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This website presents my three assignments from Curating Data. Shown together, they trace how I learned to work with data through collecting, categorising and visualising, and how each stage shapes what can be known. The assignments come from different tasks, but they share a concern with how systems, tools and classifications influence visibility and meaning.

The first assignment, a dataset about my houseplants, showed how collecting is never neutral. Choices about variables, scales and structure already guide what becomes possible later. As Acker’s work on metadata suggests, description is an active process with its own assumptions and exclusions. The spreadsheet also made me aware of the material forms that support data work, echoing points from the course about how tools and grids shape the actions we take.
The second assignment focused on Wikidata and the biography of Hu Lanqi. Translating narrative text into structured statements required decisions about categories, properties and references. Bowker and Star emphasise that classifications are social infrastructures, and that lesson became clear when certain aspects of her life did not fit neatly into the ontology. The task highlighted both the potential and the limits of collaborative knowledge graphs, and how semantic systems depend on human judgement as much as technical standards.

The third assignment examined gender representation in the Museum of Modern Art collection. By cleaning data and producing visualisations, I could see long-term patterns in acquisitions and current patterns in what is on display. Readings on visual analytics and feminist data approaches helped me understand that graphs are not simple summaries but interpretive objects. The charts made inequalities visible, but they also reflected the design choices I made while working.
Across the course, the syllabus emphasised that data practices are shaped by infrastructures, politics and histories. Bringing my assignments together in one place makes these connections clearer. Each task required decisions about structure, interpretation and emphasis. This site therefore works both as a presentation of my course work and as a reflection on how meaning is produced through data practices.

Acker, A. (2021). “Metadata.” In N. B. Thylstrup, D. Agostinho, A. Ring, C. D’Ignazio & K. Veel (eds), Uncertain Archives: Critical Keywords for Big Data, pp. 321–329. MIT Press.

Bowker, G. C. & Leigh Star, S. (2000). “Why Classifications Matter.” In Sorting Things Out: Classification and Its Consequences, pp. 319–326. MIT Press.

Cui, W. (2019). “Visual Analytics: A Comprehensive Overview.” IEEE Access, 7, 81555–81573.

Dekoloniale: Memory Culture in the Postcolonial Era. (2024). Dekoloniale. https://dekoloniale.de/en/about

Dourish, P. (2017). “Spreadsheets and Spreadsheet Events in Organizational Life.” In The Stuff of Bits: An Essay on the Materialities of Information, pp. 81–104. MIT Press.

D’Ignazio, C. & Klein, L. F. (2020). “On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints.” In Data Feminism. MIT Press.

Flyverbom, M. & Murray, J. (2018). “Datastructuring—Organizing and Curating Digital Traces into Action.” Big Data & Society, 5(2).

Ford, H. & Iliadis, A. (2023). “Wikidata as Semantic Infrastructure: Knowledge Representation, Data Labor, and Truth in a More-Than-Technical Project.” Social Media + Society, 9(3).

Kitchin, R. (2022a). “Introducing Data.” In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences, pp. 1–19. SAGE.

Kitchin, R. (2022b). “Critical Data Studies.” In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences, pp. 21–41. SAGE.

Kitchin, R. (2022c). “Small Data and Data Infrastructures.” In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences, pp. 44–59. SAGE.