Events bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges
Intensive 'collaborative hackathons' which bring together organisations from industry, government, and the third sector, with talented multi-disciplinary researchers from academia (format is currently different due to COVID-19)
Organisations act as Data Study Group 'Challenge Owners', providing real-world problems to be tackled by small groups of highly talented, carefully selected researchers
Researchers brainstorm and engineer data science solutions, presenting their work at the end of the week
"Team up with a collaborative community of motivated data science experts, to jointly create a pathway to follow-up research and translational activities"
Sebastian Vollmer, Creator of the Turing Data Study Groups
Why does the Turing run them?
Challenge Owners get to quickly prototype possible solutions to their data science challenges
Researchers get an opportunity to put knowledge into practice and go beyond individual fields of research to solve real world problems
To help initiate industry collaborations for the Institute; the ideas generated can be a seed for kick-starting larger collaborative research projects
To help with the aims of the Institute:
Applying data science to real world problems
Training the next generation
Innovating and developing world-class research in data science
Data Study Group Team. (2024). Data Study Group Final Report: Ignota Labs - Toxicity Prediction for Drug Discovery (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.13882192
Data Study Group Team. (2024). Data Study Group Final Report: Johnson Matthey - Data Driven Modelling of Chemical Processes: Towards Flexible Production of Energy Carriers for a Net-Zero Society (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.13847810
Data Study Group Team. (2024). Data Study Group Final Report: UK Centre for Ecology & Hydrology (UKCEH) - Advancing Insect Biodiversity Monitoring through Automated Sensors, Deep Learning, and Citizen Science Data (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.13687424
Data Study Group Team. (2023). Data Study Group Final Report: Towards a Deeper Understanding of Eddies using Machine Learning. The Alan Turing Institute - 10.5281/zenodo.10590207
Data Study Group Team. (2023). Data Study Group Final Report: National Biodiversity Network Trust - Spatiotemporal analysis of priority species records across England (Version 1). The Alan Turing Institute. https://doi.org/10.5281/zenodo.10063986
Data Study Group Team. (2023, October 26). Data Study Group Final Report: Sustrans - Towards Equitable Walking and Cycling Infrastructure for All. The Alan Turing Institute. https://doi.org/10.5281/zenodo.10043883