Everyday curation? Attending to data, records and record keeping in the practices of self-monitoring

Kate Weiner, Catherine Will, Flis Henwood, Ros Williams

Research output: Contribution to journalArticle

Abstract

This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’.
Original languageEnglish
Number of pages15
JournalBig Data and Society
Volume7
Issue number1
DOIs
Publication statusPublished - 22 Apr 2020

Bibliographical note

Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://
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provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage)

Keywords

  • self-tracking
  • curation
  • users
  • data power
  • partial data
  • material methods
  • Self-tracking

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