On the Reuse of Scientific Data
| Autor: | Christine L. Borgman, Bernadette M. Randles, Irene V. Pasquetto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Data collection Knowledge management Data Reuse Open Data Science Policy Knowledge Infrastructures Computer science business.industry Reuse Social and Behavioral Sciences computer.software_genre Computer Science Applications Data sharing 03 medical and health sciences Open data 030104 developmental biology Incentive Data quality Computer Science (miscellaneous) Science policy Information Science Data Curation Science Policy business lcsh:Science (General) computer Data integration lcsh:Q1-390 |
| Zdroj: | Data Science Journal, Vol 16 (2017) Pasquetto, Irene V.; Randles, Bernadette M.; & Borgman, Christine L.(2017). On the Reuse of Scientific Data. Data Science Journal, 16, 8. doi: 10.5334/dsj-2017-008. UCLA: Center for Knowledge Infrastructures. Retrieved from: http://www.escholarship.org/uc/item/4xf018wx Data Science Journal; Vol 16 (2017); 8 |
| ISSN: | 1683-1470 |
| DOI: | 10.5334/dsj-2017-008. |
| Popis: | While science policy promotes data sharing and open data, these are not ends in themselves. Arguments for data sharing are to reproduce research, to make public assets available to the public, to leverage investments in research, and to advance research and innovation. To achieve these expected benefits of data sharing, data must actually be reused by others. Data sharing practices, especially motivations and incentives, have received far more study than has data reuse, perhaps because of the array of contested concepts on which reuse rests and the disparate contexts in which it occurs. Here we explicate concepts of data, sharing, and open data as a means to examine data reuse. We explore distinctions between use and reuse of data. Lastly we propose six research questions on data reuse worthy of pursuit by the community: How can uses of data be distinguished from reuses? When is reproducibility an essential goal? When is data integration an essential goal? What are the tradeoffs between collecting new data and reusing existing data? How do motivations for data collection influence the ability to reuse data? How do standards and formats for data release influence reuse opportunities? We conclude by summarizing the implications of these questions for science policy and for investments in data reuse. |
| Databáze: | OpenAIRE |
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