Tag: Data Sharing
SIESTA Tool Update: Energy Domain
The Energy Domain tool, led by Predictia, is now live on the SIESTA dashboard with two separate interfaces. Data owners get 24-hour solar production and consumption forecasts, and researchers can work with realistic energy data anonymised through TrasgoDP’s differential privacy.
SIESTA Concepts: Synthetic Data
Some of the most valuable research data, such as health records and census microdata, is also the most sensitive. Synthetic data offers a way out: artificial populations of people who never existed, statistically faithful to the real ones. Here’s what it is, how it works and how EOSC-SIESTA puts it to use.
EOSC-SIESTA Begins Its FIDELIS Support Offer on Privacy-Preserving Data Release
EOSC-SIESTA delivers FIDELIS Support Offer #2, a hands-on training on privacy-preserving data release and trustworthy publication, running from September to October 2026.
SIESTA tool: BIDScramble
BIDScramble is an open-source tool developed by Radboud University within the EOSC-SIESTA project to facilitate the secure reuse of sensitive neuroimaging data. It transforms BIDS-formatted datasets into anonymous scrambled versions that maintain original structural and statistical properties.
Siesta Concepts: Open Science
Open Science promotes a transparent and collaborative research ecosystem, driven by Europe’s EOSC initiative. However, sharing sensitive data requires balancing openness with privacy. EOSC-SIESTA addresses this challenge by developing trusted, cloud-based environments and privacy-preserving tools that make sensitive data securely shareable and usable under FAIR principles.
Discover UC3 Energy Domain Tool: optimising solar battery management in EOSC-SIESTA
EOSC-SIESTA addresses the challenge of analyzing sensitive data without compromising privacy through its energy domain use case (UC3). Focusing on solar installations, the project utilizes trusted execution environments and anonymization techniques to secure sensitive household consumption dat
SIESTA CONCEPTS#1: Federated Learning
Federated Learning (FL) allows multiple participants to collaboratively train AI models by keeping data local and sharing only encrypted model updates. Through the SIESTA dashboard, users can easily deploy secure FL clients, which connect to a central coordinating server hosted on AI4EOSC via the Flower framework and secure gRPC protocols, enabling privacy-preserving collaboration.
SIESTA Video Series: Text Anonymization on Sensitive Data
The SIESTA Video Series brings together a set of short videos in which several of the project’s use cases present their work and main objectives. In particular, the use cases on Medical Imaging, Energy Domain, Text Anonymization on sensitive data, and Demography have contributed to this series, each explaining their respective activities within the project.…