Add entry (d46598bf) to requests.jsonl

requests.jsonl

@@ -25,3 +25,4 @@
 {"id": "9b611cd9-ae6f-4b40-8411-5c2fc2d602b8", "type": "dataset", "title": "GenData-Research/crosslab-nmc", "description": "I'd like to request for Hugging Science to feature CrossLab-NMC, an open dataset for verifying scientific claims against experimental evidence. It links 2,005 battery measurements from 227 papers to their experimental conditions, source passages, and provenance, and includes reproducible verification queries that show how evidence can be retrieved and aggregated to determine what a scientific claim is actually supported by. I think this is a useful example of an emerging need for scientific AI, where there's a growing need for systems that can trace claims back to the experiments that support them (and not just generate plausible answers). Looking forward to hearing back!", "submitted_at": "2026-09-12T13:46:05.178Z", "source": "huggingscience.co", "status": "approved", "entry_id": "gendata-research-crosslab-nmc", "slug": "GenData-Research/crosslab-nmc", "org_id": "GenData-Research", "entry_type": "Materials science (energy storage devices)", "tags": ["energy", "materials-science"], "reviewed_at": "2026-09-14T08:13:03.498122+00:00", "pr_url": "https://github.com/georgiachanning/huggingscience-dot-co/pull/45"}
 {"id": "b8c4624b-259e-4daa-aea9-a3b809d96a36", "type": "model", "title": "STRUCTURES25 — sciai-lab/structures25", "description": "Pretrained models for optimizing molecular electron densities and energies, developed by SciAI Lab, Heidelberg University. Equivariant graph neural networks learn the kinetic-plus-exchange-correlation energy functional from reference DFT data.", "submitted_at": "2026-09-16T12:15:14.548Z", "source": "huggingscience.co", "status": "approved", "entry_id": "sciai-lab-structures25", "slug": "sciai-lab/structures25", "org_id": "sciai-lab", "entry_type": "electron-density", "tags": ["chemistry"], "name": "STRUCTURES25", "reviewed_at": "2026-09-16T12:17:56.348763+00:00", "pr_url": "https://github.com/georgiachanning/huggingscience-dot-co/pull/46"}
 {"id": "d0648134-d528-44db-93ba-84512fabb11c", "type": "model", "title": "HiRO-ACEv2 — allenai/HiRO-ACEv2", "description": "The HiRO-ACEv2 framework enables efficient generation of 3 km precipitation, 10 m eastward and northward winds, and sea level pressure fields over decades of simulated climate and arbitrary regions of the globe.", "submitted_at": "2026-10-15T16:13:19.742Z", "source": "huggingscience.co", "status": "pending", "entry_id": "allenai-hiro-acev2", "slug": "allenai/HiRO-ACEv2", "org_id": "allenai", "entry_type": "Climate", "tags": ["earth-science", "climate"], "name": "HiRO-ACEv2"}
+{"id": "d46598bf-d790-4ba8-861d-50eb3527ce65", "type": "model", "title": "OlmoEarth v1.3 — allenai/OlmoEarth-v1_3-Base", "description": "OlmoEarth-v1_3-Base is a ViT-Base (128M parameters) foundation model for remote sensing tasks over Sentinel-1, Sentinel-2, and Landsat images or image time series.", "submitted_at": "2026-10-23T08:17:43.118Z", "source": "huggingscience.co", "status": "pending", "entry_id": "allenai-olmoearth-v1-3-base", "slug": "allenai/OlmoEarth-v1_3-Base", "org_id": "allenai", "entry_type": "Foundation model for remote sensing tasks", "tags": ["earth-science"], "name": "OlmoEarth v1.3"}