Reproducible science in practice @ ArturoS | 2021-05-07T10:00:00Z | 2 minutes read | Update at 2021-05-07T10:00:00Z

Reproducible Science in Practice


It refers to a series of principles, techniques, tools and practical considerations that allow the documentation, recording and preservation of data analysis pipelines — enhancing the possibilities of collaborations across borders and increasing the probabilities of replicating results by others (and yourself) in the future. Reproducibility involves using standard and well-established protocols to ensure that your code will survive outside your computer, the passing of time and that others will be able to use it as a starting point for new analysis. We will explore several of those tools: from the use of version control and Jupyter notebook in the cloud to prepare and encapsulate software environments (VM, containers) and the usage of good practices regarding licences, citation and DOIs.

Session #1

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Session #2

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La science reproductible en pratique: Il fait référence à une série de principes, techniques, outils et considérations pratiques qui permettent la documentation, l’enregistrement et la préservation des pipelines d’analyse de données - améliorant les possibilités de collaborations transfrontalières et augmentant les probabilités de répliquer les résultats par d’autres (et par vous-même) à l’avenir. . La reproductibilité implique l’utilisation de protocoles standard et bien établis pour garantir que votre code survivra en dehors de votre ordinateur, au fil du temps et que d’autres pourront l’utiliser comme point de départ pour une nouvelle analyse. Nous explorerons plusieurs de ces outils: de l’utilisation du contrôle de version et des notebooks dans le cloud pour préparer et encapsuler les environnements logiciels (VM, conteneurs) et l’utilisation de bonnes pratiques concernant les licences, les citations et les DOI.

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The program of the school is devoted to project development for astrophysics, astroparticle physics & particle physics. The aim of the school is to provide theoretical and hands-on training on Data Science and Python development.



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824064 (ESCAPE, the European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures).

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