ORCESTRA: Web application for orchestrating and sharing large multimodal data for transparent and reproducible research
Anthony Mammoliti,Minoru Nakano University Health Network
Abstract
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. Due to recent technological advances in the biological and computational sciences, a large volume of data have become available. However, their intrinsic complexity and continuous growth makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. This has made reproducing research findings more challenging, with some researchers going as far as suggesting that the biomedical sciences are experiencing a “reproducibility crisis”.
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. This flexible platform enables processing of multimodal data such as clinical, genomic and perturbation profiles of cancer samples through the use of automated processing pipelines that are user-customizable via a data versioning and orchestration tool. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI). This new platform provides a flexible solution to manage multiple versions of datasets that can be used and shared for future studies, while ensuring full transparency and reproducibility of the processing pipelines.
Funding: This project is supported by CIHR, under the frame of ERA PerMed.
Keywords: data reproducibility,web applications