Sustainable computational science: the ReScience initiative
Rougier, N. P., Hinsen, K., Alexandre, F., Arildsen, T., Barba, L., Benureau, F. C. Y., Brown, C. T., de Buyl, P., Caglayan, O., Davison, A. P., Delsuc, A., Detorakis, G., Diem, A. K., Drix, D., Enel, P., Girard, B., Guest, O., Hall, M. G., Henriques, R. N., Hinaut, X. et al, Jaron, K. S., Khamassi, M., Klein, A., Manninen, T., Marchesi, P., McGlinn, D., Metzner, C., Petchey, O. L., Plesser, H. E., Poisot, T., Ram, K., Roesch, E.  ORCID: https://orcid.org/0000-0002-8913-4173, Rossant, C., Rostami, V., Shifman, A., Stachelek, J., Stimberg, M., Stollmeier, F., Vaggi, F., Viejo, G., Vitay, J., Vostinar, A., Yurchak, R. and Zito, T.
(2017)
Sustainable computational science: the ReScience initiative.
PeerJ Computer Science, 3.
e142.
ISSN 2376-5992
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.7717/peerj-cs.142 Abstract/SummaryComputer science o ers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel con dent their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. e actual scholarship is the full so ware environment, code, and data that produced the result. is implies new work ows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically di erent from other traditional scienti c journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and so ware tests.
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Anaconda Inc. (2017). Conda. h ps://conda.io/.
Baker, M. (2016). 1, 500 scientists li the lid on reproducibility. Nature,
533(7604), 452–454. doi:10.1038/533452a
Collberg, C., Proebsting, T., Moraila, G., Shankaran, A., Shi, Z., & War-
ren, A. M. (2014). Measuring reproducibility in computer systems
research.
Collberg, C., Proebsting, T., & Warren, A. M. (2015). Repeatability and
benefaction in computer systems research — a study and a modest
proposal.
Coudert, F.-X. (2017). Reproducible research in computational chemistry of materials. Chemistry of Materials, 29(7), 2615–2617. doi:10.1021/ acs.chemmater.7b00799. eprint: h p://dx.doi.org/10.1021/acs. chemmater.7b00799
Courtes, L. & Wurmus, R. (2015). Reproducible and user-controlled so - ware environments in HPC with Guix. In S. Hunold, A. Costan, D. Gime ́nez, A. Iosup, L. Ricci, M. E. G. Requena, V. Scarano, A. L. Varbanescu, S. L. Sco , S. Lankes, J. Weidendorfer, & M. Alexander (Eds.), Euro-Par 2015: Parallel Processing Workshops. Lecture Notes in Computer Science, vol 9523. Springer, Cham. doi:10.1007/978- 3-319-27308-2 47
Crook, S. M., Davison, A. P., & Plesser, H. E. (2013). 20 years of compu- tational neuroscience. In M. J. Bower (Ed.), (Chap. Learning from the Past: Approaches for Reproducibility in Computational Neu- roscience, pp. 73–102). New York, NY: Springer New York. doi:10. 1007/978-1-4614-1424-7 4
Davison, A. P. (2012). Automated capture of experiment context for easier reproducibility in computational research. Computing in Science and Engineering, 14, 48–56. doi:10.1109/MCSE.2012.41
Docker Inc. (2017). Docker. h ps://www.docker.com/.
Donoho, D. L., Maleki, A., Rahman, I. U., Shahram, M., & Stodden, V.
(2009). Reproducible research in computational harmonic analy- sis. Computing in Science Engineering, 11(1), 8–18. doi:10.1109/ MCSE.2009.15
Guo, P. J. & Engler, D. (2011). CDE: Using system call interposition to auto- matically create portable so ware packages. In Proceedings of the 2011 usenix annual technical conference. USENIX’11. Portland, OR: USENIX Association. Retrieved from h p://dl.acm.org/citation. cfm?id=2002181.2002202
Halchenko, Y. O. & Hanke, M. (2015). Four aspects to make science open “by design” and not as an a er-thought. GigaScience. doi:10.1186/ s13742- 015- 0072- 7
Hinsen, K. (2015). Writing So ware Speci cations. Computing in Science and Engineering, 17(3). doi:10.1109/MCSE.2015.64
Ioannidis, J. P. A. (2005). Why most published research ndings are false. PLoS Medicine, 2(8). doi:10.1371/journal.pmed.0020124
Iqbal, S. A., Wallach, J. D., Khoury, M. J., Schully, S. D., & Ioannidis, J. P. A. (2016). Reproducible research practices and transparency across the biomedical literature. PLoS Biology, 14(1). doi:10.1371/journal. pbio.1002333
Janz, N. (2015). Bringing the gold standard into the class room: Replication in university teaching. International Studies Perspectives. doi:10. 1111/insp.12104
Kitzes, J., Turek, D., & Deniz, F. (Eds.). (2017). The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Oakland, CA, USA: University of California Press.
Lindholm, T. & Yellin, F. (1999). The Java Virtual Machine speci cation. Prentice Hall. Retrieved from h p://java.sun.com/docs/books/ jvms/second edition/html/VMSpecTOC.doc.html
Manninen, T., Havela, R., & Linne, M.-L. (2017). Reproducibility and comparability of computational models for astrocyte calcium ex- citability. Frontiers in Neuroinformatics, 11. doi:10.3389/fninf.2017. 00011
Mesnard, O. & Barba, L. A. (2016). Reproducible and replicable CFD: It’s harder than you think. Preprint on arXiv:1605.04339. Accepted in Comput. Sci. Eng.
Munafo, M. R., Nosek, B. A., Bishop, D. V. M., Bu on, K. S., Chambers, C. D., du Sert, N. P., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021. doi:10.1038/s41562- 016- 0021
Murta, L., Braganholo, V., Chirigati, F., Koop, D., & Freire, J. (2015). noWork ow: Capturing and analyzing provenance of scripts. In Provenance and annotation of data and processes (Vol. 8628, pp. 71– 83). Lecture Notes in Computer Science. Springer International Publishing.
Open Science Collaboration. (2015). Estimating the reproducibility of psy- chological science. Science, 349. doi:10.1126/science.aac4716
Perkel, J. (2016). Democratic databases: Science on GitHub. Nature, 538(7623), 127–128. doi:10.1038/538127a
Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Compututa- tional Biology, 9(10). doi:10.1371/journal.pcbi.1003285
Smith, A. M., E Niemeyer, K., Katz, D. S., Barba, L. A., Githinji, G., Gym- rek, M., Hu , K. D., Madan, C. R., Cabunoc Mayes, A., Moerman, K. M., Prins, P., Ram, K., Rokem, A., Teal, T. K., Valls Guimera, R., & Vanderplas, J. T. (2017). Journal of Open Source So ware (JOSS): design and rst-year review. ArXiv e-prints. arXiv: 1707 . 02264 [cs.DL]
Stachelek, J. (2016). [Re] least-cost modelling on irregular landscape graphs. ReScience, 2(1). doi:10.5281/zenodo.45852
Topalidou, M., Leblois, A., Boraud, T., & Rougier, N. P. (2015). A long journey into reproducible computational neuroscience. Frontiers in Computational Neuroscience, 9(28). doi:10.3389/fncom.2015. 00030
Topalidou, M. & Rougier, N. P. (2015). [Re] interaction between cognitive and motor cortico-basal ganglia loops during decision making: A computational study. ReScience, 1(1). doi:10.5281/zenodo.47146
Viejo, G., Girard, B., & Khamassi, M. (2016). [Re] speed/accuracy trade- o between the habitual and the goal-directed process. ReScience, 2(1). doi:10.5281/zenodo.27944
Wilson, G. (2016, January 28). So ware carpentry: Lessons learned. doi:10. 12688/f1000research.3- 62.v2
Wilson, G., Aruliah, D. A., Brown, C. T., Hong, N. P. C., Davis, M., Guy, R. T., Haddock, S. H. D., Hu , K. D., Mitchell, I. M., Plumbley, M. D., Waugh, B., White, E. P., & Wilson, P. (2014). Best practices for scienti c computing. PLoS Biology, 12(1). doi:10.1371/journal.pbio. 1001745 University Staff: Request a correction | Centaur Editors: Update this record |
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