Add entry (9cd565aa) to requests.jsonl

requests.jsonl

@@ -19,3 +19,4 @@
 {"id": "a8b16a2f-e2d4-483c-9e29-bb0df4bdd2dd", "type": "dataset", "title": "ratschlab/DeepSpotM", "description": "DeepSpot-M, a multimodal foundation model that reads the full transcriptome (the activity of every gene) from medical images.\n\nMost tumours are never profiled with spatial transcriptomics, but nearly all are imaged. DeepSpot-M is bridging that gap and as a first demonstration, we used it to build a virtual spatial transcriptomics atlas of 28,664 TCGA slides across 32 cancers, the largest resource of its kind.\n\nOlder methods predict a small, fixed set of genes. We ask a different question instead: \"how much is gene X expressed in this image?\" Every gene gets its own profile, built from its DNA and RNA sequence, its protein activity, how it behaves at the single cell level, and what's known about it in the literature. Because the model reasons over these profiles rather than memorizing a gene list, it can predict genes it never saw during training just as accurately as ones it did. \n\nTrained on one of the largest oncology datasets to date, DeepSpot-M generalizes to cancers it has never seen and even beats specialist models trained directly on those cancers. Given just a few slides from a new cohort, it adapts further and keeps improving.\n\nThe same system also enables:\n🔬 Extending targeted single-cell spatial assays toward full transcriptome-wide coverage\n🩺 Restoring degraded or low-quality spatial transcriptomics data\n🧪 In silico variant effect mapping conditioned on morphology\n💬 Natural language querying of tissue biology", "submitted_at": "2026-08-27T16:42:40.107Z", "source": "huggingscience.co", "status": "rejected", "entry_id": "ratschlab-deepspotm", "slug": "ratschlab/DeepSpotM", "org_id": "ratschlab", "entry_type": "Biology, Spatial Transcriptomics, Model, Oncology", "tags": ["biology"], "reviewed_at": "2026-08-28T09:06:20.092716+00:00", "reject_reason": "Added with the ratschlab dataset "}
 {"id": "726e8013-f85e-465c-98e9-a7b96b578d39", "type": "dataset", "title": "ulamai/UnsolvedMath", "description": "A comprehensive curated collection of 15,458 open and partially solved mathematics problems across all domains and difficulty levels, including the largest collection of Erdős problems available in machine-readable format.", "submitted_at": "2026-08-28T15:03:52.854Z", "source": "huggingscience.co", "status": "approved", "entry_id": "ulamai-unsolvedmath", "slug": "ulamai/UnsolvedMath", "org_id": "ulamai", "entry_type": "Mathematics", "tags": ["mathematics"], "reviewed_at": "2026-08-28T15:04:27.466350+00:00", "pr_url": "https://github.com/georgiachanning/huggingscience-dot-co/pull/40"}
 {"id": "98a1068c-73de-4c72-99be-26fd9d18951c", "type": "collaboration", "title": "OpenData", "description": "The Open Data Consortium is a nonprofit, open source project built with leading open-source organizations, including the Linux Foundation, under an open governance model. Members and contributors share direction. No single vendor controls it.\n\nrefferences:\nhttps://opendata.org/partners/\nhttps://opendata.org/\nhttps://huggingface.co/datasets/OpenDataFoundation/opendata", "submitted_at": "2026-08-31T10:45:30.358Z", "source": "huggingscience.co", "status": "pending", "email": "eswar@opendata.org", "institution": "Open Data Consortium"}
+{"id": "9cd565aa-2841-4440-b49d-a14f5bacef87", "type": "model", "title": "BOA — Basis Overlap Architecture — sciai-lab/boa", "description": "BOA is an equivariant graph neural network that predicts ground-state electron densities. Its message passing uses the overlap matrix of the basis functions that represent the predicted density, rather than treating the basis coefficients as generic node features.", "submitted_at": "2026-09-04T15:10:27.821Z", "source": "huggingscience.co", "status": "pending", "entry_id": "sciai-lab-boa", "slug": "sciai-lab/boa", "org_id": "sciai-lab", "entry_type": "electron-density", "tags": ["chemistry"], "name": "BOA — Basis Overlap Architecture"}