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---
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}
}
``` |