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Python Source Code - 9.1 KB - MD5: 3f4fcdd6e518d87bfae5aa9002801fd9
Jupyter Notebook - 310.2 KB - MD5: 8dd3d88988356ed97f52a42d7410e0f3
ZIP Archive - 8.7 GB - MD5: 7f71f09ad2ba7b306f5f01f6c57b620d
PIV and force data for "Ormonde, P. C., Zhu, Y., Quinn, D. B., & Moored, K. W. (2026). Rather than drafting, vortex capture dictates efficiency in three-hydrofoil schools. arXiv:2604.19121”
Apr 22, 2026
Fang, Bin, 2026, "A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions", https://doi.org/10.18130/V3/O58DEE, University of Virginia Dataverse, V3
The dataset includes GeoTIFF raster layers representing the predicted probability of each ordinal class. Files are named using a standardized convention that encodes the WASH component and ordinal class name.
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