| # 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 | |