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Automated reproducible workflows and data provenance with AiiDA

Nature Reviews Physics volume 4, page 431 (2022)Cite this article

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Data-intensive science provides many opportunities for discovery, but also brings new challenges — for example, how to manage the complex workflows and large quantities of heterogenous data that are involved?

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Acknowledgements

The author acknowledges all contributors to AiiDA (https://github.com/aiidateam/aiida-core/graphs/contributors) and the ACWF (https://github.com/aiidateam/aiida-common-workflows/graphs/contributors), as well as N. Marzari and G. Pizzi for their conception, guidance and continued support of the project. The author also thanks the many supporting projects, partners, institutions and funders (https://www.aiida.net/acknowledgements/) coordinated under the SNSF NCCR MARVEL.

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  1. National Centre of Competence in Research (NCCR) MARVEL, National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Sebastiaan P. Huber

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  1. Sebastiaan P. Huber
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Correspondence to Sebastiaan P. Huber.

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Huber, S.P. Automated reproducible workflows and data provenance with AiiDA. Nat Rev Phys 4, 431 (2022). https://doi.org/10.1038/s42254-022-00463-1

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