Cross‐disciplinary data practices in earth system science: Aligning services with reuse and reproducibility priorities. Issue 1 (22nd October 2020)

• Record Type: Journal Article
• Authors: Yan, An; Huang, Caihong; Lee, Jian‐Sin; Palmer, Carole L.
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 57:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-22
• Subjects: data curation -- data reuse -- data sharing -- earth system science -- reproducibility; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.218
• Languages: English
• ISSNs: 2373-9231

Abstract

Abstract: As a data intensive field that unites researchers from many disciplines, Earth System Science (ESS) is an ideal site for examining evolving cross‐disciplinary data practices. This paper reports on results from a survey examining data sharing, data reuse, and research reproducibility practices of ESS researchers, aimed at informing improvements in data services for interdisciplinary sciences. Data reuse was found to be very high for new and comparative analyses but very limited for reproducing research. Data sharing was also strong, mostly through supplements to published papers, with moderate use of open access repositories. At the same time, there was interesting variability in both data sharing and reuse among ESS disciplines. The most pronounced challenges to reuse and reproducibility stem from limited documentation on how data are collected and managed, practices that are poorly supported by institutions, funders, and publishers. A more refined approach to "reproducibility" is needed that aligns with priorities and practices within the research community. Just as importantly, advances in data service models for ESS and other interdisciplinary fields need to account for the diverse and distributed system of repositories and build a workforce with deeper knowledge of the complex data and methods that drive integrative systems science.