---
pretty_name: RxnMiner Dataset (reactions extracted from RxnCaption-15k)
license: cc-by-nc-sa-4.0
language:
- en
tags:
- chemistry
- reaction-extraction
- reactions
- silver-standard
- agent-trajectories
task_categories:
- image-to-text
size_categories:
- 10K<n<100K
configs:
- config_name: default
  default: true
  data_files:
  - split: train
    path: data/train-*.parquet
- config_name: trajectories
  data_files:
  - split: train
    path: trajectories/train-*.parquet
---

# RxnMiner Dataset

**129,778 concrete reactions and 15,950 generic templates** extracted by RxnMiner, without human intervention, from
**15,000 literature reaction figures** (the training split of RxnCaption-15k). Every record is linked to the figure it
came from, and the full agent session that produced it is released alongside.

This is a *silver* dataset: it is machine-extracted and has not been reviewed. For reference, RxnMiner with the same
backbone scores 71.0 macro reaction F1 (stereochemistry-aware) on the expert-reviewed benchmark
[UniParser/RxnMiner-Benchmark](https://huggingface.co/datasets/UniParser/RxnMiner-Benchmark); that score is measured on
a different set of figures and is not an estimate of the error rate of this dataset. No image in this dataset is
byte-identical to a benchmark figure (compared by SHA-256).

It accompanies the paper *RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures*
([arXiv:2611.02826](https://arxiv.org/abs/2611.02826)). Code: [github.com/rxnminer/RxnMiner](https://github.com/rxnminer/RxnMiner).

The paper describes an earlier run over the RxnCaption-11k training split (10,107 figures, 88,202 reaction records).
This release is a later run of RxnMiner over all 15,000 figures of the RxnCaption-15k training split, so its counts
differ from those reported in the paper.

| | |
|---|---|
| Figures | 15,000 (all runs finished with a complete answer) |
| Concrete reactions | 129,778 |
| Generic templates | 15,950 |
| Model | Qwen3.8-Flash-Next, deployed locally; one agent run per figure |
| Run cost | 717,187 model calls; 53.6 B input tokens, 0.85 B output tokens; median 17.5 min per figure (from `elapsed_sec`) |

## Configurations

**`default`** (5.2 GB): one row per figure.

| Field | Type | Description |
|---|---|---|
| `id` | string | Figure id (the RxnCaption-15k image name without extension) |
| `image` | image | The source figure |
| `extraction` | string (JSON) | RxnMiner's answer in the unified JSON format: `selected_examples` = concrete reactions, `reaction_templates` = generic schemes with R groups, `reaction_graph` = multistep links. Each molecule carries `smiles` and the byte-exact `E-SMILES`; `_source` records how the structure was obtained |
| `plan` | string (JSON) | The plan the agent wrote for the figure before extracting |
| `num_reactions` | int | Concrete reactions in `selected_examples` |
| `num_templates` | int | Generic templates in `reaction_templates` |
| `is_multistep` | bool | The figure shows a multistep route |
| `answer_complete` | bool | The run ended with a complete answer |
| `model` | string | Model behind the agent |
| `elapsed_sec` | float | Wall-clock time of the run |
| `tool_calls`, `llm_calls` | int | Tool calls and model calls in the run |
| `input_tokens`, `output_tokens`, `reasoning_tokens` | int | Token usage of the run |
| `source_dataset` | string | Always `RxnCaption-15k (train_set)` |

**`trajectories`** (27 GB): `id` and `trajectory`, the full agent session as JSON lines (every model message, tool
call and tool result). Credentials and internal hostnames, IP addresses and file paths that appeared in tool output
were replaced with `[REDACTED]`, `[INTERNAL-IP]` or `[INTERNAL-PATH]` (103 of the 15,000 sessions were affected).
Unlike the benchmark evaluation reported in the paper, which ran without external network access, these corpus runs
had internet access. In fewer than 100 of the 15,000 sessions the agent queried public chemistry databases (for
example PubChem) or literature search services; those calls are visible in the trajectories.

```python
import json
from datasets import load_dataset

ds = load_dataset("UniParser/RxnMiner-Dataset", split="train")
record = json.loads(ds[0]["extraction"])
for rxn in record["selected_examples"]:
    print([m.get("smiles") for m in rxn["reactants"]], ">>", [m.get("smiles") for m in rxn["products"]])

# Agent sessions are large; stream them instead of downloading all 27 GB.
traj = load_dataset("UniParser/RxnMiner-Dataset", "trajectories", split="train", streaming=True)
```

## License and attribution

The extracted records and trajectories are released under
[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). The figures come from the training split of
RxnCaption-15k ([arXiv:2511.02384](https://arxiv.org/abs/2511.02384)) and remain subject to its terms and to the
rights of the original publishers. Please cite RxnCaption when you use the figures, and the RxnMiner paper when you use
the extracted records or trajectories.

## Citation

```bibtex
@misc{hu2026rxnminer,
  title         = {RxnMiner: A Multimodal Agent for Structured Reaction Data Extraction from Chemistry Figures},
  author        = {Hu, Jinbo and Zhang, Songren and Fu, Chuanxun and Huang, Jinrui and Wong, Zhen Hao and Cui, Dongxu and
                   Fan, Haoran and Ge, Muhao and Jian, Zhen and Zhou, Jian and Luo, Chengxiang and Lyu, Han and Tao, Haoyi and
                   Huang, Chaozheng and Li, Hanzheng and Wang, ZiChen and Ma, Hengtian and Dai, Jinyang and Wang, Nan and
                   Hong, Yanhui and Fang, Xi and Ke, Guolin},
  year          = {2026},
  eprint        = {2611.02826},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2611.02826}
}
```