Orchestrating and sharing large multimodal data for transparent and reproducible research

Orchestrating and sharing large multimodal data for transparent and reproducible research

It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.

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Detalles Bibliográficos
Autores principales: Anthony Mammoliti, Petr Smirnov, Minoru Nakano, Zhaleh Safikhani, Christopher Eeles, Heewon Seo, Sisira Kadambat Nair, Arvind S. Mer, Ian Smith, Chantal Ho, Gangesh Beri, Rebecca Kusko, Massive Analysis Quality Control (MAQC) Society Board of Directors, Eva Lin, Yihong Yu, Scott Martin, Marc Hafner, Benjamin Haibe-Kains
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/da2c696db1874e349ef2975f7942b0cf
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