| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - materials-science |
| - provenance |
| - graph |
| - PROV-DM |
| - information-extraction |
| pretty_name: MatPROV |
| --- |
| |
| # MatPROV |
|
|
| **MatPROV** is a dataset of materials synthesis procedures extracted from scientific papers using large language models (LLMs) and represented in [PROV-DM](https://www.w3.org/TR/prov-dm/)–compliant structures. |
| Further details on MatPROV are described in our paper "[MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature](https://arxiv.org/abs/2509.01042).” |
|
|
| --- |
|
|
| ## Files |
|
|
| ``` |
| MatPROV/ |
| ├── MatPROV.jsonl # Main dataset (2,367 synthesis procedures) |
| ├── ground-truth/ # Expert-annotated ground truth |
| │ └─ <DOI>.json |
| ├── few-shot/. # Prompt examples used for synthesis procedure extraction |
| │ └─ <DOI>.txt |
| └── doi_status.csv # Status of each paper DOI across the pipeline |
| ``` |
|
|
| *Note: In file names under `ground-truth/` and `few-shot/`, forward slashes (`/`) in DOIs are replaced with underscores (`_`).* |
|
|
| --- |
|
|
| ## Data format |
|
|
| The main dataset file is `MatPROV.jsonl`, where each line corresponds to one paper’s structured record. |
| Each record contains: |
|
|
| - `doi`: DOI of the source paper |
| - `label`: Identifier for the extracted synthesis procedure, encoding the material's chemical composition and key synthesis characteristics (e.g., `CuGaTe2_ball-milling`) |
| - `prov_jsonld`: A PROV-JSONLD structure describing the synthesis procedure |
|
|
| ### Example |
|
|
| ```json |
| { |
| "doi": "10.1002/advs.201600035", |
| "label": "Fe1+xNb0.75Ti0.25Sb_composition variation", |
| "prov_jsonld": { |
| "@context": [ |
| {"xsd": "http://www.w3.org/2001/XMLSchema#", "prov": "http://www.w3.org/ns/prov#"}, |
| "https://openprovenance.org/prov-jsonld/context.jsonld", |
| "URL of MatPROV's context schema omitted for double-blind review" |
| ], |
| "@graph": [ |
| { |
| "@type": "Entity", |
| "@id": "e1", |
| "label": [{"@value": "Fe", "@language": "EN"}], |
| "type": [{"@value": "material"}], |
| "matprov:purity": [{"@value": "99.97%", "@type": "xsd:string"}] |
| } |
| ... |
| ] |
| } |
| } |
| ``` |
|
|
| ## Visualization |
|
|
| You can visualize the PROV-JSONLD data in MatPROV using the online tool at: https://matprov-project.github.io/prov-jsonld-viz/ |
| To do this, copy the value of the `"prov_jsonld"` field from any record in `MatPROV.jsonl` and paste it into the “PROV-JSONLD Editor” panel of the tool. |
| A directed graph of the synthesis procedure will then be generated, as shown in the figure below. |
|
|
|  |
|
|
| ## Dataset construction summary |
|
|
| - Source papers collected: 1648 |
| - **Relevant Text Extraction** |
| - 32 papers contained no synthesis-related text |
| - → 1616 papers remained |
| - **Synthesis Procedure Extraction** |
| - 48 papers contained no synthesis procedure |
| - → 1568 papers remained (final dataset) |
|
|
| The DOIs of these 1568 papers and their extracted data are included in `MatPROV.jsonl`. |
| For details on the filtering status of each DOI, see `doi_status.csv`. |
|
|
| ## Ground Truth annotations |
|
|
| - A subset of papers was manually annotated by a single domain expert. |
| - Files are stored in `ground-truth/` and named as `<DOI>.json`. |
|
|
| ## Few-shot examples |
|
|
| - Prompt examples used for LLM extraction are provided in `few-shot/`. |
| - Files are stored in `few-shot/` and named as `<DOI>.txt`. |
|
|
| ## Links |
|
|
| - Paper: https://arxiv.org/abs/2509.01042 |
| - Code: https://github.com/MatPROV-project/matprov-experiments |
|
|
| ## Citation |
|
|
| If you use MatPROV, please cite: |
|
|
| ``` |
| @inproceedings{tsuruta2025matprov, |
| title={Mat{PROV}: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature}, |
| author={Hirofumi Tsuruta and Masaya Kumagai}, |
| booktitle={NeurIPS 2025 Workshop on AI for Accelerated Materials Design}, |
| year={2025} |
| } |
| ``` |
