Journal:Data sharing at scale: A heuristic for affirming data cultures
| Full article title | Data sharing at scale: A heuristic for affirming data cultures |
|---|---|
| Journal | Data Science Journal |
| Author(s) | Poirier, Lindsay; Costelloe-Kuehn, Brandon |
| Author affiliation(s) | University of California - Davis, Rensselaer Polytechnic Institute |
| Primary contact | Email: lnpoirier at ucdavis dot edu |
| Year published | 2019 |
| Volume and issue | 18(1) |
| Page(s) | 48 |
| DOI | 10.5334/dsj-2019-048 |
| ISSN | 1683-1470 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://datascience.codata.org/articles/10.5334/dsj-2019-048/ |
| Download | https://datascience.codata.org/articles/10.5334/dsj-2019-048/galley/896/download/ (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Addressing the most pressing contemporary social, environmental, and technological challenges will require integrating insights and sharing data across disciplines, geographies, and cultures. Strengthening international data sharing networks will not only demand advancing technical, legal, and logistical infrastructure for publishing data in open, accessible formats; it will also require recognizing, respecting, and learning to work across diverse data cultures. This essay introduces a heuristic for pursuing richer characterizations of the “data cultures” at play in international, interdisciplinary data sharing. The heuristic prompts cultural analysts to query the contexts of data sharing for a particular discipline, institution, geography, or project at seven scales: the meta, macro, meso, micro, techno, data, and nano. The essay articulates examples of the diverse cultural forces acting upon and interacting with researchers in different communities at each scale. The heuristic we introduce in this essay aims to elicit from researchers the beliefs, values, practices, incentives, and restrictions that impact how they think about and approach data sharing. Rather than represent an effort to iron out differences between disciplines, this essay instead intends to showcase and affirm the diversity of traditions and modes of analysis that have shaped how data gets collected, organized, and interpreted in diverse settings.
Keywords: data sharing, data culture, ethnography, data friction, metadata
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation and grammar. In some cases important information was missing from the references, and that information was added. The original article had citations listed alphabetically; they are listed in the order they appear here due to the way the wiki works.
