Repository Approaches to Improving the Quality of Shared Data and Code

Ana Trisovic, Katherine Mika, Ceilyn Boyd, Sebastian Feger and Mercè Crosas
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Ana Trisovic: Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA
Katherine Mika: Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA
Ceilyn Boyd: Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA
Sebastian Feger: European Organization for Nuclear Research (CERN), 1, Esplanade des Particules, CH-1217 Meyrin, Switzerland
Mercè Crosas: Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA

Data, 2021, vol. 6, issue 2, 1-12

Abstract: Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.

Keywords: data quality; data repository; digital libraries; data curation; fair principles; open data; open code; gamification (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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