One of the main objectives of the VITALISE project is to provide virtual transnational access to data generated from several Living Labs (LL) throughout Europe. Since open data sharing can raise concerns, such as intellectual property issues, fear of being scooped, and ethical concerns, a solution needs to be found to make private data available publicly. Thus, in the VITALISE project, an approach to enable the open processing of LL research data by the scientific community is defined to overcome open access data processing limiting factors. Two controlled data processing workflows have been defined: synthetic data (SD) workflow and anonymised data (AD) workflow.

Synthetic Data Workflow

SD is data generated artificially by a mathematical model to replicate distributions and structures of some real data (RD). With this workflow, SD will enable the development of local analyses and then perform them remotely with RD, without real access to it.

Through the VITALISE Discovery Portal, External Researchers (ER) can explore the data from LL and request SD for a specific query. Then, they can use SD to develop local analyses and then execute them remotely with RD. Finally, ER can register the results obtained with the RD.

Anonymised Data Workflow

AD refers to de-identification data processed by anonymisation techniques. In this workflow, LL data is anonymised and transformed before uploading it to the VITALISE Node Logic. As in the SD workflow, ER can request a size-limited version of anonymised data to develop local analyses and register their results.

See More

(1) Hernandez, M.; Epelde, G.; Beristain, A.; Álvarez, R.; Molina, C.; Larrea, X.; Alberdi, A.; Timoleon, M.; Bamidis, P.; Konstantinidis, E. Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain. Electronics 2022, 11, 812. https://doi.org/10.3390/electronics11050812