Empowering AI driven discoveries through simplified FAIR data practices.

Discover how FAIR data practices are revolutionizing biomedical research. Explore our open-source tools designed to guide researchers through this transformative journey

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

There is a lot more in the pipeline. To learn more about all our work in this area, please visit our Projects page.

Sharing is caring... but also daunting?

Sharing biomedical data is essential for accelerating discoveries in human health. However, it's not as simple as uploading data anywhere. Adherence to FAIR (Findable, Accessible, Interoperable, Reusable) Principles and ethical guidelines is crucial to ensure data reusability. This involves formatting and organizing data according to standards, including metadata, and finding appropriate platforms for sharing.

Unfortunately, many researchers lack the training and support needed for these tasks, leading to inadequate or non-existent data sharing practices. It's imperative to equip researchers with the necessary tools and resources to navigate these challenges effectively. By empowering researchers, we can foster a culture of responsible and impactful data sharing in biomedical research, ultimately advancing the pace of discoveries for improving human health.

Simple guidelines and open-source tools for the win!

We believe that researchers already have enough work and responsibilities on their hands. Therefore, sharing data, software, and other research outcomes should be made very easy and quick 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 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:

SODA (Software to Organize Data Automatically) for SPARC is a desktop software intended to facilitate the data organization and submission process for SPARC investigators according to the FAIR SPARC data standards.

The AI-READI project seeks to create a flagship AI-ready and ethically-sourced dataset that will support future AI-drive research projects to provide critical insights into type 2 diabetes.

FAIRshare is a cross-platform desktop software that allows researchers to easily organize and share their biomedical research data and software according to applicable FAIR guidelines.

KnowMore is an automated knowledge discovery tool that allows users of the portal to visualize, in just a few clicks, potential similarities, differences, and connections between multiple SPARC datasets of their choice.

SPARClink is a system that queries publications using open source tools and platforms and create an interactable visualization that showcases the impact that SPARC and their FAIR data practices have in advancing the field of bioelectronic medicine.

AQUA (Advanced QUery Architecture for the SPARC Portal) improves the SPARC Portal by making the search engine smarter at understanding user search keywords, enhancing the result display, and providing users with better result filtering and sorting options.

Where do our tools make a difference?

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.