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| pretty_name: NeuroClaimBench | |
| language: | |
| - en | |
| license: cc-by-4.0 | |
| size_categories: | |
| - n<1K | |
| tags: | |
| - neuroimaging | |
| - scientific-reporting | |
| - benchmark | |
| - claim-validation | |
| configs: | |
| - config_name: cases | |
| default: true | |
| data_files: | |
| - split: test | |
| path: cases.jsonl | |
| - config_name: references | |
| data_files: | |
| - split: test | |
| path: references.jsonl | |
| - config_name: tasks | |
| data_files: | |
| - split: test | |
| path: tasks.jsonl | |
| - config_name: outcomes | |
| data_files: | |
| - split: test | |
| path: outcomes.jsonl | |
| # NeuroClaimBench v2.1 | |
| NeuroClaimBench evaluates whether a system supports or withholds a | |
| neuroimaging claim. | |
| The package contains 489 executable tasks: 279 scientific claims, 150 | |
| constructed stress controls, and 60 external-transfer claims and controls. | |
| The inventory also includes 55 cases excluded from execution because of | |
| ambiguous question-plan alignment, unavailable analysis inputs, or invalid designs. | |
| Questions, analysis plans, reference labels, and recorded CONFIRM outcomes | |
| are provided as JSONL files. | |
| **Code:** [CONFIRM](https://github.com/yuxiangwei0808/CONFIRM) · **Dataset:** [Hugging Face](https://huggingface.co/datasets/stanjsx/NeuroClaimBench) | |
| ## Load from Hugging Face | |
| Install the dataset library: | |
| ```bash | |
| python -m pip install datasets | |
| ``` | |
| Load an individual record collection: | |
| ```python | |
| from datasets import load_dataset | |
| tasks = load_dataset("stanjsx/NeuroClaimBench", "tasks", split="test", revision="v2.1.1") | |
| print(tasks[0]["task_id"]) | |
| print(tasks[0]["contract"]) | |
| ``` | |
| The configurations are `cases` (544 rows), `references` (544), `tasks` (489), | |
| and `outcomes` (489). Each uses a single `test` split. The default configuration | |
| is `cases`; these are record collections for evaluation, with links described | |
| in the [data dictionary](DATA_DICTIONARY.md). | |
| To download the complete benchmark for the command-line tools: | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| repo_id="stanjsx/NeuroClaimBench", | |
| repo_type="dataset", | |
| revision="v2.1.1", | |
| local_dir="neuroclaimbench-v2.1", | |
| ) | |
| ``` | |
| ## Install the tools | |
| Use Python 3.10 or newer. CONFIRM supplies the `neuroclaimbench` command. | |
| Download `confirm-0.1.1-py3-none-any.whl` from the | |
| [CONFIRM v0.1.1 release](https://github.com/yuxiangwei0808/CONFIRM/releases/tag/v0.1.1) | |
| into the directory containing the extracted `neuroclaimbench-v2.1` folder. | |
| Run from that parent directory: | |
| ```bash | |
| python -m venv .venv | |
| source .venv/bin/activate | |
| python -m pip install ./confirm-0.1.1-py3-none-any.whl | |
| ``` | |
| To install from source, | |
| extract `confirm-0.1.1.zip` and run `python -m pip install ./confirm-0.1.1` | |
| from its parent directory. An existing CONFIRM installation provides the | |
| same benchmark commands. | |
| ## Verify and score | |
| Run from the directory containing the extracted benchmark folder: | |
| ```bash | |
| neuroclaimbench verify --benchmark neuroclaimbench-v2.1 | |
| neuroclaimbench predictions --benchmark neuroclaimbench-v2.1 \ | |
| --standard full --out outputs/confirm-full.jsonl | |
| neuroclaimbench score --benchmark neuroclaimbench-v2.1 \ | |
| --predictions outputs/confirm-full.jsonl | |
| ``` | |
| These commands verify all files, export 489 decisions from the recorded | |
| gate results, and score agreement with the reference labels. They require | |
| no cohort data or model calls. Choose a new output filename for each export. | |
| ## Guides | |
| - [Python loading, metrics, and evaluation](USAGE.md) | |
| - [File and field definitions](DATA_DICTIONARY.md) | |
| - [Cohort data and exact-input requirements](DATA_ACCESS.md) | |
| Executing a task requires its corresponding prepared cohort files. The | |
| metadata package contains no participant tables, random-control assignment | |
| tables, or PubMed abstracts. | |