1. Caring for data: Value creation in a data-intensive research laboratory
- Author
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Barbara Prainsack, Clémence Pinel, and Christopher McKevitt
- Subjects
History ,Technology ,Science ,050905 science studies ,History and Philosophy of Science ,Sociology ,data-intensive research ,Production (economics) ,0601 history and archaeology ,care ,Marketing ,‘omics’ research ,Data value ,060101 anthropology ,Research ,05 social sciences ,General Social Sciences ,06 humanities and the arts ,Articles ,Valuation (logic) ,Databases as Topic ,data ,relational ontology ,Business ,0509 other social sciences ,valuation - Abstract
Drawing upon ethnographic observations of staff working within a research laboratory built around research and clinical data from twins, this article analyzes practices underlying the production and maintenance of a research database. While critical data studies have discussed different forms of ‘data work’ through which data are produced and turned into effective research resources, in this paper we foreground a specific form of data work, namely the affective and attentive relationships that humans build with data. Building on STS and feminist scholarship that highlights the importance of care in scientific work, we capture this specific form of data work as care. Treating data as relational entities, we discuss a set of caring practices that staff employ to produce and maintain their data, as well as the hierarchical and institutional arrangements within which these caring practices take place. We show that through acts of caring, that is, through affective and attentive engagements, researchers build long-term relationships with the data they help produce, and feel responsible for its flourishing and growth. At the same time, these practices of care – which we found to be gendered and valued differently from other practices within formal and informal reward systems – help to make data valuable for the institution. In this manner, care for data is an important practice of valuation and valorisation within data-intensive research that has so far received little explicit attention in scholarship and professional research practice.
- Published
- 2020
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