Increasing computational reproducibility or how-to share your research code

Increasing computational reproducibility or how-to share your research code

By UniBasel ReproducibiliTea

Camille Maumet

Date and time

Location

Online

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Highlights

  • 1 hour
  • Online

About this event

Science & Tech • Science

Bio:
Camille Maumet is a research scientist in neuroinformatics in the Empenn team at Inria of the University of Rennes and IRISA in France. She studies neuroimaging reproducibility. In her current research, she focuses on the variability of analytical pipelines and its impact on our ability to reuse (and use) brain imaging datasets. Camille is also an open science advocate and participate actively in national and international communities including BIDS (The brain imaging data
structure), Brainhack and the French committee for open science.


Abstract:
To the aim of improving the reproducibility and transparency of research findings there is a growing interest in sharing openly a variety of research artefacts such as: scientific papers but also data and code. While a lot has been said on how to open data (and the so-called FAIR principles), code is a very special research artefact and as such requires specific solutions for sharing. Is sharing my code on Github and adding a link in my paper enough? Should I use the Gitlab instead? If I follow the best practices in code sharing will my findings be reproducible?

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UniBasel ReproducibiliTea

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Free
Oct 16 · 6:30 AM PDT