Medicine and Health

Orchestrating and sharing large multimodal data for transparent and reproducible research

A. Mammoliti, P. Smirnov, et al.

Discover ORCESTRA, a groundbreaking cloud-based platform that revolutionizes reproducible processing of multimodal biomedical data. Developed by leading researchers including Anthony Mammoliti and Petr Smirnov, this innovative tool enhances data sharing and management for clinical and genomic research, ensuring compliance with FAIR principles.

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Abstract

Reproducibility is crucial for open science. The complexity and growth of biomedical data hinder FAIR (findable, accessible, interoperable, and reusable) data sharing. To address this, the authors created ORCESTRA (orcestra.ca), a cloud-based platform for reproducible processing of multimodal biomedical data. ORCESTRA processes clinical, genomic, and perturbation data through customizable pipelines, creates integrated, documented data objects with DOIs, and manages dataset versions for sharing.

Publisher

Nature Communications

Published On

Oct 04, 2021

Authors

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, Eva Lin, Yihong Yu, Scott Martin, Marc Hafner, Benjamin Haibe-Kains

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