes in carbon dioxide (CO2) in Cairo and major cities around the globe. To better understand the changes in CO2 from these selected major cities, this investigation also leverages complementary satellite observations of NO2, CO2, as well as an indicator of photosynthesis (i.e., solar-induced fluorescence [SIF]) from other partner agency satellites (e.g., ESA\u2019s TROPOMI and NASA\u2019s Orbiting Carbon Observatory-2 [OCO-2]). [Click here to learn more about this research](/story?id=cairo-farmlands).",
       "id": "Introduction-1647892884438",
       "title": "Introduction",
-      "__generatedText__": "<h1>NASA, ESA and JAXA, Working Together to Monitor our Atmosphere</h1>\n<p>&nbsp;<br>Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion. Humans have known for more than a century that burning fossil fuels and other materials generates smoke that contains a mixture of potentially hazardous particles and trace gases. Breathing air with a high concentration of gases from combustion can irritate airways in the human respiratory system and contribute to respiratory diseases and other health problems.</p>\n<p>Using long-term global observations from satellites and ground-based instruments, scientists have kept a watchful eye on several gases emitted by combustion for more than a decade.  These Earth observations help predict and track the pollutants that affect human health and contribute to global climate change.</p>\n<p><strong>NASA, the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA)</strong> present separate but complementary stories here, that show how data from satellites are used to enhance the scientific understanding of combustion-related trace gases. Several of these combustion-related gases negatively impact human health and some contribute to global climate change. The data also provide fundamental information for improving climate change prediction and investigating solutions to reduce air pollution in major cities around the world.</p>\n<ul>\n<li>NASA - Data from the Ozone Monitoring Instrument (OMI) show atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels for various regions in the United States, India, and China. This instrument has been tracking the annual average abundance of these gases since 2005. These charts highlight different trends in these combustion gases; in some countries the levels of these gases have decreased dramatically, while other countries show a long-term increase. \nIn the eastern US, for example, both NO2 and SO2 levels decreased dramatically from 2005 to 2015. SO2 concentrations over the Ohio River valley and western Pennsylvania fell by 80%, and NO2 levels in the region fell by more than 40%, in response to clean air legislation.</li>\n</ul>\n<p>The annual OMI data can be explored to generate maps and trends in these two combustion-related gases at both the regional and city scales. <a href>Click here to learn more about this research</a>.</p>\n<ul>\n<li><p>ESA - Carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) reveal the global and regional air quality changes of the 2021 mega-wildfires in Australia. Smoldering wood from wildfires produces CO that stays in the atmosphere for about a month. Satellite observations of CO are used to track wildfire smoke as it is transported around the world.  <a href>Click here to learn more about this research</a>. </p>\n</li>\n<li><p>JAXA \u2013 JAXA uses data from the Greenhouse Gases Observing Satellites (GOSAT and GOSAT-2) to observe and study changes in carbon dioxide (CO2) in Cairo and major cities around the globe. To better understand the changes in CO2 from these selected major cities, this investigation also leverages complementary satellite observations of NO2, CO2, as well as an indicator of photosynthesis (i.e., solar-induced fluorescence [SIF]) from other partner agency satellites (e.g., ESA\u2019s TROPOMI and NASA\u2019s Orbiting Carbon Observatory-2 [OCO-2]). <a href>Click here to learn more about this research</a>.</p>\n</li>\n</ul>\n"
+      "__generatedText__": "<h1>NASA, ESA and JAXA, Working Together to Monitor our Atmosphere</h1>\n<p>&nbsp;<br>Traffic, wildfires, and power plants all emit air pollutants and greenhouse gases as a product of combustion. Humans have known for more than a century that burning fossil fuels and other materials generates smoke that contains a mixture of potentially hazardous particles and trace gases. Breathing air with a high concentration of gases from combustion can irritate airways in the human respiratory system and contribute to respiratory diseases and other health problems.</p>\n<p>Using long-term global observations from satellites and ground-based instruments, scientists have kept a watchful eye on several gases emitted by combustion for more than a decade.  These Earth observations help predict and track the pollutants that affect human health and contribute to global climate change.</p>\n<p><strong>NASA, the European Space Agency (ESA), and the Japan Aerospace Exploration Agency (JAXA)</strong> present separate but complementary stories here, that show how data from satellites are used to enhance the scientific understanding of combustion-related trace gases. Several of these combustion-related gases negatively impact human health and some contribute to global climate change. The data also provide fundamental information for improving climate change prediction and investigating solutions to reduce air pollution in major cities around the world.</p>\n<ul>\n<li>NASA - Data from the Ozone Monitoring Instrument (OMI) show atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels for various regions in the United States, India, and China. This instrument has been tracking the annual average abundance of these gases since 2005. These charts highlight different trends in these combustion gases; in some countries the levels of these gases have decreased dramatically, while other countries show a long-term increase. \nIn the eastern US, for example, both NO2 and SO2 levels decreased dramatically from 2005 to 2015. SO2 concentrations over the Ohio River valley and western Pennsylvania fell by 80%, and NO2 levels in the region fell by more than 40%, in response to clean air legislation.</li>\n</ul>\n<p>The annual OMI data can be explored to generate maps and trends in these two combustion-related gases at both the regional and city scales. <a href=\"/story?id=air-pollution-us-india-china\">Click here to learn more about this research</a>.</p>\n<ul>\n<li><p>ESA - Carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) reveal the global and regional air quality changes of the 2021 mega-wildfires in Australia. Smoldering wood from wildfires produces CO that stays in the atmosphere for about a month. Satellite observations of CO are used to track wildfire smoke as it is transported around the world.  <a href=\"/story?id=australian-bushfires\">Click here to learn more about this research</a>. </p>\n</li>\n<li><p>JAXA \u2013 JAXA uses data from the Greenhouse Gases Observing Satellites (GOSAT and GOSAT-2) to observe and study changes in carbon dioxide (CO2) in Cairo and major cities around the globe. To better understand the changes in CO2 from these selected major cities, this investigation also leverages complementary satellite observations of NO2, CO2, as well as an indicator of photosynthesis (i.e., solar-induced fluorescence [SIF]) from other partner agency satellites (e.g., ESA\u2019s TROPOMI and NASA\u2019s Orbiting Carbon Observatory-2 [OCO-2]). <a href=\"/story?id=cairo-farmlands\">Click here to learn more about this research</a>.</p>\n</li>\n</ul>\n"
     }
   ],
   "title": "NASA, ESA and JAXA, Working Together to Monitor our Atmosphere"
diff --git a/app/public/data/dashboards/3862c9b85d87eae1.json b/app/public/data/dashboards/3862c9b85d87eae1.json
index 97ec5575d3..d5cd5deae5 100644
--- a/app/public/data/dashboards/3862c9b85d87eae1.json
+++ b/app/public/data/dashboards/3862c9b85d87eae1.json
@@ -8,35 +8,45 @@
   "features": [
     {
       "width": 2,
-      "text": "## Ocean Colour\n&nbsp;  \nOur planet is constantly changing. Some changes are fast \u2013 such as seasons and algae blooms \u2013  while others are slow and happen over decades to millennia. Some are caused by natural phenomena, while others, including global warming and ocean acidification, are caused by human activity.  Our planet\u2019s weather patterns and marked seasons make it necessary to collect data over long periods of time to see long-term changes over the normal seasonal variability. To do this, we have been using satellite sensors to collect a variety of measurements over the planet for more than two decades. \n\nOne of the most important processes in nature is primary productivity, which occurs when plants take the carbon in carbon dioxide (CO2) from the atmosphere or water and use the energy from the sun to transform that carbon into living material. Primary productivity is at the base of our food chain and the key to life on Earth. \n\nMany things affect primary productivity in land and water. The crucial ingredients are sunlight, CO2, and nutrients. In the oceans, primary productivity is mostly controlled by the amount of sunlight and the availability of nutrients. We have measured how ocean primary productivity \u2013which produces oxygen for us to breathe, supports the oceanic food webs, and helps sequester atmospheric CO2 \u2013 has changed over the past 25 years. \n\nHowever, even with this long record, it is difficult to disentangle natural drivers of change from those induced by humans, such as climate change. Why is it so hard? The ocean and the atmosphere are connected, and some of the natural changes take place over decades. For example, the Atlantic Multidecadal Oscillation (AMO) \u2013 which causes cool and warm fluctuations in sea surface temperature (SST) \u2013 may last for 20-40 years. The El Ni\u00f1o-Southern Oscillation (ENSO), a major phenomenon that takes place in the equatorial Pacific Ocean, can be as frequent as every 3 years, with variable intensity. These natural changes, or oscillations, affect SST, precipitation, and wind patterns, leading to changes in the water column and nutrients on which phytoplankton depend. Human activities, especially the increase of CO2 in the atmosphere due to the burning of fossil fuels, have also impacted global temperatures, which in turn impact ocean primary production. \n\nBut our ocean is vast and complex, which results in different amounts of primary productivity, a process important for the food we depend on and for atmospheric carbon sequestration. \n\nFor example, coastal areas, particularly those close to rivers, tend to have higher primary productivity due to nutrients coming in runoff from the land. The western coasts of continents, where wind and topography bring nutrient-rich deep water to the surface, also have high primary productivity. These areas are important for fisheries, particularly in  South America. In contrast, large areas of swirling ocean currents, known as gyres, (**Figure 1**) have low primary productivity and are considered biological deserts. A 16-year (1998-2013) analysis of trends in the five subtropical ocean gyres revealed warming and expansion of these ocean deserts, which means a decrease for the ability of our ocean to absorb CO2 from the atmosphere (Signorini et al. 2015, Leonelli et al, 2022).\n",
+      "text": "## Ocean Colour\n&nbsp;  \nOur planet is constantly changing. Some changes are fast \u2013 such as seasons and phytoplankton blooms \u2013  while others are slow and occur over decades to millennia. Some changes are caused by natural phenomena, while others, including global warming and ocean acidification, are caused by human activity. To see changes beyond the normal seasonal variability that is caused by our planet\u2019s weather patterns, it is necessary to collect data over long periods of time. We have been using satellite sensors to collect such data and to observe our planet from space for more than two decades. \n\nOne of the most important processes in nature is primary production, which occurs when plants take up carbon dioxide (CO2) from the atmosphere or the ocean and use the energy from the sun to fix the carbon that is necessary for their growth. Primary productivity is at the base of our food chain and key to all life on Earth. In the oceans, microscopic plants called phytoplankton are responsible for the majority of primary production. \n\nMany things affect primary productivity, including sunlight, CO2, and nutrients, which are the essential ingredients for photosynthesis and growth. In the oceans, phytoplankton primary productivity is mostly controlled by the amount of sunlight and the availability of nutrients. Using satellite observations, we have measured how ocean primary productivity \u2013 which produces oxygen for us to breathe, supports marine food webs, and helps to store atmospheric CO2 in the deep ocean \u2013 has changed over the past 25 years. \n\nEven with this long record, it is difficult to disentangle natural drivers of change from those induced by humans, such as climate change. Why is this so hard? The ocean and the atmosphere are connected, and some of the natural changes take place over decades. For example, the Atlantic Multidecadal Oscillation (AMO) \u2013 which causes cool and warm fluctuations in sea surface temperature (SST) \u2013 may last for 20-40 years. The El Ni\u00f1o-Southern Oscillation (ENSO), a major phenomenon that causes warming in the equatorial Pacific Ocean, can be as frequent as every 3 years, with variable intensity. These natural changes, or oscillations, affect SST, precipitation, and wind patterns, leading to changes in the water column conditions on which phytoplankton and primary production depend. Human activities, especially the increase of CO2 in the atmosphere due to the burning of fossil fuels, have also impacted global temperatures and water column conditions, which in turn impact ocean primary production. \n\nOur ocean is vast and complex, which results in regional differences in primary production. \nFor example, in coastal areas, particularly those close to rivers, primary productivion is higher due to the runoff of nutrients from land. The western coasts of the continents, where wind and topography bring nutrient-rich deep water to the surface (i.e. upwelling), also show high primary productivity. These areas are important for fisheries, particularly in South America. In contrast, in large areas of swirling ocean currents, known as gyres (Fig. 1), primary production is low and these regions are considered ocean deserts. A 16-year (1998-2013) analysis of trends in the five subtropical ocean gyres revealed warming and expansion of these ocean deserts, which means a decrease for the ability of our ocean to absorb CO2 from the atmosphere (Signorini et al. 2015, Leonelli et al, 2022).\n",
       "id": "Page 1-1652181219352",
       "title": "Page 1",
-      "__generatedText__": "<h2>Ocean Colour</h2>\n<p>&nbsp;<br>Our planet is constantly changing. Some changes are fast \u2013 such as seasons and algae blooms \u2013  while others are slow and happen over decades to millennia. Some are caused by natural phenomena, while others, including global warming and ocean acidification, are caused by human activity.  Our planet\u2019s weather patterns and marked seasons make it necessary to collect data over long periods of time to see long-term changes over the normal seasonal variability. To do this, we have been using satellite sensors to collect a variety of measurements over the planet for more than two decades. </p>\n<p>One of the most important processes in nature is primary productivity, which occurs when plants take the carbon in carbon dioxide (CO2) from the atmosphere or water and use the energy from the sun to transform that carbon into living material. Primary productivity is at the base of our food chain and the key to life on Earth. </p>\n<p>Many things affect primary productivity in land and water. The crucial ingredients are sunlight, CO2, and nutrients. In the oceans, primary productivity is mostly controlled by the amount of sunlight and the availability of nutrients. We have measured how ocean primary productivity \u2013which produces oxygen for us to breathe, supports the oceanic food webs, and helps sequester atmospheric CO2 \u2013 has changed over the past 25 years. </p>\n<p>However, even with this long record, it is difficult to disentangle natural drivers of change from those induced by humans, such as climate change. Why is it so hard? The ocean and the atmosphere are connected, and some of the natural changes take place over decades. For example, the Atlantic Multidecadal Oscillation (AMO) \u2013 which causes cool and warm fluctuations in sea surface temperature (SST) \u2013 may last for 20-40 years. The El Ni\u00f1o-Southern Oscillation (ENSO), a major phenomenon that takes place in the equatorial Pacific Ocean, can be as frequent as every 3 years, with variable intensity. These natural changes, or oscillations, affect SST, precipitation, and wind patterns, leading to changes in the water column and nutrients on which phytoplankton depend. Human activities, especially the increase of CO2 in the atmosphere due to the burning of fossil fuels, have also impacted global temperatures, which in turn impact ocean primary production. </p>\n<p>But our ocean is vast and complex, which results in different amounts of primary productivity, a process important for the food we depend on and for atmospheric carbon sequestration. </p>\n<p>For example, coastal areas, particularly those close to rivers, tend to have higher primary productivity due to nutrients coming in runoff from the land. The western coasts of continents, where wind and topography bring nutrient-rich deep water to the surface, also have high primary productivity. These areas are important for fisheries, particularly in  South America. In contrast, large areas of swirling ocean currents, known as gyres, (<strong>Figure 1</strong>) have low primary productivity and are considered biological deserts. A 16-year (1998-2013) analysis of trends in the five subtropical ocean gyres revealed warming and expansion of these ocean deserts, which means a decrease for the ability of our ocean to absorb CO2 from the atmosphere (Signorini et al. 2015, Leonelli et al, 2022).</p>\n"
+      "__generatedText__": "<h2>Ocean Colour</h2>\n<p>&nbsp;<br>Our planet is constantly changing. Some changes are fast \u2013 such as seasons and phytoplankton blooms \u2013  while others are slow and occur over decades to millennia. Some changes are caused by natural phenomena, while others, including global warming and ocean acidification, are caused by human activity. To see changes beyond the normal seasonal variability that is caused by our planet\u2019s weather patterns, it is necessary to collect data over long periods of time. We have been using satellite sensors to collect such data and to observe our planet from space for more than two decades. </p>\n<p>One of the most important processes in nature is primary production, which occurs when plants take up carbon dioxide (CO2) from the atmosphere or the ocean and use the energy from the sun to fix the carbon that is necessary for their growth. Primary productivity is at the base of our food chain and key to all life on Earth. In the oceans, microscopic plants called phytoplankton are responsible for the majority of primary production. </p>\n<p>Many things affect primary productivity, including sunlight, CO2, and nutrients, which are the essential ingredients for photosynthesis and growth. In the oceans, phytoplankton primary productivity is mostly controlled by the amount of sunlight and the availability of nutrients. Using satellite observations, we have measured how ocean primary productivity \u2013 which produces oxygen for us to breathe, supports marine food webs, and helps to store atmospheric CO2 in the deep ocean \u2013 has changed over the past 25 years. </p>\n<p>Even with this long record, it is difficult to disentangle natural drivers of change from those induced by humans, such as climate change. Why is this so hard? The ocean and the atmosphere are connected, and some of the natural changes take place over decades. For example, the Atlantic Multidecadal Oscillation (AMO) \u2013 which causes cool and warm fluctuations in sea surface temperature (SST) \u2013 may last for 20-40 years. The El Ni\u00f1o-Southern Oscillation (ENSO), a major phenomenon that causes warming in the equatorial Pacific Ocean, can be as frequent as every 3 years, with variable intensity. These natural changes, or oscillations, affect SST, precipitation, and wind patterns, leading to changes in the water column conditions on which phytoplankton and primary production depend. Human activities, especially the increase of CO2 in the atmosphere due to the burning of fossil fuels, have also impacted global temperatures and water column conditions, which in turn impact ocean primary production. </p>\n<p>Our ocean is vast and complex, which results in regional differences in primary production. \nFor example, in coastal areas, particularly those close to rivers, primary productivion is higher due to the runoff of nutrients from land. The western coasts of the continents, where wind and topography bring nutrient-rich deep water to the surface (i.e. upwelling), also show high primary productivity. These areas are important for fisheries, particularly in South America. In contrast, in large areas of swirling ocean currents, known as gyres (Fig. 1), primary production is low and these regions are considered ocean deserts. A 16-year (1998-2013) analysis of trends in the five subtropical ocean gyres revealed warming and expansion of these ocean deserts, which means a decrease for the ability of our ocean to absorb CO2 from the atmosphere (Signorini et al. 2015, Leonelli et al, 2022).</p>\n"
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/5fcf7f8818fc9a0a07622bc92947d2be0779c233/app/public/data/story-images/Global%20map%20of%20Chl-a.png<--IMG-->Figure 1. Global map of Chl-a; The polygons represent the study areas of the 5 gyres (from Signorini et al., 2015).\n",
+      "text": "<--IMG-->/data/story-images/Global%20map%20of%20Chl-a.png<--IMG-->Figure 1. Global map of Chl-a; The polygons represent the study areas of the 5 gyres (from Signorini et al., 2015).\n",
       "id": "Global chl-a-1652181660133",
-      "title": "Global chl-a"
+      "title": "Global chl-a",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Global%20map%20of%20Chl-a.png&lt;--IMG--&gt;Figure 1. Global map of Chl-a; The polygons represent the study areas of the 5 gyres (from Signorini et al., 2015).</p>\n"
     },
     {
       "width": 1,
-      "text": "Scientists study global trends in annual primary productivity because of their importance to ecosystems and human welfare. Satellite data, in combination with in situ measurements, and mathematical models to estimate primary productivity over the whole planet, allow us to take the pulse of our marine ecosystems. \n\n**Figure 2** shows the trends in annual primary production from different data sets. The first striking characteristic is the considerable variability across the ocean, reflecting its complexity. \n\nIn general, we see a decrease in net primary production in temperate oceanic regions in both the northern and southern hemispheres (up to -3%) and an increase at high latitudes (up to +4.5%). This doesn\u2019t sound like much, but over 24 years, these changes in net primary production are in the order of billions of pounds of carbon. \n\nOverall, the increases and decreases largely balance each other out; however, for organisms that depend on the primary production, local changes are the most important. \n\nThere is uncertainty in the direction, magnitude, and differences of changing primary productivity in shelf and coastal regions, which are affected by a variety of natural and anthropogenic factors. In addition, the trends observed are not all linear, meaning that they are not all changing at the same rate. \n\nGlobal primary productivity shows an increase in rates from 1998-2003 (Fig. 3), relatively stable rates 2003-2011, and a subsequent decrease in rates until 2015, after which rates showed a minor increase. This interannual variability appears to be strongly correlated to ocean-atmospheric connections: the initial increase in global annual primary production between 1998-2003 was related to ENSO and other ocean-atmosphere connections like the Indian Ocean Dipole (IOD), while the decrease in global annual primary production after 2011 was related to ENSO and the Pacific Decadal Oscillation (PDO) (Kulk et al, 2021).\n\nLong-term climate records allow us to see how our planet is changing and can provide the necessary information to mitigate negative effects to our ecosystem and way of life. \n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/1-EO-Dashboard_Water_Story_Images_2.png?raw=true)\n\nFigure 3: Annual global primary production for each year in the period between 1998 and 2018 (from Kulk et al., 2021)",
+      "text": "Scientists also study global trends in annual primary productivity because of its importance to ecosystems and human welfare. Satellite data, in combination with in situ measurements and mathematical models to estimate primary productivity for the entire planet, allow us to take the pulse of our marine ecosystems. \n\nFigure 2 shows the trends in annual primary production from different data sets. The first striking characteristic is the differences in trends across the ocean, reflecting its complexity. \n\nIn general, we see a decrease in primary production in temperate oceanic regions in both the Northern and Southern Hemisphere (up to -3% change per year) and an increase at higher latitudes near the Polar Regions (up to +4.5% change per year). This does not sound like much, but over 24 years, these changes in primary production are in the order of tonnes of carbon. There is uncertainty in the direction, magnitude and differences of changing primary productivity in shelf and coastal regions, which are affected by a variety of natural and anthropogenic factors. In addition, the trends observed are not all linear, meaning that they are not all changing at the same rate. The local differences are especially important for organisms that depend on primary production for their food.\n\nTogether, the regional increases and decreases in primary production largely balance each other out. Global primary productivity shows an increase in rates from 1998 to 2003 (Fig. 3), relatively stable rates between 2003 and 2011, and a subsequent decrease in rates until 2015, after which rates show a small increase. This interannual variability appears to be strongly correlated to ocean-atmospheric connections: the initial increase in global annual primary production between 1998 and 2003 was related to ENSO and other ocean-atmosphere connections like the Indian Ocean Dipole (IOD), while the decrease in global annual primary production after 2011 was related to ENSO and the Pacific Decadal Oscillation (PDO) (Kulk et al., 2021).\n\nLong-term climate records allow us to see how our planet is changing and can provide the necessary information to mitigate negative effects to our ecosystem and way of life. \n\n\n![](/data/story-images/1-EO-Dashboard_Water_Story_Images_2.png)\n\nFigure 3: Annual global primary production for each year in the period between 1998 and 2018 (from Kulk et al., 2021)",
       "id": "Page 2-1652182266534",
       "title": "Page 2",
-      "__generatedText__": "<p>Scientists study global trends in annual primary productivity because of their importance to ecosystems and human welfare. Satellite data, in combination with in situ measurements, and mathematical models to estimate primary productivity over the whole planet, allow us to take the pulse of our marine ecosystems. </p>\n<p><strong>Figure 2</strong> shows the trends in annual primary production from different data sets. The first striking characteristic is the considerable variability across the ocean, reflecting its complexity. </p>\n<p>In general, we see a decrease in net primary production in temperate oceanic regions in both the northern and southern hemispheres (up to -3%) and an increase at high latitudes (up to +4.5%). This doesn\u2019t sound like much, but over 24 years, these changes in net primary production are in the order of billions of pounds of carbon. </p>\n<p>Overall, the increases and decreases largely balance each other out; however, for organisms that depend on the primary production, local changes are the most important. </p>\n<p>There is uncertainty in the direction, magnitude, and differences of changing primary productivity in shelf and coastal regions, which are affected by a variety of natural and anthropogenic factors. In addition, the trends observed are not all linear, meaning that they are not all changing at the same rate. </p>\n<p>Global primary productivity shows an increase in rates from 1998-2003 (Fig. 3), relatively stable rates 2003-2011, and a subsequent decrease in rates until 2015, after which rates showed a minor increase. This interannual variability appears to be strongly correlated to ocean-atmospheric connections: the initial increase in global annual primary production between 1998-2003 was related to ENSO and other ocean-atmosphere connections like the Indian Ocean Dipole (IOD), while the decrease in global annual primary production after 2011 was related to ENSO and the Pacific Decadal Oscillation (PDO) (Kulk et al, 2021).</p>\n<p>Long-term climate records allow us to see how our planet is changing and can provide the necessary information to mitigate negative effects to our ecosystem and way of life. </p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/1-EO-Dashboard_Water_Story_Images_2.png?raw=true\" alt></p>\n<p>Figure 3: Annual global primary production for each year in the period between 1998 and 2018 (from Kulk et al., 2021)</p>\n"
+      "__generatedText__": "<p>Scientists also study global trends in annual primary productivity because of its importance to ecosystems and human welfare. Satellite data, in combination with in situ measurements and mathematical models to estimate primary productivity for the entire planet, allow us to take the pulse of our marine ecosystems. </p>\n<p>Figure 2 shows the trends in annual primary production from different data sets. The first striking characteristic is the differences in trends across the ocean, reflecting its complexity. </p>\n<p>In general, we see a decrease in primary production in temperate oceanic regions in both the Northern and Southern Hemisphere (up to -3% change per year) and an increase at higher latitudes near the Polar Regions (up to +4.5% change per year). This does not sound like much, but over 24 years, these changes in primary production are in the order of tonnes of carbon. There is uncertainty in the direction, magnitude and differences of changing primary productivity in shelf and coastal regions, which are affected by a variety of natural and anthropogenic factors. In addition, the trends observed are not all linear, meaning that they are not all changing at the same rate. The local differences are especially important for organisms that depend on primary production for their food.</p>\n<p>Together, the regional increases and decreases in primary production largely balance each other out. Global primary productivity shows an increase in rates from 1998 to 2003 (Fig. 3), relatively stable rates between 2003 and 2011, and a subsequent decrease in rates until 2015, after which rates show a small increase. This interannual variability appears to be strongly correlated to ocean-atmospheric connections: the initial increase in global annual primary production between 1998 and 2003 was related to ENSO and other ocean-atmosphere connections like the Indian Ocean Dipole (IOD), while the decrease in global annual primary production after 2011 was related to ENSO and the Pacific Decadal Oscillation (PDO) (Kulk et al., 2021).</p>\n<p>Long-term climate records allow us to see how our planet is changing and can provide the necessary information to mitigate negative effects to our ecosystem and way of life. </p>\n<p><img src=\"/data/story-images/1-EO-Dashboard_Water_Story_Images_2.png\" alt></p>\n<p>Figure 3: Annual global primary production for each year in the period between 1998 and 2018 (from Kulk et al., 2021)</p>\n"
     },
     {
       "width": 1,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/1-EO-Dashboard_Water_Story_Images.png?raw=true<--IMG-->Figure 2: (1) Linear trends in primary production between 1998-2018 given as percentage change (the gray areas represent non-significant trends; from Kulk et al., 2021) ",
+      "text": "<--IMG-->/data/story-images/1-EO-Dashboard_Water_Story_Images.png<--IMG-->Figure 2: (1) Linear trends in primary production between 1998-2018 given as percentage change (the gray areas represent non-significant trends; from Kulk et al., 2021) ",
       "id": "PP-1652181381954",
-      "title": "PP"
+      "title": "PP",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-EO-Dashboard_Water_Story_Images.png&lt;--IMG--&gt;Figure 2: (1) Linear trends in primary production between 1998-2018 given as percentage change (the gray areas represent non-significant trends; from Kulk et al., 2021) </p>\n"
     },
     {
       "width": 1,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/5fcf7f8818fc9a0a07622bc92947d2be0779c233/app/public/data/story-images/Delta%20NPP.png<--IMG-->Figure 2:  (2) Changes in primary production based on the VGPM Model (Behrenfeld and Falkowski, 1997) over the period 1998-2022. Red colors indicate increases, while bluish colors indicate decreases. Seasonally ice covered areas, like the poles, are not included in this analysis and are shown in white. \n",
+      "text": "<--IMG-->/data/story-images/Delta%20NPP.png<--IMG-->Figure 2:  (2) Changes in primary production based on the VGPM Model (Behrenfeld and Falkowski, 1997) over the period 1998-2022. Red colors indicate increases, while bluish colors indicate decreases. Seasonally ice covered areas, like the poles, are not included in this analysis and are shown in white. \n",
       "id": "Delta NPP-1652181697517",
-      "title": "Delta NPP"
+      "title": "Delta NPP",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Delta%20NPP.png&lt;--IMG--&gt;Figure 2:  (2) Changes in primary production based on the VGPM Model (Behrenfeld and Falkowski, 1997) over the period 1998-2022. Red colors indicate increases, while bluish colors indicate decreases. Seasonally ice covered areas, like the poles, are not included in this analysis and are shown in white. </p>\n"
+    },
+    {
+      "width": 1,
+      "text": "## The Interactive map below provided by NASA shows Global Ocean Primary Productivity\n&nbsp;  \nExplore more datasets by clicking the [EXPLORE DATASETS](https://eodashboard.org/explore) button at the top of this page. \n\n--\n### References:\n\n* Kulk, G.; Platt, T.; Dingle, J.; Jackson, T.; J\u00f6nsson, B.F.; Bouman, H.A.; Babin, M.; Brewin, R.J.W.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; Gonz\u00e1lez-Ben\u00edtez, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kova\u010d, \u017d.; Lutz, V.A.; Mara\u00f1\u00f3n, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Poll, W.H.v.d.; Segura, V.; Tilstone, G.H.; Uitz, J.; Dongen-Vogels, V.v.; Yoshikawa, T.; Sathyendranath, S. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826. https://doi.org/10.3390/rs12050826\n\n* Signorini S. R., Franz Bryan A., McClain Charles R., .: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends.Frontiers in Marine Science  2015  Vol. 2,  https://www.frontiersin.org/article/10.3389/fmars.2015.00001, DOI=10.3389/fmars.2015.00001   \n\n* Leonelli F. E., M. Bellacicco M., Pitarch Portero J., Organelli, E.Buongiorno Nardelli B., V. de Toma V., C. Cammarota C., S. Marullo S., R. Santoleri R. , Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations. Submitted to GRL, 2022.\n\n* Behrenfeld, M.J.,  Falkowski P.G.. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnology and Oceanography, 1997 Volume 42: 1-20\n\n",
+      "id": "References-1652181792821",
+      "title": "References",
+      "__generatedText__": "<h2>The Interactive map below provided by NASA shows Global Ocean Primary Productivity</h2>\n<p>&nbsp;<br>Explore more datasets by clicking the <a href=\"https://eodashboard.org/explore\">EXPLORE DATASETS</a> button at the top of this page. </p>\n<p>--</p>\n<h3>References:</h3>\n<ul>\n<li><p>Kulk, G.; Platt, T.; Dingle, J.; Jackson, T.; J\u00f6nsson, B.F.; Bouman, H.A.; Babin, M.; Brewin, R.J.W.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; Gonz\u00e1lez-Ben\u00edtez, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kova\u010d, \u017d.; Lutz, V.A.; Mara\u00f1\u00f3n, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Poll, W.H.v.d.; Segura, V.; Tilstone, G.H.; Uitz, J.; Dongen-Vogels, V.v.; Yoshikawa, T.; Sathyendranath, S. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826. <a href=\"https://doi.org/10.3390/rs12050826\">https://doi.org/10.3390/rs12050826</a></p>\n</li>\n<li><p>Signorini S. R., Franz Bryan A., McClain Charles R., .: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends.Frontiers in Marine Science  2015  Vol. 2,  <a href=\"https://www.frontiersin.org/article/10.3389/fmars.2015.00001\">https://www.frontiersin.org/article/10.3389/fmars.2015.00001</a>, DOI=10.3389/fmars.2015.00001   </p>\n</li>\n<li><p>Leonelli F. E., M. Bellacicco M., Pitarch Portero J., Organelli, E.Buongiorno Nardelli B., V. de Toma V., C. Cammarota C., S. Marullo S., R. Santoleri R. , Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations. Submitted to GRL, 2022.</p>\n</li>\n<li><p>Behrenfeld, M.J.,  Falkowski P.G.. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnology and Oceanography, 1997 Volume 42: 1-20</p>\n</li>\n</ul>\n"
     },
     {
       "width": 3,
@@ -50,12 +60,6 @@
       },
       "id": "W11-NPPN",
       "title": "World, NPP (NASA)"
-    },
-    {
-      "width": 1,
-      "text": "## References:\n\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; J\u00f6nsson, B.F.; Bouman, H.A.; Babin, M.; Brewin, R.J.W.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; Gonz\u00e1lez-Ben\u00edtez, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kova\u010d, \u017d.; Lutz, V.A.; Mara\u00f1\u00f3n, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Poll, W.H.v.d.; Segura, V.; Tilstone, G.H.; Uitz, J.; Dongen-Vogels, V.v.; Yoshikawa, T.; Sathyendranath, S. Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sens. 2020, 12, 826. https://doi.org/10.3390/rs12050826\n\nSignorini Sergio R., Franz Bryan A., McClain Charles R., 2015: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends, Frontiers in Marine Science    Vol. 2,  URL=https://www.frontiersin.org/article/10.3389/fmars.2015.00001    DOI=10.3389/fmars.2015.00001   \n\nLeonelli Francesca Elisa, M. Bellacicco, J. Pitarch Portero, E. Organelli, B. Buongiorno Nardelli, V. de Toma, C. Cammarota, S. Marullo, R. Santoleri (2022). Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations. Submitted to GRL.\n\nBehrenfeld, MJ, PG Falkowski, 1997. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnology and Oceanography, Volume 42: 1-20\n\n",
-      "id": "References-1652181792821",
-      "title": "References"
     }
   ],
   "title": "Ocean Story 2"
diff --git a/app/public/data/dashboards/396af4c32089ffdc.json b/app/public/data/dashboards/396af4c32089ffdc.json
index de83869922..870d820316 100644
--- a/app/public/data/dashboards/396af4c32089ffdc.json
+++ b/app/public/data/dashboards/396af4c32089ffdc.json
@@ -7,9 +7,10 @@
   "features": [
     {
       "width": 1,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/staging/app/public/data/trilateral/JP01-E8_Fig1.png?raw=true<--IMG-->*Figure 1. A multi-temporal, color-composite PALSAR-2 image at Tokyo International Airport, Haneda acquired on November 2019 (red), March 2020 (green), and May 2020 (blue).*",
+      "text": "<--IMG-->/data/trilateral/JP01-E8_Fig1.png<--IMG-->*Figure 1. A multi-temporal, color-composite PALSAR-2 image at Tokyo International Airport, Haneda acquired on November 2019 (red), March 2020 (green), and May 2020 (blue).*",
       "id": "ALOS-2-1651906072630",
-      "title": "ALOS-2"
+      "title": "ALOS-2",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/trilateral/JP01-E8_Fig1.png&lt;--IMG--&gt;<em>Figure 1. A multi-temporal, color-composite PALSAR-2 image at Tokyo International Airport, Haneda acquired on November 2019 (red), March 2020 (green), and May 2020 (blue).</em></p>\n"
     },
     {
       "width": 3,
@@ -19,15 +20,17 @@
     },
     {
       "width": 1,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/staging/app/public/data/trilateral/JP01-E13b_Animation.gif?raw=true<--IMG-->Figure 2. Sentinel-1 animation at Tokyo International Airport, Haneda from January to June 2020. Brighter pixels in parking areas of the airport may correspond with airplanes at each acquisition date.",
+      "text": "<--IMG-->/data/trilateral/JP01-E13b_Animation.gif<--IMG-->Figure 2. Sentinel-1 animation at Tokyo International Airport, Haneda from January to June 2020. Brighter pixels in parking areas of the airport may correspond with airplanes at each acquisition date.",
       "id": "Tokyo-1651906257468",
-      "title": "Tokyo"
+      "title": "Tokyo",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/trilateral/JP01-E13b_Animation.gif&lt;--IMG--&gt;Figure 2. Sentinel-1 animation at Tokyo International Airport, Haneda from January to June 2020. Brighter pixels in parking areas of the airport may correspond with airplanes at each acquisition date.</p>\n"
     },
     {
       "width": 3,
-      "text": "In addition to the SAR imagery, NASA and ESA scientists also used artificial intelligence technology and high-resolution optical imagery from Planet and [Copernicus Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) to detect and count aircraft on the ground at airports. \n\nCombining high-resolution optical remote sensing data from Planet and Sentinel-2 with SAR imagery from ALOS-2 and [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a powerful method to provide reliable time series data on the number of aircrafts grounded at airports over time. While imagery from optical satellites like Planet or [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is powerful to monitor changes in grounded aircrafts around the world systematically, their sensors cannot penetrate clouds, limiting the detection capability on cloudy or rainy days. Alternatively, SAR data, while lower resolution, can penetrate clouds. Therefore, scientists are combining these datasets to get the most comprehensive images of changes at aircraft counts at airports during the COVID-19 pandemic. \n\nThe following interactive maps show:\n\n* Aircraft detection on the San Francisco airport in the U.S. and Narita airport in Japan, using Planet imagery \n* Aircraft detection over the CDG airport in Paris (France)  using Sentinel-2 imagery and AI\n\n&nbsp; \n\nMore airports can be explored on this dashboard. Click the **EXPLORE DATASETS** button on top of this page to explore all locations.\n",
+      "text": "In addition to the SAR imagery, NASA and ESA scientists also used artificial intelligence technology and high-resolution optical imagery from Planet and [Copernicus Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) to detect and count aircraft on the ground at airports. \n\nCombining high-resolution optical remote sensing data from Planet and Sentinel-2 with SAR imagery from ALOS-2 and [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a powerful method to provide reliable time series data on the number of aircrafts grounded at airports over time. While imagery from optical satellites like Planet or [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) is powerful to monitor changes in grounded aircrafts around the world systematically, their sensors cannot penetrate clouds, limiting the detection capability on cloudy or rainy days. Alternatively, SAR data, while lower resolution, can penetrate clouds. Therefore, scientists are combining these datasets to get the most comprehensive images of changes at aircraft counts at airports during the COVID-19 pandemic. \n\nThe following interactive maps show:\n\n* Aircraft detection on the San Francisco airport in the U.S. and Narita airport in Japan, using Planet imagery \n* Aircraft detection over the CDG airport in Paris (France)  using Sentinel-2 imagery and AI\n\n&nbsp; \n\nMore airports can be explored on this dashboard. Click the **[EXPLORE DATASETS](https://eodashboard.org/explore)** button on top of this page to explore all locations.\n",
       "id": "Page 2-1651906145634",
-      "title": "Page 2"
+      "title": "Page 2",
+      "__generatedText__": "<p>In addition to the SAR imagery, NASA and ESA scientists also used artificial intelligence technology and high-resolution optical imagery from Planet and <a href=\"https://sentinel.esa.int/web/sentinel/missions/sentinel-2\">Copernicus Sentinel-2</a> to detect and count aircraft on the ground at airports. </p>\n<p>Combining high-resolution optical remote sensing data from Planet and Sentinel-2 with SAR imagery from ALOS-2 and <a href=\"https://sentinel.esa.int/web/sentinel/missions/sentinel-1\">Sentinel-1</a> is a powerful method to provide reliable time series data on the number of aircrafts grounded at airports over time. While imagery from optical satellites like Planet or <a href=\"https://sentinel.esa.int/web/sentinel/missions/sentinel-2\">Sentinel-2</a> is powerful to monitor changes in grounded aircrafts around the world systematically, their sensors cannot penetrate clouds, limiting the detection capability on cloudy or rainy days. Alternatively, SAR data, while lower resolution, can penetrate clouds. Therefore, scientists are combining these datasets to get the most comprehensive images of changes at aircraft counts at airports during the COVID-19 pandemic. </p>\n<p>The following interactive maps show:</p>\n<ul>\n<li>Aircraft detection on the San Francisco airport in the U.S. and Narita airport in Japan, using Planet imagery </li>\n<li>Aircraft detection over the CDG airport in Paris (France)  using Sentinel-2 imagery and AI</li>\n</ul>\n<p>&nbsp; </p>\n<p>More airports can be explored on this dashboard. Click the <strong><a href=\"https://eodashboard.org/explore\">EXPLORE DATASETS</a></strong> button on top of this page to explore all locations.</p>\n"
     },
     {
       "width": 1,
diff --git a/app/public/data/dashboards/4b40596e305997ac.json b/app/public/data/dashboards/4b40596e305997ac.json
index bbb0191599..67f5cca56d 100644
--- a/app/public/data/dashboards/4b40596e305997ac.json
+++ b/app/public/data/dashboards/4b40596e305997ac.json
@@ -26,10 +26,10 @@
     },
     {
       "width": 4,
-      "text": "## Why We Monitor Agricultural Production\n&nbsp;  \nAny extreme event such as conflict, severe weather, or environmental abnormalities pose threats to agricultural production and may shock the system which is why it is so critical to have them accessible, transparent, timely, and global information afforded by satellite data. For example, based on community consensus and available satellite data we understand that at the end of February 2022, conditions are generally favourable for wheat and rice production, while mixed for maize and soybeans. We also know that winter wheat is mostly dormant in the northern hemisphere, with some areas of production raising some concern in Europe, Ukraine, and the US. In the southern hemisphere,  maize is under mixed conditions in Argentina and southern Brazil. While rice conditions are favourable in most countries except for Vietnam and Brazil, soybeans are under mixed conditions in Argentina and southern Brazil. Satellite data helps us monitor these global crop conditions on a regular basis, especially in times and places where ground access is dangerous or limited.\n\n**GEOGLAM Crop Monitor for AMIS and Early Warning synthesis map showing where crops are grown around the world, and the current crop conditions as of April 28, 2022. (source: https://cropmonitor.org/)**\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/AMIS_EW_synthesis_202205.jpeg?raw=true)\n",
+      "text": "## Why We Monitor Agricultural Production\n&nbsp;  \nAny extreme event such as conflict, severe weather, or environmental abnormalities pose threats to agricultural production and may shock the system which is why it is so critical to have them accessible, transparent, timely, and global information afforded by satellite data. For example, based on community consensus and available satellite data we understand that at the end of February 2022, conditions are generally favourable for wheat and rice production, while mixed for maize and soybeans. We also know that winter wheat is mostly dormant in the northern hemisphere, with some areas of production raising some concern in Europe, Ukraine, and the US. In the southern hemisphere,  maize is under mixed conditions in Argentina and southern Brazil. While rice conditions are favourable in most countries except for Vietnam and Brazil, soybeans are under mixed conditions in Argentina and southern Brazil. Satellite data helps us monitor these global crop conditions on a regular basis, especially in times and places where ground access is dangerous or limited.\n\n**GEOGLAM Crop Monitor for AMIS and Early Warning synthesis map showing where crops are grown around the world, and the current crop conditions as of April 28, 2022. (source: https://cropmonitor.org/)**\n\n![](/data/story-images/AMIS_EW_synthesis_202205.jpeg)\n",
       "id": " -1652099257977",
       "title": " ",
-      "__generatedText__": "<h2>Why We Monitor Agricultural Production</h2>\n<p>&nbsp;<br>Any extreme event such as conflict, severe weather, or environmental abnormalities pose threats to agricultural production and may shock the system which is why it is so critical to have them accessible, transparent, timely, and global information afforded by satellite data. For example, based on community consensus and available satellite data we understand that at the end of February 2022, conditions are generally favourable for wheat and rice production, while mixed for maize and soybeans. We also know that winter wheat is mostly dormant in the northern hemisphere, with some areas of production raising some concern in Europe, Ukraine, and the US. In the southern hemisphere,  maize is under mixed conditions in Argentina and southern Brazil. While rice conditions are favourable in most countries except for Vietnam and Brazil, soybeans are under mixed conditions in Argentina and southern Brazil. Satellite data helps us monitor these global crop conditions on a regular basis, especially in times and places where ground access is dangerous or limited.</p>\n<p><strong>GEOGLAM Crop Monitor for AMIS and Early Warning synthesis map showing where crops are grown around the world, and the current crop conditions as of April 28, 2022. (source: <a href=\"https://cropmonitor.org/\">https://cropmonitor.org/</a>)</strong></p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/AMIS_EW_synthesis_202205.jpeg?raw=true\" alt></p>\n"
+      "__generatedText__": "<h2>Why We Monitor Agricultural Production</h2>\n<p>&nbsp;<br>Any extreme event such as conflict, severe weather, or environmental abnormalities pose threats to agricultural production and may shock the system which is why it is so critical to have them accessible, transparent, timely, and global information afforded by satellite data. For example, based on community consensus and available satellite data we understand that at the end of February 2022, conditions are generally favourable for wheat and rice production, while mixed for maize and soybeans. We also know that winter wheat is mostly dormant in the northern hemisphere, with some areas of production raising some concern in Europe, Ukraine, and the US. In the southern hemisphere,  maize is under mixed conditions in Argentina and southern Brazil. While rice conditions are favourable in most countries except for Vietnam and Brazil, soybeans are under mixed conditions in Argentina and southern Brazil. Satellite data helps us monitor these global crop conditions on a regular basis, especially in times and places where ground access is dangerous or limited.</p>\n<p><strong>GEOGLAM Crop Monitor for AMIS and Early Warning synthesis map showing where crops are grown around the world, and the current crop conditions as of April 28, 2022. (source: <a href=\"https://cropmonitor.org/\">https://cropmonitor.org/</a>)</strong></p>\n<p><img src=\"/data/story-images/AMIS_EW_synthesis_202205.jpeg\" alt></p>\n"
     },
     {
       "width": 2,
@@ -40,66 +40,52 @@
     },
     {
       "width": 2,
-      "text": "**(1) Normalized Difference Vegetation Index (NDVI): A measure of crop health**\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/NDVI%20AGMET.png?raw=true)\n\n*The AGMET Indicators use satellite data to measure key indicators of crop health, including NDVI and NDVI anomalies. (source: https://cropmonitor.org/tools/agmet/)*\n\n&nbsp;   \n\n**(2) Surface soil moisture: A measure of water stored in the ground and available to crops**\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/SoilMoisture.png?raw=true)\n\n*Soil moisture can be measured using satellite data and directly affects crop growth. (source: https://cropmonitor.org/tools/agmet/)*",
+      "text": "**(1) Normalized Difference Vegetation Index (NDVI): A measure of crop health**\n\n![](/data/story-images/NDVI%20AGMET.png)\n\n*The AGMET Indicators use satellite data to measure key indicators of crop health, including NDVI and NDVI anomalies. (source: https://cropmonitor.org/tools/agmet/)*\n\n&nbsp;   \n\n**(2) Surface soil moisture: A measure of water stored in the ground and available to crops**\n\n![](/data/story-images/SoilMoisture.png)\n\n*Soil moisture can be measured using satellite data and directly affects crop growth. (source: https://cropmonitor.org/tools/agmet/)*",
       "id": "AGMET NDVI-1649763449621",
       "title": " Agromet Parameters",
-      "__generatedText__": "<p><strong>(1) Normalized Difference Vegetation Index (NDVI): A measure of crop health</strong></p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/NDVI%20AGMET.png?raw=true\" alt></p>\n<p><em>The AGMET Indicators use satellite data to measure key indicators of crop health, including NDVI and NDVI anomalies. (source: <a href=\"https://cropmonitor.org/tools/agmet/\">https://cropmonitor.org/tools/agmet/</a>)</em></p>\n<p>&nbsp;   </p>\n<p><strong>(2) Surface soil moisture: A measure of water stored in the ground and available to crops</strong></p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/SoilMoisture.png?raw=true\" alt></p>\n<p><em>Soil moisture can be measured using satellite data and directly affects crop growth. (source: <a href=\"https://cropmonitor.org/tools/agmet/\">https://cropmonitor.org/tools/agmet/</a>)</em></p>\n"
+      "__generatedText__": "<p><strong>(1) Normalized Difference Vegetation Index (NDVI): A measure of crop health</strong></p>\n<p><img src=\"/data/story-images/NDVI%20AGMET.png\" alt></p>\n<p><em>The AGMET Indicators use satellite data to measure key indicators of crop health, including NDVI and NDVI anomalies. (source: <a href=\"https://cropmonitor.org/tools/agmet/\">https://cropmonitor.org/tools/agmet/</a>)</em></p>\n<p>&nbsp;   </p>\n<p><strong>(2) Surface soil moisture: A measure of water stored in the ground and available to crops</strong></p>\n<p><img src=\"/data/story-images/SoilMoisture.png\" alt></p>\n<p><em>Soil moisture can be measured using satellite data and directly affects crop growth. (source: <a href=\"https://cropmonitor.org/tools/agmet/\">https://cropmonitor.org/tools/agmet/</a>)</em></p>\n"
     },
     {
       "width": 2,
-      "text": "## Global / Regional monitor \n\n### 2022 drought in the U.S. Southern Plains region \n#### a)\tNASA Harvest-GEOGLAM AGMET \n&nbsp;  \nThe Southern Plains are a major wheat producing region in the U.S. and is monitored closely from planting to harvest given the importance of this major commodity crop. The AGMET Indicator graphic for the 2022 Southern Plains winter wheat season (above) shows below-average cumulative precipitation, NDVI, and soil moisture, consistent with drought conditions trending throughout the Southern Plains. U.S. winter wheat is typically harvested over the summer months but due to the drought conditions affecting the region, there is concern over the potential yields. With the help of satellite data, we understand several months ahead of the harvest that we might expect lower than average production as a result of the environmental indicators measured. With this knowledge comes the ability to respond and prepare appropriately while simultaneously providing market transparency.\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/AMIS-United%20States%20of%20America-Southern%20Plains-Winter%20Wheat-2022.jpeg?raw=true)\nSouthern Plains (USA) Winter Wheat 2022",
+      "text": "## Global / Regional monitor \n\n### 2022 drought in the U.S. Southern Plains region \n#### a)\tNASA Harvest-GEOGLAM AGMET \n&nbsp;  \nThe Southern Plains are a major wheat producing region in the U.S. and is monitored closely from planting to harvest given the importance of this major commodity crop. The AGMET Indicator graphic for the 2022 Southern Plains winter wheat season (above) shows below-average cumulative precipitation, NDVI, and soil moisture, consistent with drought conditions trending throughout the Southern Plains. U.S. winter wheat is typically harvested over the summer months but due to the drought conditions affecting the region, there is concern over the potential yields. With the help of satellite data, we understand several months ahead of the harvest that we might expect lower than average production as a result of the environmental indicators measured. With this knowledge comes the ability to respond and prepare appropriately while simultaneously providing market transparency.\n\n![](/data/story-images/AMIS-United%20States%20of%20America-Southern%20Plains-Winter%20Wheat-2022.jpeg)\nSouthern Plains (USA) Winter Wheat 2022",
       "id": " -1649763952645",
       "title": " ",
-      "__generatedText__": "<h2>Global / Regional monitor</h2>\n<h3>2022 drought in the U.S. Southern Plains region</h3>\n<h4>a)    NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>The Southern Plains are a major wheat producing region in the U.S. and is monitored closely from planting to harvest given the importance of this major commodity crop. The AGMET Indicator graphic for the 2022 Southern Plains winter wheat season (above) shows below-average cumulative precipitation, NDVI, and soil moisture, consistent with drought conditions trending throughout the Southern Plains. U.S. winter wheat is typically harvested over the summer months but due to the drought conditions affecting the region, there is concern over the potential yields. With the help of satellite data, we understand several months ahead of the harvest that we might expect lower than average production as a result of the environmental indicators measured. With this knowledge comes the ability to respond and prepare appropriately while simultaneously providing market transparency.</p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/AMIS-United%20States%20of%20America-Southern%20Plains-Winter%20Wheat-2022.jpeg?raw=true\" alt>\nSouthern Plains (USA) Winter Wheat 2022</p>\n"
+      "__generatedText__": "<h2>Global / Regional monitor</h2>\n<h3>2022 drought in the U.S. Southern Plains region</h3>\n<h4>a)    NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>The Southern Plains are a major wheat producing region in the U.S. and is monitored closely from planting to harvest given the importance of this major commodity crop. The AGMET Indicator graphic for the 2022 Southern Plains winter wheat season (above) shows below-average cumulative precipitation, NDVI, and soil moisture, consistent with drought conditions trending throughout the Southern Plains. U.S. winter wheat is typically harvested over the summer months but due to the drought conditions affecting the region, there is concern over the potential yields. With the help of satellite data, we understand several months ahead of the harvest that we might expect lower than average production as a result of the environmental indicators measured. With this knowledge comes the ability to respond and prepare appropriately while simultaneously providing market transparency.</p>\n<p><img src=\"/data/story-images/AMIS-United%20States%20of%20America-Southern%20Plains-Winter%20Wheat-2022.jpeg\" alt>\nSouthern Plains (USA) Winter Wheat 2022</p>\n"
     },
     {
       "width": 2,
-      "text": "#### b)\tJAXA JASMIN/Japan JASMAI with MAFF\n&nbsp;  \nJAXA with Japanese MAFF provide agrometeorological information including Precipitation, Drought Index, Soil Moisture, Solar Radiation, Surface Temperature, Vegetation Index and anomaly, etc. Rainfall, Agriculture, Land, GPM Core Observatory, SHIZUKU (GCOM-W), MODIS, Multiple Satellites (GSMaP)  bi-Monthly on JASMIN for Asia and on JASMAI for selected world areas. By using JAXA WMS with JASMIN/JASMAI products, the anomaly of Southern Plain in the US can be zoomed in with visualized map. The following are examples of composite averaged NDVI and NDVI anomaly. \n\n\nAveraged NDVI             |  NDVI Anomaly\n:-------------------------:|:-------------------------:\n![](https://github.com/aapopescu/Images/blob/main/Averaged%20NDVI.png?raw=true)  |  ![](https://github.com/aapopescu/Images/blob/main/NDVI%20Anomaly.png?raw=true)\n\nThe following time series reflect the precipitation, soil moisture and NDVI variables from Japan JASMIN/JASMAI for Kansas, USA. \n\nPrecipitation | Soil Moisture | NDVI\n:-------------------------:|:-------------------------:|:-------------------------:\n![](https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20Precipitation.png?raw=true)  |  ![](https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20Soil%20Moisture.png?raw=true)  |  ![](https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20NDVI.png?raw=true)",
+      "text": "#### b)\tJAXA JASMIN/Japan JASMAI with MAFF\n&nbsp;  \nJAXA with Japanese MAFF provide agrometeorological information including Precipitation, Drought Index, Soil Moisture, Solar Radiation, Surface Temperature, Vegetation Index and anomaly, etc. Rainfall, Agriculture, Land, GPM Core Observatory, SHIZUKU (GCOM-W), MODIS, Multiple Satellites (GSMaP)  bi-Monthly on JASMIN for Asia and on JASMAI for selected world areas. By using JAXA WMS with JASMIN/JASMAI products, the anomaly of Southern Plain in the US can be zoomed in with visualized map. The following are examples of composite averaged NDVI and NDVI anomaly. \n\n\nAveraged NDVI             |  NDVI Anomaly\n:-------------------------:|:-------------------------:\n![](/data/story-images/Averaged%20NDVI.png?raw=true)  |  ![](/data/story-images/NDVI%20Anomaly.png?raw=true)\n\nThe following time series reflect the precipitation, soil moisture and NDVI variables from Japan JASMIN/JASMAI for Kansas, USA. \n\nPrecipitation | Soil Moisture | NDVI\n:-------------------------:|:-------------------------:|:-------------------------:\n![](/data/story-images/Kansas%20Precipitation.png?raw=true)  |  ![](/data/story-images/Kansas%20Soil%20Moisture.png)  |  ![](/data/story-images/Kansas%20NDVI.png)",
       "id": " -1649764018828",
       "title": " ",
-      "__generatedText__": "<h4>b)    JAXA JASMIN/Japan JASMAI with MAFF</h4>\n<p>&nbsp;<br>JAXA with Japanese MAFF provide agrometeorological information including Precipitation, Drought Index, Soil Moisture, Solar Radiation, Surface Temperature, Vegetation Index and anomaly, etc. Rainfall, Agriculture, Land, GPM Core Observatory, SHIZUKU (GCOM-W), MODIS, Multiple Satellites (GSMaP)  bi-Monthly on JASMIN for Asia and on JASMAI for selected world areas. By using JAXA WMS with JASMIN/JASMAI products, the anomaly of Southern Plain in the US can be zoomed in with visualized map. The following are examples of composite averaged NDVI and NDVI anomaly. </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Averaged NDVI</th>\n<th align=\"center\">NDVI Anomaly</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"https://github.com/aapopescu/Images/blob/main/Averaged%20NDVI.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/aapopescu/Images/blob/main/NDVI%20Anomaly.png?raw=true\" alt></td>\n</tr>\n</tbody></table>\n<p>The following time series reflect the precipitation, soil moisture and NDVI variables from Japan JASMIN/JASMAI for Kansas, USA. </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation</th>\n<th align=\"center\">Soil Moisture</th>\n<th align=\"center\">NDVI</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20Precipitation.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20Soil%20Moisture.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/Kansas%20NDVI.png?raw=true\" alt></td>\n</tr>\n</tbody></table>\n"
+      "__generatedText__": "<h4>b)    JAXA JASMIN/Japan JASMAI with MAFF</h4>\n<p>&nbsp;<br>JAXA with Japanese MAFF provide agrometeorological information including Precipitation, Drought Index, Soil Moisture, Solar Radiation, Surface Temperature, Vegetation Index and anomaly, etc. Rainfall, Agriculture, Land, GPM Core Observatory, SHIZUKU (GCOM-W), MODIS, Multiple Satellites (GSMaP)  bi-Monthly on JASMIN for Asia and on JASMAI for selected world areas. By using JAXA WMS with JASMIN/JASMAI products, the anomaly of Southern Plain in the US can be zoomed in with visualized map. The following are examples of composite averaged NDVI and NDVI anomaly. </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Averaged NDVI</th>\n<th align=\"center\">NDVI Anomaly</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"/data/story-images/Averaged%20NDVI.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/NDVI%20Anomaly.png?raw=true\" alt></td>\n</tr>\n</tbody></table>\n<p>The following time series reflect the precipitation, soil moisture and NDVI variables from Japan JASMIN/JASMAI for Kansas, USA. </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation</th>\n<th align=\"center\">Soil Moisture</th>\n<th align=\"center\">NDVI</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"/data/story-images/Kansas%20Precipitation.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/Kansas%20Soil%20Moisture.png\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/Kansas%20NDVI.png\" alt></td>\n</tr>\n</tbody></table>\n"
     },
     {
       "width": 2,
-      "text": "### North Africa and Middle East drought \n&nbsp;  \n#### a)\tNASA Harvest-GEOGLAM AGMET \n&nbsp;  \nNot only do satellite data helps us understand the current season\u2019s productivity, but they also provide a rich historical record of agricultural measurements that enable researchers to compare outcomes to previous seasons and evaluate broader trends such as the impacts of climate change. For example, significant parts of Northern Africa and the Middle East  are experiencing drought conditions including countries such as Morocco, Tunisia (seen below), Syria and Iraq that are signifincatly impacting crop production. Earth observations are key to identifying and quatifying these impacts early and in providing support for future agricultural planning and mitigation actions when needed.  \n\n![](https://github.com/aapopescu/Images/blob/main/Sousse.png?raw=true)\n\nSousse (Northern Centre, Tunisia) Winter Wheat 2022",
+      "text": "###  The Benefits of Pairing The WorldCereal Initiative and Satellite Data \n&nbsp;  \nWorldCereal is a global Research and Development challenge aiming to engage the global agricultural community to achieve optimal results.  WorldCereal relies heavily on satellites, primarily the operational time series of the Copernicus Sentinels missions, which allow an unprecedented dynamic crop monitoring at global scale. By focusing on maize and wheat during the system demonstration, WorldCereal aims to contribute to the global food security challenge as together with rice, these crops form the world\u2019s most widely cultivated species, accounting for an estimated 42.5 percent of the world\u2019s food calorie supply (FAO).\n\n\n![](/data/story-images/worldcereal.jpeg)\n\n\nWorldCereal will not only provide a system which offers accurate, global and seasonally updated cropland extent, crop type, and irrigation maps management type (irrigated vs. non-irrigated), it will also provide the community with the necessary tools and technology to create these maps themselves for their own regions of interest and, if available, using their own reference data.\n\n## Tanzania\n&nbsp;  \n### Annual Cropland\n![](/data/story-images/Tanzania_annual_cropland_zoom.png)\n\n### Summer\n\n![](/data/story-images/Tanzania_summer_zoom.png)\n\n### Winter\n![](/data/story-images/Tanzania_winter_zoom.png)",
+      "id": "World Cereal-1650225828076",
+      "title": "World Cereal",
+      "__generatedText__": "<h3>The Benefits of Pairing The WorldCereal Initiative and Satellite Data</h3>\n<p>&nbsp;<br>WorldCereal is a global Research and Development challenge aiming to engage the global agricultural community to achieve optimal results.  WorldCereal relies heavily on satellites, primarily the operational time series of the Copernicus Sentinels missions, which allow an unprecedented dynamic crop monitoring at global scale. By focusing on maize and wheat during the system demonstration, WorldCereal aims to contribute to the global food security challenge as together with rice, these crops form the world\u2019s most widely cultivated species, accounting for an estimated 42.5 percent of the world\u2019s food calorie supply (FAO).</p>\n<p><img src=\"/data/story-images/worldcereal.jpeg\" alt></p>\n<p>WorldCereal will not only provide a system which offers accurate, global and seasonally updated cropland extent, crop type, and irrigation maps management type (irrigated vs. non-irrigated), it will also provide the community with the necessary tools and technology to create these maps themselves for their own regions of interest and, if available, using their own reference data.</p>\n<h2>Tanzania</h2>\n<p>&nbsp;  </p>\n<h3>Annual Cropland</h3>\n<p><img src=\"/data/story-images/Tanzania_annual_cropland_zoom.png\" alt></p>\n<h3>Summer</h3>\n<p><img src=\"/data/story-images/Tanzania_summer_zoom.png\" alt></p>\n<h3>Winter</h3>\n<p><img src=\"/data/story-images/Tanzania_winter_zoom.png\" alt></p>\n"
+    },
+    {
+      "width": 2,
+      "text": "### North Africa and Middle East drought \n&nbsp;  \n#### a)\tNASA Harvest-GEOGLAM AGMET \n&nbsp;  \nNot only do satellite data helps us understand the current season\u2019s productivity, but they also provide a rich historical record of agricultural measurements that enable researchers to compare outcomes to previous seasons and evaluate broader trends such as the impacts of climate change. For example, significant parts of Northern Africa and the Middle East  are experiencing drought conditions including countries such as Morocco, Tunisia (seen below), Syria and Iraq that are signifincatly impacting crop production. Earth observations are key to identifying and quatifying these impacts early and in providing support for future agricultural planning and mitigation actions when needed.  \n\n![](/data/story-images/Sousse.png?raw=true)\n\nSousse (Northern Centre, Tunisia) Winter Wheat 2022",
       "id": "a-1649754951575",
       "title": "a",
-      "__generatedText__": "<h3>North Africa and Middle East drought</h3>\n<p>&nbsp;  </p>\n<h4>a)    NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>Not only do satellite data helps us understand the current season\u2019s productivity, but they also provide a rich historical record of agricultural measurements that enable researchers to compare outcomes to previous seasons and evaluate broader trends such as the impacts of climate change. For example, significant parts of Northern Africa and the Middle East  are experiencing drought conditions including countries such as Morocco, Tunisia (seen below), Syria and Iraq that are signifincatly impacting crop production. Earth observations are key to identifying and quatifying these impacts early and in providing support for future agricultural planning and mitigation actions when needed.  </p>\n<p><img src=\"https://github.com/aapopescu/Images/blob/main/Sousse.png?raw=true\" alt></p>\n<p>Sousse (Northern Centre, Tunisia) Winter Wheat 2022</p>\n"
+      "__generatedText__": "<h3>North Africa and Middle East drought</h3>\n<p>&nbsp;  </p>\n<h4>a)    NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>Not only do satellite data helps us understand the current season\u2019s productivity, but they also provide a rich historical record of agricultural measurements that enable researchers to compare outcomes to previous seasons and evaluate broader trends such as the impacts of climate change. For example, significant parts of Northern Africa and the Middle East  are experiencing drought conditions including countries such as Morocco, Tunisia (seen below), Syria and Iraq that are signifincatly impacting crop production. Earth observations are key to identifying and quatifying these impacts early and in providing support for future agricultural planning and mitigation actions when needed.  </p>\n<p><img src=\"/data/story-images/Sousse.png?raw=true\" alt></p>\n<p>Sousse (Northern Centre, Tunisia) Winter Wheat 2022</p>\n"
     },
     {
-      "width": 2,
-      "text": "### Dry Conditions in Southern Brazil\n&nbsp;  \n#### a) NASA Harvest-GEOGLAM AGMET \n&nbsp;  \nLikewise in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop. Looking at the information provided by satellite data, the drought conditions are striking and served as a preemptive sign that governments and markets should prepare for less commodity crops coming out of this region of Brazil. This not only impacts Brazil and the global market, but also the countries that rely on the region for their food/feed imports. Earth observations can give an early indication of threats to food security should supplies end up being less than needed. Another benefit of remotely sensed satellite data is that these types of evaluations can be done anywhere in the world throughout the season.\n\n![](https://github.com/aapopescu/Images/blob/main/NASAHarvest-GEOGLAM-Brazil.png?raw=true)\nSouth Region (Brazil) Spring-planted Maize 2022",
+      "width": 3,
+      "text": "### Dry Conditions in Southern Brazil\n&nbsp;  \n#### a) NASA Harvest-GEOGLAM AGMET \n&nbsp;  \nLikewise in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop. Looking at the information provided by satellite data, the drought conditions are striking and served as a preemptive sign that governments and markets should prepare for less commodity crops coming out of this region of Brazil. This not only impacts Brazil and the global market, but also the countries that rely on the region for their food/feed imports. Earth observations can give an early indication of threats to food security should supplies end up being less than needed. Another benefit of remotely sensed satellite data is that these types of evaluations can be done anywhere in the world throughout the season.\n\n![](/data/story-images/NASAHarvest-GEOGLAM-Brazil.png?raw=true)\nSouth Region (Brazil) Spring-planted Maize 2022\n\n### Dry Conditions in Southern Brazil\n\n&nbsp;  \n#### b)\tJAXA JASMIN/Japan JASMAI with MAFF\n&nbsp;  \nAccumulated and anormal precipitation information derived from GSMaP by JASMIN/JASMAI on this March is shown in following because  in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop.\n\nPrecipitation by GSMap     |  Precipitation Anomaly by GSMaP\n:-------------------------:|:-------------------------:\n![](/data/story-images/Precipitation%20by%20GSMaP.png)  |  ![](/data/story-images/Precipitation%20anomaly%20by%20GSMaP.png)\n&nbsp;  \n\n\n\nThe following time series reflect the precipitation, soil moisture and NDVI variables from JASMIN/JASMAI for Estado de Paran\u00e1: \n&nbsp;  \n\nPrecipitation | Soil Moisture | NDVI\n:-------------------------:|:-------------------------:|:-------------------------:\n![](/data/story-images/Parana%20Precipitation.png)  |  ![](/data/story-images/Parana%20Soil%20Moisture.png)  |  ![](/data/story-images/Parana%20Precipitation.png)\n",
       "id": " -1650224977207",
       "title": " ",
-      "__generatedText__": "<h3>Dry Conditions in Southern Brazil</h3>\n<p>&nbsp;  </p>\n<h4>a) NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>Likewise in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop. Looking at the information provided by satellite data, the drought conditions are striking and served as a preemptive sign that governments and markets should prepare for less commodity crops coming out of this region of Brazil. This not only impacts Brazil and the global market, but also the countries that rely on the region for their food/feed imports. Earth observations can give an early indication of threats to food security should supplies end up being less than needed. Another benefit of remotely sensed satellite data is that these types of evaluations can be done anywhere in the world throughout the season.</p>\n<p><img src=\"https://github.com/aapopescu/Images/blob/main/NASAHarvest-GEOGLAM-Brazil.png?raw=true\" alt>\nSouth Region (Brazil) Spring-planted Maize 2022</p>\n"
+      "__generatedText__": "<h3>Dry Conditions in Southern Brazil</h3>\n<p>&nbsp;  </p>\n<h4>a) NASA Harvest-GEOGLAM AGMET</h4>\n<p>&nbsp;<br>Likewise in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop. Looking at the information provided by satellite data, the drought conditions are striking and served as a preemptive sign that governments and markets should prepare for less commodity crops coming out of this region of Brazil. This not only impacts Brazil and the global market, but also the countries that rely on the region for their food/feed imports. Earth observations can give an early indication of threats to food security should supplies end up being less than needed. Another benefit of remotely sensed satellite data is that these types of evaluations can be done anywhere in the world throughout the season.</p>\n<p><img src=\"/data/story-images/NASAHarvest-GEOGLAM-Brazil.png?raw=true\" alt>\nSouth Region (Brazil) Spring-planted Maize 2022</p>\n<h3>Dry Conditions in Southern Brazil</h3>\n<p>&nbsp;  </p>\n<h4>b)    JAXA JASMIN/Japan JASMAI with MAFF</h4>\n<p>&nbsp;<br>Accumulated and anormal precipitation information derived from GSMaP by JASMIN/JASMAI on this March is shown in following because  in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop.</p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation by GSMap</th>\n<th align=\"center\">Precipitation Anomaly by GSMaP</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"/data/story-images/Precipitation%20by%20GSMaP.png\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/Precipitation%20anomaly%20by%20GSMaP.png\" alt></td>\n</tr>\n<tr>\n<td align=\"center\">&nbsp;</td>\n<td align=\"center\"></td>\n</tr>\n</tbody></table>\n<p>The following time series reflect the precipitation, soil moisture and NDVI variables from JASMIN/JASMAI for Estado de Paran\u00e1: \n&nbsp;  </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation</th>\n<th align=\"center\">Soil Moisture</th>\n<th align=\"center\">NDVI</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"/data/story-images/Parana%20Precipitation.png\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/Parana%20Soil%20Moisture.png\" alt></td>\n<td align=\"center\"><img src=\"/data/story-images/Parana%20Precipitation.png\" alt></td>\n</tr>\n</tbody></table>\n"
     },
     {
-      "width": 2,
-      "text": "## Global / Regional monitor\n### Dry Conditions in Southern Brazil\n\n&nbsp;  \n#### b)\tJAXA JASMIN/Japan JASMAI with MAFF\n&nbsp;  \nAccumulated and anormal precipitation information derived from GSMaP by JASMIN/JASMAI on this March is shown in following because  in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop.\n\nPrecipitation by GSMap     |  Precipitation Anomaly by GSMaP\n:-------------------------:|:-------------------------:\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Precipitation%20by%20GSMaP.png?raw=true)  |  ![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Precipitation%20anomaly%20by%20GSMaP.png?raw=true)\n&nbsp;  \n\n\n\nThe following time series reflect the precipitation, soil moisture and NDVI variables from JASMIN/JASMAI for Estado de Paran\u00e1: \n&nbsp;  \n\nPrecipitation | Soil Moisture | NDVI\n:-------------------------:|:-------------------------:|:-------------------------:\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Precipitation.png?raw=true)  |  ![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Soil%20Moisture.png?raw=true)  |  ![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Precipitation.png?raw=true)\n",
-      "id": "JAXA JASMIN/Japan JASMAI with MAFF-1650225186539",
-      "title": "JAXA JASMIN/Japan JASMAI with MAFF",
-      "__generatedText__": "<h2>Global / Regional monitor</h2>\n<h3>Dry Conditions in Southern Brazil</h3>\n<p>&nbsp;  </p>\n<h4>b)    JAXA JASMIN/Japan JASMAI with MAFF</h4>\n<p>&nbsp;<br>Accumulated and anormal precipitation information derived from GSMaP by JASMIN/JASMAI on this March is shown in following because  in Southern Brazil crops did not receive the expected amount of precipitation, which negatively impacted yields of the spring planted crop.</p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation by GSMap</th>\n<th align=\"center\">Precipitation Anomaly by GSMaP</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Precipitation%20by%20GSMaP.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Precipitation%20anomaly%20by%20GSMaP.png?raw=true\" alt></td>\n</tr>\n<tr>\n<td align=\"center\">&nbsp;</td>\n<td align=\"center\"></td>\n</tr>\n</tbody></table>\n<p>The following time series reflect the precipitation, soil moisture and NDVI variables from JASMIN/JASMAI for Estado de Paran\u00e1: \n&nbsp;  </p>\n<table>\n<thead>\n<tr>\n<th align=\"center\">Precipitation</th>\n<th align=\"center\">Soil Moisture</th>\n<th align=\"center\">NDVI</th>\n</tr>\n</thead>\n<tbody><tr>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Precipitation.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Soil%20Moisture.png?raw=true\" alt></td>\n<td align=\"center\"><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Parana%20Precipitation.png?raw=true\" alt></td>\n</tr>\n</tbody></table>\n"
-    },
-    {
-      "width": 2,
-      "text": "###  The Benefits of Pairing The WorldCereal Initiative and Satellite Data \n&nbsp;  \nWorldCereal is a global Research and Development challenge aiming to engage the global agricultural community to achieve optimal results.  WorldCereal relies heavily on satellites, primarily the operational time series of the Copernicus Sentinels missions, which allow an unprecedented dynamic crop monitoring at global scale. By focusing on maize and wheat during the system demonstration, WorldCereal aims to contribute to the global food security challenge as together with rice, these crops form the world\u2019s most widely cultivated species, accounting for an estimated 42.5 percent of the world\u2019s food calorie supply (FAO).\n\n\n![](https://pbs.twimg.com/media/EfIcd-gXoAAkSaR?format=jpg&name=4096x4096)\n\n\nWorldCereal will not only provide a system which offers accurate, global and seasonally updated cropland extent, crop type, and irrigation maps management type (irrigated vs. non-irrigated), it will also provide the community with the necessary tools and technology to create these maps themselves for their own regions of interest and, if available, using their own reference data.\n\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Canada_summer.png?raw=true)\n\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Canada_winter.png?raw=true)\n\n",
-      "id": "World Cereal-1650225828076",
-      "title": "World Cereal",
-      "__generatedText__": "<h3>The Benefits of Pairing The WorldCereal Initiative and Satellite Data</h3>\n<p>&nbsp;<br>WorldCereal is a global Research and Development challenge aiming to engage the global agricultural community to achieve optimal results.  WorldCereal relies heavily on satellites, primarily the operational time series of the Copernicus Sentinels missions, which allow an unprecedented dynamic crop monitoring at global scale. By focusing on maize and wheat during the system demonstration, WorldCereal aims to contribute to the global food security challenge as together with rice, these crops form the world\u2019s most widely cultivated species, accounting for an estimated 42.5 percent of the world\u2019s food calorie supply (FAO).</p>\n<p><img src=\"https://pbs.twimg.com/media/EfIcd-gXoAAkSaR?format=jpg&name=4096x4096\" alt></p>\n<p>WorldCereal will not only provide a system which offers accurate, global and seasonally updated cropland extent, crop type, and irrigation maps management type (irrigated vs. non-irrigated), it will also provide the community with the necessary tools and technology to create these maps themselves for their own regions of interest and, if available, using their own reference data.</p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Canada_summer.png?raw=true\" alt></p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Canada_winter.png?raw=true\" alt></p>\n"
-    },
-    {
-      "width": 2,
-      "text": "## Brazil\n&nbsp;  \n### Annual cropland\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_annual_cropland.png?raw=true)\n\n\n### Summer\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_summer.png?raw=true)\n\n\n### Winter\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_winter.png?raw=true)\n",
+      "width": 1,
+      "text": "### Brazil (World Cereal)\n&nbsp;  \n### Annual cropland\n![](/data/story-images/Brazil_annual_cropland.png)\n\n\n### Summer\n\n![](/data/story-images/Brazil_summer.png)\n\n\n### Winter\n\n![](/data/story-images/Brazil_winter.png)\n",
       "id": "Brazil-1652102118413",
       "title": "Brazil",
-      "__generatedText__": "<h2>Brazil</h2>\n<p>&nbsp;  </p>\n<h3>Annual cropland</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_annual_cropland.png?raw=true\" alt></p>\n<h3>Summer</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_summer.png?raw=true\" alt></p>\n<h3>Winter</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Brazil_winter.png?raw=true\" alt></p>\n"
-    },
-    {
-      "width": 2,
-      "text": "## Tanzania\n&nbsp;  \n### Annual Cropland\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_annual_cropland_zoom.png?raw=true)\n\n### Summer\n\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_summer_zoom.png?raw=true)\n\n### Winter\n![](https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_winter_zoom.png?raw=true)",
-      "id": " -1652248601504",
-      "title": " ",
-      "__generatedText__": "<h2>Tanzania</h2>\n<p>&nbsp;  </p>\n<h3>Annual Cropland</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_annual_cropland_zoom.png?raw=true\" alt></p>\n<h3>Summer</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_summer_zoom.png?raw=true\" alt></p>\n<h3>Winter</h3>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/Tanzania_winter_zoom.png?raw=true\" alt></p>\n"
+      "__generatedText__": "<h3>Brazil (World Cereal)</h3>\n<p>&nbsp;  </p>\n<h3>Annual cropland</h3>\n<p><img src=\"/data/story-images/Brazil_annual_cropland.png\" alt></p>\n<h3>Summer</h3>\n<p><img src=\"/data/story-images/Brazil_summer.png\" alt></p>\n<h3>Winter</h3>\n<p><img src=\"/data/story-images/Brazil_winter.png\" alt></p>\n"
     },
     {
       "width": 2,
@@ -115,10 +101,10 @@
     },
     {
       "width": 2,
-      "text": "### Satellite Data Provides Critical Agricultural Insights\n\nAs data access and technology have made significant advances in recent decades, it comes as no surprise that the amount of available data can often be overwhelming and difficult to decipher. As such, tools such as the JAXA JASMAI, ESA World Cereal system and the GEOGLAM-Harvest AGMET Indicators play a key role for quick and digestible information processing and supporting key agricultural decisions. In order to increase food market stability and reduce price volatility, it is critical that market analysts, farmers, and other agricultural stakeholders have a thorough understanding of the amount of food coming to market - whether that be in line with the average amount seen in previous years or more/less. Just as with any other consumer good, supply and demand are the key drivers of agrifood markets and commodity prices. Satellite data can fill a critical gap in agricultural monitoring, enabling us to not only understand current crop conditions but prepare for potential outcomes in a given growing season, with wider-reaching impacts on market stability, supporting early action and humanitarian response, and bolstering food security.\n\n\n## Explore Datasets \n &nbsp;   \n<span style=\"color:#4059AD\">Use the **interactive map** on this page to see how NDVI from GCOM-C varies globally. The compare feature on this map allows looking at the levels of NDVI  on two different dates.</span>\n\nThis **EO Dashboard** provides access to interactive maps of NDVI and other insights on agriculture. Access these datasets by clicking on the **EXPLORE DATASETS** button at the top of the page or directly from the links below:\n\n[GCOM NDVI](https://www.eodashboard.org/?poi=World-E10e) &nbsp;   \n[GROGLAM Crop Monitor](https://www.eodashboard.org/?poi=W6-N6&indicator=N6)\n\n",
+      "text": "### Satellite Data Provides Critical Agricultural Insights\n\nAs data access and technology have made significant advances in recent decades, it comes as no surprise that the amount of available data can often be overwhelming and difficult to decipher. As such, tools such as the JAXA JASMAI, ESA World Cereal system and the GEOGLAM-Harvest AGMET Indicators play a key role for quick and digestible information processing and supporting key agricultural decisions. In order to increase food market stability and reduce price volatility, it is critical that market analysts, farmers, and other agricultural stakeholders have a thorough understanding of the amount of food coming to market - whether that be in line with the average amount seen in previous years or more/less. Just as with any other consumer good, supply and demand are the key drivers of agrifood markets and commodity prices. Satellite data can fill a critical gap in agricultural monitoring, enabling us to not only understand current crop conditions but prepare for potential outcomes in a given growing season, with wider-reaching impacts on market stability, supporting early action and humanitarian response, and bolstering food security.\n\n\n## Explore Datasets \n &nbsp;   \n<span style=\"color:#4059AD\">Use the **interactive map** on this page to see how NDVI from GCOM-C varies globally. The compare feature on this map allows looking at the levels of NDVI  on two different dates.</span>\n\nThis **EO Dashboard** provides access to interactive maps of NDVI and other insights on agriculture. Access these datasets by clicking on the **EXPLORE DATASETS** button at the top of the page or directly from the links below:\n\n[GCOM NDVI](https://www.eodashboard.org/?poi=World-E10e) &nbsp;   \n[GEOGLAM Crop Monitor](https://www.eodashboard.org/?poi=W6-N6&indicator=N6)\n\n",
       "id": "Final page-1652247980060",
       "title": "Final page",
-      "__generatedText__": "<h3>Satellite Data Provides Critical Agricultural Insights</h3>\n<p>As data access and technology have made significant advances in recent decades, it comes as no surprise that the amount of available data can often be overwhelming and difficult to decipher. As such, tools such as the JAXA JASMAI, ESA World Cereal system and the GEOGLAM-Harvest AGMET Indicators play a key role for quick and digestible information processing and supporting key agricultural decisions. In order to increase food market stability and reduce price volatility, it is critical that market analysts, farmers, and other agricultural stakeholders have a thorough understanding of the amount of food coming to market - whether that be in line with the average amount seen in previous years or more/less. Just as with any other consumer good, supply and demand are the key drivers of agrifood markets and commodity prices. Satellite data can fill a critical gap in agricultural monitoring, enabling us to not only understand current crop conditions but prepare for potential outcomes in a given growing season, with wider-reaching impacts on market stability, supporting early action and humanitarian response, and bolstering food security.</p>\n<h2>Explore Datasets</h2>\n<p> &nbsp;<br><span>Use the <strong>interactive map</strong> on this page to see how NDVI from GCOM-C varies globally. The compare feature on this map allows looking at the levels of NDVI  on two different dates.</span></p>\n<p>This <strong>EO Dashboard</strong> provides access to interactive maps of NDVI and other insights on agriculture. Access these datasets by clicking on the <strong>EXPLORE DATASETS</strong> button at the top of the page or directly from the links below:</p>\n<p><a href=\"https://www.eodashboard.org/?poi=World-E10e\">GCOM NDVI</a> &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=W6-N6&amp;indicator=N6\">GROGLAM Crop Monitor</a></p>\n"
+      "__generatedText__": "<h3>Satellite Data Provides Critical Agricultural Insights</h3>\n<p>As data access and technology have made significant advances in recent decades, it comes as no surprise that the amount of available data can often be overwhelming and difficult to decipher. As such, tools such as the JAXA JASMAI, ESA World Cereal system and the GEOGLAM-Harvest AGMET Indicators play a key role for quick and digestible information processing and supporting key agricultural decisions. In order to increase food market stability and reduce price volatility, it is critical that market analysts, farmers, and other agricultural stakeholders have a thorough understanding of the amount of food coming to market - whether that be in line with the average amount seen in previous years or more/less. Just as with any other consumer good, supply and demand are the key drivers of agrifood markets and commodity prices. Satellite data can fill a critical gap in agricultural monitoring, enabling us to not only understand current crop conditions but prepare for potential outcomes in a given growing season, with wider-reaching impacts on market stability, supporting early action and humanitarian response, and bolstering food security.</p>\n<h2>Explore Datasets</h2>\n<p> &nbsp;<br><span>Use the <strong>interactive map</strong> on this page to see how NDVI from GCOM-C varies globally. The compare feature on this map allows looking at the levels of NDVI  on two different dates.</span></p>\n<p>This <strong>EO Dashboard</strong> provides access to interactive maps of NDVI and other insights on agriculture. Access these datasets by clicking on the <strong>EXPLORE DATASETS</strong> button at the top of the page or directly from the links below:</p>\n<p><a href=\"https://www.eodashboard.org/?poi=World-E10e\">GCOM NDVI</a> &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=W6-N6&amp;indicator=N6\">GEOGLAM Crop Monitor</a></p>\n"
     }
   ],
   "title": "Satellite data applications for environmental impacts on world crop (cereal) supply and demand"
diff --git a/app/public/data/dashboards/4bcaa8555a55749d.json b/app/public/data/dashboards/4bcaa8555a55749d.json
index 19bd4432a0..741c7bef74 100644
--- a/app/public/data/dashboards/4bcaa8555a55749d.json
+++ b/app/public/data/dashboards/4bcaa8555a55749d.json
@@ -14,24 +14,24 @@
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2019267212500.CalifornaiCoastalBloom.jpg<--IMG-->Aqua/MODIS collected this view of algal blooms along the central California coast on September 24, 2019. Click on the above image to get a larger version.",
+      "text": "<--IMG-->/data/story-images/A2019267212500.CalifornaiCoastalBloom.jpg<--IMG-->Aqua/MODIS collected this view of algal blooms along the central California coast on September 24, 2019. Click on the above image to get a larger version.",
       "id": "Central California Coastal Blooms-1651562684280",
       "title": "Central California Coastal Blooms",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2019267212500.CalifornaiCoastalBloom.jpg\">https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2019267212500.CalifornaiCoastalBloom.jpg</a>&lt;--IMG--&gt;Aqua/MODIS collected this view of algal blooms along the central California coast on September 24, 2019. Click on the above image to get a larger version.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/A2019267212500.CalifornaiCoastalBloom.jpg&lt;--IMG--&gt;Aqua/MODIS collected this view of algal blooms along the central California coast on September 24, 2019. Click on the above image to get a larger version.</p>\n"
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%201.png<--IMG-->Lake Erie Predicted bloom severity data to 2021-05-09",
+      "text": "<--IMG-->/data/story-images/NASA%20Ocean%20Fig%201.png<--IMG-->Lake Erie Predicted bloom severity data to 2021-05-09",
       "id": "Lake Erie-1652179631815",
       "title": "Lake Erie",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%201.png\">https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%201.png</a>&lt;--IMG--&gt;Lake Erie Predicted bloom severity data to 2021-05-09</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/NASA%20Ocean%20Fig%201.png&lt;--IMG--&gt;Lake Erie Predicted bloom severity data to 2021-05-09</p>\n"
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%202.png<--IMG-->HAB Occurrence (days)",
+      "text": "<--IMG-->/data/story-images/NASA%20Ocean%20Fig%202.png<--IMG-->HAB Occurrence (days)",
       "id": "HAB-1652179664849",
       "title": "HAB",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%202.png\">https://raw.githubusercontent.com/eurodatacube/eodash/3eea082b1e9b156c2f70c0751c04e0706eaa1335/app/public/data/story-images/NASA%20Ocean%20Fig%202.png</a>&lt;--IMG--&gt;HAB Occurrence (days)</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/NASA%20Ocean%20Fig%202.png&lt;--IMG--&gt;HAB Occurrence (days)</p>\n"
     }
   ],
   "title": "NASA Ocean Story 1"
diff --git a/app/public/data/dashboards/6be3fcfd33789f76.json b/app/public/data/dashboards/6be3fcfd33789f76.json
index 22c2c94ef4..09cc1806d6 100644
--- a/app/public/data/dashboards/6be3fcfd33789f76.json
+++ b/app/public/data/dashboards/6be3fcfd33789f76.json
@@ -8,10 +8,10 @@
   "features": [
     {
       "width": 2,
-      "text": "## Sea Ice Freeboard and Thickness from Satellite Altimetry\n&nbsp;  \n### NASA's ICESat-2 and ATLAS\n&nbsp;  \nNASA provides data collected over the Arctic Ocean by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) that show monthly sea ice freeboard from 2018 to the present. Sea ice freeboard is the amount of sea ice and snow cover that is visible above the surface of the ocean and, along with snow depth, is a critical measurement for accurately measuring the thickness of sea ice in the polar oceans. Click here to learn more about this research.\n \nNASA\u2019s ICESat-2 mission has been measuring the height of the Earth\u2019s features from space with a laser ever since its launch in September 2018. The Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 shoots pulses of light towards the Earth\u2019s surface. ATLAS counts the particles of light (i.e., photons) that bounce off the Earth\u2019s surface and return to the instrument\u2019s sensors, measuring the round-trip time of each photon with extreme accuracy. Scientists can then take the total elapsed time to generate a height measurement of the particular surface that each photon bounced from. When enough photon measurements have been collected, scientists clump these measurements together to form a \u201cphoton cloud\u201d that produces continuous surface elevation measurements as the satellite collects more and more data along its orbit. ICESat-2 collects data over the same location every 91 days, and this allows scientists to develop a picture of how the heights at certain spots around the globe are changing over time.\n \nCollecting repeated height measurements of the Earth\u2019s ice from space is an important way to view changes happening over the course of seasons and years. While ICESat-2 can only measure the height of Earth\u2019s ice, scientists utilize special formulas (based on Archimedes' principle) to take those height measurements and turn them into measurements of sea ice freeboard that includes the ice above and below the waterline.\n \n",
+      "text": "## Sea Ice Freeboard and Thickness from Satellite Altimetry\n&nbsp;  \n### NASA's ICESat-2 and ATLAS\n&nbsp;  \nNASA provides data collected over the Arctic Ocean by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) that show monthly sea ice freeboard from 2018 to the present. Sea ice freeboard is the amount of sea ice and snow cover that is visible above the surface of the ocean and, along with snow depth, is a critical measurement for accurately measuring the thickness of sea ice in the polar oceans. Click [here](https://icesat-2.gsfc.nasa.gov/science) to learn more about this research.\n \nNASA\u2019s ICESat-2 mission has been measuring the height of the Earth\u2019s features from space with a laser ever since its launch in September 2018. The Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 shoots pulses of light towards the Earth\u2019s surface. ATLAS counts the particles of light (i.e., photons) that bounce off the Earth\u2019s surface and return to the instrument\u2019s sensors, measuring the round-trip time of each photon with extreme accuracy. Scientists can then take the total elapsed time to generate a height measurement of the particular surface that each photon bounced from. When enough photon measurements have been collected, scientists clump these measurements together to form a \u201cphoton cloud\u201d that produces continuous surface elevation measurements as the satellite collects more and more data along its orbit. ICESat-2 collects data over the same location every 91 days, and this allows scientists to develop a picture of how the heights at certain spots around the globe are changing over time.\n \nCollecting repeated height measurements of the Earth\u2019s ice from space is an important way to view changes happening over the course of seasons and years. While ICESat-2 can only measure the height of Earth\u2019s ice, scientists utilize special formulas (based on Archimedes' principle) to take those height measurements and turn them into measurements of sea ice freeboard that includes the ice above and below the waterline.\n \n",
       "id": "IceSAT-2-1652251792610",
       "title": "IceSAT-2",
-      "__generatedText__": "<h2>Sea Ice Freeboard and Thickness from Satellite Altimetry</h2>\n<p>&nbsp;  </p>\n<h3>NASA&#39;s ICESat-2 and ATLAS</h3>\n<p>&nbsp;<br>NASA provides data collected over the Arctic Ocean by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) that show monthly sea ice freeboard from 2018 to the present. Sea ice freeboard is the amount of sea ice and snow cover that is visible above the surface of the ocean and, along with snow depth, is a critical measurement for accurately measuring the thickness of sea ice in the polar oceans. Click here to learn more about this research.</p>\n<p>NASA\u2019s ICESat-2 mission has been measuring the height of the Earth\u2019s features from space with a laser ever since its launch in September 2018. The Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 shoots pulses of light towards the Earth\u2019s surface. ATLAS counts the particles of light (i.e., photons) that bounce off the Earth\u2019s surface and return to the instrument\u2019s sensors, measuring the round-trip time of each photon with extreme accuracy. Scientists can then take the total elapsed time to generate a height measurement of the particular surface that each photon bounced from. When enough photon measurements have been collected, scientists clump these measurements together to form a \u201cphoton cloud\u201d that produces continuous surface elevation measurements as the satellite collects more and more data along its orbit. ICESat-2 collects data over the same location every 91 days, and this allows scientists to develop a picture of how the heights at certain spots around the globe are changing over time.</p>\n<p>Collecting repeated height measurements of the Earth\u2019s ice from space is an important way to view changes happening over the course of seasons and years. While ICESat-2 can only measure the height of Earth\u2019s ice, scientists utilize special formulas (based on Archimedes&#39; principle) to take those height measurements and turn them into measurements of sea ice freeboard that includes the ice above and below the waterline.</p>\n"
+      "__generatedText__": "<h2>Sea Ice Freeboard and Thickness from Satellite Altimetry</h2>\n<p>&nbsp;  </p>\n<h3>NASA&#39;s ICESat-2 and ATLAS</h3>\n<p>&nbsp;<br>NASA provides data collected over the Arctic Ocean by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) that show monthly sea ice freeboard from 2018 to the present. Sea ice freeboard is the amount of sea ice and snow cover that is visible above the surface of the ocean and, along with snow depth, is a critical measurement for accurately measuring the thickness of sea ice in the polar oceans. Click <a href=\"https://icesat-2.gsfc.nasa.gov/science\">here</a> to learn more about this research.</p>\n<p>NASA\u2019s ICESat-2 mission has been measuring the height of the Earth\u2019s features from space with a laser ever since its launch in September 2018. The Advanced Topographic Laser Altimeter System (ATLAS) onboard ICESat-2 shoots pulses of light towards the Earth\u2019s surface. ATLAS counts the particles of light (i.e., photons) that bounce off the Earth\u2019s surface and return to the instrument\u2019s sensors, measuring the round-trip time of each photon with extreme accuracy. Scientists can then take the total elapsed time to generate a height measurement of the particular surface that each photon bounced from. When enough photon measurements have been collected, scientists clump these measurements together to form a \u201cphoton cloud\u201d that produces continuous surface elevation measurements as the satellite collects more and more data along its orbit. ICESat-2 collects data over the same location every 91 days, and this allows scientists to develop a picture of how the heights at certain spots around the globe are changing over time.</p>\n<p>Collecting repeated height measurements of the Earth\u2019s ice from space is an important way to view changes happening over the course of seasons and years. While ICESat-2 can only measure the height of Earth\u2019s ice, scientists utilize special formulas (based on Archimedes&#39; principle) to take those height measurements and turn them into measurements of sea ice freeboard that includes the ice above and below the waterline.</p>\n"
     },
     {
       "width": 2,
@@ -28,10 +28,10 @@
     },
     {
       "width": 1,
-      "text": "### ESA's CryoSat-2 and Envisat\n&nbsp;  \nWhile measurements from ICESat-2 can be used to determine the height and freeboard of polar sea ice, scientists need a little help from ESA and the CryoSat-2 mission to measure sea ice thickness. Researchers at ESA provide monthly measurements of sea ice thickness from the CryoSat-2 and Envisat radar altimeters. \n\nWatch this video to learn how ESA measures sea ice thickness from space.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9einyMSOmHE\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
+      "text": "### ESA's CryoSat-2 and Envisat\n&nbsp;  \nWhile measurements from ICESat-2 can be used to determine the height and freeboard of polar sea ice, scientists also rely on ESA and the CryoSat-2 mission to measure sea ice thickness. Researchers at ESA provide monthly measurements of sea ice thickness from the CryoSat-2 and Envisat radar altimeters and other supporting missions. \n\nWatch this video to learn how ESA measures sea ice thickness from space.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9einyMSOmHE\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
       "id": "Cryosat-1652251780072",
       "title": "Cryosat",
-      "__generatedText__": "<h3>ESA&#39;s CryoSat-2 and Envisat</h3>\n<p>&nbsp;<br>While measurements from ICESat-2 can be used to determine the height and freeboard of polar sea ice, scientists need a little help from ESA and the CryoSat-2 mission to measure sea ice thickness. Researchers at ESA provide monthly measurements of sea ice thickness from the CryoSat-2 and Envisat radar altimeters. </p>\n<p>Watch this video to learn how ESA measures sea ice thickness from space.</p>\n&lt;iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9einyMSOmHE\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen&gt;&lt;/iframe&gt;\n"
+      "__generatedText__": "<h3>ESA&#39;s CryoSat-2 and Envisat</h3>\n<p>&nbsp;<br>While measurements from ICESat-2 can be used to determine the height and freeboard of polar sea ice, scientists also rely on ESA and the CryoSat-2 mission to measure sea ice thickness. Researchers at ESA provide monthly measurements of sea ice thickness from the CryoSat-2 and Envisat radar altimeters and other supporting missions. </p>\n<p>Watch this video to learn how ESA measures sea ice thickness from space.</p>\n&lt;iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9einyMSOmHE\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen&gt;&lt;/iframe&gt;\n"
     },
     {
       "width": 3,
@@ -46,10 +46,10 @@
     },
     {
       "width": 1,
-      "text": " ESA has been building radar altimeters since the early 1990s. Radar altimeters use a microwave signal to measure sea ice freeboard. In this frequency band, the signal can penetrate further into the snow that often lies on top of sea ice. To better understand how far the radar signals can penetrate the snowpack, separate field or airborne campaigns are needed to validate the satellite signals. Ongoing research is comparing altimeter signals at different frequencies to calculate how much snow lies on top of the sea ice. This is important as the amount of snow can also impact the freeboard\u2014for instance a heavy snow-load can weigh the sea ice down and may even push it under water.\n \nOnce the freeboard has been established, it can be converted to sea ice thickness. The ESA Climate Change Initiative Sea Ice Thickness data set presented here shows the decrease in winter sea ice thickness over the past 15 years using satellite information from ESA\u2019s Envisat and CryoSat-2 missions.",
+      "text": " ESA has been building radar altimeters since the early 1990s. Radar altimeters use a microwave signal to measure sea ice freeboard. In this frequency band, the signal can penetrate further into snow that lies on sea ice. To better understand how far the microwaves can penetrate the snowpack, complementary field or airborne campaigns are needed to validate the satellite data. Researchers are comparing altimeter signals at different frequencies to calculate how much snow lies on top of the sea ice. This is important as the amount of snow can also impact the freeboard\u2014for instance a heavy snow-load can weigh the sea ice down and may even push it under water.\n \nResearchers can calculate sea ice thickness once the freeboard has been established. The ESA Climate Change Initiative Sea Ice Thickness data set presented here shows the decrease in winter sea ice thickness over the past 15 years using information from ESA\u2019s Envisat and CryoSat-2 satellite missions.",
       "id": "Envisat-1652251767198",
       "title": "Envisat",
-      "__generatedText__": "<p> ESA has been building radar altimeters since the early 1990s. Radar altimeters use a microwave signal to measure sea ice freeboard. In this frequency band, the signal can penetrate further into the snow that often lies on top of sea ice. To better understand how far the radar signals can penetrate the snowpack, separate field or airborne campaigns are needed to validate the satellite signals. Ongoing research is comparing altimeter signals at different frequencies to calculate how much snow lies on top of the sea ice. This is important as the amount of snow can also impact the freeboard\u2014for instance a heavy snow-load can weigh the sea ice down and may even push it under water.</p>\n<p>Once the freeboard has been established, it can be converted to sea ice thickness. The ESA Climate Change Initiative Sea Ice Thickness data set presented here shows the decrease in winter sea ice thickness over the past 15 years using satellite information from ESA\u2019s Envisat and CryoSat-2 missions.</p>\n"
+      "__generatedText__": "<p> ESA has been building radar altimeters since the early 1990s. Radar altimeters use a microwave signal to measure sea ice freeboard. In this frequency band, the signal can penetrate further into snow that lies on sea ice. To better understand how far the microwaves can penetrate the snowpack, complementary field or airborne campaigns are needed to validate the satellite data. Researchers are comparing altimeter signals at different frequencies to calculate how much snow lies on top of the sea ice. This is important as the amount of snow can also impact the freeboard\u2014for instance a heavy snow-load can weigh the sea ice down and may even push it under water.</p>\n<p>Researchers can calculate sea ice thickness once the freeboard has been established. The ESA Climate Change Initiative Sea Ice Thickness data set presented here shows the decrease in winter sea ice thickness over the past 15 years using information from ESA\u2019s Envisat and CryoSat-2 satellite missions.</p>\n"
     },
     {
       "width": 3,
diff --git a/app/public/data/dashboards/6ea3293aade90541.json b/app/public/data/dashboards/6ea3293aade90541.json
index ee4d5cc109..8fb3066d8c 100644
--- a/app/public/data/dashboards/6ea3293aade90541.json
+++ b/app/public/data/dashboards/6ea3293aade90541.json
@@ -7,52 +7,106 @@
   },
   "features": [
     {
-      "width": 2,
-      "text": "<--IMG-->https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2021071.LakeErie.crop.small.jpg<--IMG-->At the end of winter the ice is nearly all gone from Lake Erie, and growing phytoplankton populations are revealed mingling with suspended sediments in this relatively shallow Great Lake.\n\nThe above Aqua/MODIS image was collected on March 12, 2021. Click on it to get a larger version that includes all of the Great Lakes.",
+      "width": 1,
+      "text": "## Following the Activity of Phytoplankton \u2013 The Miracle Plants in The Ocean\n&nbsp;  \nWhen most of us think of plants, we envision the vibrant colors of a beautiful garden, a lush rainforest, or even a healthy bowl of salad. Plants provide us with essential nutrients, and they are critical to maintaining carbon dioxide (CO<sub>2</sub>) levels in the atmosphere and regulating Earth\u2019s climate. \n\nJust as important to sustaining life on land, however, are plants in the oceans, which have been studied with advanced satellites and monitoring instruments for more than 30 years.\n\nBefore satellite sensors, we thought the open ocean was plain and lacking any significant features. For many years, we depended on measurements taken only from oceanographic ships, but these were laborious and only covered a tiny portion of the ocean at a given time. With a daily focus on the entire Earth, satellite images have revealed a view of the ocean that is as rich and complex as that of land.   \n\nAbout half of the oxygen we take in every breath comes from microscopic plants in the ocean called phytoplankton. Similar to land-based plants, phytoplankton are at the base of the food chain, and they release oxygen during photosynthesis \u2013 a process in which energy from the sun is used to convert CO<sub>2</sub> into carbon that is used for growth. Phytoplankton come in different shapes and sizes, and different species have different roles in the ocean ecosystem.  \n\nAs with land vegetation, there are areas of the ocean where phytoplankton are in abundance and other areas where phytoplankton are scarce. One big difference, however, is that phytoplankton are much more difficult to see than a tree in a forest. Satellites give scientists the data to find and measure the amounts of phytoplankton, and to determine ocean primary productivity, which measures the mass of carbon per area of the ocean over time (mg C m<sup>-2</sup> yr<sup>-1</sup>).",
+      "id": "introduction-1647902900871",
+      "title": " ",
+      "__generatedText__": "<h2>Following the Activity of Phytoplankton \u2013 The Miracle Plants in The Ocean</h2>\n<p>&nbsp;<br>When most of us think of plants, we envision the vibrant colors of a beautiful garden, a lush rainforest, or even a healthy bowl of salad. Plants provide us with essential nutrients, and they are critical to maintaining carbon dioxide (CO<sub>2</sub>) levels in the atmosphere and regulating Earth\u2019s climate. </p>\n<p>Just as important to sustaining life on land, however, are plants in the oceans, which have been studied with advanced satellites and monitoring instruments for more than 30 years.</p>\n<p>Before satellite sensors, we thought the open ocean was plain and lacking any significant features. For many years, we depended on measurements taken only from oceanographic ships, but these were laborious and only covered a tiny portion of the ocean at a given time. With a daily focus on the entire Earth, satellite images have revealed a view of the ocean that is as rich and complex as that of land.   </p>\n<p>About half of the oxygen we take in every breath comes from microscopic plants in the ocean called phytoplankton. Similar to land-based plants, phytoplankton are at the base of the food chain, and they release oxygen during photosynthesis \u2013 a process in which energy from the sun is used to convert CO<sub>2</sub> into carbon that is used for growth. Phytoplankton come in different shapes and sizes, and different species have different roles in the ocean ecosystem.  </p>\n<p>As with land vegetation, there are areas of the ocean where phytoplankton are in abundance and other areas where phytoplankton are scarce. One big difference, however, is that phytoplankton are much more difficult to see than a tree in a forest. Satellites give scientists the data to find and measure the amounts of phytoplankton, and to determine ocean primary productivity, which measures the mass of carbon per area of the ocean over time (mg C m<sup>-2</sup> yr<sup>-1</sup>).</p>\n"
+    },
+    {
+      "width": 3,
+      "text": "<--IMG-->/data/story-images/A2021071.LakeErie.crop.small.jpg<--IMG-->At the end of winter the ice is nearly all gone from Lake Erie, and growing phytoplankton populations are revealed mingling with suspended sediments in this relatively shallow Great Lake.\n\nThe above Aqua/MODIS image was collected on March 12, 2021. Click on it to get a larger version that includes all of the Great Lakes.",
       "id": "Lake Erie-1651555257284",
       "title": "Lake Erie",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2021071.LakeErie.crop.small.jpg\">https://oceancolor.gsfc.nasa.gov/gallery/feature/images/A2021071.LakeErie.crop.small.jpg</a>&lt;--IMG--&gt;At the end of winter the ice is nearly all gone from Lake Erie, and growing phytoplankton populations are revealed mingling with suspended sediments in this relatively shallow Great Lake.</p>\n<p>The above Aqua/MODIS image was collected on March 12, 2021. Click on it to get a larger version that includes all of the Great Lakes.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/A2021071.LakeErie.crop.small.jpg&lt;--IMG--&gt;At the end of winter the ice is nearly all gone from Lake Erie, and growing phytoplankton populations are revealed mingling with suspended sediments in this relatively shallow Great Lake.</p>\n<p>The above Aqua/MODIS image was collected on March 12, 2021. Click on it to get a larger version that includes all of the Great Lakes.</p>\n"
     },
     {
-      "width": 2,
-      "text": "## Following the Activity of Phytoplankton \u2013 The Miracle Plants in The Ocean\n&nbsp;  \nWhen most of us think of plants, we envision the vibrant colors of a beautiful garden, a lush rainforest, or even a healthy bowl of salad. Plants provide us with essential nutrients, and they are critical to maintaining carbon dioxide (CO<sub>2</sub>) levels in the atmosphere and regulating Earth\u2019s climate. \n\nJust as important to sustaining life on the ground, however, are plants in the oceans, which have been studied with advanced satellites and monitoring instruments for more than 30 years.\n\nBefore satellite sensors, we thought the open ocean was plain and lacking any significant features.  For many years, we depended on measurements taken only from oceanographic ships, but these were slow and only covered a tiny portion of the ocean at a given time. With a daily focus on the entire Earth, satellite images have revealed a view of the ocean that is as rich and complex as that of land.   \n\nHalf of the oxygen we take in every breath comes from microscopic plants in the ocean called phytoplankton. Similar to land-based plants, phytoplankton are at the base of the food chain, and they release oxygen during photosynthesis \u2013 the process of using energy from CO<sub>2</sub> to grow. Phytoplankton come in different shapes, and different species have different roles in the ocean ecosystem.  \n\nAs with land vegetation, there are areas of the ocean where phytoplankton are in abundance, and others where they are scarce. One big difference, however, is that phytoplankton are much more difficult to see than a tree in a forest. Satellites give scientists the data to find and measure the amounts of phytoplankton, and to determine ocean primary productivity, which measures the mass of carbon per area of the ocean over time (mg C m<sup>-2</sup> yr<sup>-1</sup>). ",
-      "id": "introduction-1647902900871",
+      "width": 1,
+      "text": "### Plants and the Climate\n&nbsp;  \nPrimary productivity is important to the Earth\u2019s environment, notably by capturing large amounts of the CO<sub>2</sub> released from the burning of fossil fuels, which causes global warming. In fact, it is estimated that oceans have captured about half of the CO<sub>2</sub> released by burning fossil fuels. \n\nThe Net Primary Productivity (NPP), also known as the net carbon gain by plants, or the measure of plant growth, can be measured via satellite. NASA, JAXA, and ESA base their estimates on slightly different input datasets and methodologies (*Behrenfeld et al. 2005, Kameda et al, 2005; Kulk et al, 2021*). Nevertheless, all three agencies provide comparable NPP estimates, as highlighted in Fig. 1 which shows maps of global NPP in 2020.  \n\nAll three NPP estimates also show phytoplankton variability between locations (Fig. 1) and seasons (Fig. 2). This is because phytoplankton primary productivity is affected by conditions in the water column, including temperature, light, and nutrients which, in turn, are themselves influenced by variations in oceanic and atmospheric processes at different spatial and temporal scales. ",
+      "id": "page 2-1647902968281",
+      "title": "page 2",
+      "__generatedText__": "<h3>Plants and the Climate</h3>\n<p>&nbsp;<br>Primary productivity is important to the Earth\u2019s environment, notably by capturing large amounts of the CO<sub>2</sub> released from the burning of fossil fuels, which causes global warming. In fact, it is estimated that oceans have captured about half of the CO<sub>2</sub> released by burning fossil fuels. </p>\n<p>The Net Primary Productivity (NPP), also known as the net carbon gain by plants, or the measure of plant growth, can be measured via satellite. NASA, JAXA, and ESA base their estimates on slightly different input datasets and methodologies (<em>Behrenfeld et al. 2005, Kameda et al, 2005; Kulk et al, 2021</em>). Nevertheless, all three agencies provide comparable NPP estimates, as highlighted in Fig. 1 which shows maps of global NPP in 2020.  </p>\n<p>All three NPP estimates also show phytoplankton variability between locations (Fig. 1) and seasons (Fig. 2). This is because phytoplankton primary productivity is affected by conditions in the water column, including temperature, light, and nutrients which, in turn, are themselves influenced by variations in oceanic and atmospheric processes at different spatial and temporal scales. </p>\n"
+    },
+    {
+      "width": 3,
+      "text": "<--IMG-->/data/story-images/Ocean%20joint%20figure%201.png<--IMG-->Figure 1: Net primary productivity (in mg C m-2 yr-1), annual mean for 2020\n",
+      "id": " -1652438933194",
       "title": " ",
-      "__generatedText__": "<h2>Following the Activity of Phytoplankton \u2013 The Miracle Plants in The Ocean</h2>\n<p>&nbsp;<br>When most of us think of plants, we envision the vibrant colors of a beautiful garden, a lush rainforest, or even a healthy bowl of salad. Plants provide us with essential nutrients, and they are critical to maintaining carbon dioxide (CO<sub>2</sub>) levels in the atmosphere and regulating Earth\u2019s climate. </p>\n<p>Just as important to sustaining life on the ground, however, are plants in the oceans, which have been studied with advanced satellites and monitoring instruments for more than 30 years.</p>\n<p>Before satellite sensors, we thought the open ocean was plain and lacking any significant features.  For many years, we depended on measurements taken only from oceanographic ships, but these were slow and only covered a tiny portion of the ocean at a given time. With a daily focus on the entire Earth, satellite images have revealed a view of the ocean that is as rich and complex as that of land.   </p>\n<p>Half of the oxygen we take in every breath comes from microscopic plants in the ocean called phytoplankton. Similar to land-based plants, phytoplankton are at the base of the food chain, and they release oxygen during photosynthesis \u2013 the process of using energy from CO<sub>2</sub> to grow. Phytoplankton come in different shapes, and different species have different roles in the ocean ecosystem.  </p>\n<p>As with land vegetation, there are areas of the ocean where phytoplankton are in abundance, and others where they are scarce. One big difference, however, is that phytoplankton are much more difficult to see than a tree in a forest. Satellites give scientists the data to find and measure the amounts of phytoplankton, and to determine ocean primary productivity, which measures the mass of carbon per area of the ocean over time (mg C m<sup>-2</sup> yr<sup>-1</sup>). </p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Ocean%20joint%20figure%201.png&lt;--IMG--&gt;Figure 1: Net primary productivity (in mg C m-2 yr-1), annual mean for 2020</p>\n"
     },
     {
-      "width": 2,
-      "text": "### Plants and the Climate\n&nbsp;  \nPrimary productivity is important to Earth\u2019s environment, notably by capturing large amounts of the CO<sub>2</sub> released from the burning of fossil fuels, which causes global warming.  In fact, it is estimated that oceans have captured about half of the CO<sub>2</sub> released by burning fossil fuels.\n \nThe Net Primary Productivity (NPP), also known as the net carbon gain by plants, or the measure of plant growth, can be measured via satellite. NASA, JAXA, and ESA base their estimates on slightly different input datasets and methodologies (*Behrenfeld et al. 2005, Kameda et al, 2005; Kulk et al, 2021*). Nevertheless, all three agencies provide comparable NPP estimates, as highlighted in **Figure. 1** which shows maps of global NPP in 2020.  \n\n![](https://raw.githubusercontent.com/eurodatacube/eodash/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-EO_Dashboard_Water_Quality_Phytoplankton_Production.gif)\n\nFigure 1. Maps of global Phytoplankton Primary Production (NPP) in 2020\n\nAll three NPP estimates also show phytoplankton variability between locations (**Figure. 1**) and seasons (**Figure. 2**). This is because phytoplankton primary productivity is affected by conditions in the water column, including temperature, light, and nutrients which, in turn, are themselves influenced by variations in oceanic and atmospheric processes at different spatial and temporal scales. \n\n### Interactive Map of NPP\n\nExplore the following interactive map to observe the variability of NPP across 2020 globally. \n\nYou can also access more advanced features by viewing this indicator in [**Exploration mode**](https://www.eodashboard.org/?poi=NPP-NPP)\n\n&nbsp; ",
-      "id": "page 2-1647902968281",
-      "title": "page 2",
-      "__generatedText__": "<h3>Plants and the Climate</h3>\n<p>&nbsp;<br>Primary productivity is important to Earth\u2019s environment, notably by capturing large amounts of the CO<sub>2</sub> released from the burning of fossil fuels, which causes global warming.  In fact, it is estimated that oceans have captured about half of the CO<sub>2</sub> released by burning fossil fuels.</p>\n<p>The Net Primary Productivity (NPP), also known as the net carbon gain by plants, or the measure of plant growth, can be measured via satellite. NASA, JAXA, and ESA base their estimates on slightly different input datasets and methodologies (<em>Behrenfeld et al. 2005, Kameda et al, 2005; Kulk et al, 2021</em>). Nevertheless, all three agencies provide comparable NPP estimates, as highlighted in <strong>Figure. 1</strong> which shows maps of global NPP in 2020.  </p>\n<p><img src=\"https://raw.githubusercontent.com/eurodatacube/eodash/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-EO_Dashboard_Water_Quality_Phytoplankton_Production.gif\" alt></p>\n<p>Figure 1. Maps of global Phytoplankton Primary Production (NPP) in 2020</p>\n<p>All three NPP estimates also show phytoplankton variability between locations (<strong>Figure. 1</strong>) and seasons (<strong>Figure. 2</strong>). This is because phytoplankton primary productivity is affected by conditions in the water column, including temperature, light, and nutrients which, in turn, are themselves influenced by variations in oceanic and atmospheric processes at different spatial and temporal scales. </p>\n<h3>Interactive Map of NPP</h3>\n<p>Explore the following interactive map to observe the variability of NPP across 2020 globally. </p>\n<p>You can also access more advanced features by viewing this indicator in <a href=\"https://www.eodashboard.org/?poi=NPP-NPP\"><strong>Exploration mode</strong></a></p>\n<p>&nbsp; </p>\n"
+      "width": 1,
+      "text": "For example, coastal areas have high levels of phytoplankton productivity due to nutrient-rich waters. These nutrients are brought into the water column by river runoff or coastal upwelling of deep water. Productivity in tropical water is low throughout the year due to warm and highly stratified surface waters that prevent the nutrient-rich deep waters from reaching the surface. \n\nAt mid-latitudes, a seasonal cycle is observed, with the lowest productivity in winter due to light scarcity, followed in spring by productivity blooms due to the combination of light and well mixed, nutrient-rich water. In the summer, productivity declines despite strong light availability, due to increased stratification and nutrient depletion by the spring/early summer phytoplankton blooms. \n\nAt high latitudes, stratification is weak year-round, but scarcity or absence of light in winter/autumn leads to lower levels of productivity. In spring and summer, similarly to temperate areas, a higher level of productivity is observed due to the combination of sunlight and nutrient abundance.\nMeasuring primary productivity in our oceans is critical to understanding the effects of global warming, now and for the next generation. Our research satellites allow us to do this from the advantageous position of space.",
+      "id": " -1652439009656",
+      "title": " ",
+      "__generatedText__": "<p>For example, coastal areas have high levels of phytoplankton productivity due to nutrient-rich waters. These nutrients are brought into the water column by river runoff or coastal upwelling of deep water. Productivity in tropical water is low throughout the year due to warm and highly stratified surface waters that prevent the nutrient-rich deep waters from reaching the surface. </p>\n<p>At mid-latitudes, a seasonal cycle is observed, with the lowest productivity in winter due to light scarcity, followed in spring by productivity blooms due to the combination of light and well mixed, nutrient-rich water. In the summer, productivity declines despite strong light availability, due to increased stratification and nutrient depletion by the spring/early summer phytoplankton blooms. </p>\n<p>At high latitudes, stratification is weak year-round, but scarcity or absence of light in winter/autumn leads to lower levels of productivity. In spring and summer, similarly to temperate areas, a higher level of productivity is observed due to the combination of sunlight and nutrient abundance.\nMeasuring primary productivity in our oceans is critical to understanding the effects of global warming, now and for the next generation. Our research satellites allow us to do this from the advantageous position of space.</p>\n"
+    },
+    {
+      "width": 3,
+      "text": "![](/data/story-images/1-EO_Dashboard_Water_Quality_Phytoplankton_Production.gif)\n\nFigure 1. Maps of global Phytoplankton Primary Production (NPP) in 2020, from ESA BICEP Project\n\n![](/data/story-images/NPP_2020a.gif)\n\nFigure 2. NPP 2020 from GCOM-C\n\n",
+      "id": "Page 3-1651660044419",
+      "title": "Page 3",
+      "__generatedText__": "<p><img src=\"/data/story-images/1-EO_Dashboard_Water_Quality_Phytoplankton_Production.gif\" alt></p>\n<p>Figure 1. Maps of global Phytoplankton Primary Production (NPP) in 2020, from ESA BICEP Project</p>\n<p><img src=\"/data/story-images/NPP_2020a.gif\" alt></p>\n<p>Figure 2. NPP 2020 from GCOM-C</p>\n"
+    },
+    {
+      "width": 4,
+      "text": "## Explore Datasets \n &nbsp;   \n<span style=\"color:#4059AD\">Use the **interactive maps on the next pages** to see how NPP levels vary globally. The compare feature on the maps allows looking at the NPP on two different dates.</span>\n\nThis **EO Dashboard** provides access to interactive maps of NPP but also water quality at regional level. Access these datasets by clicking on the **[EXPLORE DATASETS](https://eodashboard.org/explore)** button at the top of the page or directly from the links below:\n\n[NASA NPP](https://www.eodashboard.org/?poi=W11-NPPN) &nbsp;   \n[ESA NPP](https://www.eodashboard.org/?poi=NPP-NPP)  &nbsp;   \n[JAXA NPP](https://www.eodashboard.org/?poi=World-N11)\n&nbsp;   \n[Water Quality Maps - Chl-a and TSM](https://www.eodashboard.org/?poi=US04-N3a2&indicator=N3a2)\n&nbsp;   \n[Water Quality Time Series - Chl-a and TSM](https://www.eodashboard.org/?poi=IT10-N3b&indicator=N3b)",
+      "id": " -1652438200762",
+      "title": " ",
+      "__generatedText__": "<h2>Explore Datasets</h2>\n<p> &nbsp;<br><span>Use the <strong>interactive maps on the next pages</strong> to see how NPP levels vary globally. The compare feature on the maps allows looking at the NPP on two different dates.</span></p>\n<p>This <strong>EO Dashboard</strong> provides access to interactive maps of NPP but also water quality at regional level. Access these datasets by clicking on the <strong><a href=\"https://eodashboard.org/explore\">EXPLORE DATASETS</a></strong> button at the top of the page or directly from the links below:</p>\n<p><a href=\"https://www.eodashboard.org/?poi=W11-NPPN\">NASA NPP</a> &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=NPP-NPP\">ESA NPP</a>  &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=World-N11\">JAXA NPP</a>\n&nbsp;<br><a href=\"https://www.eodashboard.org/?poi=US04-N3a2&amp;indicator=N3a2\">Water Quality Maps - Chl-a and TSM</a>\n&nbsp;<br><a href=\"https://www.eodashboard.org/?poi=IT10-N3b&amp;indicator=N3b\">Water Quality Time Series - Chl-a and TSM</a></p>\n"
     },
     {
-      "width": 2,
+      "width": 1,
+      "text": "## Monthly global Marine Phytoplankton Primary Production\n\nThis dataset is produced in the frame of project Biological Pump and Carbon Exchange Processes (BICEP)",
+      "id": " -1652439424855",
+      "title": " ",
+      "__generatedText__": "<h2>Monthly global Marine Phytoplankton Primary Production</h2>\n<p>This dataset is produced in the frame of project Biological Pump and Carbon Exchange Processes (BICEP)</p>\n"
+    },
+    {
+      "width": 3,
       "mapInfo": {
         "zoom": 2,
         "center": {
           "lng": 0.17578125,
-          "lat": -17.644022027872726
+          "lat": -0.7031073524364783
         }
       },
       "id": "NPP-NPP",
       "title": "World, NPP (BICEP)"
     },
     {
-      "width": 2,
-      "text": "For example, coastal areas have high levels of phytoplankton productivity due to nutrient-rich waters. These nutrients are brought by river runoff or coastal upwelling. Productivity in tropical water is low throughout the year due to warm and highly stratified surface water that prevents the nutrient-rich deep waters from reaching the surface. \n\nAt mid-latitudes, a seasonal cycle is observed, with the lowest productivity in winter due to light scarcity, followed in spring by productivity blooms due to the combination of light and well mixed, nutrient-rich water. In the summer, productivity declines despite strong light availability, due to increased stratification and nutrient depletion by the spring/early summer phytoplankton blooms. \n\nAt high latitudes, stratification is weak year-round, but scarcity or absence of light in winter/autumn leads to lower levels of productivity. In spring and summer, similarly to temperate areas, a higher level of productivity is observed due to the combination of sunlight and nutrient abundance.\nMeasuring primary productivity in our oceans is critical to understanding the effects of global warming, now and for the next generation. Our research satellites allow us to do this from the advantageous position of space.\n\n![](https://raw.githubusercontent.com/eurodatacube/eodash/213f6bfdc83233f7c667b37ef78bce59f14ebe7a/app/public/data/story-images/NPP_2020a.gif)\n\nFigure 2. NPP 2020\n\n### Interactive Map of Ocean Primary Productivity from GCOM-C\n\nExplore the following interactive map to observe the variability of NPP across 2020 globally. \n\nYou can also access more advanced features by viewing this indicator in [**Exploration mode**](https://www.eodashboard.org/?poi=World-N11)\n&nbsp;  \n",
-      "id": "Page 3-1651660044419",
-      "title": "Page 3",
-      "__generatedText__": "<p>For example, coastal areas have high levels of phytoplankton productivity due to nutrient-rich waters. These nutrients are brought by river runoff or coastal upwelling. Productivity in tropical water is low throughout the year due to warm and highly stratified surface water that prevents the nutrient-rich deep waters from reaching the surface. </p>\n<p>At mid-latitudes, a seasonal cycle is observed, with the lowest productivity in winter due to light scarcity, followed in spring by productivity blooms due to the combination of light and well mixed, nutrient-rich water. In the summer, productivity declines despite strong light availability, due to increased stratification and nutrient depletion by the spring/early summer phytoplankton blooms. </p>\n<p>At high latitudes, stratification is weak year-round, but scarcity or absence of light in winter/autumn leads to lower levels of productivity. In spring and summer, similarly to temperate areas, a higher level of productivity is observed due to the combination of sunlight and nutrient abundance.\nMeasuring primary productivity in our oceans is critical to understanding the effects of global warming, now and for the next generation. Our research satellites allow us to do this from the advantageous position of space.</p>\n<p><img src=\"https://raw.githubusercontent.com/eurodatacube/eodash/213f6bfdc83233f7c667b37ef78bce59f14ebe7a/app/public/data/story-images/NPP_2020a.gif\" alt></p>\n<p>Figure 2. NPP 2020</p>\n<h3>Interactive Map of Ocean Primary Productivity from GCOM-C</h3>\n<p>Explore the following interactive map to observe the variability of NPP across 2020 globally. </p>\n<p>You can also access more advanced features by viewing this indicator in <a href=\"https://www.eodashboard.org/?poi=World-N11\"><strong>Exploration mode</strong></a>\n&nbsp;  </p>\n"
+      "width": 1,
+      "text": "## Ocean Primary Productivity product provided by NASA",
+      "id": " -1652439481676",
+      "title": " ",
+      "__generatedText__": "<h2>Ocean Primary Productivity product provided by NASA</h2>\n"
+    },
+    {
+      "width": 3,
+      "mapInfo": {
+        "zoom": 2,
+        "center": {
+          "lng": 0.1737505196481637,
+          "lat": -24.5271348225978
+        }
+      },
+      "id": "W11-NPPN",
+      "title": "World, NPP (NASA)"
     },
     {
-      "width": 2,
+      "width": 1,
+      "text": "## GCOM-C Ocean net primary productivity (ONPP) provided by JAXA\n&nbsp;  \nNet primary productivity [mgC/m2/day] is gross photosynthetic carbon fixation minus the carbon respired to support maintenance requirements of the whole plant.\n",
+      "id": " -1652439543055",
+      "title": " ",
+      "__generatedText__": "<h2>GCOM-C Ocean net primary productivity (ONPP) provided by JAXA</h2>\n<p>&nbsp;<br>Net primary productivity [mgC/m2/day] is gross photosynthetic carbon fixation minus the carbon respired to support maintenance requirements of the whole plant.</p>\n"
+    },
+    {
+      "width": 3,
       "mapInfo": {
-        "zoom": 3,
+        "zoom": 2,
         "center": {
-          "lng": 0.7910156250000001,
-          "lat": 0.7031073524364909
+          "lng": -14.947904587125848,
+          "lat": -20.632784250388028
         }
       },
       "id": "World-N11",
diff --git a/app/public/data/dashboards/92bc845659abfed6.json b/app/public/data/dashboards/92bc845659abfed6.json
index 60afc4ad87..b6bf7fc8f2 100644
--- a/app/public/data/dashboards/92bc845659abfed6.json
+++ b/app/public/data/dashboards/92bc845659abfed6.json
@@ -14,10 +14,10 @@
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true<--IMG--> ",
+      "text": "<--IMG-->/data/story-images/1-sea-ice.jpg<--IMG--> ",
       "id": "Sea Ice-1651663365516",
       "title": "Sea Ice",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true\">https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true</a>&lt;--IMG--&gt; </p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-sea-ice.jpg&lt;--IMG--&gt; </p>\n"
     }
   ],
   "title": "Cryo - joint story 1"
diff --git a/app/public/data/dashboards/a785d7a3c536a452.json b/app/public/data/dashboards/a785d7a3c536a452.json
index 5b0035c691..5acb2caa3b 100644
--- a/app/public/data/dashboards/a785d7a3c536a452.json
+++ b/app/public/data/dashboards/a785d7a3c536a452.json
@@ -14,17 +14,29 @@
     },
     {
       "width": 2,
-      "text": " ![](https://github.com/aapopescu/Images/blob/main/10-leafy-vegetation.jpg?raw=true)",
+      "text": " ![](/data/story-images/2-biomass.jpg)",
       "id": " -1651522741155",
       "title": " ",
-      "__generatedText__": "<p> <img src=\"https://github.com/aapopescu/Images/blob/main/10-leafy-vegetation.jpg?raw=true\" alt></p>\n"
+      "__generatedText__": "<p> <img src=\"/data/story-images/2-biomass.jpg\" alt></p>\n"
     },
     {
-      "width": 4,
-      "text": "Spaceborne satellite missions will soon provide globally consistent measurements of forests that can be used to estimate biomass.  However, different space missions using different sensors and technologies (e.g., radar, lidar) often produce different estimates of biomass. Such disparities are a potential source of confusion, so it is highly desirable to produce a single most accurate biomass dataset by combining the strengths of the individual sensors.\n\nThe world\u2019s Earth observation biomass community is undertaking a collaboration aimed at resolving discrepancies between space missions and producing harmonized estimates of biomass and uncertainty at a policy-relevant, jurisdictional-level scale. \n\n\n![](https://github.com/eurodatacube/eodash/blob/9e7c9e5cecc459b8a5e794e5e2ce4a3949ece36a/app/public/data/story-images/Biomass_Figure1.png?raw=true)\n\n\n## Existing Biomass Products\n&nbsp;  \nSeveral current projects are producing continental- to global-scale biomass maps. These include: \n\n*\t**ESA\u2019s CCI-Biomass global maps** at a scale of 100 meters for the years 2010, 2017, 2018, and 2020; \n\n*\t**JPL\u2019s global 100 meter map** for 2020 and a time series of 10 kilometer biomass maps from 2000 through 2020; \n\n*\t**NASA GEDI\u2019s 1 kilometer map** for 2020 covering latitudes between approximately 52\u00b0 N and 52\u00b0 S; \n\n*\t**NASA\u2019s ICESat-2 30 meter boreal map** for 2020; and\n\n*\t**The UK National Centre for Earth Observation (EO) 100 meter time series of maps** for Africa from 2007 to 2017.\n\nKey global biomass production and validation teams, including national experts, are engaged in this effort. These teams share the goal of producing harmonized biomass products for use in the UNFCCC Global Stocktake process.",
+      "width": 2,
+      "text": "Spaceborne satellite missions will soon provide globally consistent measurements of forests that can be used to estimate biomass.  However, different space missions using different sensors and technologies (e.g., radar, lidar) often produce different estimates of biomass. Such disparities are a potential source of confusion, so it is highly desirable to produce a single most accurate biomass dataset by combining the strengths of the individual sensors.\n\nThe world\u2019s Earth observation biomass community is undertaking a collaboration aimed at resolving discrepancies between space missions and producing harmonized estimates of biomass and uncertainty at a policy-relevant, jurisdictional-level scale. \n\n\n![](/data/story-images/Biomass_Figure1.png)\n\n\n## Existing Biomass Products\n&nbsp;  \nSeveral current projects are producing continental- to global-scale biomass maps. These include: \n\n*\t**ESA\u2019s CCI-Biomass global maps** at a scale of 100 meters for the years 2010, 2017, 2018, and 2020; \n\n*\t**JPL\u2019s global 100 meter map** for 2020 and a time series of 10 kilometer biomass maps from 2000 through 2020; \n\n*\t**NASA GEDI\u2019s 1 kilometer map** for 2020 covering latitudes between approximately 52\u00b0 N and 52\u00b0 S; \n\n*\t**NASA\u2019s ICESat-2 30 meter boreal map** for 2020; and\n\n*\t**The UK National Centre for Earth Observation (EO) 100 meter time series of maps** for Africa from 2007 to 2017.\n\nKey global biomass production and validation teams, including national experts, are engaged in this effort. These teams share the goal of producing harmonized biomass products for use in the UNFCCC Global Stocktake process.\n\n## Explore Datasets \n &nbsp;   \n<span style=\"color:#4059AD\">Use the **interactive map** on this page to zoom in on the Africa Woody Biomass map. \n\nThe NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK\u2019s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see CEOS biomass.",
       "id": " -1651522406153",
       "title": " ",
-      "__generatedText__": "<p>Spaceborne satellite missions will soon provide globally consistent measurements of forests that can be used to estimate biomass.  However, different space missions using different sensors and technologies (e.g., radar, lidar) often produce different estimates of biomass. Such disparities are a potential source of confusion, so it is highly desirable to produce a single most accurate biomass dataset by combining the strengths of the individual sensors.</p>\n<p>The world\u2019s Earth observation biomass community is undertaking a collaboration aimed at resolving discrepancies between space missions and producing harmonized estimates of biomass and uncertainty at a policy-relevant, jurisdictional-level scale. </p>\n<p><img src=\"https://github.com/eurodatacube/eodash/blob/9e7c9e5cecc459b8a5e794e5e2ce4a3949ece36a/app/public/data/story-images/Biomass_Figure1.png?raw=true\" alt></p>\n<h2>Existing Biomass Products</h2>\n<p>&nbsp;<br>Several current projects are producing continental- to global-scale biomass maps. These include: </p>\n<ul>\n<li><p>   <strong>ESA\u2019s CCI-Biomass global maps</strong> at a scale of 100 meters for the years 2010, 2017, 2018, and 2020; </p>\n</li>\n<li><p>   <strong>JPL\u2019s global 100 meter map</strong> for 2020 and a time series of 10 kilometer biomass maps from 2000 through 2020; </p>\n</li>\n<li><p>   <strong>NASA GEDI\u2019s 1 kilometer map</strong> for 2020 covering latitudes between approximately 52\u00b0 N and 52\u00b0 S; </p>\n</li>\n<li><p>   <strong>NASA\u2019s ICESat-2 30 meter boreal map</strong> for 2020; and</p>\n</li>\n<li><p>   <strong>The UK National Centre for Earth Observation (EO) 100 meter time series of maps</strong> for Africa from 2007 to 2017.</p>\n</li>\n</ul>\n<p>Key global biomass production and validation teams, including national experts, are engaged in this effort. These teams share the goal of producing harmonized biomass products for use in the UNFCCC Global Stocktake process.</p>\n"
+      "__generatedText__": "<p>Spaceborne satellite missions will soon provide globally consistent measurements of forests that can be used to estimate biomass.  However, different space missions using different sensors and technologies (e.g., radar, lidar) often produce different estimates of biomass. Such disparities are a potential source of confusion, so it is highly desirable to produce a single most accurate biomass dataset by combining the strengths of the individual sensors.</p>\n<p>The world\u2019s Earth observation biomass community is undertaking a collaboration aimed at resolving discrepancies between space missions and producing harmonized estimates of biomass and uncertainty at a policy-relevant, jurisdictional-level scale. </p>\n<p><img src=\"/data/story-images/Biomass_Figure1.png\" alt></p>\n<h2>Existing Biomass Products</h2>\n<p>&nbsp;<br>Several current projects are producing continental- to global-scale biomass maps. These include: </p>\n<ul>\n<li><p>   <strong>ESA\u2019s CCI-Biomass global maps</strong> at a scale of 100 meters for the years 2010, 2017, 2018, and 2020; </p>\n</li>\n<li><p>   <strong>JPL\u2019s global 100 meter map</strong> for 2020 and a time series of 10 kilometer biomass maps from 2000 through 2020; </p>\n</li>\n<li><p>   <strong>NASA GEDI\u2019s 1 kilometer map</strong> for 2020 covering latitudes between approximately 52\u00b0 N and 52\u00b0 S; </p>\n</li>\n<li><p>   <strong>NASA\u2019s ICESat-2 30 meter boreal map</strong> for 2020; and</p>\n</li>\n<li><p>   <strong>The UK National Centre for Earth Observation (EO) 100 meter time series of maps</strong> for Africa from 2007 to 2017.</p>\n</li>\n</ul>\n<p>Key global biomass production and validation teams, including national experts, are engaged in this effort. These teams share the goal of producing harmonized biomass products for use in the UNFCCC Global Stocktake process.</p>\n<h2>Explore Datasets</h2>\n<p> &nbsp;<br><span>Use the <strong>interactive map</strong> on this page to zoom in on the Africa Woody Biomass map. </p>\n<p>The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK\u2019s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. For more information see CEOS biomass.</p>\n"
+    },
+    {
+      "width": 2,
+      "mapInfo": {
+        "zoom": 3,
+        "center": {
+          "lng": 16.875000000000004,
+          "lat": 1.5818302639606454
+        }
+      },
+      "id": "W12-NCEO",
+      "title": "World, NCEO Africa Biomass"
     }
   ],
   "title": "BIOMASS - Joint Story"
diff --git a/app/public/data/dashboards/ac447995674c2091.json b/app/public/data/dashboards/ac447995674c2091.json
index fe876c967c..7e835c4a3a 100644
--- a/app/public/data/dashboards/ac447995674c2091.json
+++ b/app/public/data/dashboards/ac447995674c2091.json
@@ -20,9 +20,10 @@
     },
     {
       "width": 2,
-      "text": "## Nightlights in the Port of Dunkirk (France)\n&nbsp;  \nThe High Definition Nightlights dataset is processed to eliminate light sources like moonlight reflectance and other interferences. The darker shades are places with less light while the lighter shades of yellow are areas with more light.\n\nThe Port of Dunkirk is the third-largest maritime port in northern France. Located on the busy North Sea, the port is well known for the comings and goings of heavy cargo barges. In early March, following French government guidance, the port began to limit activity in response to the novel coronavirus. \n\nResearchers are using night lights to track variations in energy use, migration, and transportation at the port in response to social distancing and lockdown measures. \n\nThey are able to visualize the extent social distancing measures impacted various economic activities based on whether light pollution increased or decreased, which highways were shut down, and which cities stayed the same.\n\n![](https://www.cruisemapper.com/images/ports/9901-841799c4b49.jpg)\n*Port of Dunkirk. Image source: https://www.cruisemapper.com/ports/dunkirk-port-9901* \n\n",
+      "text": "## Nightlights in the Port of Dunkirk (France)\n&nbsp;  \nThe High Definition Nightlights dataset is processed to eliminate light sources like moonlight reflectance and other interferences. The darker shades are places with less light while the lighter shades of yellow are areas with more light.\n\nThe Port of Dunkirk is the third-largest maritime port in northern France. Located on the busy North Sea, the port is well known for the comings and goings of heavy cargo barges. In early March, following French government guidance, the port began to limit activity in response to the novel coronavirus. \n\nResearchers are using night lights to track variations in energy use, migration, and transportation at the port in response to social distancing and lockdown measures. \n\nThey are able to visualize the extent social distancing measures impacted various economic activities based on whether light pollution increased or decreased, which highways were shut down, and which cities stayed the same.\n\n![](/data/story-images/9901-841799c4b49.jpg)\n*Port of Dunkirk. Image source: https://www.cruisemapper.com/ports/dunkirk-port-9901* \n\n",
       "id": "Page 1-1651908156613",
-      "title": "Page 1"
+      "title": "Page 1",
+      "__generatedText__": "<h2>Nightlights in the Port of Dunkirk (France)</h2>\n<p>&nbsp;<br>The High Definition Nightlights dataset is processed to eliminate light sources like moonlight reflectance and other interferences. The darker shades are places with less light while the lighter shades of yellow are areas with more light.</p>\n<p>The Port of Dunkirk is the third-largest maritime port in northern France. Located on the busy North Sea, the port is well known for the comings and goings of heavy cargo barges. In early March, following French government guidance, the port began to limit activity in response to the novel coronavirus. </p>\n<p>Researchers are using night lights to track variations in energy use, migration, and transportation at the port in response to social distancing and lockdown measures. </p>\n<p>They are able to visualize the extent social distancing measures impacted various economic activities based on whether light pollution increased or decreased, which highways were shut down, and which cities stayed the same.</p>\n<p><img src=\"/data/story-images/9901-841799c4b49.jpg\" alt>\n<em>Port of Dunkirk. Image source: <a href=\"https://www.cruisemapper.com/ports/dunkirk-port-9901\">https://www.cruisemapper.com/ports/dunkirk-port-9901</a></em> </p>\n"
     },
     {
       "width": 2,
diff --git a/app/public/data/dashboards/adc20ad9b73231b6.json b/app/public/data/dashboards/adc20ad9b73231b6.json
index 1ae8587505..350b84e10b 100644
--- a/app/public/data/dashboards/adc20ad9b73231b6.json
+++ b/app/public/data/dashboards/adc20ad9b73231b6.json
@@ -6,23 +6,44 @@
   },
   "features": [
     {
-      "width": 1,
-      "text": "<--IMG-->https://oceancolor.gsfc.nasa.gov/gallery/feature/images/V2021110.Japan.crop.small.jpg<--IMG-->The above NOAA 20 and Suomi-NPP VIIRS composite depicts phytoplankton communities around Japan and shows that the sea is still turbulent in the 21st century.\nThe data composing the above image were collected on April 20, 2021. \nClick on the image to get a larger version with Typhoon Surigae spinning in the lower left corner.",
-      "id": "Japan and the Turbulent Sea-1651557744642",
-      "title": "Japan and the Turbulent Sea"
-    },
-    {
-      "width": 3,
-      "text": "## Satellite Sensors Tracking Ocean Color Changes to Predict Phytoplankton Health \n&nbsp;  \nUnderstanding climate change effects in the marine environment \u2013 including water temperature and ocean currents \u2013 is vital to managing the sustainable use of these resources.  \n\nThese changes are reflected in microscopic plant- and animal-based organisms known as phytoplankton and zooplankton, respectively, as well as in invertebrate larvae and fish throughout the food chain.\n\nFor example, the northwest Pacific Oyashio front region (*Fig. 1*) is considered one of the world\u2019s highest productivity waters. In spring (April-May), the phytoplankton bloom comes later in colder years, earlier in warmer years. The relationship between the bloom period and the growth period of predators may have an impact on higher-level ecosystems which are also used as marine resources.\n\nSatellite sensors that observe ocean color help us estimate the spatial distribution and temporal changes in phytoplankton mass, their productivity and ability to absorb carbon dioxide (CO<sub>2</sub>), and related environmental variables that impact ecosystem health. Satellites provide important information to adequately carry out ecosystem-based management for sustainable use of the marine biological resources.\n\n### References\n[1] IOCCG (2009). Remote Sensing in Fisheries and Aquaculture. Forget, M.-H., Stuart, V. and Platt, T. (eds.), Reports of the International Ocean-Colour Coordinating Group, No. 8, IOCCG, Dartmouth, Canada., https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf\n\n[2] Chiba et al., 2009, Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio\u2013Oyashio currents in the western North Pacific, https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x\n\n[3] Siswanto et al., 2016, Meridional and seasonal footprints of the Pacific Decadal Oscillation on phytoplankton biomass in the northwestern Pacific Ocean, J Oceanogr (2016) 72:465\u2013477 https://doi.org/10.1007/s10872-016-0367-z\n\n[4] Kameda, T., Ishizaka, J. Size-Fractionated Primary Production Estimated by a Two-Phytoplankton Community Model Applicable to Ocean Color Remote Sensing. J Oceanogr 61, 663\u2013672 (2005). https://doi.org/10.1007/s10872-005-0074-7\n",
+      "width": 2,
+      "text": "## Satellite Sensors Tracking Ocean Color Changes to Predict Phytoplankton Health \n&nbsp;  \nUnderstanding climate change effects in the marine environment \u2013 including water temperature and ocean currents \u2013 is vital to managing the sustainable use of these resources.  \n\nThese changes are reflected in microscopic plant- and animal-based organisms known as phytoplankton and zooplankton, respectively, as well as in invertebrate larvae and fish throughout the food chain.\n\nFor example, the northwest Pacific Oyashio front region (Figure 1.) is considered one of the world\u2019s highest productivity waters. In spring (April-May), the phytoplankton bloom comes later in colder years, and earlier in warmer years. The relationship between the bloom period and the growth period of predators may have an impact on higher-level ecosystems which are also used as marine resources.\n\n![](/data/story-images/1-EO_PIX_Ocean_REDTIDE-2.png)\n\nFigure 1. Northwest Pacific Oyahio front region\n\nSatellite sensors that observe ocean color help us estimate the spatial distribution and temporal changes in phytoplankton mass, their productivity and ability to absorb carbon dioxide (CO<sub>2</sub>), and related environmental variables that impact ecosystem health. Satellites provide important information to adequately carry out ecosystem-based management for the sustainable use of marine biological resources.\n\n## Explore Datasets \n &nbsp;   \n<span style=\"color:#4059AD\">Use the **interactive map** to see how Ocean NPP vary globally. The compare feature on this map allows looking at NPP on two different dates.</span>\n",
       "id": "Page 1-1651557692510",
       "title": "Page 1",
-      "__generatedText__": "<h2>Satellite Sensors Tracking Ocean Color Changes to Predict Phytoplankton Health</h2>\n<p>&nbsp;<br>Understanding climate change effects in the marine environment \u2013 including water temperature and ocean currents \u2013 is vital to managing the sustainable use of these resources.  </p>\n<p>These changes are reflected in microscopic plant- and animal-based organisms known as phytoplankton and zooplankton, respectively, as well as in invertebrate larvae and fish throughout the food chain.</p>\n<p>For example, the northwest Pacific Oyashio front region (<em>Fig. 1</em>) is considered one of the world\u2019s highest productivity waters. In spring (April-May), the phytoplankton bloom comes later in colder years, earlier in warmer years. The relationship between the bloom period and the growth period of predators may have an impact on higher-level ecosystems which are also used as marine resources.</p>\n<p>Satellite sensors that observe ocean color help us estimate the spatial distribution and temporal changes in phytoplankton mass, their productivity and ability to absorb carbon dioxide (CO<sub>2</sub>), and related environmental variables that impact ecosystem health. Satellites provide important information to adequately carry out ecosystem-based management for sustainable use of the marine biological resources.</p>\n<h3>References</h3>\n<p>[1] IOCCG (2009). Remote Sensing in Fisheries and Aquaculture. Forget, M.-H., Stuart, V. and Platt, T. (eds.), Reports of the International Ocean-Colour Coordinating Group, No. 8, IOCCG, Dartmouth, Canada., <a href=\"https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf\">https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf</a></p>\n<p>[2] Chiba et al., 2009, Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio\u2013Oyashio currents in the western North Pacific, <a href=\"https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x\">https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x</a></p>\n<p>[3] Siswanto et al., 2016, Meridional and seasonal footprints of the Pacific Decadal Oscillation on phytoplankton biomass in the northwestern Pacific Ocean, J Oceanogr (2016) 72:465\u2013477 <a href=\"https://doi.org/10.1007/s10872-016-0367-z\">https://doi.org/10.1007/s10872-016-0367-z</a></p>\n<p>[4] Kameda, T., Ishizaka, J. Size-Fractionated Primary Production Estimated by a Two-Phytoplankton Community Model Applicable to Ocean Color Remote Sensing. J Oceanogr 61, 663\u2013672 (2005). <a href=\"https://doi.org/10.1007/s10872-005-0074-7\">https://doi.org/10.1007/s10872-005-0074-7</a></p>\n"
+      "__generatedText__": "<h2>Satellite Sensors Tracking Ocean Color Changes to Predict Phytoplankton Health</h2>\n<p>&nbsp;<br>Understanding climate change effects in the marine environment \u2013 including water temperature and ocean currents \u2013 is vital to managing the sustainable use of these resources.  </p>\n<p>These changes are reflected in microscopic plant- and animal-based organisms known as phytoplankton and zooplankton, respectively, as well as in invertebrate larvae and fish throughout the food chain.</p>\n<p>For example, the northwest Pacific Oyashio front region (Figure 1.) is considered one of the world\u2019s highest productivity waters. In spring (April-May), the phytoplankton bloom comes later in colder years, and earlier in warmer years. The relationship between the bloom period and the growth period of predators may have an impact on higher-level ecosystems which are also used as marine resources.</p>\n<p><img src=\"/data/story-images/1-EO_PIX_Ocean_REDTIDE-2.png\" alt></p>\n<p>Figure 1. Northwest Pacific Oyahio front region</p>\n<p>Satellite sensors that observe ocean color help us estimate the spatial distribution and temporal changes in phytoplankton mass, their productivity and ability to absorb carbon dioxide (CO<sub>2</sub>), and related environmental variables that impact ecosystem health. Satellites provide important information to adequately carry out ecosystem-based management for the sustainable use of marine biological resources.</p>\n<h2>Explore Datasets</h2>\n<p> &nbsp;<br><span>Use the <strong>interactive map</strong> to see how Ocean NPP vary globally. The compare feature on this map allows looking at NPP on two different dates.</span></p>\n"
+    },
+    {
+      "width": 2,
+      "mapInfo": {
+        "zoom": 5,
+        "center": {
+          "lng": 136.51611328125003,
+          "lat": 34.84987503195418
+        }
+      },
+      "id": "World-N11",
+      "title": "Global, Ocean Primary Productivity (GCOM-C)"
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://raw.githubusercontent.com/eurodatacube/eodash/213f6bfdc83233f7c667b37ef78bce59f14ebe7a/app/public/data/story-images/SGLI_nppKI_Global_2020_12.gif<--IMG-->NPP",
-      "id": " NPP-1651557951362",
-      "title": " NPP"
+      "text": "<--IMG-->/data/story-images/1-EO_PIX_Ocean_Global_NPP.png<--IMG-->Figure 2. Global Ocean NPP (net primary productivity)",
+      "id": "Japan and the Turbulent Sea-1651557744642",
+      "title": "Japan and the Turbulent Sea",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-EO_PIX_Ocean_Global_NPP.png&lt;--IMG--&gt;Figure 2. Global Ocean NPP (net primary productivity)</p>\n"
+    },
+    {
+      "width": 2,
+      "text": "![](/data/story-images/1-EO_PIX_Ocean_Global_REDTIDE.png)\n\nFigure 3. Northwest Pacific Oyashio front region, one of the world\u2019s highest productivity waters. \n\n![](/data/story-images/Oyashio%20and%20time%20series.png)\n\nFigure 4. Map of NPP around Oyashio region, and four-year timeseries of NPP in the area. The NPP is derived using SGLI by Kameda and Ishizaka 2005[4].\n\n### References\n[1] IOCCG (2009). Remote Sensing in Fisheries and Aquaculture. Forget, M.-H., Stuart, V. and Platt, T. (eds.), Reports of the International Ocean-Colour Coordinating Group, No. 8, IOCCG, Dartmouth, Canada., https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf\n\n[2] Chiba et al., 2009, Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio\u2013Oyashio currents in the western North Pacific, https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x\n\n[3] Siswanto et al., 2016, Meridional and seasonal footprints of the Pacific Decadal Oscillation on phytoplankton biomass in the northwestern Pacific Ocean, J Oceanogr (2016) 72:465\u2013477 https://doi.org/10.1007/s10872-016-0367-z\n\n[4] NPP Product <https://www.eodashboard.org/?poi=World-N11&indicator=N11>",
+      "id": " -1652435834156",
+      "title": " ",
+      "__generatedText__": "<p><img src=\"/data/story-images/1-EO_PIX_Ocean_Global_REDTIDE.png\" alt></p>\n<p>Figure 3. Northwest Pacific Oyashio front region, one of the world\u2019s highest productivity waters. </p>\n<p><img src=\"/data/story-images/Oyashio%20and%20time%20series.png\" alt></p>\n<p>Figure 4. Map of NPP around Oyashio region, and four-year timeseries of NPP in the area. The NPP is derived using SGLI by Kameda and Ishizaka 2005[4].</p>\n<h3>References</h3>\n<p>[1] IOCCG (2009). Remote Sensing in Fisheries and Aquaculture. Forget, M.-H., Stuart, V. and Platt, T. (eds.), Reports of the International Ocean-Colour Coordinating Group, No. 8, IOCCG, Dartmouth, Canada., <a href=\"https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf\">https://ioccg.org/wp-content/uploads/2015/10/ioccg-report-08.pdf</a></p>\n<p>[2] Chiba et al., 2009, Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio\u2013Oyashio currents in the western North Pacific, <a href=\"https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x\">https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2486.2009.01890.x</a></p>\n<p>[3] Siswanto et al., 2016, Meridional and seasonal footprints of the Pacific Decadal Oscillation on phytoplankton biomass in the northwestern Pacific Ocean, J Oceanogr (2016) 72:465\u2013477 <a href=\"https://doi.org/10.1007/s10872-016-0367-z\">https://doi.org/10.1007/s10872-016-0367-z</a></p>\n<p>[4] NPP Product <a href=\"https://www.eodashboard.org/?poi=World-N11&amp;indicator=N11\">https://www.eodashboard.org/?poi=World-N11&amp;indicator=N11</a></p>\n"
+    },
+    {
+      "width": 4,
+      "text": "## Explore Datasets \n &nbsp;   \nThis **EO Dashboard** provides access to three interactive maps of global ocean NPP from.  \n\nAccess these datasets by clicking on the **[EXPLORE DATASETS](https://eodashboard.org/explore)** button at the top of the page or directly from the links below:\n\n[NASA NPP](https://www.eodashboard.org/?poi=W11-NPPN&indicator=NPPN) &nbsp;   \n[ESA NPP](https://www.eodashboard.org/?poi=NPP-NPP&indicator=NPP)  &nbsp;   \n[JAXA NPP](https://www.eodashboard.org/?poi=World-N11&indicator=N11)\n",
+      "id": " -1652436596100",
+      "title": " ",
+      "__generatedText__": "<h2>Explore Datasets</h2>\n<p> &nbsp;<br>This <strong>EO Dashboard</strong> provides access to three interactive maps of global ocean NPP from.  </p>\n<p>Access these datasets by clicking on the <strong><a href=\"https://eodashboard.org/explore\">EXPLORE DATASETS</a></strong> button at the top of the page or directly from the links below:</p>\n<p><a href=\"https://www.eodashboard.org/?poi=W11-NPPN&amp;indicator=NPPN\">NASA NPP</a> &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=NPP-NPP&amp;indicator=NPP\">ESA NPP</a>  &nbsp;<br><a href=\"https://www.eodashboard.org/?poi=World-N11&amp;indicator=N11\">JAXA NPP</a></p>\n"
     }
   ],
   "title": "JAXA - Ocean Story 1"
diff --git a/app/public/data/dashboards/afea38efc80e9846.json b/app/public/data/dashboards/afea38efc80e9846.json
index ae0eedb541..7ac6c75661 100644
--- a/app/public/data/dashboards/afea38efc80e9846.json
+++ b/app/public/data/dashboards/afea38efc80e9846.json
@@ -8,10 +8,10 @@
   "features": [
     {
       "width": 3,
-      "text": "<--IMG-->https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/09/carbon_monoxide_from_fires_in_australia/23487447-1-eng-GB/Carbon_monoxide_from_fires_in_Australia_article.jpg<--IMG-->This image uses information from the Copernicus  Sentinel-5P mission  and shows the average carbon monoxide concentrations from 1 November 2019 to 31 January 2020. Carbon monoxide is commonly associated with traffic, but the concentrations here are due to the bushfires. Naturally, once in the air, it can cause problems for humans by reducing the amount of oxygen that can be transported in the bloodstream.\n*Copyright: contains modified Copernicus Sentinel data (2019-20), processed by ESA*\n",
+      "text": "<--IMG-->/data/story-images/Carbon_monoxide_from_fires_in_Australia_article.jpg<--IMG-->This image uses information from the Copernicus  Sentinel-5P mission  and shows the average carbon monoxide concentrations from 1 November 2019 to 31 January 2020. Carbon monoxide is commonly associated with traffic, but the concentrations here are due to the bushfires. Naturally, once in the air, it can cause problems for humans by reducing the amount of oxygen that can be transported in the bloodstream.\n*Copyright: contains modified Copernicus Sentinel data (2019-20), processed by ESA*\n",
       "id": "TROPOMI CO Australia-1652003467661",
       "title": "TROPOMI CO Australia",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/09/carbon_monoxide_from_fires_in_australia/23487447-1-eng-GB/Carbon_monoxide_from_fires_in_Australia_article.jpg\">https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/09/carbon_monoxide_from_fires_in_australia/23487447-1-eng-GB/Carbon_monoxide_from_fires_in_Australia_article.jpg</a>&lt;--IMG--&gt;This image uses information from the Copernicus  Sentinel-5P mission  and shows the average carbon monoxide concentrations from 1 November 2019 to 31 January 2020. Carbon monoxide is commonly associated with traffic, but the concentrations here are due to the bushfires. Naturally, once in the air, it can cause problems for humans by reducing the amount of oxygen that can be transported in the bloodstream.\n<em>Copyright: contains modified Copernicus Sentinel data (2019-20), processed by ESA</em></p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Carbon_monoxide_from_fires_in_Australia_article.jpg&lt;--IMG--&gt;This image uses information from the Copernicus  Sentinel-5P mission  and shows the average carbon monoxide concentrations from 1 November 2019 to 31 January 2020. Carbon monoxide is commonly associated with traffic, but the concentrations here are due to the bushfires. Naturally, once in the air, it can cause problems for humans by reducing the amount of oxygen that can be transported in the bloodstream.\n<em>Copyright: contains modified Copernicus Sentinel data (2019-20), processed by ESA</em></p>\n"
     },
     {
       "width": 1,
@@ -22,10 +22,10 @@
     },
     {
       "width": 3,
-      "text": "<--IMG-->https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/08/monitoring_air_quality/23426746-1-eng-GB/Monitoring_air_quality_pillars.jpg<--IMG-->Sentinel-5 Precursor is the first Copernicus mission dedicated to monitoring our atmosphere. This satellite carries the state-of-the-art Tropomi instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, methane, carbon monoxide and aerosols \u2013 all of which affect the air we breathe and our climate. \nSource:  [esa.int](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/Introducing_Sentinel-5P)",
+      "text": "<--IMG-->/data/story-images/Monitoring_air_quality_pillars.jpg<--IMG-->Sentinel-5 Precursor is the first Copernicus mission dedicated to monitoring our atmosphere. This satellite carries the state-of-the-art Tropomi instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, methane, carbon monoxide and aerosols \u2013 all of which affect the air we breathe and our climate. \nSource:  [esa.int](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/Introducing_Sentinel-5P)",
       "id": "TROPOMI-1652005423851",
       "title": "TROPOMI",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/08/monitoring_air_quality/23426746-1-eng-GB/Monitoring_air_quality_pillars.jpg\">https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2021/08/monitoring_air_quality/23426746-1-eng-GB/Monitoring_air_quality_pillars.jpg</a>&lt;--IMG--&gt;Sentinel-5 Precursor is the first Copernicus mission dedicated to monitoring our atmosphere. This satellite carries the state-of-the-art Tropomi instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, methane, carbon monoxide and aerosols \u2013 all of which affect the air we breathe and our climate. \nSource:  <a href=\"https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/Introducing_Sentinel-5P\">esa.int</a></p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Monitoring_air_quality_pillars.jpg&lt;--IMG--&gt;Sentinel-5 Precursor is the first Copernicus mission dedicated to monitoring our atmosphere. This satellite carries the state-of-the-art Tropomi instrument to map a multitude of trace gases such as nitrogen dioxide, ozone, formaldehyde, methane, carbon monoxide and aerosols \u2013 all of which affect the air we breathe and our climate. \nSource:  <a href=\"https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-5P/Introducing_Sentinel-5P\">esa.int</a></p>\n"
     },
     {
       "width": 1,
diff --git a/app/public/data/dashboards/bd6db303fd0fb8e8.json b/app/public/data/dashboards/bd6db303fd0fb8e8.json
index 37bda3db6c..a490c845ec 100644
--- a/app/public/data/dashboards/bd6db303fd0fb8e8.json
+++ b/app/public/data/dashboards/bd6db303fd0fb8e8.json
@@ -8,10 +8,10 @@
   "features": [
     {
       "width": 2,
-      "text": "<--IMG-->https://www.nasa.gov/sites/default/files/styles/full_width_feature/public/aura_0.jpg<--IMG-->NASA Aura. Source: [www.nasa.gov](https://www.nasa.gov/directorates/heo/scan/services/missions/earth/Aura.html)",
+      "text": "<--IMG-->/data/story-images/aura_0.jpg<--IMG-->NASA Aura. Source: [www.nasa.gov](https://www.nasa.gov/directorates/heo/scan/services/missions/earth/Aura.html)",
       "id": "WHO Air Pollution-1650232992366",
       "title": "NASA Aura Satellite",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://www.nasa.gov/sites/default/files/styles/full_width_feature/public/aura_0.jpg\">https://www.nasa.gov/sites/default/files/styles/full_width_feature/public/aura_0.jpg</a>&lt;--IMG--&gt;NASA Aura. Source: <a href=\"https://www.nasa.gov/directorates/heo/scan/services/missions/earth/Aura.html\">www.nasa.gov</a></p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/aura_0.jpg&lt;--IMG--&gt;NASA Aura. Source: <a href=\"https://www.nasa.gov/directorates/heo/scan/services/missions/earth/Aura.html\">www.nasa.gov</a></p>\n"
     },
     {
       "width": 2,
@@ -22,45 +22,45 @@
     },
     {
       "width": 1,
-      "text": "## <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Flag_of_the_United_States_%28Pantone%29.svg/1280px-Flag_of_the_United_States_%28Pantone%29.svg.png\" width=\"50\">  UNITED STATES \n&nbsp;  \nFor example, this image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States.  (Note: this website features an interactive tool that allows you to draw a box around areas of interest and see timeseries data for that region). \nThe decreases in the U.S. can be attributed in large part to improved emissions from cars and trucks, as well as a reduction in the sulfur content in fuel. Some improvement is also the result of technology that does a better job of trapping nitrogen and sulfur oxides from power plant smokestacks.\n&nbsp; \n\n![Ohio River](https://www.nps.gov/articles/images/1049_1.png)\n\nOhio River Valley. Source: [National Park Service](https://www.nps.gov/index.htm)\n",
+      "text": "## <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Flag_of_the_United_States_%28Pantone%29.svg/1280px-Flag_of_the_United_States_%28Pantone%29.svg.png\" width=\"50\">  UNITED STATES \n&nbsp;  \nFor example, this image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States.  (Note: this website features an interactive tool that allows you to draw a box around areas of interest and see timeseries data for that region). \nThe decreases in the U.S. can be attributed in large part to improved emissions from cars and trucks, as well as a reduction in the sulfur content in fuel. Some improvement is also the result of technology that does a better job of trapping nitrogen and sulfur oxides from power plant smokestacks.\n&nbsp; \n\n![Ohio River](/data/story-images/1049_1.png)\n\nOhio River Valley. Source: [National Park Service](https://www.nps.gov/index.htm)\n",
       "id": "Air Pollution and Health-1650232865941",
       "title": "Air Pollution and Health",
-      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Flag_of_the_United_States_%28Pantone%29.svg/1280px-Flag_of_the_United_States_%28Pantone%29.svg.png\" width=\"50\">  UNITED STATES</h2>\n<p>&nbsp;<br>For example, this image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States.  (Note: this website features an interactive tool that allows you to draw a box around areas of interest and see timeseries data for that region). \nThe decreases in the U.S. can be attributed in large part to improved emissions from cars and trucks, as well as a reduction in the sulfur content in fuel. Some improvement is also the result of technology that does a better job of trapping nitrogen and sulfur oxides from power plant smokestacks.\n&nbsp; </p>\n<p><img src=\"https://www.nps.gov/articles/images/1049_1.png\" alt=\"Ohio River\"></p>\n<p>Ohio River Valley. Source: <a href=\"https://www.nps.gov/index.htm\">National Park Service</a></p>\n"
+      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Flag_of_the_United_States_%28Pantone%29.svg/1280px-Flag_of_the_United_States_%28Pantone%29.svg.png\" width=\"50\">  UNITED STATES</h2>\n<p>&nbsp;<br>For example, this image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States.  (Note: this website features an interactive tool that allows you to draw a box around areas of interest and see timeseries data for that region). \nThe decreases in the U.S. can be attributed in large part to improved emissions from cars and trucks, as well as a reduction in the sulfur content in fuel. Some improvement is also the result of technology that does a better job of trapping nitrogen and sulfur oxides from power plant smokestacks.\n&nbsp; </p>\n<p><img src=\"/data/story-images/1049_1.png\" alt=\"Ohio River\"></p>\n<p>Ohio River Valley. Source: <a href=\"https://www.nps.gov/index.htm\">National Park Service</a></p>\n"
     },
     {
       "width": 3,
-      "text": "<--IMG-->https://github.com/aapopescu/Images/blob/main/SO2NO2-USA.png?raw=true<--IMG-->**USA** \n&nbsp; This image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States. The decreases in the U.S. can be attributed to the improved catalytic converters in cars and trucks and well as a reduction in the sulfur content  of fuel that is cleaner than previous years. Some of this improvement is also the result of the implementation of newer technology that does a better job of trapping the nitrogen and sulfur oxides in powerplant smokestacks.",
+      "text": "<--IMG-->/data/story-images/SO2NO2-USA.png<--IMG-->**USA** \n&nbsp; This image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States. The decreases in the U.S. can be attributed to the improved catalytic converters in cars and trucks and well as a reduction in the sulfur content  of fuel that is cleaner than previous years. Some of this improvement is also the result of the implementation of newer technology that does a better job of trapping the nitrogen and sulfur oxides in powerplant smokestacks.",
       "id": "SO2 and NO2 Change from 2005 (%)-1650233325165",
       "title": "SO2 and NO2 Change from 2005 (%)",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/aapopescu/Images/blob/main/SO2NO2-USA.png?raw=true\">https://github.com/aapopescu/Images/blob/main/SO2NO2-USA.png?raw=true</a>&lt;--IMG--&gt;<strong>USA</strong> \n&nbsp; This image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States. The decreases in the U.S. can be attributed to the improved catalytic converters in cars and trucks and well as a reduction in the sulfur content  of fuel that is cleaner than previous years. Some of this improvement is also the result of the implementation of newer technology that does a better job of trapping the nitrogen and sulfur oxides in powerplant smokestacks.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/SO2NO2-USA.png&lt;--IMG--&gt;<strong>USA</strong> \n&nbsp; This image shows decreases in SO2 and NO2 in the Ohio River Valley region of the United States. The decreases in the U.S. can be attributed to the improved catalytic converters in cars and trucks and well as a reduction in the sulfur content  of fuel that is cleaner than previous years. Some of this improvement is also the result of the implementation of newer technology that does a better job of trapping the nitrogen and sulfur oxides in powerplant smokestacks.</p>\n"
     },
     {
       "width": 1,
-      "text": "##  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Flag_of_the_People%27s_Republic_of_China.svg/800px-Flag_of_the_People%27s_Republic_of_China.svg.png\" width=\"50\">  CHINA\n&nbsp;  \nSatellite observations for northeastern China show a different story. The data for SO2 (red) show a 90% reduction in this pollutant from 2005-2021. The atmospheric levels of NO2 initially increased after 2005,   then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. \n\nThis highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.\n\n<a title=\"User:Quintucket, CC0, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Northeast_China.svg\"><img width=\"512\" alt=\"Northeast China\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg/512px-Northeast_China.svg.png\"></a>\nNortheast China. Source: [User:Quintucket, CC0, via Wikimedia](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg)",
+      "text": "##  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Flag_of_the_People%27s_Republic_of_China.svg/800px-Flag_of_the_People%27s_Republic_of_China.svg.png\" width=\"50\">  CHINA\n&nbsp;  \nSatellite observations for northeastern China show a different story. The data for SO2 (red) show a 90% reduction in this pollutant from 2005-2021. The atmospheric levels of NO2 initially increased after 2005,   then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. \n\nThis highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.\n\n<a title=\"User:Quintucket, CC0, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Northeast_China.svg\"><img width=\"512\" alt=\"Northeast China\" src=\"/data/story-images/512px-Northeast_China.svg.png\"></a>\nNortheast China. Source: [User:Quintucket, CC0, via Wikimedia](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg)",
       "id": "CHINA-1652008295080",
       "title": "CHINA",
-      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Flag_of_the_People%27s_Republic_of_China.svg/800px-Flag_of_the_People%27s_Republic_of_China.svg.png\" width=\"50\">  CHINA</h2>\n<p>&nbsp;<br>Satellite observations for northeastern China show a different story. The data for SO2 (red) show a 90% reduction in this pollutant from 2005-2021. The atmospheric levels of NO2 initially increased after 2005,   then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. </p>\n<p>This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.</p>\n<p><a title=\"User:Quintucket, CC0, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Northeast_China.svg\"><img width=\"512\" alt=\"Northeast China\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg/512px-Northeast_China.svg.png\"></a>\nNortheast China. Source: <a href=\"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg\">User:Quintucket, CC0, via Wikimedia</a></p>\n"
+      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Flag_of_the_People%27s_Republic_of_China.svg/800px-Flag_of_the_People%27s_Republic_of_China.svg.png\" width=\"50\">  CHINA</h2>\n<p>&nbsp;<br>Satellite observations for northeastern China show a different story. The data for SO2 (red) show a 90% reduction in this pollutant from 2005-2021. The atmospheric levels of NO2 initially increased after 2005,   then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. </p>\n<p>This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.</p>\n<p><a title=\"User:Quintucket, CC0, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Northeast_China.svg\"><img width=\"512\" alt=\"Northeast China\" src=\"/data/story-images/512px-Northeast_China.svg.png\"></a>\nNortheast China. Source: <a href=\"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a5/Northeast_China.svg\">User:Quintucket, CC0, via Wikimedia</a></p>\n"
     },
     {
       "width": 3,
-      "text": "<--IMG-->https://github.com/aapopescu/Images/blob/main/SO2NO2-China.png?raw=true<--IMG-->**CHINA** \n&nbsp; \nThe satellite observations for northeastern China show a different year-to-year story. The timeseries for SO2 (red) shows a 90% reduction in this  pollutant, while the atmospheric levels of NO2 initially increased after 2005  and then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.",
+      "text": "<--IMG-->/data/story-images/SO2NO2-China.png<--IMG-->**CHINA** \n&nbsp; \nThe satellite observations for northeastern China show a different year-to-year story. The timeseries for SO2 (red) shows a 90% reduction in this  pollutant, while the atmospheric levels of NO2 initially increased after 2005  and then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.",
       "id": "SO2 amd NO2 Change from 2005 (%)-1650233425751",
       "title": "SO2 amd NO2 Change from 2005 (%)",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/aapopescu/Images/blob/main/SO2NO2-China.png?raw=true\">https://github.com/aapopescu/Images/blob/main/SO2NO2-China.png?raw=true</a>&lt;--IMG--&gt;<strong>CHINA</strong> \n&nbsp; \nThe satellite observations for northeastern China show a different year-to-year story. The timeseries for SO2 (red) shows a 90% reduction in this  pollutant, while the atmospheric levels of NO2 initially increased after 2005  and then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/SO2NO2-China.png&lt;--IMG--&gt;<strong>CHINA</strong> \n&nbsp; \nThe satellite observations for northeastern China show a different year-to-year story. The timeseries for SO2 (red) shows a 90% reduction in this  pollutant, while the atmospheric levels of NO2 initially increased after 2005  and then started to decrease after 2011. As of 2021 the atmospheric NO2 abundance is approximately 20% lower than at the start of the OMI record. This highlights the different strategies and technologies employed to reduce these two pollutants. While SO2 primarily comes from coal and diesel fuel, the NO2 is released by all combustion sources and as a result a bit more difficult to control.</p>\n"
     },
     {
       "width": 1,
-      "text": "##  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Flag_of_India.svg/1280px-Flag_of_India.svg.png\" width=\"50\"> INDIA \n&nbsp;  \nThe timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005-2018, which then leveled off, perhaps due to the deployment of air pollution control technology. The significant drop in both SO2 and NO2 in 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.\n\n![](https://mdoner.gov.in/contentimages/images/Road_Map.jpg)\nNorth-east India. Source: [Ministry of development of North East Region](https://mdoner.gov.in/road-map-only-nh-)\n\n",
+      "text": "##  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Flag_of_India.svg/1280px-Flag_of_India.svg.png\" width=\"50\"> INDIA \n&nbsp;  \nThe timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005-2018, which then leveled off, perhaps due to the deployment of air pollution control technology. The significant drop in both SO2 and NO2 in 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.\n\n![](/data/story-images/Road_Map.jpg)\nNorth-east India. Source: [Ministry of development of North East Region](https://mdoner.gov.in/road-map-only-nh-)\n\n",
       "id": "INDIA-1652008442641",
       "title": "INDIA",
-      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Flag_of_India.svg/1280px-Flag_of_India.svg.png\" width=\"50\"> INDIA</h2>\n<p>&nbsp;<br>The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005-2018, which then leveled off, perhaps due to the deployment of air pollution control technology. The significant drop in both SO2 and NO2 in 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.</p>\n<p><img src=\"https://mdoner.gov.in/contentimages/images/Road_Map.jpg\" alt>\nNorth-east India. Source: <a href=\"https://mdoner.gov.in/road-map-only-nh-\">Ministry of development of North East Region</a></p>\n"
+      "__generatedText__": "<h2><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Flag_of_India.svg/1280px-Flag_of_India.svg.png\" width=\"50\"> INDIA</h2>\n<p>&nbsp;<br>The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005-2018, which then leveled off, perhaps due to the deployment of air pollution control technology. The significant drop in both SO2 and NO2 in 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.</p>\n<p><img src=\"/data/story-images/Road_Map.jpg\" alt>\nNorth-east India. Source: <a href=\"https://mdoner.gov.in/road-map-only-nh-\">Ministry of development of North East Region</a></p>\n"
     },
     {
       "width": 3,
-      "text": "<--IMG-->https://github.com/aapopescu/Images/blob/main/SO2NO2-India.png?raw=true<--IMG-->**INDIA** \n&nbsp; The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005 to 2018. Interestingly, the amount of pollution in this region appears to have leveled off over the past few years, perhaps a sign that air pollution control technology has started to be deployed on large pollution sources. The significant drop in both SO2 and NO2 for 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.",
+      "text": "<--IMG-->/data/story-images/SO2NO2-India.png<--IMG-->**INDIA** \n&nbsp; The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005 to 2018. Interestingly, the amount of pollution in this region appears to have leveled off over the past few years, perhaps a sign that air pollution control technology has started to be deployed on large pollution sources. The significant drop in both SO2 and NO2 for 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.",
       "id": "SO2 and NO2 Change from 2005 (%)-1650233501346",
       "title": "SO2 and NO2 Change from 2005 (%)",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/aapopescu/Images/blob/main/SO2NO2-India.png?raw=true\">https://github.com/aapopescu/Images/blob/main/SO2NO2-India.png?raw=true</a>&lt;--IMG--&gt;<strong>INDIA</strong> \n&nbsp; The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005 to 2018. Interestingly, the amount of pollution in this region appears to have leveled off over the past few years, perhaps a sign that air pollution control technology has started to be deployed on large pollution sources. The significant drop in both SO2 and NO2 for 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/SO2NO2-India.png&lt;--IMG--&gt;<strong>INDIA</strong> \n&nbsp; The timeseries charts for northeastern India show a steady increase for both NO2 and SO2 from 2005 to 2018. Interestingly, the amount of pollution in this region appears to have leveled off over the past few years, perhaps a sign that air pollution control technology has started to be deployed on large pollution sources. The significant drop in both SO2 and NO2 for 2020 is most likely the result of the prolonged COVID-19 lockdown in India that resulted in a reduction in fossil fuel combustion.</p>\n"
     },
     {
       "width": 2,
diff --git a/app/public/data/dashboards/d6a638e79a7de361.json b/app/public/data/dashboards/d6a638e79a7de361.json
index 6035c19046..765777446b 100644
--- a/app/public/data/dashboards/d6a638e79a7de361.json
+++ b/app/public/data/dashboards/d6a638e79a7de361.json
@@ -7,9 +7,10 @@
   "features": [
     {
       "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/12-ship-traffic.jpg?raw=true<--IMG-->Ship traffic",
+      "text": "<--IMG-->/data/story-images/12-ship-traffic.jpg<--IMG-->Ship traffic",
       "id": "Ship traffic-1651904866820",
-      "title": "Ship traffic"
+      "title": "Ship traffic",
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/12-ship-traffic.jpg&lt;--IMG--&gt;Ship traffic</p>\n"
     },
     {
       "width": 2,
diff --git a/app/public/data/dashboards/e45036dd512afea1.json b/app/public/data/dashboards/e45036dd512afea1.json
index 3d15fcbb30..0d6e1660c1 100644
--- a/app/public/data/dashboards/e45036dd512afea1.json
+++ b/app/public/data/dashboards/e45036dd512afea1.json
@@ -14,17 +14,17 @@
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true<--IMG--> ",
+      "text": "<--IMG-->/data/story-images/1-sea-ice.jpg<--IMG--> ",
       "id": " -1651663567542",
       "title": " ",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true\">https://github.com/eurodatacube/eodash/blob/d3628aaf9d9c16628212f4256efcdc8a93ef4197/app/public/data/story-images/1-sea-ice.jpg?raw=true</a>&lt;--IMG--&gt; </p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-sea-ice.jpg&lt;--IMG--&gt; </p>\n"
     },
     {
       "width": 1,
-      "text": "## Explore Datasets\n\nThis interactive globe view displays the global, sea cce concentration product from JAXA's GCOM-W (Global Change Observation Mission - Water)\n\n\n",
+      "text": "## Explore Datasets\n\nThis interactive globe view displays the global, sea ice concentration product from JAXA's GCOM-W (Global Change Observation Mission - Water)\n\n\n",
       "id": " -1652251121816",
       "title": " ",
-      "__generatedText__": "<h2>Explore Datasets</h2>\n<p>This interactive globe view displays the global, sea cce concentration product from JAXA&#39;s GCOM-W (Global Change Observation Mission - Water)</p>\n"
+      "__generatedText__": "<h2>Explore Datasets</h2>\n<p>This interactive globe view displays the global, sea ice concentration product from JAXA&#39;s GCOM-W (Global Change Observation Mission - Water)</p>\n"
     },
     {
       "width": 3,
@@ -46,10 +46,10 @@
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/ICEBridgePlane.png?raw=true<--IMG-->Ice bridge plane",
+      "text": "<--IMG-->/data/story-images/1-EO_Dashboard_Cryo-Pix_May_12.jpeg<--IMG-->Ice bridge plane",
       "id": "Ice bridge plane-1652251495171",
       "title": "Ice bridge plane",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/ICEBridgePlane.png?raw=true\">https://github.com/eurodatacube/eodash/blob/staging/app/public/data/story-images/ICEBridgePlane.png?raw=true</a>&lt;--IMG--&gt;Ice bridge plane</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/1-EO_Dashboard_Cryo-Pix_May_12.jpeg&lt;--IMG--&gt;Ice bridge plane</p>\n"
     },
     {
       "width": 2,
@@ -60,10 +60,10 @@
     },
     {
       "width": 2,
-      "text": "<--IMG-->https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2009/11/icebreaking_ships/10040180-2-eng-GB/Icebreaking_ships_pillars.jpg<--IMG-->Shipping routes",
+      "text": "<--IMG-->/data/story-images/Icebreaking_ships_pillars.jpg<--IMG-->Shipping routes",
       "id": " -1651663707409",
       "title": " ",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2009/11/icebreaking_ships/10040180-2-eng-GB/Icebreaking_ships_pillars.jpg\">https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2009/11/icebreaking_ships/10040180-2-eng-GB/Icebreaking_ships_pillars.jpg</a>&lt;--IMG--&gt;Shipping routes</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Icebreaking_ships_pillars.jpg&lt;--IMG--&gt;Shipping routes</p>\n"
     }
   ],
   "title": "Cryo - JAXA 1"
diff --git a/app/public/data/dashboards/fe9c083d830b9f90.json b/app/public/data/dashboards/fe9c083d830b9f90.json
index dc39b746c3..82de666671 100644
--- a/app/public/data/dashboards/fe9c083d830b9f90.json
+++ b/app/public/data/dashboards/fe9c083d830b9f90.json
@@ -8,10 +8,10 @@
   "features": [
     {
       "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/3-agriculture-crops.jpg?raw=true<--IMG-->Agriculture",
+      "text": "<--IMG-->/data/story-images/3-agriculture-crops.jpg<--IMG-->Agriculture",
       "id": "Cairo-1652033099336",
       "title": "Cairo",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/3-agriculture-crops.jpg?raw=true\">https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/3-agriculture-crops.jpg?raw=true</a>&lt;--IMG--&gt;Agriculture</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/3-agriculture-crops.jpg&lt;--IMG--&gt;Agriculture</p>\n"
     },
     {
       "width": 2,
@@ -21,18 +21,25 @@
       "__generatedText__": "<h1>Crops Consume Cairo\u2019s Carbon Dioxide</h1>\n<h2>Story of Cairo</h2>\n<p>&nbsp;<br>Cairo, the capital city of Egypt, is home to more than 20 million people, making it the most populated city in Africa. The city is located on the fertile floodplain of the Nile River and is surrounded by farmland, with a high concentration of farmland to the north. The farmland stands out in satellite imagery against the drier deserts of Egypt, and during the growing season crops absorb atmospheric carbon dioxide (CO2) that can be measured from space. </p>\n<p>The following maps show examples of the changes of atmospheric CO2 concentrations based on the growth and photosynthetic activity of crops on nearby farms.</p>\n<p>We can observe the emission of CO2 from cars, power plants, and other fossil-fuel burning activities from space using the Japanese Aerospace Exploration Agency\u2019s Greenhouse gasses Observing Satellite (GOSAT). We can also observe how crops on surrounding farmlands consume CO2 by looking at solar-induced chlorophyll fluorescence (SIF). SIF is an innovative measurement that serves as a proxy of plant photosynthetic activity. Crops and other plants absorb white light from the sun and when photosynthesis occurs, they emit some of the unused energy from the sun as a red glow, known as SIF. </p>\n<p>By combining GOSAT data, TROPOMI SIF data, surface wind data, and a statistical megacity emission inventory, we can monitor daily atmospheric changes of CO2. GOSAT observes tropospheric partial column density of carbon dioxide (CO2) over global megacities and provides the CO2 enhancement from lower atmosphere (0-4 km) density minus upper atmosphere (4-12 km) density. </p>\n<p>In this research, researchers from the Japanese Aerospace Exploration Agency demonstrate how CO2 levels over Cairo change over time based on trends in combustion, SIF levels, and wind direction.</p>\n<p>Egypt has two growing seasons, one in the summer and one in the winter. In the summer, the map below shows there were low CO2 levels over Cairo, correlated with high SIF.</p>\n"
     },
     {
-      "width": 2,
-      "text": "# 2018-06-30\n\nEgypt has two growing seasons, one in the summer and one in the winter. In the summer, GOSAT data from the 30 of June, 2018 shows low CO2 levels over Cairo, correlated with high solar-induced chlorophyll fluorescence (SIF) over the farmlands of the Nile River Delta. Winds are moving from the high SIF areas to the north over the large city of Cairo.\n\n# 2019-02-01 \n\nDuring the winter crop season we see very high SIF in the Delta region. GOSAT data from the first of February, 2019, show high levels of CO2 and the winds moving over Cairo from the east.\n\n # 2019-02-01\n\nBetween crop seasons, we see much lower SIF over the Nile Delta showing no photosynthetic activity. As winds move from the north, we see high CO2 levels over Cairo.",
-      "id": " -1652035505919",
+      "width": 1,
+      "text": "<--IMG-->/data/story-images/Wind_Cairo_2018-06-30_rev.png<--IMG-->Egypt has two growing seasons, one in the summer and one in the winter. In the summer, GOSAT data from the 30 of June, 2018 shows low CO2 levels over Cairo, correlated with high solar-induced chlorophyll fluorescence (SIF) over the farmlands of the Nile River Delta. Winds are moving from the high SIF areas to the north over the large city of Cairo.",
+      "id": " -1652450674679",
       "title": " ",
-      "__generatedText__": "<h1>2018-06-30</h1>\n<p>Egypt has two growing seasons, one in the summer and one in the winter. In the summer, GOSAT data from the 30 of June, 2018 shows low CO2 levels over Cairo, correlated with high solar-induced chlorophyll fluorescence (SIF) over the farmlands of the Nile River Delta. Winds are moving from the high SIF areas to the north over the large city of Cairo.</p>\n<h1>2019-02-01</h1>\n<p>During the winter crop season we see very high SIF in the Delta region. GOSAT data from the first of February, 2019, show high levels of CO2 and the winds moving over Cairo from the east.</p>\n<h1>2019-02-01</h1>\n<p>Between crop seasons, we see much lower SIF over the Nile Delta showing no photosynthetic activity. As winds move from the north, we see high CO2 levels over Cairo.</p>\n"
+      "__generatedText__": "<p>&lt;--IMG--&gt;/data/story-images/Wind_Cairo_2018-06-30_rev.png&lt;--IMG--&gt;Egypt has two growing seasons, one in the summer and one in the winter. In the summer, GOSAT data from the 30 of June, 2018 shows low CO2 levels over Cairo, correlated with high solar-induced chlorophyll fluorescence (SIF) over the farmlands of the Nile River Delta. Winds are moving from the high SIF areas to the north over the large city of Cairo.</p>\n"
     },
     {
-      "width": 2,
-      "text": "<--IMG-->https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/WindCairo.gif?raw=true<--IMG-->Wind diagram over Cairo Airport on 2018-06-30, 2019-02-01 and 2019-10",
-      "id": "2018-06-30    -1652034650875",
-      "title": "2018-06-30    ",
-      "__generatedText__": "<p>&lt;--IMG--&gt;<a href=\"https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/WindCairo.gif?raw=true\">https://github.com/eurodatacube/eodash/blob/aapopescu-patch-4/app/public/data/story-images/WindCairo.gif?raw=true</a>&lt;--IMG--&gt;Wind diagram over Cairo Airport on 2018-06-30, 2019-02-01 and 2019-10</p>\n"
+      "width": 1,
+      "text": "<--IMG--> /data/story-images/Wind_Cairo_2019-02-01_rev.png<--IMG--> During the winter crop season we see very high SIF in the Delta region. GOSAT data from the first of February, 2019, show high levels of CO2 and the winds moving over Cairo from the east.",
+      "id": " -1652450717846",
+      "title": " ",
+      "__generatedText__": "<p>&lt;--IMG--&gt; /data/story-images/Wind_Cairo_2019-02-01_rev.png&lt;--IMG--&gt; During the winter crop season we see very high SIF in the Delta region. GOSAT data from the first of February, 2019, show high levels of CO2 and the winds moving over Cairo from the east.</p>\n"
+    },
+    {
+      "width": 1,
+      "text": "<--IMG--> /data/story-images/Wind_Cairo_2019-10-05_rev.png<--IMG--> Between crop seasons, we see much lower SIF over the Nile Delta showing no photosynthetic activity. As winds move from the north, we see high CO2 levels over Cairo.",
+      "id": " -1652450726876",
+      "title": " ",
+      "__generatedText__": "<p>&lt;--IMG--&gt; /data/story-images/Wind_Cairo_2019-10-05_rev.png&lt;--IMG--&gt; Between crop seasons, we see much lower SIF over the Nile Delta showing no photosynthetic activity. As winds move from the north, we see high CO2 levels over Cairo.</p>\n"
     },
     {
       "width": 2,
diff --git a/app/public/data/story-images/1-EO_Dashboard_Cryo-Pix_May_12.jpeg b/app/public/data/story-images/1-EO_Dashboard_Cryo-Pix_May_12.jpeg
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diff --git a/app/public/data/story-images/A2021071.LakeErie.crop.small.jpg b/app/public/data/story-images/A2021071.LakeErie.crop.small.jpg
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diff --git a/app/public/data/story-images/Carbon_monoxide_from_fires_in_Australia_article.jpg b/app/public/data/story-images/Carbon_monoxide_from_fires_in_Australia_article.jpg
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diff --git a/app/public/data/story-images/Monitoring_air_quality_pillars.jpg b/app/public/data/story-images/Monitoring_air_quality_pillars.jpg
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diff --git a/app/public/data/story-images/Oyashio and time series.png b/app/public/data/story-images/Oyashio and time series.png
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diff --git a/app/public/data/story-images/Road_Map.jpg b/app/public/data/story-images/Road_Map.jpg
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diff --git a/app/public/data/story-images/SO2NO2-China.png b/app/public/data/story-images/SO2NO2-China.png
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diff --git a/app/public/data/story-images/SO2NO2-India.png b/app/public/data/story-images/SO2NO2-India.png
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diff --git a/app/public/data/story-images/SO2NO2-USA.png b/app/public/data/story-images/SO2NO2-USA.png
new file mode 100644
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diff --git a/app/public/data/story-images/aura_0.jpg b/app/public/data/story-images/aura_0.jpg
new file mode 100644
index 0000000000..f7119b95ba
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diff --git a/app/public/data/story-images/worldcereal.jpeg b/app/public/data/story-images/worldcereal.jpeg
new file mode 100644
index 0000000000..53a237f8b0
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diff --git a/app/public/eodash-data/stories/N11.md b/app/public/eodash-data/stories/N11.md
index 1d9f8c44a5..10a813ba0e 100644
--- a/app/public/eodash-data/stories/N11.md
+++ b/app/public/eodash-data/stories/N11.md
@@ -6,5 +6,5 @@ JAXA's GCOM-C mission aims to establish and demonstrate a global, long-term sate
 
 Access the [Global Change Observation Mission - Climate (GCOM-C) Webpage](https://suzaku.eorc.jaxa.jp/GCOM_C/index.html)
 
-Read more about the product: https://suzaku.eorc.jaxa.jp/GCOM_C/data/files/Hirawake_ATBD_ONPP_Hirawake_ver3.3.pdf 
+Read more about the product: https://doi.org/10.1007/s10872-005-0074-7
 
diff --git a/app/public/index.html b/app/public/index.html
index 19c7a4cd5a..f33adb9d2a 100644
--- a/app/public/index.html
+++ b/app/public/index.html
@@ -5,8 +5,8 @@
     <meta http-equiv="X-UA-Compatible" content="IE=edge">
     <meta name="viewport" content="width=device-width,initial-scale=1.0">
     <title><%= htmlWebpackPlugin.options.title %></title>
-    <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:100,300,400,500,700,900">
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@mdi/font@latest/css/materialdesignicons.min.css">
+    <link rel="stylesheet" href="./css/roboto.css">
+    <link rel="stylesheet" href="./css/materialdesignicons.min.css">
     <link rel="stylesheet" href="./spinkit.min.css">
   </head>
   <body>
diff --git a/app/src/appConfig.js b/app/src/appConfig.js
index c120bf6fc2..ce30ff5f13 100644
--- a/app/src/appConfig.js
+++ b/app/src/appConfig.js
@@ -307,6 +307,6 @@ module.exports = [
     feedbackTwitterHandles: ['ESA_EO', 'esa', 'NASAEarth', 'NASA', 'JAXA_en', 'JAXA_jp', 'eurodatacube'],
     countDownTimer: '2020-06-25T13:30:00.000+02:00',
     countDownMatch: ['eodashboard.org', 'www.eodashboard.org', 'eodash-trilateral.eox.at'],
-    enableStories: process.env.NODE_ENV === 'development' || process.env.NODE_ENV === 'staging',
+    enableStories: true,
   },
 ];
diff --git a/app/src/components/CustomDashboardGrid.vue b/app/src/components/CustomDashboardGrid.vue
index 622f8a1b16..0224243853 100644
--- a/app/src/components/CustomDashboardGrid.vue
+++ b/app/src/components/CustomDashboardGrid.vue
@@ -460,6 +460,7 @@ export default {
     showTextCurrent: null,
     tooltipTrigger: false,
     numberOfRows: null,
+    ro: null,
   }),
   computed: {
     ...mapGetters('dashboard', {
@@ -540,7 +541,7 @@ export default {
       immediate: true,
       deep: true,
       handler(features) {
-        if (features) {
+        if (features && !this.localFeatures) {
           this.parseFeatures(features);
         }
       },
@@ -564,9 +565,36 @@ export default {
         this.getNumberOfRows();
       }
     },
+    currentRow(newRow) {
+      const query = { ...this.$route.query };
+      if (newRow === 0) {
+        delete query.page;
+      } else {
+        query.page = newRow;
+      }
+      this.$router.replace({ query }).catch(() => {});
+    },
   },
   mounted() {
     this.isMounted = true;
+    setTimeout(() => {
+      if (this.$route.query.page) {
+        this.goStep(Number(this.$route.query.page));
+      }
+    });
+
+    this.ro = new ResizeObserver(() => {
+      setTimeout(() => {
+        if (document.querySelector('.scrollContainer').scrollTop > 0) {
+          this.goStep(0);
+        }
+      });
+    })
+      .observe(document.querySelector('.scrollContainer'));
+  },
+  beforeDestroy() {
+    delete this.ro;
+    zoom.close();
   },
   methods: {
     ...mapActions('dashboard', [
@@ -660,11 +688,15 @@ export default {
       if (this.currentRow === 1 && direction === -1) {
         position = 0; // scroll back to story intro
       } else {
+        const container = document.querySelector('#elementsContainer');
+        if (!container) {
+          return;
+        }
         const startingPoint = document.querySelector('#elementsContainer').offsetTop;
         const rowHeight = window.innerHeight
           - this.$vuetify.application.top
           - this.$vuetify.application.footer;
-        const target = rowHeight * (this.currentRow - 1 + direction);
+        const target = rowHeight * ((this.currentRow || 0) - 1 + direction);
         position = startingPoint + target;
       }
       this.$emit('scrollTo', { target: position });
diff --git a/app/src/components/GlobalFooter.vue b/app/src/components/GlobalFooter.vue
index 56dbdc9f0a..259f9e649e 100644
--- a/app/src/components/GlobalFooter.vue
+++ b/app/src/components/GlobalFooter.vue
@@ -66,7 +66,9 @@ export default {
   mounted() {
     this.fixFullHeight();
     window.addEventListener('resize', () => {
-      this.fixFullHeight();
+      setTimeout(() => {
+        this.fixFullHeight();
+      }, 200);
     });
   },
   methods: {
diff --git a/app/src/components/GlobalHeader.vue b/app/src/components/GlobalHeader.vue
index 8b308adf68..dcbf1a97bd 100644
--- a/app/src/components/GlobalHeader.vue
+++ b/app/src/components/GlobalHeader.vue
@@ -99,9 +99,7 @@
           text
           color="primary"
           block
-          @click="$store.state.indicators.selectedIndicator
-            ? $router.go(-1)
-            : $router.push({ name: 'explore' })"
+          :to="{ name: 'explore' }"
         >
           Explore Datasets
       </v-btn>
@@ -262,9 +260,7 @@
         text
         dark
         small
-        @click="$store.state.indicators.selectedIndicator
-          ? $router.go(-1)
-          : $router.push({ name: 'explore' })"
+        :to="{ name: 'explore' }"
       >
         Explore Datasets
       </v-btn>
diff --git a/app/src/config/stories.json b/app/src/config/stories.json
index b59b8f70d5..8b8110e3d2 100644
--- a/app/src/config/stories.json
+++ b/app/src/config/stories.json
@@ -44,8 +44,8 @@
         "originalDashboardId": "a785d7a3c536a452",
         "title": "A view from space monitors land cover and tree to track aboveground biomass and carbon content",
         "subtitle": "Read more about taking stock of global biomass maps",
-        "image": "./data/story-images/10-leafy-vegetation.jpg",
-        "imagePlaceholder": "./data/story-images/10-leafy-vegetation_placeholder.jpeg"  
+        "image": "./data/story-images/Biomass.jpeg",
+        "imagePlaceholder": "./data/story-images/Biomass_placeholder.jpeg"  
       }
     },
     "covid-19": {
diff --git a/app/src/config/themes.json b/app/src/config/themes.json
index 3dda6ecc73..c6fcb8f7f0 100644
--- a/app/src/config/themes.json
+++ b/app/src/config/themes.json
@@ -31,7 +31,7 @@
     "name": "Cryosphere",
     "slug": "cryosphere",
     "color": "#42C7B8",
-    "description": "Tracking Ice and Snow’s relationship to Climate",
+    "description": "Tracking Ice and Snow’s Relationship to Climate",
     "longDescription": "The Arctic region shows changes most characteristic of global warming."
   },
   {
diff --git a/app/src/config/trilateral.js b/app/src/config/trilateral.js
index 95933a7c18..0bc29ecebe 100644
--- a/app/src/config/trilateral.js
+++ b/app/src/config/trilateral.js
@@ -203,7 +203,7 @@ export const indicatorsDefinition = Object.freeze({
     indicator: 'NDVI GCOM-C',
     class: 'agriculture',
     story: '/eodash-data/stories/E10e',
-    themes: ['agriculture'],
+    themes: ['agriculture', 'biomass-and-landcover'],
   },
   E11: {
     indicator: 'Volume of activity at shopping centers',
@@ -347,7 +347,7 @@ export const indicatorsDefinition = Object.freeze({
   N12: {
     indicator: 'Sea Ice Concentration (GCOM-W)',
     class: 'water',
-    themes: ['covid-19', 'oceans'],
+    themes: ['covid-19', 'oceans', 'cryosphere'],
     baseLayers: [{
       ...baseLayers.cloudless,
       visible: true,
@@ -399,7 +399,7 @@ export const indicatorsDefinition = Object.freeze({
     indicator: 'Solar Induced Chlorophyll Fluorescence',
     story: '/eodash-data/stories/SIF',
     class: 'agriculture',
-    themes: ['biomass-and-landcover'],
+    themes: ['biomass-and-landcover', 'agriculture'],
     largeSubAoi: true,
     maxMapZoom: 8,
   },
@@ -419,19 +419,19 @@ export const indicatorsDefinition = Object.freeze({
     indicator: 'SIE',
     story: '/eodash-data/stories/SIE',
     class: 'water',
-    themes: ['oceans'],
+    themes: ['oceans', 'cryosphere'],
   },
   SIC: {
     indicator: 'SIC',
     story: '/eodash-data/stories/SIC',
     class: 'water',
-    themes: ['oceans'],
+    themes: ['oceans', 'cryosphere'],
   },
   SITI: {
     indicator: 'SITI',
     story: '/eodash-data/stories/SITI',
     class: 'water',
-    themes: ['oceans'],
+    themes: ['oceans', 'cryosphere'],
   },
   NCEO: {
     indicator: 'NCEO',
@@ -995,7 +995,8 @@ export const globalIndicators = [
           opacity: 1,
           url: 'https://8ib71h0627.execute-api.us-east-1.amazonaws.com/v1/{z}/{x}/{y}@1x.png?url=s3://covid-eo-data/OMNO2d_HRM/OMI_trno2_0.10x0.10_{time}_Col3_V4.nc.tif&resampling_method=bilinear&bidx=1&rescale=0%2C1.08398547e16&color_map=reds',
           name: 'Air Quality (NASA)',
-          dateFormatFunction: (date) => DateTime.fromISO(date).toFormat('yyyyMM'),
+          dateFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('yyyyMM'),
+          labelFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('LLL yyyy'),
           legendUrl: 'eodash-data/data/no2Legend.png',
           areaIndicator: {
             url: 'https://8ib71h0627.execute-api.us-east-1.amazonaws.com/v1/timelapse',
@@ -1091,6 +1092,7 @@ export const globalIndicators = [
           url: 'https://8ib71h0627.execute-api.us-east-1.amazonaws.com/v1/{z}/{x}/{y}@1x?url=s3://covid-eo-data/OMNO2d_HRMDifference/OMI_trno2_0.10x0.10_{time}_Col3_V4.nc.tif&resampling_method=bilinear&bidx=1&rescale=-3e15%2C3e15&color_map=rdbu_r',
           name: 'Air Quality (NASA)',
           dateFormatFunction: (date) => DateTime.fromISO(date).toFormat('yyyyMM'),
+          labelFormatFunction: (date) => DateTime.fromISO(date).toFormat('LLL yyyy'),
           legendUrl: 'data/trilateral/N1-NO2DiffLegend.png',
           disableCompare: true,
         },
@@ -1213,7 +1215,7 @@ export const globalIndicators = [
         lastColorCode: null,
         aoi: null,
         aoiID: 'W5',
-        time: availableDates['no2-monthly-diff'],
+        time: getDailyDates('2020-01-01', '2021-10-15'),
         inputData: [''],
         display: {
           protocol: 'xyz',
@@ -1258,6 +1260,7 @@ export const globalIndicators = [
           maxZoom: 13,
           minMapZoom: 2,
           dateFormatFunction: (date) => DateTime.fromISO(date).toFormat('yyyy-MM-dd'),
+          labelFormatFunction: (date) => DateTime.fromISO(date).toFormat('LLL yyyy'),
           /*
           customAreaIndicator: true,
           areaIndicator: {
@@ -1346,7 +1349,7 @@ export const globalIndicators = [
           url: 'https://staging-raster.delta-backend.xyz/cog/tiles/WebMercatorQuad/{z}/{x}/{y}?url={time}&resampling_method=bilinear&rescale=0.0,4.0&bidx=1&colormap_name=plasma',
           name: 'Sea Ice Thickness (ICESat-2)',
           dateFormatFunction: (date) => `${date[1]}`,
-          labelFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('yyyy-MM'),
+          labelFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('LLL yyyy'),
           legendUrl: 'eodash-data/data/SeaIceThicknessCCI.PNG',
         },
       },
@@ -1384,7 +1387,7 @@ export const globalIndicators = [
           url: 'https://staging-raster.delta-backend.xyz/cog/tiles/WebMercatorQuad/{z}/{x}/{y}?{time}&resampling_method=bilinear&rescale=0.0,1500.0&bidx=1&colormap_name=jet',
           name: 'NPP (NASA)',
           dateFormatFunction: (date) => `url=${date[1]}`,
-          labelFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('yyyy-MM'),
+          labelFormatFunction: (date) => DateTime.fromISO(date[0]).toFormat('LLL yyyy'),
           legendUrl: 'eodash-data/data/nppn_legend.png',
         },
       },
@@ -1505,6 +1508,7 @@ export const globalIndicators = [
           maxZoom: 13,
           minMapZoom: 2,
           dateFormatFunction: (date) => DateTime.fromISO(date).toFormat('yyyy-MM-dd'),
+          labelFormatFunction: (date) => DateTime.fromISO(date).toFormat('LLL yyyy'),
           /*
           customAreaIndicator: true,
           areaIndicator: {
@@ -1547,6 +1551,7 @@ export const globalIndicators = [
           maxZoom: 13,
           minMapZoom: 2,
           dateFormatFunction: (date) => DateTime.fromISO(date).toFormat('yyyy-MM-dd'),
+          labelFormatFunction: (date) => DateTime.fromISO(date).toFormat('LLL yyyy'),
           /*
           customAreaIndicator: true,
           areaIndicator: {
@@ -1776,6 +1781,7 @@ export const globalIndicators = [
           layers: 'ONPP-GCOMC-World-Monthly',
           minZoom: 1,
           dateFormatFunction: (date) => DateTime.fromISO(date).toFormat("yyyy-MM-dd'T'hh:mm:ss'.000Z'"),
+          labelFormatFunction: (date) => DateTime.fromISO(date).toFormat('LLL yyyy'),
           legendUrl: 'eodash-data/data/N11_legend.png',
         }],
       },
diff --git a/app/src/views/Dashboard.vue b/app/src/views/Dashboard.vue
index 463c87c6d1..87994d0ba8 100644
--- a/app/src/views/Dashboard.vue
+++ b/app/src/views/Dashboard.vue
@@ -321,6 +321,9 @@ export default {
       }
     }, 2000);
   },
+  beforeDestroy() {
+    this.$store.commit('indicators/SET_SELECTED_INDICATOR', null);
+  },
   methods: {
     setDataPanelWidth(enable) {
       if (enable) {
diff --git a/app/src/views/DashboardCustom.vue b/app/src/views/DashboardCustom.vue
index 1b12efe874..8e7df70d7e 100644
--- a/app/src/views/DashboardCustom.vue
+++ b/app/src/views/DashboardCustom.vue
@@ -441,6 +441,15 @@ import CustomDashboardGrid from '@/components/CustomDashboardGrid.vue';
 import Modal from '@/components/Modal.vue';
 
 export default {
+  metaInfo() {
+    const { appConfig } = this.$store.state.config;
+    return {
+      title: appConfig ? appConfig.branding.appName : 'eodash',
+      link: [
+        { rel: 'apple-touch-icon', href: `${appConfig.pageMeta.imagePath}/apple-touch-icon-192x192.png` },
+      ],
+    };
+  },
   components: {
     CustomDashboardGrid,
     GlobalHeader,
@@ -536,10 +545,12 @@ export default {
     ]),
     newDashboard() {
       return this.$store.state.dashboard.dashboardConfig
-        && !this.$store.state.dashboard?.dashboardConfig?.marketingInfo;
+        && !this.$store.state.dashboard?.dashboardConfig?.marketingInfo
+        && !this.officialDashboard;
     },
     hasEditingPrivilege() {
-      return this.$store.state.dashboard?.dashboardConfig?.editKey;
+      return this.$store.state.dashboard?.dashboardConfig?.editKey
+        && !this.officialDashboard;
     },
     rootLink() {
       return document.location.origin;
@@ -613,7 +624,7 @@ export default {
       }
     }
 
-    if (this.dashboardConfig) {
+    if (this.dashboardConfig && !this.officialDashboard) {
       if (this.dashboardConfig.title) {
         this.dashboardTitle = this.dashboardConfig.title;
       }
@@ -627,6 +638,18 @@ export default {
         });
       }
     }
+    if (this.officialDashboard && this.storyModeEnabled) {
+      if (!this.getCurrentTheme) {
+        if (existingConfiguration) {
+          const currentTheme = Object.entries(storiesConfig[this.appConfig.id])
+            .find((stories) => Object.values(stories[1]).includes(existingConfiguration))[0];
+          this.loadTheme(currentTheme);
+        }
+      }
+    } else {
+      this.loadTheme(null);
+    }
+
     if (!this.dashboardConfig) {
       this.$store.commit('indicators/SET_SELECTED_INDICATOR', null);
     }
@@ -634,7 +657,7 @@ export default {
     this.initialLoading = false;
   },
   beforeDestroy() {
-    if (!this.hasEditingPrivilege && !this.newDashboard) {
+    if (!this.hasEditingPrivilege && !this.newDashboard && !this.officialDashboard) {
       this.reconnecting = true;
       this.disconnect();
       this.reconnecting = false;