Research institutes and large research projects continue to amass an ever-expanding wealth of valuable data. Meaningful interpretation and discoveries require the data to be of the highest quality. FAIRLYZ focuses on making the data reusable and ready for AI.
The Private FAIRLYZ Metadata Commons has a triple objective:
- Make your data FAIR for reuse within an organization or for Open Science
- Foster collaborative data cleaning and annotation
- Protect your data through an on-premise data curation tool customizable for ML/AI add-ons
Using FAIRLYZ, your organization or project can make clinical or multi-omics data analysis-ready. Its cloud-based metadata registry interoperates with an on-premise data processing and QC toolkit to offer dataset profiling, cleaning, and integration tools. Depending on your use case, it can be implemented as a comprehensive research data commons or as a standalone data curation platform interoperating with existing data repositories, commons, and portals.
The FAIRLYZ Private Metadata Commons main technical features:
- Supports FAIR data principles
- Adds “analyzable” as a 5th principle
- Guides researchers in data management tasks during the lifecycle of a study
- Collects metadata linking to the location of the data
- Interoperates with an AI-driven data visitation platform that runs in the computing environment where the data is located
- Ensures trustworthy data analysis through semi-automated data curation and QC gamification of data curation
- Data versioning of reused data that respects data authorship
Are you interested in learning more about FAIRLYZ and discussing your specific needs? We’re here to help!