Journal:Open data: Accountability and transparency
| Full article title | Open data: Accountability and transparency |
|---|---|
| Journal | Big Data and Society |
| Author(s) | Mayernik, Matthew S. |
| Author affiliation(s) | University Corporation for Atmospheric Research |
| Primary contact | Email: mayernik at ucar dot edu |
| Year published | 2017 |
| Volume and issue | 4(2) |
| DOI | 10.1177/2053951717718853 |
| ISSN | 2053-9517 |
| Distribution license | Creative Commons Attribution-NonCommercial 4.0 International |
| Website | http://journals.sagepub.com/doi/10.1177/2053951717718853 |
| Download | http://journals.sagepub.com/doi/pdf/10.1177/2053951717718853 (PDF) |
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Abstract
The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. In high-level visions of open data, researchers’ data and metadata practices are expected to be robust and structured. The integration of the internet into scientific institutions amplifies these expectations. When examined critically, however, the data and metadata practices of scholarly researchers often appear incomplete or deficient. The concepts of “accountability” and “transparency” provide insight in understanding these perceived gaps. Researchers’ primary accountabilities are related to meeting the expectations of research competency, not to external standards of data deposition or metadata creation. Likewise, making data open in a transparent way can involve a significant investment of time and resources with no obvious benefits. This paper uses differing notions of accountability and transparency to conceptualize “open data” as the result of ongoing achievements, not one-time acts.
Keywords: Open data, accountability, transparency, data policy, data, metadata
Introduction
The movements by national governments, funding agencies, universities, and research communities toward “open data” face many difficult challenges. As a slate of recent studies have shown, the phrase “open data” itself faces at least two central questions, namely (1) what are “data”?[1][2] and (2) what is “open”?[3][4][5] In the face of the vagueness of these terms, individuals, research projects, communities, and organizations define “data” and “openness” in a variety of ways, often via informal norms in lieu of codified policies.
References
- ↑ Borgman, C.L. (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press. pp. 416. ISBN 9780262028561.
- ↑ Leonelli, S. (2015). "What Counts as Scientific Data? A Relational Framework". Philosophy of Science 82 (5): 810–821. doi:10.1086/684083. PMC PMC4747116. PMID 26869734. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747116.
- ↑ Levin, N.; Leonelli, S.; Weckowska, D. et al. (2016). "How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice". Bulletin of Science, Technology, and Society 36 (2): 128–141. doi:10.1177/0270467616668760. PMC PMC5066505. PMID 27807390. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066505.
- ↑ Pasquetto, I.V.; Sands, A.E.; Darch, P.T. et al. (2016). "Open Data in Scientific Settings: From Policy to Practice". Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems 2016: 1585-1596. doi:10.1145/2858036.2858543.
- ↑ Pomerantz, J.; Peek, R. (2016). "Fifty shades of open". First Monday 21 (5). doi:10.5210/fm.v21i5.6360.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance.
