@article {734,
	title = {Orchestrating and sharing large multimodal data for transparent and reproducible research.},
	journal = {Nat Commun},
	volume = {12},
	year = {2021},
	month = {2021 10 04},
	pages = {5797},
	abstract = {<p>Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.</p>},
	issn = {2041-1723},
	doi = {10.1038/s41467-021-25974-w},
	author = {Mammoliti, Anthony and Smirnov, Petr and Nakano, Minoru and Safikhani, Zhaleh and Eeles, Christopher and Seo, Heewon and Nair, Sisira Kadambat and Mer, Arvind S and Smith, Ian and Ho, Chantal and Beri, Gangesh and Kusko, Rebecca and Lin, Eva and Yu, Yihong and Martin, Scott and Hafner, Marc and Haibe-Kains, Benjamin}
}
