domsoos/replicatorbench

LLM Benchmarking Project — Dataset (Scientific Replication Benchmark)

This repository contains the data-only portion of the Center for Open Science (COS) LLM Benchmarking Project. The dataset supports benchmarking LLM agents on core parts of the scientific research lifecycle—especially replication—including:

• Information extraction from scientific papers into structured JSON
• Research design and analysis planning
• (Optional) execution support using provided replication datasets and code
• Scientific interpretation using human reference materials and expected outputs

Each numbered folder corresponds to one study instance in the benchmark.

Dataset contents (per study)

Each study folder typically contains:

• original_paper.pdf
  The published paper used as the primary input.

• initial_details.txt
  Brief notes to orient the replication attempt (e.g., key outcomes, hints, pointers).

• replication_data/
  Data and scripts required to reproduce analyses (common formats: .csv, .dta, .rds, .R, .do, etc.).

• human_preregistration.(pdf|docx)
  Human-created preregistration describing the replication plan.

• human_report.(pdf|docx)
  Human-created replication report describing analyses and findings.

• expected_post_registration*.json
  Expert-annotated ground truth structured outputs used for evaluation.

  • expected_post_registration.json is the primary reference.
  • expected_post_registration_2.json, _3.json, etc. are acceptable alternative variants where applicable.

Some studies include multiple acceptable ground-truth variants to capture permissible differences in annotation or representation.

Repository structure

At the dataset root, folders like 1/, 2/, 10/, 11/, etc. are study IDs.

Example:

text
.
├── 1/
│   ├── expected_post_registration.json
│   ├── expected_post_registration_2.json
│   ├── human_preregistration.pdf
│   ├── human_report.pdf
│   ├── initial_details.txt
│   ├── original_paper.pdf
│   └── replication_data/
│       ├── <data files>
│       └── <analysis scripts>

Intended uses

This dataset is intended for:

• Benchmarking LLM agents that extract structured study metadata from papers
• Evaluating LLM systems that generate replication plans and analysis specifications
• Comparing model outputs against expert-annotated expected JSON and human reference docs

Not intended for

• Clinical or other high-stakes decision-making
• Producing definitive judgments about the original papers
• Training models to reproduce copyrighted texts verbatim

Quickstart (local)

Iterate over studies and load ground truth

python
from pathlib import Path
import json

root = Path(".")
study_dirs = sorted(
    [p for p in root.iterdir() if p.is_dir() and p.name.isdigit()],
    key=lambda p: int(p.name)
)

for study in study_dirs:
    gt = study / "expected_post_registration.json"
    if gt.exists():
        data = json.loads(gt.read_text(encoding="utf-8"))
        print(study.name, "ground truth keys:", list(data.keys())[:10])

Using with the main pipeline repository (recommended)

If you are using the LLM Benchmarking Project codebase, point the pipeline/evaluators at a given study directory:

bash
make evaluate-extract STUDY=/path/to/llm-benchmarking-data/1

The expected JSON format is defined by the main repository’s templates/schemas. Use those schemas to validate or format model outputs.

Notes on multiple expected JSON variants

Some studies include expected_post_registration_2.json, expected_post_registration_3.json, etc. This is intentional:

• Some fields allow multiple equivalent representations
• Annotation may vary slightly without changing meaning
• Evaluators may accept any variant depending on scoring rules

If you implement your own scorer, consider:

• Exact matching for strictly defined fields
• More tolerant matching for lists, notes, or fields with legitimate paraphrase/format variation

File formats

You may encounter:

• R artifacts: .R, .rds
• Stata artifacts: .do, .dta
• CSV/tabular data: .csv
• Documents: .pdf, .docx
• Structured evaluation targets: .json

Reproducing analyses may require R and/or Stata depending on the study.

Licensing, copyright, and redistribution (important)

This repository is released under Apache 2.0 for COS-authored materials and annotations (for example: benchmark scaffolding, expected JSON outputs, and other COS-created files).

However, some contents may be third-party materials, including (but not limited to):

• original_paper.pdf (publisher copyright may apply)
• replication_data/ (may have its own license/terms from the original authors)
• external scripts or datasets included for replication

You are responsible for ensuring you have the right to redistribute third-party files publicly (e.g., GitHub / Hugging Face).

Common options if redistribution is restricted:

• Remove third-party PDFs and provide DOI/URL references instead
• Keep restricted files in a private location and publish only COS-authored annotations
• Add per-study LICENSE / NOTICE files inside each study folder where terms are known

Large files (Git LFS recommendation)

If hosting on GitHub, consider Git LFS for PDFs and large datasets:

bash
git lfs install
git lfs track "*.pdf" "*.dta" "*.rds"
git add .gitattributes

Citation

If you use this dataset in academic work, please cite it as:

bibtex
@dataset{cos_llm_benchmarking_data_2026,
  author    = {Center for Open Science},
  title     = {LLM Benchmarking Project: Scientific Replication Benchmark Data},
  year      = {2026},
  publisher = {Center for Open Science},
  note      = {Benchmark dataset for evaluating LLM agents on scientific replication tasks}
}

Acknowledgements

This project is funded by Coefficient Giving as part of its “Benchmarking LLM Agents on Consequential Real-World Tasks” program. We thank the annotators who contributed to the ground-truth post-registrations for the extraction stage.

Contact

For questions about this dataset:

Shakhlo Nematova
Research Scientist, Center for Open Science
shakhlo@cos.io