PCI - Statistics and machine learning

We are excited to share our project to create a Peer Community In for our community: statistics and machine learning.

PCI offers an alternative to traditional scientific publishing: It functions through peer reviews of preprints typically hosted on arXiv, bioRxiv, and similar platforms. Recommended articles can then be published in a PCI-friendly journal or submitted elsewhere. Several communities have built successful PCIs, such as Ecology and Evolutionary biology. Many journals, including PLOS ONE, ISME, PeerJ Computer Science, and Computo are already PCI-friendly and accept recommended articles.

More ressources on PCI

• Youtube video "Why be a recommender for PCI"
• More about the Peer Community journal

Join us

If you wish to be part of this adventure, let use now through this form.

The following people have already either pledge to submit an article or wish to become a recommender:

• Erwan Scornet, Sorbonne Université
• Sandrine Dudoit, UC Berkeley
• Ryan Giordano, UC Berkeley
• Émilie Devijver, CNRS, Grenoble
• Sébastien Da Veiga, ENSAI
• Alice Cleynen, CNRS, Montpellier
• Bertrand Thirion, Inria MIND
• Laurent Jacob, CNRS, Sorbonne University
• Nathalie Vialaneix, INRAE Toulouse
• Chloé Azencott, Mines ParisTech
• Julyan Arbel, Inria, Grenoble
• Sylvain Le Corff, Sorbonne University
• Franck Picard, CNRS, ENS Lyon
• Julien chiquet, INRAE, Paris Saclay
• Marie-Pierre Etienne, Agro Campus Rennes
• Pierre Neuvial, CNRS, Univ. Paul Sabatier
• Nelle Varoquaux, CNRS, Université Grenoble Alpes
• Mathurin Massias, Inria Lyon
• François-David Collin, Univ. Montpellier
• Ghislain Durif, CNRS, ENS Lyon