assignment1

Introduction
This project documents my collection of houseplants in a self-made Excel dataset. Each plant is listed as a row with details that describe its background, condition and care. The aim was to turn my plants into structured data and explore how everyday practices of care can be represented in a spreadsheet. I focused on variables that show time and maintenance rather than physical traits like height or colour. This choice reflects how data always involves selection and simplification. As Dourish (2017) points out, spreadsheets actively shape the way we organise information. By turning personal care routines into data, the project explores how everyday knowledge can become part of digital infrastructures

Data Collection Process
To build the dataset, I photographed each plant and entered the information by hand into Excel. This manual process required care and attention, as I checked that names, categories and dates were entered consistently. Working this way made me aware of how data entry is also a form of maintenance. As Acker (2021) explains, metadata and documentation shape how information can be preserved and understood later. Each step – from typing to checking – was part of creating reliable data while showing the small, ongoing labour that data work involves.

Variables & Design Decisions
The dataset includes nine variables: image, species (Latin), year of acquisition, origin, health, care intensity, location in home, sun exposure, last observed, and notes on last observation. These variables capture both practical and descriptive aspects of each plant. I focused on information that reflects time, care and environment rather than physical traits like size or colour. This choice makes the dataset more about ongoing maintenance than static features. Following Kitchen (2022), such design decisions show how small datasets reflect personal and situated contexts. The spreadsheet layout – each plant as a row and each attribute as a column – also shapes what becomes visible. The order of columns moves from visual and descriptive details to temporal and environmental ones, trying to show a logic that mirrors how I observe and care for the plants in daily life. This clarified for me what Flyverbom & Murray (2018) describe in terms of datastructuring never being neutral but actively organising traces of everyday life into comparable information.

Design Refinements
The ‘health’ and ‘care intensity’ scales were self-defined rather than scientific. Health is rated as good, fair or poor, describing visible condition and growth. Care intensity is rated as low, medium or high, based on how much attention each plant needs. These categories help structure the dataset but are shaped by my own judgement and experience. As Kitchin (2022) notes, data are always influenced by human decisions. The process of rating my plants made this clear, as my assessments depended on personal impressions rather than objective measurement.
The ‘sun exposure’ variable adds to the location data by showing how environmental factors relate to plant care and condition. In future versions, the dataset could include details such as humidity, temperature or watering frequency to give a fuller picture of each plant’s surroundings. Adding a column for plant changes or growth over time could also make it easier to see development and decay. Together, these changes would make the dataset more dynamic and more useful for comparing patterns across time.

Reflection & Conclusion
I was very interested in longevity – how both plants and their data continue over time. This shaped my choices around the dataset’s temporal and maintenance variables. A different focus – such as material traits or social relationships – might have led to more physical or relational variables, shifting the dataset from documenting ongoing life to capturing form or social meaning instead. The process of naming, sorting and checking my entries reflected what Acker (2021) calls metadata – the ongoing work that keeps data understandable and alive. Working in a spreadsheet also made me aware of how this format shapes thought and action, as Dourish (2017) describes. I learned that even small, personal datasets carry bias and values, and that curating data is an ongoing process of attention rather than a one-time task. Like plants, data also require maintenance. Without regular updates or documentation, the dataset could lose meaning over time but with structure and care it can continue to stay relevant. Maintaining the dataset also means taking responsibility for its accuracy and future readability, recognising that data care extends beyond creation into preservation.

References
Acker, A. (2021). Metadata. In N. Bonde Thylstrup, D. Agostinho, A. Ring, C. D’Ignazio, & K. Veel (Eds.), Uncertain Archives: Critical Keywords for Big Data (pp. 321-29.). The MIT Press.
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). The MIT Press. https://pdfcoffee.com/the-stuff-of-bits-an-essay-on-the-materia-paul-dourish-pdf-free.html
Flyverbom, M., & Murray, J. (2018). Datastructuring—Organizing and Curating Digital Traces into Action. Big Data & Society, 5(2). https://doi.org/10.1177/2053951718799114
Kitchen, R. (2022). Small Data and Data Infrastructures. In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (pp. 44–59). SAGE Publications Ltd.
Kitchin, R. (2022a). Critical Data Studies. In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (pp. 21–41). SAGE Publications Ltd.
Kitchin, R. (2022b). Introducing Data. In The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (pp. 1–19). SAGE Publications Ltd.