We are happy to announce the results of the SPACIOUS Open Call for Resources!
The call invited researchers to bring forward ambitious, data-intensive science cases built around the “code-to-data” approach, moving analysis closer to major ESA datasets rather than transferring massive volumes of data across systems.
After evaluation by an international Resource Allocation Committee (RAC), two projects were selected for immediate implementation, with two additional proposals placed on a reserve list.
The Selected Projects
- Joris De Ridder: Deep learning across Gaia and complementary surveys.
This project will combine the full Gaia dataset with other photometric catalogues to discover and characterise new types of variable stars using state-of-the-art machine learning techniques. It represents a powerful example of how SPACIOUS can enable cross-survey, data-driven discovery. - Carlos Allende Prieto: Accelerating Gaia analysis with cloud technologies.
This project will apply machine learning and cloud-native tools to refine stellar parameters derived from Gaia spectroscopy and photometry. It will also serve as a key test case for scalable analysis within the SPACIOUS platform.
The selected teams will receive dedicated computing and storage resources, as well as hands-on support to adapt their workflows to the SPACIOUS Big Data Analytics Framework using tools such as Spark, Dask, and Jupyter Notebooks.
These first community challenges mark an important step in opening the SPACIOUS infrastructure to the wider scientific community. More opportunities will follow as the platform continues to grow.