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| "description": "This course will introduce students to conceptual organization of workflows as a way to conduct reproducible analyses", | ||
| "author": [], | ||
| "contents": "\n\nContents\nInstructor\nTutor\nImportant links\nCourse description\nPredictable daily schedule\nLearning objectives\nSessions (subject to change)\nCourse requirements\nComputing\nTextbook\n\n\n\n\n\nFigure 1: Workflow example using the tidyverse. Note the program box around the workflow and the iterative nature of the analytical process described. Source: R for Data Science https://r4ds.had.co.nz/\n\n\n\nInstructor\nJulien Brun ([email protected])\nTutor\nCasey O’Hara ([email protected])\nImportant links\nCourse syllabus\nCode of Conduct\nCourse description\nThe generation and analysis of environmental data is often a complex, multi-step process that may involve the collaboration of many people. Increasingly tools that document and help to organize workflows are being used to ensure reproducibility, shareability, and transparency of the results. This course will introduce students to the conceptual organization of workflows (including code, documents, and data) as a way to conduct reproducible analyses. These concepts will be combined with the practice of various software tools and collaborative coding techniques to develop and manage multi-step analytical workflows as a team.\nPredictable daily schedule\nCourse dates: Monday (2021-08-02) - Friday (2021-08-06)\nEDS 214 is an intensive 1-week long 2-unit course. Students should plan to attend all scheduled sessions. All course requirements will be completed between 8am and 5pm PST (M - F), i.e. you are not expected to do additional work for EDS 214 outside of those hours, unless you are working with the Teaching Assistant in student hours.\nDaily schedule (subject to change):\nTime (PST)\nActivity\n9:00am - 10:00am\nLecture 1 (60 min)\n10:00am - 10:10am\nBreak 1 (10 min)\n10:10am - 11:30am\nInteractive Session 1 (80 min)\n11:30am - 12:00am\nFlex time (30 min)\n12:00am - 1:15pm\nLunch (75 min)\n1:15pm - 2:00pm\nLecture 2 (45 min)\n2:00pm - 2:10pm\nBreak 2 (10 min)\n2:10pm - 3:10pm\nInteractive Session 2 (60 min)\n3:10pm - 3:20pm\nBreak 3 (10 min)\n3:20pm - 4:30pm\nGroup projects and/or flex time (70 min)\nLearning objectives\nThe goal of EDS 214 (Analytical Workflows and Scientific Reproducibility) is to expose incoming MEDS students to “good enough” practices of scientific programming develop skills in environmental data science to produce reproducible research. By the end of the course, students should be able to:\nDevelop knowledge in scientific analytical workflows To learn how to make your data-riven research reproducible, it is important to develop scientific workflows that will be relying on programming to accomplish the necessary tasks to go from the raw data to the results of your analysis (figures, new data, publications, …). Scripting languages, even better open ones such as R and python, are well-suited for scientists to develop reproducible scientific workflows, but are not the only tools you will need to develop reproducible and collaborative workflows\nLearn how to code in a collaborative manner by practicing techniques such as code review and pair programming. Become comfortable asking for and conducting code review using git and GitHub to create pull request, ask feedback from peers, and merge changes into the main repository. Practice pair programming to cement the collaborative development of reproducible analytical workflows\nPractice documenting code and data in a systematic way that will enable your collaborators, including your future self, to understand and reuse your work\nSessions (subject to change)\n\n\n\nDay / Session\nTopics\nInteractive Sessions\nFlex Sessions\nMonday 8/23: morning\nReproducible workflows\nPlanning things: from diagrams to pseudo code\nthe Markdown syntax\nMonday 8/23: afternoon\nCollaborating with Github 101\nForking and Pull Requests\nGithub collaboration Hands-on\nTuesday 8/24: morning\nGithub conflicts\nManaging data-driven projects as a team\nDebugging with RStudio\nTuesday 8/24: afternoon\ngit commit messages\nMEDS IT team (Brad & Kat) – Computing resources\n\nWednesday 8/25: morning (late start)\nWorking on a remote server\nRStudio server\nthe command line\nWednesday 8/25: afternoon\nIntroduction to group project]\nGroup project\nGroup project Q&A\nThursday 8/26: morning\nCoding as a team – Code review and pair programming\nGroup project\nMeet your next instructor – Jim Frew, Bren School, UCSB\nThursday 8/26: afternoon\nDocumenting things\nGroup project\nCapstone project proposal – Jamie\nFriday 8/27: morning\nSharing things\nGroup project\nXaringan\nFriday 8/27 afternoon\nProject presentations\nProject presentations\n\nCourse requirements\nComputing\nMinimum MEDS device requirements\nHave a ready to be used GitHub Account (https://github.com/)\nTextbook\nR for Data Science: https://r4ds.had.co.nz/\nThe Practice of Reproducible Research: http://www.practicereproducibleresearch.org/\n\n\n\n", | ||
| "last_modified": "2022-02-24T22:42:02-08:00" | ||
| "last_modified": "2022-02-24T23:19:59-08:00" | ||
| }, | ||
| { | ||
| "path": "README.html", | ||
| "author": [], | ||
| "contents": "\n\nContents\nEDS 214: Analytical Workflows and Scientific Reproducibility\nInstructor\nCourse description\n\n\nEDS 214: Analytical Workflows and Scientific Reproducibility\nInstructor\nJulien Brun ([email protected])\nCourse description\nThe generation and analysis of environmental data is often a complex, multi-step process that may involve the collaboration of many people. Increasingly tools that document help to organize and document workflows are being used to ensure reproducibility and transparency of the results. This course will introduce students to conceptual organization of workflows as a way to conduct reproducible analyses, as well as various software tools that help users to manage multi-step processes that requires tools for storing, managing and sharing workflows, code, documents and data.\n\nThis website template is made with distill by RStudio as an optional starting point for teachers in the Master of Environmental Data Science Program at the Bren School (UC Santa Barbara).\nClick here for a template preview.\n\n\n", | ||
| "last_modified": "2022-02-24T22:42:04-08:00" | ||
| "last_modified": "2022-02-24T23:19:59-08:00" | ||
| }, | ||
| { | ||
| "path": "resources.html", | ||
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