| Autor: |
David Schoch, Chung-hong Chan, Claudia Wagner, Arnim Bleier |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
EPJ Data Science, Vol 13, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2193-1127 |
| DOI: |
10.1140/epjds/s13688-024-00514-w |
| Popis: |
Abstract Open science practices have been widely discussed and have been implemented with varying success in different disciplines. We argue that computational-x disciplines such as computational social science, are also susceptible to the symptoms of the crises, but in terms of reproducibility. We expand the binary definition of reproducibility into a tier system which allows increasing levels of reproducibility based on external verifiability to counteract the practice of open-washing. We provide solutions for barriers in Computational Social Science that hinder researchers from obtaining the highest level of reproducibility, including the use of alternate data sources and considering reproducibility proactively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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