Literature

Required and optional readings

Core reading

All Data Are Local

Loukissas (2019) is the core text. The book is open access.

Read together in class:

Introduction, pp. 1–12 — Session 2
Chapter 1, Local Origins, pp. 13–26 — Session 3 and Session 4
Chapter 2, A Place for Plant Data, pp. 27–54 — Session 5 and Session 6
Chapter 6, Models of Local Practice, pp. 161–188 — Session 8 and Session 9
Chapter 7, Local Ends, pp. 189–196 — Session 10

Optional extensions:

Chapter 3, Collecting Infrastructures
Chapter 5, Market, Place, Interface

Introduction to Modern Statistics (2e)

Çetinkaya-Rundel and Hardin (2024) is the statistics text. The book is free online, and the source files are on GitHub.

Required self-study sections:

1.2.1–1.2.4
2.1.1
4.1
5.1
5.2 — focus on the mean
5.3
5.5

The course uses the second edition, version dated 31 March 2026.

The assignments page lists these sections with links into the statistics cheat sheet.

Optional reading

The resources page describes four optional learning resources. The reference list below holds their full details.

References

Çetinkaya-Rundel, Mine, and Johanna Hardin. 2024. Introduction to Modern Statistics. 2nd ed. OpenIntro. https://openintrostat.github.io/ims/.

Kunin, Daniel, Jingru Guo, Tyler Dae Devlin, and Daniel Xiang. n.d. Seeing Theory: A Visual Introduction to Probability and Statistics. Interactive website. Accessed September 16, 2026. https://seeing-theory.brown.edu/.

Loukissas, Yanni Alexander. 2019. All Data Are Local: Thinking Critically in a Data-Driven Society. The MIT Press. https://doi.org/10.7551/mitpress/11543.001.0001.

Swiss Federal Data Protection and Information Commissioner. n.d. Legal Basis for Data Protection. Official guidance. Accessed September 16, 2026. https://www.edoeb.admin.ch/en/legal-basis-data-protection.

The Carpentries. 2026. Data Carpentry Lessons. Lesson collection. https://datacarpentry.org/lessons/.

University of Mannheim. n.d. MaDaLi² — Mannheim Data Literacy. E-learning course. Accessed September 16, 2026. https://www.uni-mannheim.de/en/about/teaching/teaching-projects/data-literacy/e-learning-course-mannheim-data-literacy-madali2/.