Do you need to create slides on workflowr for an upcoming presentation? This repository is for you!
First, thanks for sharing workflowr with your colleagues. Second, below are various resources you can adapt and reuse for your presentations.
I usually release my content with the permissive Creative Commons Attribution Internal Public License (CC-BY). This gives you lots of freedom. You can adapt and reuse as you like. The main requirement is that you attribute my content as the original source.
Recently I have been using icons provided by the Noun Project. These are also licensed with CC-BY, and thus must be attributed. My strategy is to have a slide at the end of the presentation that includes all the icons with their attributions. On any slide that uses an icon, I insert a link to this attribution slide. If you use only a few slides from one of my presentations, you can also copy the attribution slide, and then remove any of the icons you didn't include.
If you're looking for the hex sticker or logos, you can find these in the separate repository workflowr-assets. It contains the logos in various styles and in multiple file formats.
I was invited by Balasubramanian Narasimhan to give two guest lectures on reproducible research and workflowr for the course STATS 352, Topics in Computing for Data Science, a survey course he co-teaches with John Chambers.
The first lecture was essentially a remix of my previous presentation for the Why R? webinar series. I added some extra background slides to the introduction about the lack of computational reproducibility of published scientific articles. I also added the slides on workflowr.io from my previous presentation at the Toronto Workshop on Reproducibility.
The second lecture was more interactive. The presentation mostly consisted of updated slides from the UserR! 2020 tutorial. Since it was short and mostly unchanged, I didn't create a shareable link for it.
- Slides: https://bit.ly/2024-stanford-workflowr
- License: CC-BY
- Attribution: "2024 Stanford STATS 352: Reproducible research with workflowr" by John Blischak, https://bit.ly/2024-stanford-workflowr
This presentation was a virtual poster. The dimensions had to be A0, and we were encouraged to create a single slide like a traditional poster. However, this was very hard to read in the SpatialChat platform, so I also included extra slides after the initial poster to walk through each section separately.
- Slides (virtual poster): https://bit.ly/r-med-2023-workflowr
- Event page
- License: CC-BY
- Attribution: "R/Medicine 2023: Facilitating reproducible and open research with workflowr and workflowr.io" by John Blischak, https://bit.ly/r-med-2023-workflowr
- Slides: https://bit.ly/workflowr-toronto
- Video: https://youtu.be/RrcaGukYDyE
- License: CC-BY
- Attribution: "2021 Toronto Workshop on Reproducibility: Facilitating reproducible and open research with workflowr and workflowr.io" by John Blischak, https://bit.ly/workflowr-toronto
- Slides: https://bit.ly/why-r-workflowr
- Video: https://youtu.be/3yX73UBN2SU
- License: CC-BY
- Attribution: "Why R? Webinar: Reproducible research with workflowr" by John Blischak, https://bit.ly/why-r-workflowr
- Slides (5-24): https://bit.ly/bdr-slides-day3
- License: CC-BY
- Attribution: John Blischak, Bioinformatics Data Reproducibility Bootcamp, Aug 12-14, 2020
- Slides: https://bit.ly/useR-workflowr-slides
- License: CC-BY
- Attribution: "Create and share reproducible code with R Markdown and workflowr" by John Blischak and Peter Carbonetto, https://bit.ly/useR-workflowr-slides
The workflowr paper has various figures to demonstrate key concepts.
- URL: https://doi.org/10.12688/f1000research.20843.1
- License: CC-BY
- Attribution: Blischak JD, Carbonetto P, and Stephens M. Creating and sharing reproducible research code the workflowr way [version 1; peer review: 3 approved]. F1000Research 2019, 8:1749 (https://doi.org/10.12688/f1000research.20843.1)
- Slides: https://speakerdeck.com/jdblischak/the-workflowr-r-package-a-framework-for-reproducible-and-collaborative-data-science
- Video: https://youtu.be/GrqM2VqIQ20
- License: CC-BY
- Attribution: John Blischak, "The workflowr R package: a framework for reproducible and collaborative data science", useR! 2018