| --- |
| 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} |
| } |
| ``` |
|
|
