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The NNPDF collaboration determines the structure of theproton using Machine Learning methods. This is the main repository of thefitting and analysis frameworks. In particular it contains all the necessarytools toreproduce theNNPDF4.0 PDF determinations.

The documentation is available athttps://docs.nnpdf.science/

See theNNPDF installation guidefor instructions on how to install and use the code.As a first step we recommend to follow one of thetutorials.

We follow a rolling development model where the tip of the master branch isexpected to be stable, tested and correct. For more information see ourreleases and compatibility policy.

This code is described in the followingpaper:

@article{NNPDF:2021uiq,    author = "Ball, Richard D. and others",    collaboration = "NNPDF",    title = "{An open-source machine learning framework for global analyses of parton distributions}",    eprint = "2109.02671",    archivePrefix = "arXiv",    primaryClass = "hep-ph",    reportNumber = "Edinburgh 2021/13, Nikhef-2021-020, TIF-UNIMI-2021-12",    doi = "10.1140/epjc/s10052-021-09747-9",    journal = "Eur. Phys. J. C",    volume = "81",    number = "10",    pages = "958",    year = "2021"}

If you use the code to produce new results in a scientific publication, pleasefollow theCitation Policy,particularly in regards to the papers relevant for QCD NNLO and EW NLOcalculations incorporated in the NNPDF dataset.

We welcome bug reports or feature requests sent to theissuetracker. You may use the issue trackerfor help and questions as well.

If you would like contribute to the code, please follow theContributionGuidelines.