FAIR assessment as an opportunity to foster open science and scientific crowdsourcing in systems biomedicine: the example of the COVID-19 Disease Map#

Authors#

Irina-Afrodita Balaur, Danielle Welter, Adrien Rougny, Esther Thea Inau, Alexander Mazein, Soumyabrata Ghosh, Reinhard Schneider, Dagmar Waltemath, Marek Ostaszewski, Venkata Pardhasaradhi Satagopam

Abstract#

Motivation:#

The Disease Maps Project https://disease-maps.org focuses on the development of disease-specific comprehensive structured knowledge repositories supporting translational medicine research. These disease maps require continuous interdisciplinary collaboration, and they should be reusable and interoperable. Adhering to the Findable, Accessible, Interoperable and Reusable (FAIR) principles enhances the utility of such digital assets. We used the RDA FAIR Data Maturity Model, which assesses adherence to FAIR principles using one or multiple indicators and performed a FAIR assessment of the Molecular Interaction NEtwoRk VisuAlization (MINERVA) Platform. MINERVA is a standalone webserver that allows users to manage, explore and analyze disease maps and their related data manually or programmatically. We exemplify the FAIR assessment on the COVID-19 Disease Map, which is a large-scale community project under the umbrella of the Disease Maps Project, aiming to investigate molecular mechanisms of the SARS- CoV-2 infection.

Results:#

We summarize the results of the FAIR assessment, and we discuss the FAIR features supported by both the disease map itself (e.g. use of annotations and systems biology standards) and the MINERVA Platform (e.g. online availability, authentication). We also outline steps to further improve the FAIRness of disease maps and of the MINERVA Platform, and to better connect these resources to other ongoing scientific initiatives supporting FAIR in computational systems biomedicine.