ROUND TABLE BRAZIL

PARSEC PROJECT

Workflow management and reproducibility

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

Re-watch on Youtube Access Materials
Foto - @goumbik

Guests

Shelley Stall

AGU - USA
Romain David

ERINHA - France
Gerard Subsol

ICAR/LIRMM, CNRS/Univ. Montpellier - France
Marc Chaumont

ICAR/LIRMM, CNRS/Univ. Montpellier/Univ. Nîmes - France
Laure Berti-Esquille

Espace-Dev/IRD, - France
Ali Ben Abbes

Fondation Biodiversité - France
Pedro Luiz Pizzigatti Corrêa

PCS/EPUSP
Jeaneth Machicao

USP
Jorge Rady de Almeida Júnior

PCS/EPUSP

Schedule – 25/mar/2021 (thursday)

Access: Videoconference

Time Activity Guests
1:00-1:15pm (BRT) Opening - Introduction over Data Science Experiments and FAIR approach Pedro Luiz Pizzigatti Corrêa - PCS/EPUSP
1:15-1:30pm (BRT) General problem of Reproducibility in Data Science. Shelley Stall AGU - USA
Romain David ERINHA - France
1:30-2:00pm (BRT) Challenges in reproducibility of Deep Learning experiments - presentation of an example Ali Ben Abbes Fondation Biodiversité - France
Laure Berti-Esquille Espace-Dev/IRD, - France
Marc Chaumont ICAR/LIRMM, CNRS/Univ. Montpellier/Univ. Nîmes - France
Gerard Subsol ICAR/LIRMM, CNRS/Univ. Montpellier - France
2:00-2:15pm (BRT) Experiences on reproducibility of paper experiments. Jeaneth Machicao- PCS/EPUSP - Brazil
Pedro Corrêa - PCS/EPUSP - Brazil
2:15-2:45pm (BRT) Round Table about Reproducibility in Data Science Experiments as a FAIR approach Moderator:
Jorge Rady de Almeida Junior- EPUSP – C2D - Brazil
2:45-3:00pm (BRT) Closure Pedro Luiz Pizzigatti Corrêa- PCS/EPUSP

Parsec Project:
Building New Tools for Data Sharing and Reuse through a Transnational Investigation of the Socioeconomic Impacts of Protected Areas

Project objectives:

  • Predict the socioeconomic outcomes of natural protected areas (PAs) on rural communities using a novel combination of satellite imagery and artificial intelligence;
  • Determine the influence of PAs on consumption expenditure and asset health of rural communities;
  • Improve future environmental decision-making;
  • Improve digital connections between researchers, their funding, publications and data;
  • Improve recommendations for the research data workflow and skills for research teams;
  • Increase the number of citations to data sets and better attribute them to the data creator;
  • Promote credit for open and FAIR data management and preservation for data reuse;
  • Provide tools for researchers to view how the data they have deposited is used and cited.



    • Participating countries:

  • BRAZIL: Universidade de São Paulo - FAPESP (P. Pizzigatti Corrêa) plus postdoc and technical support (FAPESP)
  • FRANCE: Foundation for Research on Biodiversity, University of Toulouse III - ANR (N. Mouquet)
  • JAPAN: National Institute of Information & Communications Technology, Research Institute for Humanity and Nature - JST (Y. Murayama)
  • USA: American Geophysical Union - NSF (S. Stall)
  • Cooperating partners: NCI, Australia (L. Wyborn), BGS, UK (H. Glaves)
  • Associated organisations DataCite, ORCID, ESIP, RDA, EDI, WDS, AST, JWP, TNC

    • Presented by

    Parsec
    Belmont
    Fapesp
    Poli USP
    PCS
    Grupo de Pesquisa e Extensao de Big Data da EPUSP
    C2D

    Register now to participate - limited seats!

    Register!

    Contact

    Questions? Keep in touch!

    Email:
    pedro.correa@usp.br
    Email:
    danton.vellenich@usp.br

    References and 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