Datasets:

stanjsx
/

NeuroClaimBench

Install CONFIRM using the quickstart. Run the commands below from the benchmark directory containing cases.jsonl. In the CONFIRM source package, that directory is benchmark/neuroclaimbench-v2.1/.

Verify the benchmark

neuroclaimbench verify --benchmark .

Verification checks SHA-256 checksums, record schemas, unique IDs, links between records, and inventory counts. The summary command performs the same verification and prints the inventory.

Track	Inventory cases	Executable tasks
Scientific claims	332	279
Constructed stress controls	150	150
External-transfer claims and controls	62	60
Total	544	489

Each executable case has one task. The 55 excluded cases comprise 40 with ambiguous question-plan alignment and 15 with unavailable analysis inputs or invalid designs. The benchmark has 268 score-eligible references: 96 literature references and 172 constructed controls. The 221 executable tasks with unresolved references contribute to coverage counts and are excluded from reference-agreement metrics.

Load records in Python

from bench.benchmark import BenchmarkDataset

benchmark = BenchmarkDataset(".")
cases = {case.benchmark_case_id: case for case in benchmark.cases}
references = {ref.reference_id: ref for ref in benchmark.references}

task = benchmark.tasks[0]
case = cases[task.benchmark_case_id]
reference = references[case.reference_id]
print(case.question)
print(task.contract.model_dump())
print(reference.disposition, reference.score_eligible)

The loader validates record schemas. Use neuroclaimbench verify for checksum and cross-record checks. Join tasks and outcomes on task_id; join a task to its case through benchmark_case_id, then to its reference through reference_id. The data dictionary defines the files and fields.

Score recorded CONFIRM decisions

neuroclaimbench predictions --benchmark . \
  --standard full --out ../outputs/confirm-full.jsonl
neuroclaimbench score --benchmark . \
  --predictions ../outputs/confirm-full.jsonl

Choose screen, replicate, or full. The predictions command applies the selected standard to the recorded gate vectors. It writes a new file and exports an error row for any outcome lacking a completed gate vector.

Score another system

Give your system the question, frozen analysis plan, and required data. Reserve the reference labels and recorded CONFIRM outcomes for evaluation. Write one JSON object per task to a JSONL file:

{"task_id": "<exact task_id from tasks.jsonl>", "status": "completed", "supported": false}

Use a JSON boolean for supported. For an execution failure or a missing verdict, use an error row:

{"task_id": "<exact task_id from tasks.jsonl>", "status": "error", "error": "Input cohort unavailable"}

Pass the file to neuroclaimbench score --benchmark . --predictions FILE. The scorer requires unique, known task IDs and complete coverage by default. --allow-partial scores a subset and reports omitted tasks. Use complete predictions when comparing systems.

Metrics and error handling

Metric	Numerator	Denominator
Literature recall	Supported decisions	Evaluated literature references labeled `confirm`
Literature false support	Supported decisions	Evaluated literature references labeled `abstain`
Control false support	Supported decisions	Evaluated constructed controls

Under the paper's scoring rule, execution errors count as withheld and remain in the evaluated denominator. Output reports their count separately from completed decisions. Missing tasks are excluded from denominators when --allow-partial is specified.

Metrics are grouped by track. Literature metrics are also grouped by reference strength: strict, provisional, and combined. Each metric reports supported, evaluated, completed, eligible, errors, and missing counts. evaluated includes submitted error rows; completed counts valid decisions. A rate is null when its denominator is zero. The output's partial flag indicates missing tasks or execution errors.

The scorer returns point estimates. The distributed summary tables also provide the paper's Wilson and cluster-bootstrap intervals for recorded results.

Match the paper's scalar comparison

The main scientific comparison uses 46 scalar literature claims: 31 labeled confirm and 15 labeled abstain. The default all scope also includes brain-wide claims, giving 51 and 19 scientific literature references, respectively. Select the scalar scope to match the main comparison:

neuroclaimbench score --benchmark . \
  --predictions ../outputs/confirm-full.jsonl --literature-scope scalar

This filters literature metrics to scalar tasks and includes all constructed controls. Supply all 489 predictions; the scorer selects the metric subsets. Expected results for the scientific scalar set and the 150 stress controls:

Standard	Literature recall	Literature false support	Control false support
Screen	19/31	6/15	5/150
Replicate	15/31	2/15	1/150
Full	15/31	2/15	0/150

Literature reference agreement and CONFIRM support measure different things: references summarize the adjudicated literature evidence, while CONFIRM support means the selected reporting checks passed.

Execute tasks

Obtain the prepared inputs specified in the data-access guide, then run:

neuroclaimbench run --benchmark . --data-root path/to/frozen-cohorts \
  --standard full --out ../outputs/rerun

Supply additional --data-root arguments for separate directories. Each task's discovery and replication files must be together in one supplied directory. Repeat --task-id to select specific tasks. Use a new output directory for each run.

The command verifies cohort hashes before execution and writes per-task results and predictions.jsonl. Unavailable or mismatched inputs produce error rows. Exit code 1 indicates at least one failed task. Score the output with the same score command; include --allow-partial when running a subset.

File integrity and provenance

SHA256SUMS covers every distributed file except itself. The manifests identify the package contents, schema, and record hashes. Source paths and hashes inside scientific records identify the artifacts used to construct the benchmark. Runtime input files are located through --data-root.