About
FAIRshare is a cross-platform desktop software that allows researchers to easily organize and share their biomedical data and software according to applicable FAIR guidelines. The first phase of development of FAIRshare is focused on supporting COVID-19 and other infectious diseases related data and software. Our ultimate goal is to provide support for data and software from all fields of biomedical research.
What are the FAIR Data Principles?
- The FAIR Data Principles are a set of guidelines for curating and sharing research data such that they are optimaly reusable by both humans and machines.
Why are FAIR data practices important?
- FAIR data practices are crucial for ensuring reproducibility of scientific findings, promoting reuse of data, and enabling AI/ML-driven analysis to ultimately increase the pace of new discoveries. They also ensure proper credits to data generators.
What are the challenges?
- Support is lacking to educate and assist the researchers, manual curation is not suitable to manage the amount of data being generated, and there is a lack of standard approach across research labs.
What does FAIRshare do?
- FAIRshare is a desktop software that streamlines the process of making biomedical research data and software FAIR by combining intuitive user interface and automation.
Development Approach
FAIRshare is distributed as an open-source desktop application with an MIT License. Anyone is free to fork our GitHub repository and make their own changes if they would like. If you would like to submit a feature modification, or feature suggestion, please feel free to submit an issue on the repository.
Funding
FAIRshare is funded through a supplemental award from the National Institute of Allergy and Infectious Diseases (NIAID).
Team Members
Research Partners
The first phase of development of FAIRshare is focused on supporting COVID-19 and other infectious diseases data and software. FAIRshare is developed in collaboration with Dr. Zicheng Hu (UCSF) and his colleagues at University of California, San Francisco and Stanford University who are conducting a clinical trial for a novel treatment approach for COVID-19. They will be providing overall guidance on the applicable FAIR guidelines as well as conducting beta testing of the software.
Timeline
November 2020 - Birth of FAIRshare
The first prototype of FAIRshare is developed for demo purpose by the FAIR Data Innovations Hub.
September 2021 - Aug 2022 - Phase I development
The base framework of the software will be developed and support will be provided for COVID-19 and other infectious diseases related data types (genomics, immunology, etc.) and software.
Impact
Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool
Citation
Patel, B., Soundarajan, S., Ménager, H., & Hu, Z. (2023). Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-02463-x
Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool
Citation
Patel, B., Soundarajan, S., Ménager, H., & Hu, Z. (2022). Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.04.18.488694
Making biomedical research software FAIR with FAIRshare
Poster presented at Intelligent Systems for Molecular Biology (ISMB) 2022 and Bioinformatics Open Source Conference (BOSC) 2022Citation
Bhavesh Patel, & Soundarajan, S. (2022). Making biomedical research software FAIR with FAIRshare. F1000 Research Limited. https://doi.org/10.7490/F1000RESEARCH.1119054.1
Making biomedical research software findable, accessible, interoperable, reusable (FAIR) with FAIRshare
Slides presented at Intelligent Systems for Molecular Biology (ISMB) 2022 and Bioinformatics Open Source Conference (BOSC) 2022Citation
Bhavesh Patel, & Soundarajan, S. (2022). Making biomedical research software findable, accessible, interoperable, reusable (FAIR) with FAIRshare. F1000 Research Limited. https://doi.org/10.7490/F1000RESEARCH.1119055.1