Update AgentSLR dataset release with labelled only rows

README.md

@@ -4,7 +4,7 @@ language:
 - en
 viewer: true
 size_categories:
-- 100K<n<1M
+- 10K<n<100K
 license: cc-by-4.0
 configs:
 - config_name: Harvest Metadata and Screening
@@ -24,10 +24,6 @@ configs:
     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
@@ -84,7 +80,7 @@ tags:
 # AgentSLR: Priority Pathogens Dataset
 
 <p align="left">
-  <a href="https://arxiv.org/abs/2603.22327" style="display:inline-flex;vertical-align:top;align-items:center;gap:6px;height:38px;box-sizing:border-box;padding:0 12px;margin:0 8px 8px 0;border:1px solid #d4d4d8;border-radius:6px;text-decoration:none;color:inherit;background:#ffffff;">
+  <a href="https://arxiv.org" style="display:inline-flex;vertical-align:top;align-items:center;gap:6px;height:38px;box-sizing:border-box;padding:0 12px;margin:0 8px 8px 0;border:1px solid #d4d4d8;border-radius:6px;text-decoration:none;color:inherit;background:#ffffff;">
     <span style="display:inline-flex;align-items:center;justify-content:center;width:16px;height:16px;line-height:1;flex:0 0 16px;">
       <img src="assets/arxiv.svg" alt="Paper" width="15" style="display:block;">
     </span>
@@ -104,17 +100,17 @@ tags:
   </a>
 </p>
 
-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.
+This dataset accompanies the paper **Evaluating AI-based Scientific Knowledge Synthesis with Epidemiological Systematic Reviews**. It provides the data component of the AgentSLR evaluation harness. This covers article metadata, human abstract and full text screening labels, and structured human data extractions for epidemiological parameters, transmission models, and outbreaks across WHO 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.
+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.
 
 ![AgentSLR overview](assets/hero.jpg)
 
-***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.*
+***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:
+The released screening labels cover seven priority pathogens:
 
 - Marburg virus
 - Ebola virus
@@ -123,33 +119,31 @@ The release covers nine priority pathogens:
 - 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.
+This release includes `16,248` downloaded article records matched to PERG human screening labels and deduplicated by Covidence ID. It also includes `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](https://github.com/oxrml/agentslr).
+Harvest metadata was generated on **26 January 2026 (UTC)**. The full AgentSLR workflow, covering harvesting, PDF retrieval, OCR and Markdown conversion, screening, full text processing, extraction and report generation, is available on [GitHub](https://github.com/oxrml/agentslr).
 
-> 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).
+> The screening records are the matched subset for which article full texts were downloaded and converted to Markdown during the evaluation run. This release does not redistribute PDFs or OCR text.
 
 ---
 
-## PERG (Humans) and AgentSLR Pathogen Coverage
+## Paper Evaluation Coverage and Released Records
 
-The table below mirrors the review-overlap summary from the [paper](https://arxiv.org).
+The table below mirrors the deduplicated review overlap summary from the paper and the released records. `AgentSLR Matched` denotes the downloaded corpus that matched with PERG labelled subset.
 
-| Pathogen | PERG* | AgentSLR | Matched |
-| --- | ---: | ---: | ---: |
-| <span style="color:#6aa84f;">●</span> Marburg virus | 2,593 | 6,501 | 762 (29.4%) |
-| <span style="color:#6aa84f;">●</span> Ebola virus | 11,605 | 23,226 | 3,938 (33.9%) |
-| <span style="color:#6aa84f;">●</span> Lassa fever | 2,131 | 6,514 | 647 (30.4%) |
-| <span style="color:#6aa84f;">●</span> SARS-CoV-1 | 12,280 | 7,540 | 1,967 (16.0%) |
-| <span style="color:#6aa84f;">●</span> Zika virus | 10,510 | 3,103 | 2,128 (20.2%) |
-| <span style="color:#f1a340;">●</span> MERS-CoV | 19,656 | 23,204 | 5,675 (28.9%) |
-| <span style="color:#f1a340;">●</span> Nipah virus | 1,458 | 5,103 | 664 (45.5%) |
-| <span style="color:#d6604d;">●</span> Rift Valley fever virus | - | 6,810 | - |
-| <span style="color:#d6604d;">●</span> CCHF virus | - | 3,478 | - |
-| **Total<sup>†</sup>** | **60,233** | **75,191** | **15,781 (26.2%)** |
+| Pathogen | PERG* | AgentSLR Matched |
+| --- | ---: | ---: |
+| <span style="color:#6aa84f;">●</span> Marburg virus | 2,593 | 801 (30.9%) |
+| <span style="color:#6aa84f;">●</span> Ebola virus | 11,605 | 4,119 (35.5%) |
+| <span style="color:#6aa84f;">●</span> Lassa fever | 2,131 | 667 (31.3%) |
+| <span style="color:#6aa84f;">●</span> SARS-CoV-1 | 12,280 | 2,047 (16.7%) |
+| <span style="color:#6aa84f;">●</span> Zika virus | 10,510 | 2,164 (20.6%) |
+| <span style="color:#f1a340;">●</span> MERS-CoV | 19,656 | 5,714 (29.1%) |
+| <span style="color:#f1a340;">●</span> Nipah virus | 1,458 | 736 (50.5%) |
+| <span style="color:#d6604d;">●</span> Rift Valley fever virus | - | - |
+| <span style="color:#d6604d;">●</span> CCHF virus | - | - |
+| **Total<sup>†</sup>** | **60,233** | **16,248 (27.0%)** |
 
 <span style="color:#6aa84f;">●</span> Published PERG review &nbsp;&nbsp; <span style="color:#f1a340;">●</span> In data extraction by PERG &nbsp;&nbsp; <span style="color:#d6604d;">●</span> Screening not yet conducted by PERG
 
@@ -162,12 +156,12 @@ The table below mirrors the review-overlap summary from the [paper](https://arxi
 
 The dataset is organised into four config types:
 
-- **Harvest Metadata and Screening**: one config with nine pathogen splits
+- **Harvest Metadata and Screening**: one config with seven 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.
+The harvest config contains downloaded PERG matched screening rows, deduplicated by `covidence_id`. The `perg_subset` and `downloaded` columns are `True` for all released screening records. 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.
 
@@ -189,9 +183,9 @@ lassa_outbreaks = load_dataset(repo_id, "Outbreak Extraction - Lassa")
 
 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](https://docs.openalex.org/additional-help/faq)) and notes that original copyright remains with the source for PDFs ([full-text PDF docs](https://docs.openalex.org/download-all-data/full-text-pdfs)). PubMed provides citations and abstracts rather than full-text articles ([About PubMed](https://pubmed.ncbi.nlm.nih.gov/about/)), and NLM does not claim copyright on PubMed abstracts, though publishers or authors may retain rights in the underlying materials ([NCBI Policies](https://www.ncbi.nlm.nih.gov/home/about/policies/), [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/)).
+The legal status of underlying sources is not uniform. OpenAlex releases its data under **CC0** ([FAQ](https://docs.openalex.org/additional-help/faq)) and notes that original copyright remains with the source for PDFs ([OpenAlex PDF docs](https://docs.openalex.org/download-all-data/full-text-pdfs)). PubMed provides citations and abstracts rather than full text articles ([About PubMed](https://pubmed.ncbi.nlm.nih.gov/about/)), and NLM does not claim copyright on PubMed abstracts, though publishers or authors may retain rights in the underlying materials ([NCBI Policies](https://www.ncbi.nlm.nih.gov/home/about/policies/), [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/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.
+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 and 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.*
 
@@ -202,14 +196,10 @@ This release provides metadata and structured outputs only. Downstream redistrib
 If you use the paper, dataset or codebase, please cite our paper:
 
 ```bibtex
-@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}, 
+@article{padarha2026agentslr,
+  title={AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI},
+  author={Padarha, Shreyansh and Kearns, Ryan Othniel and Naidoo, Tristan and Yang, Lingyi and Borchmann, {\L}ukasz and B{\l}aszczyk, Piotr and Morgenstern, Christian and McCabe, Ruth and Bhatia, Sangeeta and Torr, Philip H. and Foerster, Jakob and Hale, Scott A. and Rawson, Thomas and Cori, Anne and Semenova, Elizaveta and Mahdi, Adam},
+  year={2026}
 }
 ```
 
@@ -223,4 +213,4 @@ When citing our work, please also cite the `epireview` R package, which underpin
   note = {R package version 1.4.4},
   url = {https://github.com/mrc-ide/epireview}
 }
-```
+```

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