Simplifying FAIR data practices to empower AI-driven discoveries
We develop open source tools, standards, and guidelines that support biomedical researchers in preparing and sharing data, software, and other research outcomes such that they are FAIR, i.e. optimally reusable by both humans and machines
What are we working on?
SODA for SPARC
Easily make bioelectronic, neurophysiology, and other similar research data and computational models FAIR following the NIH SPARC guidelines
AI-READI
Generating a flagship AI-ready and ethically-sourced dataset to support future AI-driven discoveries in diabetes
Codefair
codefair is your personal assistant when it comes to making your research software reusable and especially complying with the Findable, Accessible, Interoperable, Reusable (FAIR) Principles for Research Software.
FAIR Biomedical Research Software (FAIR-BioRS) guidelines
The FAIR-BioRS guidelines are a set of minimal and actionable step-by-step instructions for making biomedical research software FAIR
There is a lot more in the pipeline. To learn more about all our work in this area, please visit our Projects page.
The wave of FAIR Data practices
The FAIR (Findable, Accessible, Interoperable, Reusable) Principles are a set of instructions for sharing data and other research outcomes such that they are optimally reusable. Since their publication in 2016, they have been promoted and adopted by a large amount of stakeholders in research data including the National Institutes of Health (NIH).
Sharing is caring... but also daunting?
However, sharing FAIR data is not trivial as this involves formatting data into specific file format, organizing data files consistently, including metadata according to applicable standards, uploading data to a suitable repository, and more. This adds significant burden on researchers who are neither trained nor supported to do these. As result, they are either not sharing making data FAIR or not doing it properly.
Simple guidelines and open-source tools for the win!
We believe that researchers already have enough work and responsibilities on their hands. Therefore, making data, software, and other research outcomes FAIR should be made very easy for them. We are trying to achieve that through two main approaches:
Developing minimal, step-by-step, and actionable guidelines for preparing and sharing datasets, software, and other research outcomes such that they are FAIR and AI-Ready
Developing open-source and free tools that streamline these tasks and minimize researchers' time and effort through a combination of intuitive user interfaces, AI, and automation.
A little bit about us
FAIR Data Innovations Hub is a division of the California Medical Innovations Institute (CalMI2), a non profit biomedical research organization located in San Diego, California. We started this division in 2019 and now have a multidisciplinary team of enthusiasts about FAIR Data practices and software development.
Current Projects
These are the projects we are working on at the moment:
We won't be able to do this alone!
Our work would not be possible without our incredible collaborators at various institutions all over the world.
Disclaimer: All logos are used with adequate permissions. Opinions, interpretations, conclusions and recommendations are those of the FAIR Data Innovations Hub and are not necessarily endorsed by the other organizations mentioned on this website.