From Data Creator to Data Reuser: Distance Matters

• URL: http://arxiv.org/abs/2402.07926v2
• Date: Wed, 28 Aug 2024 18:53:17 GMT
• Title: From Data Creator to Data Reuser: Distance Matters
• Authors: Christine L. Borgman, Paul T. Groth,
• Abstract summary: Open science policies focus more heavily on data sharing than on reuse. The value of data reuse lies in relationships between creators and reusers. We develop the theoretical construct of distance between data creator and data reuser.
• Score: 0.847136673632881
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange, rather than mere transactions between stakeholders, investments in data management and knowledge infrastructures can be made more wisely. Drawing upon empirical studies of data sharing and reuse, we develop the theoretical construct of distance between data creator and data reuser, identifying six distance dimensions that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality. We address the social and socio-technical aspects of these dimensions, exploring ways in which they may decrease -- or increase -- distances between creators and reusers. Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies. 'It takes a village' to share research data -- and a village to reuse data. Our aim is to provoke new research questions, new research, and new investments in effective and efficient circulation of research data; and to identify criteria for investments at each stage of data and research life cycles.

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