OpenScience : Différence entre versions

Version actuelle en date du 21 novembre 2025 à 14:04

Open science encompasses open data, open source code and open access publication.

• Definitions of reproducibility, replicability, etc.
• National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press.

Open Access

Open access: https://guides.biblio.polymtl.ca/diffusion_recherche with a link to send files for open access submission on institutional repository

Research Data Management

• Polytechnique's libguide https://guides.biblio.polymtl.ca/donneesrecherche
  • Public datasets should be put on Borealis https://borealisdata.ca
• Politique de gestion des données de recherche https://www.polymtl.ca/renseignements-generaux/documents-officiels/6-recherche-et-innovation (https://share.polymtl.ca/alfresco/service/api/node/content/workspace/SpacesStore/436d0b4a-0f4c-4cda-afd3-421337a239a4?a=false&guest=true)
• Open Source in Lab Management, Julien Cohen-Adad https://arxiv.org/abs/2405.07774
• Tools: DataLad (Handbook), https://snakemake.github.io/

Reproducibility

• The Turing Way https://the-turing-way.netlify.app
• Findable, Accessible, Interoperable, Reusable (FAIR) research
  • WorldFAIR Final Policy Brief: Enabling Global FAIR Data - Recommendations for Research Infrastructures https://zenodo.org/records/14236140
  • 10 Things for Curating Reproducible and FAIR Research https://zenodo.org/records/6797657
• Courses
  • GEOG 712 Reproducible Research Workflow with GitHub and R, Antonio Paez https://github.com/paezha/Reproducible-Research-Workflow
  • Brainhack school https://school-brainhack.github.io
  • Tools for Reproducible Research, Karl Broman, https://kbroman.org/Tools4RR/
  • R for Reproducible Scientific Analysis https://swcarpentry.github.io/r-novice-gapminder/
  • Reproducible Research Introduction https://datacarpentry.github.io/rr-intro/
  • Compute Ontario Summer School https://www.computeontario.ca/2025-summer-school:
    • Reproducible Research Practices and Tools https://training.computeontario.ca/courses/course/view.php?id=131
    • Research Data Management: A Global Perspective on Making Data FAIRhttps://training.computeontario.ca/courses/course/view.php?id=132
    • Depositing in Borealis, the Canadian Dataverse Repository https://training.computeontario.ca/courses/course/view.php?id=148
• Tips for Publishing Research Code https://github.com/paperswithcode/releasing-research-code/tree/master
• Reproducible research best practices @JupyterCon https://www.kaggle.com/code/rtatman/reproducible-research-best-practices-jupytercon
• Curating Data Sets for Reproducibility https://research-reuse.github.io Reproducibility Framework
• Journal club initiative https://reproducibilitea.org
• REproducible Research In Transportation Engineering (RERITE) Working Group https://www.rerite.org Hands-on Tutorial
• Tools
  • writing papers with the code: https://quarto.org/ (see also knitr for R)

Replicability

Reproducibility is not sufficient, replicating is also important.

• https://rescience.github.io/
• Institute For Replication https://i4replication.org
  • Replication Games https://i4replication.org/blog%20Games.html

Misc

From my webpage written in 2009: "I am a supporter of open science for many reasons, both from a philosophical and moral point of view, and from a practical point of view. From a philosophical and moral point of view, it is the right thing to do, especially for publicly funded research institutions and it allows reproducible research: why should you trust my claims if you cannot replicate my work? From a practical point of view, it is a better method (open source software is a better software development technique) and my research benefits from collaboration and sharing code and data with you, as you reference my research and release publicly your improvements in turn). Join the movement!" https://nicolas.saunier.confins.net/