Reading Assignments

Every week, each student is asked to read and discuss a series of reading assignments. The topics of the reading assignments include reproducibitity in science, open source practices, and applications to climate science, among others.

You have to submit a total of two paragraphs per each separate article. The first is a summary paragraph and the second must highlight an important idea of the reading. This last one can be focused in something that caught your attention and you would like to expand a little bit more. You are more than welcome to orient this second paragraph into something related to your own research or area of interest. A good length for each one of the paragraphs is between 80 and 120 words.

You will submit your reading assignments in bCourses. Reading assignments will be due every Wednesday at 9pm (Exception: the first one will be due Thursday Jan 26). No late reading assignments will be accepted unless there is a medical situation or other documented major emergency. In that case, you will need to submit in Ed a private note to the instructors with a supporting evidence showing that you are unable to complete the assignment.

You can drop two readings without need of justification. Notice that this applies to INDIVIDUAL readings. For example, if the weekly reading consists of 4 papers, you can drop a maximum of two of them. If you drop two readings in one week, you cannot drop any other one without penalty.

Each paragraph per reading assignment gives 1 point (a total of 2 per reading). The final points for the reading assignments is the sum of all the readings. Notice that this means that the maximum credit you can obtain per week depends on the number of readings that week.

• #1, due Jan 26, 2023: Keith Baggerly and the Potti & Nevins Cancer Scandal. These are actually two videos, not reading, but the same format applies (short summary, then idea highlight; in this case only one paragraph of eacy kind is to be submitted, as the 60 minutes video is a short overview meant to give you context, while the talk contains more dense ideas):

  • 2012, 60 minutes segment about Duke Clinical Trials (also available on vimeo).

  • 2018, The Importance of Reproducible Research in High-Throughput Biology, talk at UW.

• #2, due Feb 1, 2023: Developing open source scientific practice.

• #3, due Feb 8, 2023: Core concepts and The Reinhart and Rogoff Controversy

  • Barba 2018, Terminologies for Reproducible Research

  • The Reinhart and Rogoff Controversy. Read the article in The Conversation and the report article with the critique to the original paper. For these two articles, you just need to write two paragraphs instead of four.

• #4, due Feb 15, 2023: Reproducibility and Computational Challenges in Physics

  • The Limits of Reproducibility in Numerical Simulation.

  • Mesnard and Barba 2017, Reproducible and Replicable Computational Fluid Dynamics: It’s Harder Than You Think.

• #5, due Feb 22, 2023: Jupyter in Computational Research

  • Granger and Pérez 2021, Jupyter: Thinking and Storytelling With Code and Data.

  • Using Jupyter for Reproducible Scientific Workflows.

  • Jupyter in Computational Science.

  • Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks.

• #6, due Mar 1, 2023: Earth and Climate Science in the Cloud

  • Gentemann et al. 2016 “Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave.”. For this paper, start thinking about reproducibility issues with it. The lead author, Dr. Chelle Gentemann, will be our guest lecturer on March 6th, and she will discuss aspects of this work. Later we will follow up with a reproducibility attempt around this paper.

  • Gentemann et al. 2021, Science Storms the Cloud.

  • Open Source Science: A Conversation with Leaders at NASA and UC Berkeley | Oct. 26, 2022. For this assignment, we are asking you to summarize and comment on the ~1.5hrs long panel discussion.

• #7, due Mar 8, 2023: Cultivating good Programming Practices

  • Yukihiro Matsumoto, Treating Code as an Essay. You can find the reading in Chapter 29 of the book Beautiful Code.

  • Williams et al. 2000, Strengthening the Case for Pair Programming.

• #8, due Mar 16, 2023: Open Source Software and Open Science - these are four easy “ten simple rules for…”:

  • Ten Simple Rules for the Open Development of Scientific Software.

  • Ten simple rules for documenting scientific software.

  • Ten simple rules for helping newcomers become contributors to open projects.

  • Ten Simple Rules for Cultivating Open Science and Collaborative R&D.

• #9, due Mar 22, 2023. Earth and Climate Science in the Cloud

  • Liu et al. 2019, improving reproducibility in Earth science research.

  • Feulner 2016, Science under Societal Scrutiny: Reproducibility in Climate Science.

  • Abernathey et al. 2021, Cloud-Native Repositories for Big Scientific Data.

• #10, due Apr 6, 2023. Open Source and Open Science

  • Open Source Software Policy Options for NASA Earth and Space Sciences, 2018. Chapter 4, Lessons Learned from Community Perspectives.

  • Open Science by Design, Realizing a Vision for 21st Century Research (2018). Chapter 4, A Vision for Open Science by Design.

  • Turk 2013, How to Scale a Code in the Human Dimension

• #11, due Apr 12, 2023. Principles for Data Analysis Workflows.

  • Stoudt et al. 2021, Principles for data analysis workflows. One of the authors of this paper, Ciera Martinez, will lead give the invited lecture on Monday 04/17.

Future reading assignments

• Foundational classics

  • Schwab, M., Karrenbach, N., Claerbout, J. (2000) Making scientific computations reproducible, Comp. Sci. Eng. 2(6):61–67, doi: 10.1109/5992.881708.

  • Reproducible Research, by the Yale Law School Roundtable on Data and Code Sharing, Comp. Sci. Eng. 12(5): 8–13 (Sept.-Oct. 2010), doi:10.1109/mcse.2010.113.

• Earth and climate science

  • Hoffimann et al. 2021, Geostatistical Learning: Challenges and Opportunities

  • Geyer et al. 2021, Limits of reproducibility and hydrodynamic noise in atmospheric regional modelling.