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Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

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  • Marcel LaFlamme
  • Marion Poetz
  • Daniel Spichtinger

Abstract

Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation. Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated. Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification. We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse. Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.

Suggested Citation

  • Marcel LaFlamme & Marion Poetz & Daniel Spichtinger, 2022. "Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0272153
    DOI: 10.1371/journal.pone.0272153
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    References listed on IDEAS

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    1. Kathleen Gregory & Paul Groth & Helena Cousijn & Andrea Scharnhorst & Sally Wyatt, 2019. "Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(5), pages 419-432, May.
    2. Xiaoguang Wang & Qingyu Duan & Mengli Liang, 2021. "Understanding the process of data reuse: An extensive review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(9), pages 1161-1182, September.
    3. Christine L. Borgman & Andrea Scharnhorst & Milena S. Golshan, 2019. "Digital data archives as knowledge infrastructures: Mediating data sharing and reuse," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(8), pages 888-904, August.
    4. Youngseek Kim & Ayoung Yoon, 2017. "Scientists' data reuse behaviors: A multilevel analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2709-2719, December.
    5. Susanne Beck & Carsten Bergenholtz & Marcel Bogers & Tiare-Maria Brasseur & Marie Louise Conradsen & Diletta Di Marco & Andreas P. Distel & Leonhard Dobusch & Daniel Dörler & Agnes Effert & Benedikt F, 2022. "The Open Innovation in Science research field: a collaborative conceptualisation approach," Industry and Innovation, Taylor & Francis Journals, vol. 29(2), pages 136-185, February.
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    1. Merilyn Riley & Kerin Robinson & Monique F Kilkenny & Sandra G Leggat, 2024. "The knowledge and reuse practices of researchers utilising government health information assets, Victoria, Australia, 2008–2020," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-20, February.

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