0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessThis work was supported by an AusIndustry R and D tax incentive program from the Department of Industry, Science, Energy and Resources, Australia, to SlowVoice Pty Ltd. (IR 2101990) and Fellowship (GNT 1110200) and Investigator grant (GNT 1197234) to A-L Ponsonby by the National Health and Medical Research Council of Australia.
Alexander Gruen, Karl Mattingly, Ellen Morwitch, Frederik Bossaerts, Manning Clifford, Chad Nash, John P A Ioannidis, Anne‐Louise Ponsonby (2023). Machine learning augmentation reduces prediction error in collective forecasting: development and validation across prediction markets with application to COVID events. , 96, DOI: https://doi.org/10.1016/j.ebiom.2023.104783.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2023
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.ebiom.2023.104783
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
