datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
biosses-sts
BIOSSES
An MTEB dataset
Massive Text Embedding Benchmark
Biomedical Semantic Similarity Estimation.
Task category
t2t
Domains
Medical
Reference
https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["BIOSSES"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biosses-sts.bio-mqm-datasetThis dataset is compiled from the official Amazon repository (all respective licensing applies).
It contains system translations, multiple references, and their quality evaluation on the MQM scale. It accompanies the ACL 2024 paper Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains.
Watch a brief 4 minutes-long video.
Abstract: We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this… See the full description on the dataset page: https://huggingface.co/datasets/zouhar/bio-mqm-dataset.biology
CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society
Github: https://github.com/lightaime/camel
Website: https://www.camel-ai.org/
Arxiv Paper: https://arxiv.org/abs/2303.17760
Dataset Summary
Biology dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 biology topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
We provide… See the full description on the dataset page: https://huggingface.co/datasets/camel-ai/biology.BioTrove
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
Description
See the BioTrove-Train dataset card on HuggingFace to access the samller BioTrove-Train dataset (40M)
BioTrove comprises well-processed metadata with full taxa information and URLs pointing to image files. The metadata can be used to filter specific categories, visualize data distribution, and manage imbalance effectively. We provide a collection of… See the full description on the dataset page: https://huggingface.co/datasets/BGLab/BioTrove.bio-mcp-data
Bio-MCP-Data
A repository containing biological datasets that will be used by BIO-MCP MCP (Model Context Protocol) standard.
About
This repository hosts biological data assets formatted to be compatible with the Model Context Protocol, enabling AI models to efficiently access and process biological information. The data is managed using Git Large File Storage (LFS) to handle large biological datasets.
Purpose
Provide standardized biological datasets for AI… See the full description on the dataset page: https://huggingface.co/datasets/longevity-genie/bio-mcp-data.SPACCC_Tokenizer
The Tokenizer for Clinical Cases Written in Spanish
Introduction
This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish.
This model was created using the Apache… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/SPACCC_Tokenizer.bias_in_bios
Bias in Bios
Bias in Bios was created by (De-Artega et al., 2019) and published under the MIT license (https://github.com/microsoft/biosbias). The dataset is used to investigate bias in NLP models. It consists of textual biographies used to predict professional occupations, the sensitive attribute is the gender (binary).
The version shared here is the version proposed by (Ravgofel et al., 2020) which slightly smaller due to the unavailability of 5,557 biographies.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/LabHC/bias_in_bios.biomedical_lectures_v2
Vidore Benchmark 2 - MIT Dataset (Multilingual)
This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of MIT courses in anatomy (precisely tissue interactions).
Dataset Summary
The dataset contain queries in the following languages : ["english", "french", "german", "spanish"]. Each query was originaly in "english" (see… See the full description on the dataset page: https://huggingface.co/datasets/vidore/biomedical_lectures_v2.biorxiv-clustering-p2p
BiorxivClusteringP2P.v2
An MTEB dataset
Massive Text Embedding Benchmark
Clustering of titles+abstract from biorxiv across 26 categories.
Task category
t2c
Domains
Academic, Written
Reference
https://api.biorxiv.org/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["BiorxivClusteringP2P.v2"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biorxiv-clustering-p2p.MedThinkVQA
MedThinkVQA
MedThinkVQA is an expert-annotated benchmark for multi-image diagnostic reasoning in radiology. Unlike prior medical VQA benchmarks that typically contain at most one image per case, MedThinkVQA requires models to extract evidence from each image, integrate cross-view information, and perform differential-diagnosis reasoning.
Links
GitHub: https://github.com/benluwang/MedThinkVQA
Leaderboard: https://benluwang.github.io/MedThinkVQA/
Submission Guide:… See the full description on the dataset page: https://huggingface.co/datasets/bio-nlp-umass/MedThinkVQA.BioRel
Dataset Card for BioRel
Dataset Summary
BioRel Dataset Summary:
BioRel is a comprehensive dataset designed for biomedical relation extraction, leveraging the vast amount of electronic biomedical literature available.
Developed using the Unified Medical Language System (UMLS) as a knowledge base and Medline articles as a corpus, BioRel utilizes Metamap for entity identification and linking, and employs distant supervision for relation labeling.
The training set… See the full description on the dataset page: https://huggingface.co/datasets/DFKI-SLT/BioRel.HESRT
HESRT
Human-reviewed, accession-level spatial-omics artifacts with tissue images, expression matrices and source provenance.
Samples (GSMs)
Studies (GSEs)
Expression observations
Compressed packages
4,468
507
37,054,005
413.94 GB
Each sample is a self-contained, checksummed tar.zst package. Browse the catalog, select the accessions you need, then download those samples. The full collection contains approximately 647.72 GB of uncompressed member data; do not clone… See the full description on the dataset page: https://huggingface.co/datasets/Biogod/HESRT.CodiEsp_corpus
Introduction
These are the train, development, test and background sets of the CodiEsp corpus. Train and development have gold standard annotations. The unannotated background and test sets are distributed together. All documents are released in the context of the CodiEsp track for CLEF ehealth 2020 (http://temu.bsc.es/codiesp/).
The CodiEsp corpus contains manually coded clinical cases. All documents are in Spanish language and CIE10 is the coding terminology (it is the Spanish… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/CodiEsp_corpus.agieval-gaokao-biology
Dataset Card for "agieval-gaokao-biology"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.
This dataset contains the contents of the Gaokao Biology subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .
Citation:
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}… See the full description on the dataset page: https://huggingface.co/datasets/hails/agieval-gaokao-biology.wikipedia-biology
Dataset Card for wikipedia-biology
Dataset Summary
The dataset consists of text from 87045 Wikipedia articles created by processing all articles in the Wikipedia categories Branches of biology, Biological concepts, Eukaryote biology and Biology terminology, as well as their subcategories recursively till a depth of 4. It was originally created for the purpose of unlearning the domain of biology, although it may be used for other purposes such as biology fine-tuning.
It… See the full description on the dataset page: https://huggingface.co/datasets/jd5697/wikipedia-biology.Bio-Domain-Transfertrustworthy-biology-agents-traces
Trustworthy Biology Agents — Run Traces
Raw execution traces from 1,329 agent runs across three coding agents on three
biology benchmarks — BiomniBench-DA, BixBench, and CompBioBench. This is the scrubbed
trace bundle for the study in
manu-tej/ai-scientists; the write-up
lives in that repo's RESULTS.md.
The motivating question is not only whether an agent reaches the right answer, but
whether it behaves like a trustworthy analyst when the task is ambiguous,
under-specified, or… See the full description on the dataset page: https://huggingface.co/datasets/amanutej/trustworthy-biology-agents-traces.wmdp-bio-forget-corpusBioT5_finetune_dataset
References
For more information, please refer to our paper and GitHub repository.
Paper:
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
GitHub: https://github.com/QizhiPei/BioT5
BioTrove-Train
BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity
Description
See the BioTrove dataset card on HuggingFace to access the main BioTrove dataset (161.9M)
BioTrove comprises well-processed metadata with full taxa information and URLs pointing to image files. The metadata can be used to filter specific categories, visualize data distribution, and manage imbalance effectively. We provide a collection of software… See the full description on the dataset page: https://huggingface.co/datasets/BGLab/BioTrove-Train.ProfNER_corpus_NER
Description
Gold standard annotations for profession detection in Spanish COVID-19 tweets
The entire corpus contains 10,000 annotated tweets. It has been split into training, validation, and test (60-20-20). The current version contains the training and development set of the shared task with Gold Standard annotations. In addition, it contains the unannotated test, and background sets will be released.
For Named Entity Recognition, profession detection, annotations are distributed… See the full description on the dataset page: https://huggingface.co/datasets/Biomedical-TeMU/ProfNER_corpus_NER.bioburden_labelled_datapubmed_bioasq_2022
PubMed BioASQ 2022 Corpus
This dataset contains the PubMed abstracts corpus from the BioASQ 2022 challenge, comprising approximately 23 million biomedical documents with MeSH (Medical Subject Headings) annotations.
Purpose
This is a convenience collection of the PubMed corpus from the BioASQ 2022 Challenge, reformatted for easier use in retrieval and QA systems. The original source is the BioASQ challenge data. We created this processed version with multiple formats (JSON… See the full description on the dataset page: https://huggingface.co/datasets/jmhb/pubmed_bioasq_2022.daily-bio-newsBiomed-Enriched
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
Dataset Authors
Rian Touchent, Nathan Godey & Eric de la ClergerieSorbonne Université, INRIA Paris
Overview
Biomed-Enriched is a PubMed-derived dataset created using a two-stage annotation process. Initially, Llama 3.1 70B Instruct annotated 400K paragraphs for document type, domain, and educational quality. These annotations were then… See the full description on the dataset page: https://huggingface.co/datasets/almanach/Biomed-Enriched.bio-lens
🌿 iNaturalist Bronze Dataset (Research-Grade, Deduplicated)
Description
This dataset contains research-grade observations from iNaturalist, processed through a bronze-layer pipeline that includes:
It's a curated dataset for biodiversity analysis based on community sourced observations, contains millions of images thus can be used for biology model training.
The intent of this data is to train specialist vision models capable of identifying species with high… See the full description on the dataset page: https://huggingface.co/datasets/HirakoSan/bio-lens.agieval-gaokao-biology
Dataset Card for "agieval-gaokao-biology"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
MIT License
Copyright (c) Microsoft Corporation.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell… See the full description on the dataset page: https://huggingface.co/datasets/dmayhem93/agieval-gaokao-biology.biorxiv-clustering-s2s
BiorxivClusteringS2S.v2
An MTEB dataset
Massive Text Embedding Benchmark
Clustering of titles from biorxiv across 26 categories.
Task category
t2c
Domains
Academic, Written
Reference
https://api.biorxiv.org/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["BiorxivClusteringS2S.v2"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/biorxiv-clustering-s2s.Bioinformatics
🔭 Overview
R2MED: First Reasoning-Driven Medical Retrieval Benchmark
R2MED is a high-quality, high-resolution synthetic information retrieval (IR) dataset designed for medical scenarios. It contains 876 queries with three retrieval tasks, five medical scenarios, and twelve body systems.
Dataset
#Q
#D
Avg. Pos
Q-Len
D-Len
Biology
103
57359
3.6
115.2
83.6
Bioinformatics77
47473
2.9
273.8
150.5
Medical Sciences
88
34810
2.8
107.1
122.7
MedXpertQA-Exam
97… See the full description on the dataset page: https://huggingface.co/datasets/R2MED/Bioinformatics.BioMed-R1-Eval
Disentangling Reasoning and Knowledge in Medical Large Language Models
This is the evaluation dataset accompanying our paper, comprising 11 publicly available biomedical benchmarks. We disentangle each benchmark question into either medical reasoning or medical knowledge categories.
Additionally, we provide a set of adversarial reasoning traces designed to evaluate the robustness of medical reasoning models.
For more details, please refer to our GitHub.
If you find this work useful… See the full description on the dataset page: https://huggingface.co/datasets/zou-lab/BioMed-R1-Eval.