From nostalgia to knowledge: Considering the personal dimensions of data lifecycles

Gretchen R. Stahlman

Journal of the Association for Information Science & Technology, 2022, vol. 73, issue 12, 1692-1705

Abstract: Data lifecycle models are used to visualize the stages data resources pass through from creation to completion of a research project and beyond. Although helpful, these models tend to depict idealized and impersonal circumstances that often involve curation actions taken by researchers themselves, while neglecting to represent dynamics particular to researchers' lives and careers. Meanwhile, research data are still infrequently managed successfully according to these models, which is a particular concern for “long tail” data, where sharing tends to depend on individual researchers' decisions and preservation actions. The paper addresses these considerations by exploring the ways in which long tail data lifecycles are entwined with human career lifecycles. Through interviews with astronomers, six affective dimensions that may impact data and career lifecycle dynamics are identified: painstakingness, loose ends, altruism, intellectual passion, legacy, and nostalgia. Building upon these insights and holistically drawing connections between human information behavior and data lifecycle modeling literature and frameworks, a researcher‐centered lifecycle model is proposed that seeks to depict the unique motivations and changing needs of researchers to share data throughout the courses of their careers. The paper concludes with suggestions for theoretical development and curatorial interventions in current data lifecycle modeling and data management practice.

Date: 2022
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