Week 4 - Reproducible and FAIR data
Learning goals
- Understand the implications of data management and organization for reproducible and FAIR (Findable, Accessible, Interoperable, and Reusable) data science.
- Operationalize reproducibility and the FAIR principles by adopting good and responsible data management, code, and workflow documentation practices in your daily work.
- Apply strategies to mitigate issues that could prevent reproducibility.
Slides
In-class exercise
Renv for R (graded)
Open the project example in the class data GitHub repository, week 4 in RStudio.
Inspect files and documentation. Take a quick look. What opportunities for improvement can you spot in this project (README, file naming, and organization)?
- Let’s look together at the scripts. Any issues when you try to run it?
- Create a
renv.lockfile for the project - Organize the files in a way that would make things better (optional)
Venv for Python (optional)
- Let’s use the terminal in VS Code on the tsosie server and see how we can set up a virtual environment for Python
Binder (optional)
- Create a project on GitHub using the data and code from the project example (give a good name to it!)
- “Binderize” your example repo
- Share the link to your repo
Recommended readings
Marwick B, Boettiger C, Mullen L. 2018. Packaging data analytical work reproducibly using R (and friends). PeerJ Preprints 6:e3192v2 https://doi.org/10.7287/peerj.preprints.3192v2.
Powers, S. M., & Hampton, S. E. (2019). Open science, reproducibility, and transparency in ecology. Ecological applications : a publication of the Ecological Society of America, 29(1), e01822. https://doi.org/10.1002/eap.1822.
PyPA Guides. (Apr. 4, 2023). Installing packages using pip and virtual environments. https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#creating-a-virtual-environment.
Ushey, K. (April, 6, 2023). Renv: Project environments (Version 0.17.3) [Computer software]. Retrieved from https://cran.r-project.org/web/packages/renv/renv.pdf.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3, 160018. https://doi.org/10.1038/sdata.2016.18Links
Other suggested readings and resources are noted in the slide deck.