If these data could talk
| Autor: | Valerie Gibson, Mercè Crosas, Thomas Pasquier, Aaron M. Ellison, Margo Seltzer, Christopher R. Jones, Emery R. Boose, Matthew K. Lau, Ben Couturier, Ana Trisovic |
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| Přispěvatelé: | Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University [Cambridge], European Organization for Nuclear Research (CERN), Cavendish Laboratory, University of Cambridge [UK] (CAM), Institute for Quantitative Social Sciences |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Statistics and Probability 010504 meteorology & atmospheric sciences Computer science Library and Information Sciences Research management 010603 evolutionary biology 01 natural sciences Education law.invention Software law [INFO]Computer Science [cs] Data Analysis and Statistics 0105 earth and related environmental sciences Research data Data source business.industry Comment Data science Computer Science Applications Open data Formalism (philosophy of mathematics) CLARITY Statistics Probability and Uncertainty business Information Systems |
| Zdroj: | Scientific Data Scientific Data, Nature Publishing Group, 2017, 4, pp.170114. ⟨10.1038/sdata.2017.114⟩ Pasquier, T, Lau, M K, Trisovic, A, Boose, E R, Couturier, B, Crosas, M, Ellison, A M, Gibson, V, Jones, C R & Seltzer, M 2017, ' If these data could talk ', Scientific Data, vol. 4, 170114 . https://doi.org/10.1038/sdata.2017.114 |
| ISSN: | 2052-4463 |
| Popis: | In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientific fields exhibit distressingly low rates of reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and efficiency of reporting, which contributes to issues of reproducibility. Data provenance aids both reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers. |
| Databáze: | OpenAIRE |
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