Orchestrating and sharing large multimodal data for transparent and reproducible research.
Orchestrating and sharing large multimodal data for transparent and reproducible research.
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DOI：
10.1038/s41467-021-25974-w
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发表时间：
2021-10-04
影响因子：
16.6
通讯作者：
Haibe-Kains B
中科院分区：
文献类型：
--
作者：
Mammoliti A;Smirnov P;Nakano M;Safikhani Z;Eeles C;Seo H;Nair SK;Mer AS;Smith I;Ho C;Beri G;Kusko R;Massive Analysis Quality Control (MAQC) Society Board of Directors;Lin E;Yu Y;Martin S;Hafner M;Haibe-Kains B
可重复性对于开放科学至关重要，因为对于独立研究小组无法重复的研究结果，无论其有效性如何，其相关性有限。因此，对于科学家来说，足够详细地描述他们的实验至关重要，这样实验才能被复制、审查、质疑和发展。然而，生物医学数据的内在复杂性和不断增长使得以公平（可查找、可访问、可互操作和可重用）的方式处理、分析和与社区共享变得越来越困难。为了克服这些问题，我们创建了一个名为 ORCESTRA (orcestra.ca) 的基于云的平台，它为多模式生物医学数据的可重复处理提供了灵活的框架。它能够通过用户可定制的自动化处理管道处理癌症样本的临床、基因组和扰动概况。 ORCESTRA 创建具有持久标识符 (DOI) 的集成且完整记录的数据对象，并管理多个数据集版本，这些版本可以共享以供将来的研究使用。众所周知，科学研究的很大一部分是难以重现的。在这里，作者提出了一个名为 ORCESTRA 的云计算平台，该平台可以使用可定制的管道和记录良好的数据对象促进多模式生物医学数据的可重复处理。
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. 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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影响因子：
82.9
作者：
Gao, Hui;Korn, Joshua M.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
11.2
作者：
Hatzis C;Bedard PL;Birkbak NJ;Beck AH;Aerts HJ;Stem DF;Shi L;Clarke R;Quackenbush J;Haibe-Kains B
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Haibe-Kains B
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Ghandi, Mahmoud;Huang, Franklin W.;Sellers, William R.
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Sellers, William R.
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通讯作者：
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作者：
Ahmed，Azza E.;Heldenbrand，Jacob;Mainzer，Liudmila S.
通讯作者：
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