datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MedConclusion-Compact
MedConclusion-Compact
MedConclusion is a large-scale dataset of 5.7M PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains.
This repository… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/MedConclusion-Compact.MedConclusion
MedConclusion
MedConclusion is a large-scale dataset of 5.7M PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains.
This repository contains the… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/MedConclusion.cua-harm-recovery
CUA Harm Recovery Preference Dataset
This dataset contains human preference judgments for evaluating recovery plans in computer use agent (CUA) harm scenarios introduced in Human-Guided Harm Recovery for Computer Use Agents
Dataset Summary
The dataset contains 1,130 annotated plan pairs across 226 unique harm scenarios in computer use contexts. Each pair consists of two recovery plans (Plan A and Plan B) that were evaluated by human annotators to determine which plan… See the full description on the dataset page: https://huggingface.co/datasets/christykl/cua-harm-recovery.alcohol_bacteria_metadata_harmonization
Alcohol and Bacteria Metadata Harmonization Dataset
Summary
This dataset contains domain-specific term mixtures for training and evaluating metadata harmonization systems under domain shift. Each configuration includes a defined ratio of alcohol-related and bacteria-related terms to support experiments on generalization and domain adaptation. Each entry includes a term representation, its corresponding harmonized standard, and metadata such as variation type and source… See the full description on the dataset page: https://huggingface.co/datasets/netrias/alcohol_bacteria_metadata_harmonization.autonomous-driving-minimal-harm-gradient-pathfinding-v0.1
What this dataset tests
Whether a system can navigatea minimal-harm gradient through a driving scene.
The task is to identify the paththat minimizes total deformationacross all agents.
Required outputs
gradient vectors across actions
minimal harm path
deformation score
stability margin
Use case
Second layer of ethical navigation stack.
Transforms ethical cost fieldinto an actionable path.
Evaluation
Predictions must:
describe gradient… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-minimal-harm-gradient-pathfinding-v0.1.infosec_harmful_behaviors
Infosec Harmful Behaviors
Offensive-security instruction prompts for refusal-direction research and abliteration of code/security models.
Dataset Details
This dataset contains infosec-domain harmful prompts intended to elicit refusal behavior from aligned instruction models. It is designed as the harmful side of a harmful/harmless contrast pair, analogous to mlabonne/harmful_behaviors but focused on offensive-security and malicious-coding requests.
Rows:
train:… See the full description on the dataset page: https://huggingface.co/datasets/zaakirio/infosec_harmful_behaviors.clinical-harm-benefit-integrity-v0.1
What this dataset tests
Safety must constrain conclusions.
Benefit claims must stay inside harm evidence.
Why it exists
A common failure is safety spin.
Harms get buried.
Language says “safe” or “well tolerated” without support.
This set forces explicit harm–benefit balance.
Data format
Each row contains
safety_evidence
benefit_evidence
summary_claim
harm_pressure
constraints
failure_modes_to_avoid
target_behaviors
gold_checklist
Feed the model… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-harm-benefit-integrity-v0.1.Math-Forge-Hard
Math-Forge-Hard Dataset
Overview
The Math-Forge-Hard dataset is a collection of challenging math problems designed to test and improve problem-solving skills. This dataset includes a variety of word problems that cover different mathematical concepts, making it a valuable resource for students, educators, and researchers.
Dataset Details
Modalities
Text: The dataset primarily contains text data, including math word problems.
Formats… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Math-Forge-Hard.coding_harmless_prompts
Coding Harmless Prompts
Benign coding and technical prompts for the harmless side of infosec refusal-direction extraction.
Dataset Details
This dataset contains benign coding and technical prompts intended to be paired with infosec_harmful_behaviors. The contrast helps isolate malicious coding intent rather than a general coding or technical-domain direction.
Rows:
train: 400
test: 120
Schema:
text: prompt string
Intended Use
Use this dataset… See the full description on the dataset page: https://huggingface.co/datasets/zaakirio/coding_harmless_prompts.cancer_metadata_harmonization
Cancer Metadata Harmonization Dataset
Summary
This dataset contains cancer-related terms for training and evaluating metadata harmonization systems in the biomedical domain. Each entry includes a term representation, its corresponding harmonized standard, and metadata such as semantic type, variation type, and source terminology. Term representations include standard forms as well as lexical variations (e.g., synonyms, abbreviations) and are harmonized to biomedical… See the full description on the dataset page: https://huggingface.co/datasets/netrias/cancer_metadata_harmonization.recruiter-harvesting-dataset-v1
🕵️♂️ Recruiter Harvesting & Spam Forensic Dataset (v1.0)
Maintainer: Cata Risk Lab | Project: V.I.P.E.R.
🛡️ Dataset Summary
This dataset contains labeled examples of recruitment communications, categorized into "Harvesting" (Predatory/Spam) and "Legitimate" (Professional/Retained Search).
It was created to train and benchmark the V.I.P.E.R. (Vendor Integrity & Personnel Email Reconnaissance) auditing engine.
📂 Structure
text: The raw body content of the… See the full description on the dataset page: https://huggingface.co/datasets/Cata-Risk-Lab/recruiter-harvesting-dataset-v1.