Module topics to add to LH manual

[hdolinh]

Technical

• tidy-data.qmd (best placed after cleaning-and-manipulating-data.qmd; or maybe this can be non-technical and is focused on the concepts of tidy data)
• I would move up git-branches.qmd and place it after intro-to-git-github.qmd
• github-pages.qmd
• I think creating functions and creating packages should be two different modules, so: creating-functions-r.qmd and creating-packages-r.qmd
• reproducible-reports.qmd
• bayesian-modeling.qmd
• time-series-forecasting.qmd (Jeanette taught this with the Delta Science group and I know many gov agencies are interested in this topic)
• shiny.qmd (refer to the session Jeanette led with Delta Science and Sam's MEDS workshop)
• intro-to-command-line.qmd (Open Science for Synthesis: Gulf Research Program has a lesson on day 3 and Software Carpentries has a lesson; we should check to see if the library offers a lesson too so we can compliment their workshop vs repeating)
• data-visualization-maps.qmd (I think we should think about what is an overall technical data viz module and then what are additional technical data viz modules. Additional data viz modules could be: creating maps, interactive visualizations, infographics, data viz for scientific reports, data viz for stats, shiny, flexdashboard)
• remote-computing.qmd
• python-clusters.qmd
• intro-to-python.qmd (we should see what current materials NCEAS has on this and compare it with the library's lesson)
• parallel-programming.qmd
• software-design.qmd (we need to think through what this means 100% and if it needs to be split out)
• spatial-analysis-python.qmd
• cloud-computing.qmd
• google-earth-engine.qmd
• parllelization-dask.qmd
• opening-data.qmd (I think this could be a potentially useful module for r or data science beginners; which packages to use in r and possibly python; opening big data; opening specific file types - or maybe this can be combined with something else)

Non-technical

• metadata-best-practices.qmd
• fair-and-care-principles.qmd
• team-science-collaboration-best-practices.qmd
• reproducible-surveys.qmd
• data-repositories.qmd (I think a non-technical module on where to find environmental data would be useful for practice, research, etc. and would allow us to highlight packages like palmerpenguins and lterdatasampler)
• I think reproducibility and provenance can each be their own module, so: data-provenance.qmd and reproducible-workflows.qmd
• open-science.qmd
• data-management-best-practices.qmd (I think a module on how to organize your data/files/etc, and manage large data would be helpful)
• missing-data.qmd (Jeanette did a session on missing data with the Delta Science group, and Allison Horst co-authored a book on missing data called The Missing Book which has some course-like materials)
• data-visualizations-best-practices.qmd (I think this could go hand-in-hand with the technical data viz module and could discuss when to use specific visualizations, picking color-blind friendly palettes, what makes a good visualization vs a bad one, data viz ethics and more)
• teach-me-how-to-google.qmd
• virtual-collaboration-best-practices.qmd (this was a section included in the NEON Onboarding book and would be helpful for researchers and orgs who work remotely)
• environmental-data.qmd (I think a module on different data types and structures could be useful, esp with large data and we can pull this from the Scalable Computing Course)
• small thing but but documenting-data.qmd has a typo

[camilavargasp]

Notes

• metadata-best-practices.qmd --> documenting data takes care of this
• fair-and-care-principles.qmd --> data ethics covers this topic
• data-repositories.qmd --> covered in data publishing
• data-provenance.qmd and reproducible-workflows.qmd --> lesson is tied in together. We can think more about this and see if it makes sense to separate these topics.
• added environmental-data.qmd, but maybe rethink of the name? Following the Scalable computing curriculum, could it be data-structures-and-formats instead?

Questions

• [ ] Where is the material for team-science-collaboration-best-practices.qmd?