Fall 2023
UC Merced
Prof. Dan Hicks (they/them)
or

GRAN 140
TÞ 1:30-2:45pm

week date topic readings assignment
Some fundamental tools
1 Aug 24

Motivation: What researchers can learn from software engineering
2 Aug 29

git and R Markdown
  • Ram, Karthik. 2013. “Git Can Facilitate Greater Reproducibility and Increased Transparency in Science.” Source Code for Biology and Medicine 8 (1): 7. https://doi.org/10.1186/1751-0473-8-7.
  • Optional: Blischak, John D., Emily R. Davenport, and Greg Wilson. 2016. “A Quick Introduction to Version Control with Git and GitHub.” PLOS Computational Biology 12 (1): e1004668. https://doi.org/10.1371/journal.pcbi.1004668.
  • Optional: Baumer, Ben, Mine Cetinkaya-Rundel, Andrew Bray, Linda Loi, and Nicholas J. Horton. 2014. “R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics.” ArXiv:1402.1894 [Stat], February. http://arxiv.org/abs/1402.1894.

Lab 1: Git
3 Sep 05

Functions and debugging

Lab 2: Debugging
4 Sep 12

Data journeys

Lab 3: Functional programming
5 Sep 19

Work week
  • No new reading
  • In-class time to work on labs and projects

Project: Proposal
Exploratory data analysis
6 Sep 26

EDA
  • ch. 7, "Phenomena," of Brown, James Robert. 2002. Smoke and Mirrors: How Science Reflects Reality. Routledge. link
  • chs. 2 and 4 of Peng, Roger D., and Elizabeth Matsui. 2016. The Art of Data Science: A Guide for Anyone Who Works with Data. Leanpub. link
  • Optional: Huebner, Vach, and le Cessie. 2016. "A Systematic Approach to Initial Data Analysis Is Good Research Practice." The Journal of Thoracic and Cardiovascular Surgery 151 (1): 25-27. https://doi.org/10.1016/j.jtcvs.2015.09.085.
  • Optional: Zuur, Alain F., Elena N. Ieno, and Chris S. Elphick. 2010. “A Protocol for Data Exploration to Avoid Common Statistical Problems.” Methods in Ecology and Evolution 1 (1): 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x.
7 Oct 03

EDA

No new readings

Lab 4: EDA
8 Oct 10

Data justice

Project: Data Journey Narrative
9 Oct 17

Covid contrarianism
  • Lee, Crystal, Tanya Yang, Gabrielle Inchoco, Graham M. Jones, and Arvind Satyanarayan. 2021. “Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online.” Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, May, 1–18. https://doi.org/10.1145/3411764.3445211.
  • Douglass, Rex W. 2020. “How to Be Curious Instead of Contrarian About COVID-19: Eight Data Science Lessons From ‘Coronavirus Perspective’ (Epstein 2020).” March 30, 2020. Link.
10 Oct 24

Work week
Reproducibility and replicability
11 Oct 31

The replication crisis
  • Hicks, Dan. 2021. "Open science, the replication crisis, and environmental public health." Accountability in Research. https://doi.org/10.1080/08989621.2021.1962713.
  • Bailey, David H., and Jonathan Borwein (Jon). n.d. “The Reinhart-Rogoff Error – or How Not to Excel at Economics.” The Conversation. Accessed May 15, 2020. Link.
  • Optional: Jager, Leah R., and Jeffrey T. Leek. 2014. “An Estimate of the Science-Wise False Discovery Rate and Application to the Top Medical Literature.” Biostatistics 15 (1): 1–12. https://doi.org/10.1093/biostatistics/kxt007.
  • Optional: Smaldino, Paul, Matthew Adam Turner, and Pablo Andrés Contreras Kallens. 2019. “Open Science and Modified Funding Lotteries Can Impede the Natural Selection of Bad Science.” Royal Society Open Science 6 (7): 190194. https://doi.org/10.1098/rsos.190194.

Project: EDA
12 Nov 07

Code review
  • Bryan, Jenny. 2018. “Code Smells and Feels.” Presented at the useR 2018, July 21. https://www.youtube.com/watch?v=7oyiPBjLAWY.
  • Postolovski, Tash. 2020. “Your Code Review Checklist: 14 Things to Include.” July 6, 2020. Link.
  • Buckens, Wouter. 2019. “Self-Documenting Is a Myth, and How to Make Your Code Self-Documenting.” Woubuc. August 3, 2019. Link.
  • Note: We'll be doing code review and trying to reproduce Table 1 from the article below. Read as background, not necessarily for a full understanding.
  • Zhou, Xiaodan, Kevin Josey, Leila Kamareddine, Miah C. Caine, Tianjia Liu, Loretta J. Mickley, Matthew Cooper, and Francesca Dominici. 2021. “Excess of COVID-19 Cases and Deaths Due to Fine Particulate Matter Exposure during the 2020 Wildfires in the United States.” Science Advances 7 (33): eabi8789. https://doi.org/10.1126/sciadv.abi8789.

Lab 5: Code Review
13 Nov 14

Project management and sharing data
  • Laskowski, Kate. n.d. “What to Do When You Don’t Trust Your Data Anymore – Laskowski Lab at UC Davis.” Accessed January 29, 2020. Link.
  • Noble, William Stafford. 2009. “A Quick Guide to Organizing Computational Biology Projects.” PLOS Computational Biology 5 (7): e1000424. https://doi.org/10.1371/journal.pcbi.1000424.
  • Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1): 1–9. https://doi.org/10.1038/sdata.2016.18.
  • Jennings, Lydia, Talia Anderson, Andrew Martinez, Rogena Sterling, Dominique David Chavez, Ibrahim Garba, Maui Hudson, Nanibaa’ A. Garrison, and Stephanie Russo Carroll. 2023. “Applying the ‘CARE Principles for Indigenous Data Governance’ to Ecology and Biodiversity Research.” Nature Ecology & Evolution, August, 1–5. https://doi.org/10.1038/s41559-023-02161-2.

Lab 6: Reproducibility
14 Nov 21

Thanksgiving break
15 Nov 28

Work week
16 Dec 05

Failure
  • On Tuesday we'll be discussing Lab 6
  • Redish, A. David, Erich Kummerfeld, Rebecca Lea Morris, and Alan C. Love. 2018. “Opinion: Reproducibility Failures Are Essential to Scientific Inquiry.” Proceedings of the National Academy of Sciences 115 (20): 5042–46. https://doi.org/10.1073/pnas.1806370115.
  • Feest, Uljana. 2019. “Why Replication Is Overrated.” Philosophy of Science 86 (5): 895–905. https://doi.org/10.1086/705451.

Project: Code Review
Dec 13

Project presentations (9:30am, GRAN 140)
Hard deadline for everything

Project: Report & Presentation