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Erika Austhof edited this page Oct 17, 2022 · 19 revisions

Welcome to the LearningDataScience wiki!

This Wiki is for the Roots for Resilience Research Assistantship. Learn more about Roots for Resilience from the UArizona Data Science Institute.

Tasklist

  • See if python-climate-indices would be a good option for getting drought data for thesis project
  • Determine a project to use for the R for Reproducibility workshop, maybe the Rain analysis?
  • Write a function to merge PRISM data, maybe a loop?
  • Cyverse Intro Training

Updates

  • 10/19/2022

Previous Updates

  • 10/12/2022
  • 10/5/2022: This past week I attended an API for scientists workshop which was really great. I also turned my main thesis project into a GitHub repository and shared it with my collaborators. We're going to start working on R code together. I also explored README templates, folder project template structures, and best practices for data management plans. I also helped guide a project that I'm on in some better data management practices we could implement for the project.
    • Issues: I don't have any issues this week - I am really excited about learning all these new tools and implementing them in my work!
    • Takeaways: Having a base understanding of an API was really helpful in order to know how to use them. The python-climate-indices, as well as the CRan prism project both use APIs but I didn't previously understood how they worked, so coding was difficult; now I feel like I could try it again with more success.
  • 9/28/2022: This week we discussed project management in the FOSS workshops. I really liked the idea of a binding document for a project that details expectations, who is doing what, and the goals/objectives for a project. I was really intimidated about the research object...it seems like a massive project that would take a long time to complete. I'm hoping with the FOSS workshops we'll get more understanding of how to do this type of work in the future.
    • Issues: This week I'm having issues with getting all the health data I need for my project, working through data use agreements, IRB applications, and working with partners to get data at a level that makes sense. We are making slow progress!
    • Takeaways: I merged all of the data for my project with CO and cleaned up my R code so that my filepaths are relational, rather than hard-coded to my own folder. I am planning to turn this R project into a GitHub for my collaborators to provide comments on my code as well!
  • 9/21/2022: This week we started the FOSS workshops and discussed open science. I also attended the Reproducibility in R workshops where we learned about branches and forking in GitHub and overall project management using git and GitHub.
    • Issues: This week I am having issues with pulling in multiple datasets offline and merging them in R. Since I'm working with large datasets of climate data, I've figured out how to do it manually, but it takes awhile, and I'd like to learn how to do it more efficiently.
    • Takeaways: I am seeing how I could use R for my thesis work more clearly. I like the idea of setting up a private repository for my collaborators across states so that we can work together on code and data.
  • 9/14/2022: This week I set up my notebook for the R4R cohort, and this is the first edition of the notebook for the 9/14 deadline. Feel free to navigate the pages on the right, and see updates down below for my learning progress so far.
  • 9/8/2022: This week we started the pre-workshop for FOSS, and learned about the differences between open science and FAIR principles. Check out the associated new pages for: Docker, FAIR principles, and FOSS workshops for learning activities.
    • Issues: I am still unsure how I will use Docker in my own work, but am excited to learn more as FOSS continues.
    • Takeaways: I was excited to learn about data standards in public health, and I am getting more comfortable with navigating GitHub!
  • 9/1/2022: This week I reviewed the lectures from previous sessions and got my computer set up for RStudio and GitHub.
    • Issues: R programming is still new to me, but I am working through a self-paced course to help with coding skills.
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