Round Table Brazil - PARSEC Project - Workflow management and reproducibility
Bibliographic record
Abstract
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 www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.254 | 0.138 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".