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, semantic annotation, 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 safely curate their data before sharing it 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.
FAIRlyz QC uses a data‑visiting approach in which raw data never needs to be moved or copied. Instead, FAIRlyz QC is deployed where the data resides, leveraging modern edge‑computing principles to enable AI‑assisted data science, research, and analytics directly within the organization’s own environment. No individual‑level (row‑level) data is transmitted to the AI, only metadata such as variable names, types, and categories. FAIRLYZ automates the detection of errors, inconsistencies, missingness, and protocol deviations, then harmonizes and validates data so it meets the standards required for statistical modeling, AI analysis, and clinical decision‑support workflows.
Why Data Visiting for QC?
| Traditional Approach | FAIRlyz Data Visiting |
|---|---|
| Files uploaded to a server | Files read from your local disk or bucket |
| 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 or copy 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 raw data, it only shares metadata, semantic annotations, digital twins, data location and access information, and QC data analysis results. In medicine, 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
