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Throughout this course, my view of data has changed from seeing it as something neutral to recognising how much it depends on choices, tools and attention. Bringing my three assignments together makes this shift visible. Each project sits at a different stage of working with data – collecting, organising and presenting – and each showed me how interpretation is part of every step.

The houseplant dataset, in assignment 1, made this clear from the beginning. Turning everyday observations into a spreadsheet required deciding what information mattered. By focusing on care, condition and time, I highlighted some aspects of the plants while leaving others in the background. Entering everything by hand showed me how much ongoing work goes into even the simplest dataset. Readings by Acker and Dourish helped me see that the structure of the spreadsheet didn’t just store information – it shaped how I noticed and understood it.

The work on Hu Lanqi, assignment 2, moved my attention from personal data to shared systems. Adding her biography to Wikidata meant turning narrative text into short, structured statements. This required selecting what details could be expressed clearly and which parts of her life were harder to capture in this format. Bowker & Star’s work on classification helped me understand how systems like Wikidata make some information easy to share while making other details harder to express. Editing her entry felt like curating inside a framework defined by community rules, technical limits and my own judgement.

The MoMA analysis, in assignment 3, shifted the focus again, this time to how data is shown to an audience. Creating two different visuals – a line chart and a pie chart – revealed how design choices influence interpretation. The line chart supported a broad overview of acquisition patterns, while the pie chart drew attention to who is missing from display. Ideas from visual analytics and feminist data practice helped me recognise how a visualisation can guide the viewer toward certain insights and away from others.
Together, these assignments show that curating data is not only about arranging information but about shaping how meaning is produced. Whether through choosing variables, structuring information for others to use, or designing a visual, each step influences what becomes visible. This site gathers my work as a reflection on how attention, structure and care matter when working with data.

Curatorial Statement

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.

Collecting: Houseplant Dataset

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This project began with documenting my own houseplants in a spreadsheet. Each plant became a row, described through variables such as care intensity, condition and time of last observation. Building the dataset by hand made me aware of how collecting data involves many decisions: what to include, how to name it and how to keep it consistent.

Rather than focusing on physical traits, I chose variables that reflect ongoing maintenance. This highlighted how data can represent routines and attention, not just objects. Readings by Acker and Dourish helped me recognise that the spreadsheet itself shapes how information is organised and that data collection is a form of work that continues after the initial entry.

This assignment introduced the idea that even personal datasets carry interpretations and that collecting data is already a curatorial process.

Categorising: Hu Lanqi on Wikidata

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This assignment focused on turning a narrative biography of Hu Lanqi into structured data on Wikidata. Working from the Dekoloniale project, my group identified key information - such as occupations, affiliations and significant events - and translated it into short statements using Wikidata’s property system.

This process made me aware of how shared data infrastructures shape what can be represented. Some aspects of Lanqi’s life could be added easily, while more complex experiences were harder to express within the platform’s structure. Bowker & Star’s discussion of classification systems helped me see how these choices affect what becomes visible in collaborative knowledge projects.

Editing Wikidata felt like curating within a framework defined by community rules and technical standards. The assignment showed how organising data in a shared environment requires both careful interpretation and attention to structure.

Displaying: Gender in the MoMA Collection

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In this assignment, I examined gender representation in the Museum of Modern Art’s collection using two different visual approaches. After cleaning and organising the dataset, I created a line chart to show long-term acquisition patterns and a pie chart to show which works are currently on display.

Comparing these visualisations revealed how design choices influence interpretation. The line chart provided a broad overview of acquisitions, while the pie chart highlighted uneven visibility in the galleries. Ideas from visual analytics and feminist data practice helped me understand how visualisations do not simply present information but guide the viewer toward certain insights.

This assignment demonstrated how displaying data is a curatorial act: the way a visual is constructed shapes what becomes noticeable and what remains in the background.