Distributed FAIR information systems to enable federated learning and reasoning

Data sources from different owners increase considerably in value when they can be combined.

However, the data is often too large or too vulnerable to be published. For this reason, the interest in federated solutions for big data analysis is growing.

In this project, IT specialists from UvA, VU, Leiden University and TNO are working together with a series of applications (such as life scientists in GO-FAIR and ASTRON) to develop the architecture for a network of FAIR data hubs and services.

The resulting architecture offers learning and reasoning processes with complete transparency, and anticipates heterogeneous data with different reliability.

The project provides service demonstrations based on a federation of at least three different data hubs. This project relies on knowledge about privacy, fairness and accuracy from the other projects.

This call is closed

The projects have been awarded, at the bottom of this page the current projects.

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