Round Table Brazil - PARSEC Project - Workflow management and reproducibility
Creators
- 1. Polytechnic School of University of São Paulo
- 2. American Geophysical Union
- 3. ERINHA-AISBL
- 4. Fondation for Research on Biodiversity
- 5. University of Montpellier
- 6. University of Montpellier, University of Nimes
Description
As a forum for experience exchange, the ROUND TABLE BRAZIL - Workflow management and reproducibility, invites data scientist and data professionals to discuss experiences on the subject.
It was published in 2016 the ‘FAIR Guiding Principles for scientific data management and stewardship’ which became a standard for workflow management to create replicable and reproducible data experiments.
How these ideas are implemented on daily data science experiments?
And what challenges are faced?
March, 25th 2021
1:00pm to 3:00pm (Brazilian Time)
Escola Politécnica - Videoconference
Round Table Website: http://wds.poli.usp.br/round-table-brazil-2021/
Recording: https://www.youtube.com/watch?v=0UGNaKOV6Ss
This workshop is part of the PARSEC project generously funded by the Belmont Forum.
Resources:
National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. https://doi.org/10.17226/25303.
Victoria Stodden, Marcia McNutt, David H. Bailey, Ewa Deelman, Yolanda Gil, Brooks Hanson, Michael A. Heroux, John P.A. Ioannidis and Michela Taufer (December 8, 2016)
Science 354 (6317), 1240-1241. [doi: 10.1126/science.aah6168]
Wilkinson, MD, Dumontier, M, Aalbersberg, IjJ, et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3: 160018. DOI: https://doi.org/10.1038/sdata.2016.18
https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
Pineau, J., Vincent-Lamarre, P., Sinha, K., Larivière, V., Beygelzimer, A., d’Alché-Buc, F., & Larochelle, H. (2020). Improving reproducibility in machine learning research (a report from the NeurIPS 2019 Reproducibility Program). arXiv preprint arXiv :2003.12206.
Harris, Jenine K.; Johnson, Kimberly J.; Carothers, Bobbi J.; Combs, Todd B.; Luke, Douglas A.; Wang, Xiaoyan (2018). "Use of reproducible research practices in public health: A survey of public health analysts". PLOS ONE. 13 (9): e0202447.
Hartley, M., & Olsson, T. S. (2020). dtoolAI : Reproducibility for Deep Learning. Patterns, 1(5), 100073.
https://the-turing-way.netlify.app/welcome
Reproducible Research in Computational Science, R. Peng, Science, Dec. 2011:1226-1227
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