Datasets:

OxRML
/

AgentSLR

pretty_name: 'AgentSLR: Priority Pathogens Dataset'
language:
  - en
viewer: true
size_categories:
  - 100K<n<1M
license: cc-by-4.0
configs:
  - config_name: Harvest Metadata and Screening
    default: true
    data_files:
      - split: marburg
        path: data/harvest_metadata_and_screening/marburg.parquet
      - split: ebola
        path: data/harvest_metadata_and_screening/ebola.parquet
      - split: lassa
        path: data/harvest_metadata_and_screening/lassa.parquet
      - split: sars
        path: data/harvest_metadata_and_screening/sars.parquet
      - split: zika
        path: data/harvest_metadata_and_screening/zika.parquet
      - split: mers
        path: data/harvest_metadata_and_screening/mers.parquet
      - split: nipah
        path: data/harvest_metadata_and_screening/nipah.parquet
      - split: rvf
        path: data/harvest_metadata_and_screening/rvf.parquet
      - split: cchf
        path: data/harvest_metadata_and_screening/cchf.parquet
  - config_name: Parameter Extraction - Ebola
    data_files:
      - split: ebola
        path: data/parameter_extractions_ebola/ebola.parquet
  - config_name: Parameter Extraction - Lassa
    data_files:
      - split: lassa
        path: data/parameter_extractions_lassa/lassa.parquet
  - config_name: Parameter Extraction - SARS
    data_files:
      - split: sars
        path: data/parameter_extractions_sars/sars.parquet
  - config_name: Parameter Extraction - Zika
    data_files:
      - split: zika
        path: data/parameter_extractions_zika/zika.parquet
  - config_name: Transmission Model Extraction - Ebola
    data_files:
      - split: ebola
        path: data/transmission_model_extractions_ebola/ebola.parquet
  - config_name: Transmission Model Extraction - Lassa
    data_files:
      - split: lassa
        path: data/transmission_model_extractions_lassa/lassa.parquet
  - config_name: Transmission Model Extraction - SARS
    data_files:
      - split: sars
        path: data/transmission_model_extractions_sars/sars.parquet
  - config_name: Transmission Model Extraction - Zika
    data_files:
      - split: zika
        path: data/transmission_model_extractions_zika/zika.parquet
  - config_name: Outbreak Extraction - Lassa
    data_files:
      - split: lassa
        path: data/outbreak_extractions_lassa/lassa.parquet
  - config_name: Outbreak Extraction - Zika
    data_files:
      - split: zika
        path: data/outbreak_extractions_zika/zika.parquet
task_categories:
  - question-answering
  - text-classification
  - table-question-answering
tags:
  - AI4Science
  - Epidemiology
  - Agents
  - Public-Health
  - Evidence-Synthesis
  - Systematic-Review

AgentSLR: Priority Pathogens Dataset

Paper Codebase Project Website

This dataset accompanies the paper AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI. It brings together large-scale research articles that undergo the scientific rigours required to create systematic literature reviews. We present the metadata of articles, human abstract and article screening labels, and structured human data extractions for epidemiological parameters, transmission models, and outbreaks across WHO-designated priority pathogens.

Human labels in this release come from real-world reviews conducted by the Pathogen Epidemiology Review Group (PERG) at Imperial College London. These labels reflect expert review decisions rather than synthetic annotation, and they ground the evaluation of AgentSLR in operational epidemiological review workflows.

Figure: Data flow through a systematic literature review: a large corpus of harvested articles is progressively filtered through abstract and full-text screening to yield a relevant subset, which then undergoes structured data extraction across three output types (parameters, transmission models and outbreaks) that feed into living review generation.

The release covers nine priority pathogens:

Marburg virus
Ebola virus
Lassa fever
SARS-CoV-1
Zika virus
MERS-CoV
Nipah virus
Rift Valley fever virus
CCHF virus

This release includes 218,325 harvested article records, 37,155 PERG-linked human screening records across seven pathogens, 3,808 human parameter extractions, 687 human transmission-model extractions and 189 human outbreak extractions.

Harvest metadata was generated on 26 January 2026 (UTC). The full AgentSLR toolkit, covering harvesting, PDF retrieval, OCR/PDF-to-Markdown conversion, screening, full-text processing, extraction and report generation, is available on GitHub.

This release contains broad harvesting metadata, but downloadable full text is narrower: roughly 40% of records in the January 2026 harvest yielded a downloadable PDF, with variation driven by open-access status, publisher availability, hosting platform and retrieval route (including proxy and institutional access).

PERG (Humans) and AgentSLR Pathogen Coverage

The table below mirrors the review-overlap summary from the paper.

Pathogen	PERG*	AgentSLR	Matched
● Marburg virus	2,593	6,501	762 (29.4%)
● Ebola virus	11,605	23,226	3,938 (33.9%)
● Lassa fever	2,131	6,514	647 (30.4%)
● SARS-CoV-1	12,280	7,540	1,967 (16.0%)
● Zika virus	10,510	3,103	2,128 (20.2%)
● MERS-CoV	19,656	23,204	5,675 (28.9%)
● Nipah virus	1,458	5,103	664 (45.5%)
● Rift Valley fever virus	-	6,810	-
● CCHF virus	-	3,478	-
Total^†	60,233	75,191	15,781 (26.2%)

● Published PERG review ● In data extraction by PERG ● Screening not yet conducted by PERG

* Articles post deduplication and empty abstract removal.
† Excludes Rift Valley fever virus and CCHF article counts, matching the paper table.

Dataset Organisation

The dataset is organised into four config types:

Harvest Metadata and Screening: one config with nine pathogen splits
Parameter Extraction - {Pathogen}: one config per pathogen
Transmission Model Extraction - {Pathogen}: one config per pathogen
Outbreak Extraction - {Pathogen}: one config per pathogen

The harvest config contains PERG screening labels for all nine pathogens. For RVF and CCHF, screening columns are present but null as PERG labels were not available for this release. Human screening labels are only populated where perg_subset == True. The covidence_id key links screened articles in the harvest table to their corresponding human extraction records.

As data extraction schemas vary by pathogen, each pathogen for which human data extraction has been concluded is published as an individual config on the Hub, covering Ebola, Lassa, SARS and Zika for parameters and transmission models, and Lassa and Zika for outbreaks.

Using datasets:

from datasets import load_dataset

repo_id = "OxRML/AgentSLR"

marburg_harvest = load_dataset(repo_id, "Harvest Metadata and Screening", split="marburg")
ebola_parameters = load_dataset(repo_id, "Parameter Extraction - Ebola", split="ebola")
zika_models = load_dataset(repo_id, "Transmission Model Extraction - Zika")
lassa_outbreaks = load_dataset(repo_id, "Outbreak Extraction - Lassa")

Access, Copyright and Licensing

This repository distributes structured review data, bibliographic metadata, identifiers, URLs and abstracts where present in source records. It does not redistribute publisher PDFs.

The legal status of underlying sources is not uniform. OpenAlex releases its data under CC0 (FAQ) and notes that original copyright remains with the source for PDFs (full-text PDF docs). PubMed provides citations and abstracts rather than full-text articles (About PubMed), and NLM does not claim copyright on PubMed abstracts, though publishers or authors may retain rights in the underlying materials (NCBI Policies, PubMed Disclaimer).

This release provides metadata and structured outputs only. Downstream redistribution of article text or PDFs should follow source-specific rights and licences. To run the full AgentSLR pipeline, use the main codebase for PDF retrieval, OCR/PDF-to-Markdown conversion, full-text screening and structured data extraction.

NOTE: This summary is provided for transparency and reproducibility and should not be treated as legal advice.

Citation

If you use the paper, dataset or codebase, please cite our paper:

@misc{padarha2026agentslr,
      title={AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI}, 
      author={Shreyansh Padarha and Ryan Othniel Kearns and Tristan Naidoo and Lingyi Yang and Łukasz Borchmann and Piotr BŁaszczyk and Christian Morgenstern and Ruth McCabe and Sangeeta Bhatia and Philip H. Torr and Jakob Foerster and Scott A. Hale and Thomas Rawson and Anne Cori and Elizaveta Semenova and Adam Mahdi},
      year={2026},
      eprint={2603.22327},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2603.22327}, 
}

When citing our work, please also cite the epireview R package, which underpins the PERG manual review workflows and structured data schemas this dataset builds on:

@Manual{epireview2025,
  title = {epireview: Tools to update and summarise the latest pathogen data from the Pathogen Epidemiology Review Group (PERG)},
  author = {Tristan Naidoo and Rebecca Nash and Christian Morgenstern and Patrick Doohan and Ruth McCabe and Joshua Lambert and Richard Sheppard and Cosmo Santoni and Thomas Rawson and Shazia Ruybal-Pes{\'a}ntez and Juliette H Unwin and Gina Cuomo-Dannenburg and Kelly McCain and Joseph Hicks and Anne Cori and Sangeeta Bhatia},
  year = {2025},
  note = {R package version 1.4.4},
  url = {https://github.com/mrc-ide/epireview}
}