datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SciFigAlign
SciFigAlign
Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence
Paper · Code · Dataset
Scientific figure assessment in peer review is not natural-image IQA. A figure must be legible, support the manuscript’s claims, and present a clear visual hierarchy.
This dataset is the full SciFigAlign corpus: 3,857 figure crops from 3,126 ICLR / NeurIPS / ICML papers, labeled on four peer-review dimensions (1–5): Clarity, Relevance, Informativeness… See the full description on the dataset page: https://huggingface.co/datasets/haihanlamu/SciFigAlign.Handwritten-Computer-Science-Notes-Dataset
English Handwritten Computer Science Notes Dataset
This dataset contains high-resolution images of handwritten computer science notes written in English. It includes algorithm explanations, code snippets, flowcharts, theoretical content, and annotations. The dataset is designed to support AI research in handwriting recognition, OCR, and document understanding specifically for computer science education.
Contact
For queries or collaborations related to this dataset… See the full description on the dataset page: https://huggingface.co/datasets/HumynLabs/Handwritten-Computer-Science-Notes-Dataset.SCIN-Dermatology-Raw-Images
SCIN-Dermatology-Raw-Images
This dataset contains 6,517 patient-submitted photographs organized into 3,061 clinical cases of common skin diseases. The source images are curated from the public Google Skin Condition Image Network (SCIN) corpus, cleansed of quality and gradability conflicts, and paired with complete patient-reported demographics, clinical symptoms, and dermatologist gradings.
Dataset Structure
This repository follows the standard Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/HawkFranklin-Research/SCIN-Dermatology-Raw-Images.SCIN
SCIN Dataset
Dataset Description
This dataset is a copy of SCIN Dataset which is shared with the license CC BY 4.0.
This dataset contains 5033 samples in its train split.
This dataset includes image data.
Splits
train: 5033 samples
Data Fields
The dataset includes the following columns:
case_id: String data
images: Sequence of image data
source: String data
image_paths: Sequence of strings (file paths to images)
image_shot_types: Sequence… See the full description on the dataset page: https://huggingface.co/datasets/ekacare/SCIN.ALD-E-ImageMiner
ALD-E-ImageMiner
ALD-E-ImageMiner is a benchmark package for scientific figure understanding in atomic layer deposition and atomic layer etching literature. This Hugging Face export is organized for ImageFolder loading while preserving the repository's source split membership and panel-level annotations.
🗂️ Source Data Summary
This package was generated from the sciknoworg/ALD-E-ImageMiner GitHub repository, using icdar2026-competition-data, and contains 1951… See the full description on the dataset page: https://huggingface.co/datasets/SciKnowOrg/ALD-E-ImageMiner.Sci-ImageMiner
Sci-ImageMiner
Sci-ImageMiner is a scientific-image dataset for multimodal figure
understanding. The initial release focuses on figures extracted from atomic
layer deposition (ALD) and atomic layer etching (ALE) publications in materials
science. The schema is designed to support future scientific domains,
subdomains, image types, data sources, and annotation tasks.
This dataset contains mixed-rights content. Metadata and annotations are
described as CC BY 4.0 in the upstream… See the full description on the dataset page: https://huggingface.co/datasets/SciKnowOrg/Sci-ImageMiner.scissors-cut-cystic-artery
Dataset Card for Dataset Name
This is a FiftyOne dataset with 179 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/scissors-cut-cystic-artery")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/scissors-cut-cystic-artery.cosmos_predict-scissors-cut-cystic-artery
Dataset Card for laparoscopy_image2world_test10_grouped
This is a FiftyOne dataset with 10 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("pjramg/cosmos_predict-scissors-cut-cystic-artery")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/pjramg/cosmos_predict-scissors-cut-cystic-artery.rock-paper-scissor-datasetrock-paper-scissor