Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- George Washington University, Washington, DC (United States)
- Rutgers University, Piscataway, NJ (United States)
- University of Illinois, Champaign, IL (United States)
- University of Tennessee, Knoxville, TN (United States)
Reproducibility is an essential ingredient of the scientific enterprise. The ability to reproduce results builds trust that we can rely on the results as foundations for future scientific exploration. Presently, the fields of computational and computing sciences provide two opposing definitions of reproducible and replicable. In computational sciences, reproducible research means authors provide all necessary data and computer codes to run analyses again, so others can re-obtain the results (J. Claerbout et al., 1992). The concept was adopted and extended by several communities, where it was distinguished from replication: collecting new data to address the same question, and arriving at consistent findings (Peng et al. 2006). The Association of Computing Machinery (ACM), representing computer science and industry professionals, recently established a reproducibility initiative, adopting essentially opposite definitions. The purpose of this report is to raise awareness of the opposite definitions and propose a path to a compatible taxonomy.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1481626
- Report Number(s):
- SAND-2018-11186; 669580
- Country of Publication:
- United States
- Language:
- English
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