datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OlmoEarth-v1-Potomac-Sewage-Spill-2026
OlmoEarth-v1-Potomac-Sewage-Spill-2026
Validated, time-aligned reference dataset supporting GeoAI tracking of the 2026 Potomac River sewage spill (Glen Echo, MD — 240–300 million gallons released from the 72-inch Potomac Interceptor on January 19, 2026). Provides event timeline, hydrologic context, monitoring station locations, and AOI corridor polygons for AI2's downstream Sentinel-1/2 plume detection.
8 USGS gauges · 846 daily flow records · 4 CBP/CBF stations in AOI · 37 NHD… See the full description on the dataset page: https://huggingface.co/datasets/BAIGroup/OlmoEarth-v1-Potomac-Sewage-Spill-2026.OlmoEarth-v1-Potomac-Sewage-Spill-2026
OlmoEarth-v1-Potomac-Sewage-Spill-2026
Validated, time-aligned reference dataset supporting GeoAI tracking of the 2026 Potomac River sewage spill (Glen Echo, MD — 240–300 million gallons released from the 72-inch Potomac Interceptor on January 19, 2026). Provides event timeline, hydrologic context, monitoring station locations, and AOI corridor polygons for AI2's downstream Sentinel-1/2 plume detection.
8 USGS gauges · 846 daily flow records · 4 CBP/CBF stations in AOI · 37 NHD… See the full description on the dataset page: https://huggingface.co/datasets/2imi9/OlmoEarth-v1-Potomac-Sewage-Spill-2026.pot-o-quantum-88
pot-o-quantum-88
888,888 synthetic PoT-O challenges for tensor + MML path optimization in a Planck–tensor–realm lineage used to probe REALMS / realms-devkit themes: Planck-scale framing (Part I), tensor-network / entropy toy scales (Part IV §3–§4), and coarse-resolution “realm” tensor shapes (including 8×8 and 88×8-style blocks).
Program scale (8.88M lineage): each row carries nominal_lineage_M: 8.88 (documentation of the Quantum-88 / 8.88M Tribewarez program scale). This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/pot-o-quantum-88.synthetic-pot-o-challanges-22-22k
synthetic-pot-o-challanges-22-22k
Large synthetic PoT-O (Proof of Tensor Optimizations) challenge set for tensor shape / dtype specs and MML (Minimum Message Length) path optimization. 22,222 examples aligned with generation signature 22.2222 (param_signature field) and MML targets sampled around 0.222222.
Format (JSONL)
Same schema as classic PoT-O challenge JSONL, plus optional lineage field:
challenge — Classic challenge string: tensor:shape=[M… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/synthetic-pot-o-challanges-22-22k.