U.S. Department of EnergyOffice of Scientific and Technical Information
Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences.
Abstract
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.
- Authors:
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1481626
- Report Number(s):
- SAND2018-11186
669580
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Heroux, Michael A., Barba, Lorena, Parashar, Manish, Stodden, Victoria, and Taufer, Michela. Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences.. United States: N. p., 2018.
Web. doi:10.2172/1481626.
Heroux, Michael A., Barba, Lorena, Parashar, Manish, Stodden, Victoria, & Taufer, Michela. Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences.. United States. https://doi.org/10.2172/1481626
Heroux, Michael A., Barba, Lorena, Parashar, Manish, Stodden, Victoria, and Taufer, Michela. 2018.
"Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences.". United States. https://doi.org/10.2172/1481626. https://www.osti.gov/servlets/purl/1481626.
@article{osti_1481626,
title = {Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences.},
author = {Heroux, Michael A. and Barba, Lorena and Parashar, Manish and Stodden, Victoria and Taufer, Michela},
abstractNote = {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.},
doi = {10.2172/1481626},
url = {https://www.osti.gov/biblio/1481626},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {10}
}