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Accepted Paper:
Paper short abstract:
To demonstrate the ways data are always degrown and regrown, or territorialized (Deleuze and Guattari 1972), this presentation focuses on experimental practices with, and care for, laboratory animals in a bioscience laboratory.
Paper long abstract:
All scientific experimental processes involve a kind of intentional loss, as substances are concentrated, condensed, and distilled. In bioscience specifically, experiments are a lossy practice, for example when DNA is extracted from tissue in chemical reactions. This production of smooth and commensurate elements, which we label data, is informed by, and enacts, scientific values of abstraction and standardization. Yet, all data are not simply materialized but emerge from acts of transformation and translation (cf. Callon 1984), and decontextualization (Loukissas 2019). In this way, information is simultaneously contracted and expanded, as it both sheds and absorbs new characteristics in the process of becoming data.
To demonstrate the ways data are always degrown and regrown, or territorialized (Deleuze and Guattari 1972), this presentation focuses on experimental practices with, and care for, laboratory animals. For example, as bioscientists record information about live mice to a computer database, the slippery relationship between digital reproduction and animals demands a continual recalibration. Similarly, mice behavior deemed irrelevant by scientific models is left out of data production processes and noted only anecdotally, while those of concern to project research questions are carefully documented and datafied; further, these variables of relevance are not fixed but similarly drift. Drawing from two years of ethnography in a bioscience laboratory located near Tokyo, this presentation argues that imaginations of data as an smooth space, and its use and circulation as an objective and autonomous matter, fails to capture the ongoing vacillation of data in production and practice.
Degrowing data: valuing and practicing intentional data loss
Session 1 Tuesday 16 July, 2024, -