FAIRlyz is a data QC reporting platform guiding organizations to manage, share and reuse well-annotated quality data that is ready for AI analysis. By visiting the data for AI-driven curation and quality control (QC), FAIRlyz protects sensitive information. This, along with synchronization with a central registry, forms the core functionality of the platform. FAIRlyz demonstrates broad applicability, ranging from enabling individual researchers to secure funding for data reuse to empowering organizations in strengthening internal data integrity and oversight.
FAIRlyz is available in two versions:
- A Private Metadata Commons for AI/ML Data Curation.
- A Public FAIRlyz.com Registry, promotes biomedical data sharing, igniting collaboration and fundraising opportunities around data reuse.
Although countless scientists have dedicated their careers to groundbreaking discoveries, the rise of AI in medicine introduces significant new challenges. Researchers who generate data often lack effective tools to share it, while those seeking data struggle with limited access. Within large organizations, these issues are compounded by persistent concerns over data quality and re-usability.
Why Data Visiting for QC?
| Traditional Approach | FAIRlyz Data Visiting |
|---|---|
| Files uploaded to a server | Files read from your local disk |
| Data leaves your organization | Data stays on your computer |
| Privacy and compliance risk | Maximum privacy by design |
| Your data may be rejected after sharing | Share a QC report or digital twin before sharing data |
Only QC scores, statistics, digital twins, and metadata you choose to share are ever shared, not raw data.
How does FAIRlyz help you?
FAIRlyz champions data AI‑readiness, ensuring it’s done right.
The FAIRlyz platform promotes a data integrity strategy that validates data usability and re-usability for analytics. There is no need to move sensitive data, as the FAIRlyz QC tool visits the data in its compute environment, allowing data managers to monitor data quality, versioning, and reuse for available datasets. FAIRlyz QC does not share data, it only shares metadata, digital twins, data location and access information, and QC data analysis results. By helping grow the sample size of an AI/ML, bioinformatics, or meta-analysis study, FAIRlyz speeds up the journey from initial research and lab discoveries to life-saving treatments, bringing faster advancements to healthcare.
The acronym FAIRlyz is derived from the acronyms FAIR and anaLYZable. FAIRlyz follows FAIR data principles, by evaluating and supporting data that is Findable, Accessible, Interoperable, Reusable, and adds anaLYZable as the 5th principle. FAIRlyz was developed through funding from a National Institute of Health NIAID contract.
If you still have questions, we have compiled answers to commonly asked questions in our FAQ page.
Funders & Sponsors
