FAIR stands for Findable, Accessible, Interoperable and Reusable.
We envision crossing borders of data silos, analyzing them by making data and services FAIR.
Our research focuses on methods for machine actionable data and services to foster data-driven science and innovation.
FAIR Data Management
Guide practitioners to develop a value-oriented FAIR data management policy to enable organizations to manage their data through its lifecycle and support their data driven business models
Distributed Analytics Platforms
The FIT Data Analytics Train platform provides a solution to gain full benefits of distributed data, without sharing any data. Analytics algorithms visit the decentral data centres and return (and travel on) with trained models of what they have learned from the data.
Persistent Identifiers (PID) used for managing and sharing digital resources in complex data-intensive production and research. PID systems identify digital objects (such as data, software) globally uniquely and make them findable both for human and machine users.
FAIR Capability Maturity Models and Assessment
Making your data FAIR is a journey: each organization decides the best path for themselves. Capability Maturity model helps organizations to identify their critical process for their goals and guides them to improve those for achieving FAIR data.