Dataset Viewer
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7bdf8e576b9b4c3daaf9d587b429b2f6
| 2025-09-18T11:40:40 |
PET/CT Imaging
|
https://github.com/microsoft/lymphoma-segmentation-dnn/tree/main; https://microsoft.github.io/aiforgoodlab/resource.html?d=PETCT-Imaging-15
|
Lymphoma lesion segmentation and quantitation from FDG PET/CT images using deep neural networks
| 0 |
GB
|
Healthcare Imaging
|
anonymous
|
2c8ab04f729d4bbca922acbaf56dde3d
| 2025-09-18T13:14:22 |
MSnLib
|
https://www.nature.com/articles/s41592-025-02813-0
|
A large-scale, open multi-stage fragmentation mass spectral library for 30,000 unique small molecules.
| 21.6 |
GB
|
Natural products discovery, drug discovery, medicinal chemistry, structural chemistry, Metabolomics
|
Ludovico Comito
|
460f8634d2324de9a4912e5ea28a7133
| 2025-09-18T14:13:58 |
Reporting of Surgically Removed Lymph Nodes for Breast Tumors
|
https://meridian.allenpress.com/aplm/article/146/11/1308/487735/Reporting-of-Surgically-Removed-Lymph-Nodes-for
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evidence-based pathology reporting of different types of cancers, with the inclusion of all parameters deemed to be relevant for best patient care and future data collection
| 0 |
GB
|
Healthcare
|
Pranjal Dubey
|
47d7e22550c440f5951961f3a6c0ce33
| 2025-09-18T14:16:51 |
Nuclear Fusion Dataset
|
https://www.kaggle.com/datasets/adebusayoadewunmi/nuclearfusion-data/data
|
This data was simulated to give a view of what a nuclear fusion experiment data would be like and it does not hold any representation of any real-life experiment. But the features were selected based on the creator's understanding of some components that would be necessary for a successful ignition.
| 35 |
MB
|
Magnetic Field Fluctutations, Leak Percentage, Plasma Instabilities, etc.
|
anonymous
|
96d131a55c084040b83a9de4bf07a1fb
| 2025-09-18T14:20:19 |
Dataset of Solution-based Inorganic Materials Synthesis Procedures Extracted from the Scientific Literature
|
https://doi.org/10.6084/m9.figshare.16583387.v4
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here are 20,037 hydrothermal synthesis reactions and 15,638 precipitation synthesis reactions. Each record corresponds to a synthesis procedure extracted from a paragraph and is represented as an individual JSON objec
| 161 |
MB
|
Material Synthesis
|
Pranjal Dubey
|
4a78a154811845ba902184a1d37fa3f4
| 2025-09-18T14:23:31 |
Human Reference Interactome (HuRI)
|
https://interactome-atlas.org/
|
Human protein–protein interactome from large scale yeast two hybrid screens (HI-III-20). ~52k curated binary PPIs across ~8.3k proteins with QC and assay metadata. Good for link prediction, graph learning, and protein function modeling. Check source for redistribution limits.
| 162 |
MB
|
interactomics; proteomics; systems biology; bioinformatics
|
-
|
44510458008b4e65b983f34f96d518a9
| 2025-09-18T14:23:33 |
Human Reference Interactome (HuRI)
|
https://interactome-atlas.org/
|
Human protein–protein interactome from large scale yeast two hybrid screens (HI-III-20). ~52k curated binary PPIs across ~8.3k proteins with QC and assay metadata. Good for link prediction, graph learning, and protein function modeling. Check source for redistribution limits.
| 162 |
MB
|
interactomics; proteomics; systems biology; bioinformatics
|
-
|
dccf6b447e934e5eba2bb0e0f31def88
| 2025-09-18T14:28:47 |
Human Reference Interactome (HuRI)
|
https://www.interactome-atlas.org/
|
Human protein–protein interactome from large scale yeast two hybrid screens (HI-III-20). ~52k curated binary PPIs across ~8.3k proteins with QC and assay metadata. Good for link prediction, graph learning, and protein function modeling. Check source for redistribution limits.
| 162 |
MB
|
interactomics; proteomics; systems biology; bioinformatics
|
-
|
e2773a85b36d4b0e9876bbbd1eedaddc
| 2025-09-18T14:29:32 |
Human Reference Interactome (HuRI)
|
https://www.interactome-atlas.org/
|
Human protein–protein interactome from large scale yeast two hybrid screens (HI-III-20). ~52k curated binary PPIs across ~8.3k proteins with QC and assay metadata. Good for link prediction, graph learning, and protein function modeling. Check source for redistribution limits.
| 162 |
MB
|
interactomics; proteomics; systems biology; bioinformatics
|
Samuel
|
cacc9d4e686c43cdbd700e1ebfbef5df
| 2025-09-18T14:43:12 |
Human Reference Interactome (HuRI)
|
https://interactome-atlas.org/
|
Human protein–protein interactome from large scale yeast two hybrid screens (HI-III-20). ~52k curated binary PPIs across ~8.3k proteins with QC and assay metadata. Good for link prediction, graph learning, and protein function modeling. Check source for redistribution limits.
| 162 |
MB
|
interactomics; proteomics; systems biology; bioinformatics
|
Samuel
|
e7d8117398a04d87bb3870cbe0afcb91
| 2025-09-18T15:00:43 |
Awadhi Dataset
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https://github.com/kmi-linguistics/awadhi
|
POS Tagged Awadhi Dataset
| 26 |
MB
|
Linguistics
|
Rishav Chandra Varma
|
121c2b53fdd547d68e8202d518b1b876
| 2025-09-18T15:06:13 |
Bhojpuri - Voice Text Dataset
|
https://huggingface.co/datasets/KonthouKabiAI/bhojpuri-lr-v3
|
Audio - Text pairs of Bhojpuri Dataset of 24.6k pairs
| 7 |
GB
|
Linguistics
|
Rishav Chandra Varma
|
3d0cac830c6b4eff8545468af7815f88
| 2025-09-18T15:08:36 |
English-Bhojpuri SMT System: Insights from the Karaka Model
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https://github.com/shashwatup9k/BHLTR/tree/master
|
Bhojpuri POS Tagged Dataset and Parallel Corpora Dataset
| 20 |
MB
|
Linguistics
|
Rishav Chandra Varma
|
c7ef4f68480c4cb189086f31c7f1badb
| 2025-09-18T16:48:26 |
Protein Data Bank (PDB)
|
https://www.rcsb.org/
|
The Protein Data Bank (PDB) is the global archive of experimentally determined 3D structures of proteins, nucleic acids, and complex assemblies, widely used for structural biology and drug discovery research. Check source for redistribution limits.
Data is fragmented across many file formats, schema changes over time, no consistent ML-friendly embeddings. Metadata parsing requires domain-specific parsers.
| 2 |
TB
|
Structural Biology, Bioinformatics, Drug Discovery
|
anonymous
|
fa7f8dd520e24de486ea4f8127c7432a
| 2025-09-18T16:49:36 |
IFBench
|
https://huggingface.co/collections/allenai/ifbench-683f590687f61b512558cdf1
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One of the most used benchmarks for instruction following, yet HF dataset card is quite dry. Not directly a science dataset but given its importance for the industry I believe it deserves a better HF page.
| 200 |
MB
|
LLM
|
Lucas Fernandes Martins
|
b0eb9affc50449dca24863b8c20baf15
| 2025-09-18T16:58:40 |
PubChem BioAssay Dataset
|
https://pubchem.ncbi.nlm.nih.gov/source/
|
Large-scale dataset containing bioactivity results for millions of chemical compounds tested in high-throughput screening assays. Provides assay outcomes, compound structures, and associated metadata. Check source for redistribution limits.
Critical resource for training ML models in drug discovery, virtual screening, and activity prediction. Enables foundation models for bioactivity and chemical property prediction.
| 150 |
GB
|
Chemistry, Drug Discovery, Bioinformatics
|
Naga Adithya Kaushik
|
314286105a074c64a8d1709efd9fe16d
| 2025-09-18T17:03:48 |
NASA Exoplanet Archive
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https://exoplanetarchive.ipac.caltech.edu/
|
Catalog of confirmed exoplanets, stellar parameters, orbital characteristics, and light curves collected from multiple NASA missions. Provides both tabular metadata and time-series photometry.
| 10 |
TB
|
Astrophysics, Space Science
|
Naga Adithya Kaushik
|
a4df3c0954bf4e45ad3fcb88901627e9
| 2025-09-18T17:50:43 |
NASA’s Solar Dynamics Observatory (SDO)
|
https://huggingface.co/nasa-ibm-ai4science/Surya-1.0
|
Surya is a 366M-parameter transformer model pretrained on 9 years (≈218 TB) of multi-instrument data from NASA’s Solar Dynamics Observatory (SDO), including 8 Atmospheric Imaging Assembly (AIA) channels and 5 Helioseismic and Magnetic Imager (HMI) products.
| 218 |
TB
|
Astrophysics, Space Science, Heliophysics
|
anonymous
|
aed2ada9cd274485956f2df52fe8b03e
| 2025-09-18T18:25:10 |
BindingDB
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https://www.bindingdb.org/rwd/bind/chemsearch/marvin/Download.jsp
|
BindingDB is a FAIRsharing knowledgebase that accelerates biomedical research and innovation by extracting, accumulating, organizing, and annotating protein-ligand interaction datasets.
| 3 |
GB
|
ligand-prot interactions
|
anonymous
|
a2f68ce72b914f34bc18ad6f154e0233
| 2025-09-18T19:37:03 |
BubbleML_2
|
https://huggingface.co/datasets/hpcforge/BubbleML_2
|
BubbleML_2 is a high-fidelity dataset of boiling simulations in 2D for three fluids (FC-72, Liquid N2 and R515B). It provides paired time-series fields stored in HDF5 (.hdf5) files together with metadata (.json).
| 2.35 |
TB
|
PDEs
|
Sheikh Md Shakeel Hassan
|
a183190b77584a0a8ce79908c5319133
| 2025-09-18T20:01:26 |
Rubin's Legacy Survey of Space and Time (LSST)
|
https://data.lsst.cloud/
|
The Rubin Observatory’s Legacy Survey of Space and Time (LSST) is a 10-year astronomical survey scheduled to begin in 2025 at Cerro Pachón in Chile, using an 8.4-meter telescope and the world’s largest 3.2-gigapixel digital camera. It will scan the southern sky every few nights in six optical bands (u, g, r, i, z, y), producing about 15 terabytes of data per night and ultimately a dataset exceeding tens of petabytes. By building both a deep static map of the universe and a time-domain “movie of the sky,” LSST will enable discoveries ranging from the nature of dark matter and dark energy to the inventory of solar system bodies, the structure of the Milky Way, and the detection of rare, fast-changing cosmic transients.
| 1,000 |
TB
|
Astronomy, Cosmology, Physics
|
anonymous
|
73a136bf76e74e25b5153c92922b64a4
| 2025-09-18T20:03:17 |
Rubin's Legacy Survey of Space and Time (LSST)
|
https://data.lsst.cloud/
|
The Rubin Observatory’s Legacy Survey of Space and Time (LSST) is a 10-year astronomical survey scheduled to begin in 2025 at Cerro Pachón in Chile, using an 8.4-meter telescope and the world’s largest 3.2-gigapixel digital camera. It will scan the southern sky every few nights in six optical bands (u, g, r, i, z, y), producing about 15 terabytes of data per night and ultimately a dataset exceeding tens of petabytes. By building both a deep static map of the universe and a time-domain “movie of the sky,” LSST will enable discoveries ranging from the nature of dark matter and dark energy to the inventory of solar system bodies, the structure of the Milky Way, and the detection of rare, fast-changing cosmic transients.
| 1,000 |
TB
|
Astronomy, Cosmology, Physics
|
Steven Dillmann
|
e4eb7e1ce61a41348d49b1d86b3558a9
| 2025-09-18T20:05:36 |
Multimodal Universe
|
https://huggingface.co/MultimodalUniverse
|
The Multimodal Universe dataset is a large scale collection of multimodal astronomical data, including images, spectra, and light curves, which aims to enable research into foundation models for astrophysics and beyond.
| 100 |
TB
|
Astronomy, Cosmology, Physics
|
Steven Dillmann
|
a92f734164304dd799e9914f7dded1e1
| 2025-09-18T20:11:53 |
Chandra Source Catalog (CSC)
|
https://cda.cfa.harvard.edu/cscweb/index.do
|
The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed
individual source studies and statistical studies of large samples of X-ray sources detected in ACIS
and HRC-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides
carefully-curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial,
photometric, spectral, and temporal source properties, as well as science-ready X-ray data products.
| 40 |
GB
|
Astronomy, Cosmology, Physics
|
Steven Dillmann
|
08b4872696ea4451aa65363ff22902c3
| 2025-09-18T20:12:12 |
Chandra Source Catalog (CSC)
|
https://cda.cfa.harvard.edu/cscweb/index.do
|
The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed
individual source studies and statistical studies of large samples of X-ray sources detected in ACIS
and HRC-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides
carefully-curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial,
photometric, spectral, and temporal source properties, as well as science-ready X-ray data products.
| 40 |
TB
|
Astronomy, Cosmology, Physics
|
Steven Dillmann
|
83c03815e5784d1fb3aceb7d3c98b25d
| 2025-09-19T01:33:58 |
Smith42/galaxies
|
https://huggingface.co/datasets/Smith42/galaxies
|
8.6 million galaxy images from the DESI Legacy Survey, crossmatched with galaxy metadata.
| 1.1 |
TB
|
Astronomy
|
Mike Smith
|
5d30e4f6963f4241895c2cb1abfa4c6a
| 2025-09-19T02:34:11 |
Multimodal Universe
|
https://github.com/MultimodalUniverse/MultimodalUniverse
|
The Multimodal Universe dataset is a large scale collection of multimodal astronomical data, including images, spectra, and light curves, which aims to enable research into foundation models for astrophysics and beyond.
| 120 |
TB
|
Astronomy
|
Mike Smith
|
2f4525f502d14595b7a16c417407fd53
| 2025-09-19T02:41:17 |
ROVER-Gen
|
https://huggingface.co/datasets/cheryyunl/ROVER-Gen
|
Logical puzzle solving and instruction following dataset for large vision models. Given an image and the task (prompt), the dataset consists of a final result (image) after executing the task on this image.
| 0 |
GB
|
logical puzzle solving, visual instruction following
|
SB
|
5e633ca577df48368ca9a95832f081d9
| 2025-09-19T02:48:20 |
bird-species
|
https://huggingface.co/datasets/Ez-Clap/bird-species
|
This dataset contains images of Belgian birds and their species. Dataset is split into train (6.78k rows), validation (215 rows) and test (215 rows).
| 163 |
MB
|
bird-species
|
SB
|
28b52169b90c495e870e898681491c13
| 2025-09-19T02:54:33 |
Wild Life Dataset
|
https://lila.science/datasets/wcscameratraps
|
This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the Wildlife Conservation Society. The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle).
| 0 |
MB
|
wildlife
|
SB
|
67c59f7c54774d119ac61d65be1903bd
| 2025-09-19T06:57:31 |
ECOTOX
|
https://cfpub.epa.gov/ecotox/
|
ECOTOX is a comprehensive Knowledgebase providing single chemical environmental toxicity data on aquatic and terrestrial species.
| 0 |
GB
|
chemistry
|
Rahul Maity
|
fc394bc9e68f496382c259081b497c8e
| 2025-09-19T08:40:06 |
emg2pose
|
https://github.com/facebookresearch/emg2pose
|
A dataset of Surface electromyography (sEMG) recordings paired with ground-truth, motion-capture recordings of the hands. Data loading, baseline model training, and baseline model evaluation code are provided.
| 431 |
GB
|
Surface Electromyography
|
MatteoFasulo
|
c585ff9115574c759db6cde676465c75
| 2025-09-19T08:42:11 |
Generic non-invasive neuromotor interface for HCI
|
https://fb-ctrl-oss.s3.amazonaws.com/generic-neuromotor-interface-data
|
Surface electromyography (sEMG) data and training models associated with the paper "A generic non-invasive neuromotor interface for human-computer interaction".
The dataset contains sEMG recordings from 100 participants in each of the three tasks described in the paper: discrete_gestures, handwriting, and wrist.
| 150 |
GB
|
Surface Electromyography
|
MatteoFasulo
|
3915860e8db542adaee2ae21e1275376
| 2025-09-19T08:56:58 |
clinical_trials_gov
|
https://clinicaltrials.gov
|
Clinical Trial Study information is crucial for drug development/repurposing and while the information is publicly available, it comes in really bad formats which varies for different diseases (as each has different endpoint) and different studies (Phase I/II/III). The information is available within data vendors but behind proprietary walls
| 0 |
GB
|
medicine
|
anonymous
|
5499c9b6ab8d4a36b86eef244e3a6970
| 2025-09-19T08:57:53 |
clinical_trials_gov
|
https://clinicaltrials.gov
|
Clinical Trial Study information is crucial for drug development/repurposing and while the information is publicly available, it comes in really bad formats which varies for different diseases (as each has different endpoint) and different studies (Phase I/II/III). The information is available within data vendors but behind proprietary walls
| 0 |
GB
|
medicine
|
anonymous
|
98b0da12a1c9439198fb6aefb2becd8c
| 2025-09-19T09:04:55 |
clinical_trials_gov
|
https://clinicaltrials.gov
|
Clinical Trial Study information is crucial for drug development/repurposing and while the information is publicly available, it comes in really bad formats which varies for different diseases (as each has different endpoint) and different studies (Phase I/II/III). The information is available within data vendors but behind proprietary walls
| 4 |
GB
|
healthcare
|
Piotr Kaniewski
|
64d8d78da2b3451781c828ac199d7ed8
| 2025-09-19T09:14:12 |
pubmed_literature
|
https://pubmed.ncbi.nlm.nih.gov/download/
|
Biomedical Literature is crucial for drug discovery and while its available at API service, its not available in a single place in a structured format.
| 30 |
GB
|
biomedicine
|
Piotr Kaniewski
|
71a05fedb7ac4519a05af58171e1fb61
| 2025-09-19T09:16:30 |
daily_med_labels
|
https://dailymed.nlm.nih.gov/dailymed/
|
daily med contains labels of fda approved drugs/medicines yet its not availalbe in a structured format that doesnt require manual scraping
| 30 |
GB
|
healthcare
|
Piotr Kaniewski
|
7c8408a366e54a4892e90627b0597678
| 2025-09-19T09:38:28 |
ontology_mapping
|
https://www.ebi.ac.uk/ols4/
|
Ontologies are essential in knowledge discovery/bioinformatics; yet mapping between them is a non trivial task
| 10 |
GB
|
bioinformatics
|
Piotr Kaniewski
|
c2849283ae1a41d9b6c7a20c98098588
| 2025-09-19T14:32:01 |
GeneReviews
|
https://huggingface.co/datasets/Tonic/GeneReviews
|
GeneReviews Dataset Extraction
This project extracts text and metadata from GeneReviews® chapters downloaded from NCBI Bookshelf and creates a structured dataset in Hugging Face format.
Overview
GeneReviews® is an international point-of-care resource for clinicians, providing clinically relevant and medically actionable information for inherited conditions. This project processes the XML files from the GeneReviews database and creates a structured dataset suitable for machine learning and research applications.
📈 Dataset Statistics:
Total Records: 929 GeneReviews chapters
Average Abstract Length: 899.6 characters
Average Content Length: 56,377.9 characters
Total References: 13,683 references across all chapters
Average References per Chapter: 14.7
Chapters with >100 references: 12 chapters
Total Keywords: 9,616
Unique Keywords: 6,824
| 16 |
MB
|
Clinical reviews of genetic conditions
|
Joseph [open/acc] Pollack
|
6ae0293a58324f64a91e33d895c61e14
| 2025-09-19T18:51:33 |
GV-Rep
|
https://zenodo.org/records/11502840
|
A large-scale Genetic variants (GVs) dataset featuring a comprehensive collection of 7 million GV records with detailed annotations and one clinician verified dataset. Paper: https://arxiv.org/abs/2407.16940 , Github: https://github.com/bowang-lab/genomic-FM
| 30 |
GB
|
Genomics
|
Mohammed Hamdy
|
75e0431bb75241baa8f876820ff0fedc
| 2025-09-19T19:03:50 |
Biology-Instructions
|
https://github.com/hhnqqq/Biology-Instructions
|
A large-scale multi-omics biological sequences-related instruction-tuning dataset including DNA, RNA, proteins, and multi-molecules. Paper: https://arxiv.org/abs/2412.19191
| 100 |
MB
|
Multi-omics
|
Mohammed Hamdy
|
71fcbca937a0443a920606be6dcc4060
| 2025-09-19T19:05:48 |
International Brain Lab Data
|
http://internationalbrainlab.com/data; https://www.biorxiv.org/content/10.1101/2023.07.04.547681v4
|
Multiple Brain-Related Datasets. A key challenge is that this dataset contains data from multiple experiments and different modalities. The dataset comprises 459 Neuropixel experimental sessions encompassing 699 distinct recordings (probe insertions). These recordings were obtained from 139 subjects performing the IBL task across 12 different laboratories. Spike-sorting analysis yielded 621,733 units, of which 75,708 were classified as good quality. In total, 241 brain regions were recorded(from their website).
| 50 |
TB
|
Multisite Neural Recording Local Field Potential(LFP)
|
Ken Fahmidur Rahman
|
a77b360636984b4ea3391c1757cdca50
| 2025-09-19T19:34:53 |
Insect-1M
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https://uark-cviu.github.io/projects/insect-foundation/
|
Large-scale Insect Dataset comprising more than a million images with hierarchical labels from the high to the low taxonomy level, including class, order, family, genus, and species. Paper: https://arxiv.org/abs/2502.09906
| 100 |
GB
|
Entomology
|
Mohammed Hamdy
|
92b0add1ee9b446fb234f13438ea93e0
| 2025-09-20T08:04:57 |
ToT-Biology
|
https://huggingface.co/datasets/moremilk/ToT-Biology
|
The ToT-Biology dataset emphasizes mechanistic understanding and explanatory biological reasoning, rather than just providing correct answers. It aims to train AI models in interpretability and logical deduction within the biological realm. Spanning a wide range of biological complexities, it starts with foundational concepts in cell biology, genetics, and ecology, and progresses to advanced areas like systems biology, synthetic biology, and computational biophysics. The dataset incorporates real-world applications across diverse fields such as medicine, agriculture, environmental science, and bioengineering, ensuring practical relevance alongside rigorous theoretical exploration.
| 80 |
MB
|
Biology
|
SHIVAM DUBEY
|
README.md exists but content is empty.
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Size of downloaded dataset files:
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24.2 kB
Number of rows:
45