datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sma-evidence-graph
SMA Evidence Graph
An open-source, evidence-first dataset for Spinal Muscular Atrophy (SMA) drug research.
Description
This dataset contains structured evidence extracted from PubMed papers, clinical trials
from ClinicalTrials.gov, computationally generated hypotheses, AI-designed molecules,
and DiffDock molecular docking results — all linking gene targets to potential
therapeutic interventions for SMA.
Built by a researcher who has SMA, this dataset aims to accelerate… See the full description on the dataset page: https://huggingface.co/datasets/SMAResearch/sma-evidence-graph.cbeeg-evidence-graphs
CBEEG — Compute-Budgeted Exploitability Evidence Graphs (derived artifacts)
Reproducible derived artifacts for the paper Compute-Budgeted Exploitability
Evidence Graphs for Prospective Vulnerability Triage (Alpay & Alpay). The paper
frames prospective CVE triage as a leakage-safe, compute-budgeted evidence
selection problem: for each CVE we admit only public evidence visible by a fixed
decision time, select a few documents under a budget, and attach an auditable
evidence… See the full description on the dataset page: https://huggingface.co/datasets/Lightcap/cbeeg-evidence-graphs.douvras-scientific-ci-evidence-graph
Douvras Scientific CI Evidence Graph v0.1
Synthetic protocol dataset for linking a claim to its paper, repository,
dataset, seed and reproduced metric. It contains 30 records from six toy paper
instances (20 train, 5 validation and 5 frozen test), split by paper_id.
The labels distinguish REPRODUCED, PARTIAL, FAILED and INCONCLUSIVE.
Shortcuts and leakage fail closed. No real paper, code, dataset or result is
included, and this release is not a reproduction benchmark.
clinical-evidence-dependency-graph-reasoning-v0.1
Clinical Multi-Evidence State Integration v0.1
Overview
Clinical Multi-Evidence State Integration v0.1 is a structured clinical-reasoning benchmark designed to test whether an AI system can integrate multiple sequential evidence events into a coherent final clinical state.
The benchmark evaluates more than final-answer classification.
A system must determine:
how each evidence event affects each tracked clinical item;
whether an item should be confirmed… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-evidence-dependency-graph-reasoning-v0.1.