---
license: odc-by
task_categories:
- text-generation
- question-answering
tags:
- scientific-papers
- arxiv
- citation-prediction
- author-prediction
- collaboration-prediction
- research-forecasting
size_categories:
- 100K<n<1M
language:
- en
---

# PreScience: A Benchmark for Forecasting Scientific Contributions

## Dataset Summary

Can AI systems trained on the scientific record up to a fixed point in time forecast the scientific advances that follow? Such a capability could help researchers identify collaborators and impactful research directions, and anticipate which problems and methods will become central next. We introduce PreScience, a scientific forecasting benchmark that decomposes the research process into four interdependent generative tasks: collaborator prediction, prior work selection, contribution generation, and impact prediction. PreScience is a carefully curated dataset of 98,000 recent AI-related research papers (titles and abstracts), featuring disambiguated author identities, temporally aligned scholarly metadata, and a structured graph of companion author publication histories and citations spanning 502,000 total papers. 

## Dataset Statistics

| Split | Target Papers | Total Papers | Unique Authors | Date Range |
|-------|--------------|--------------|----------------|-------------|
| Train | 44,990 | 373,716 | 106,913 | Oct 2023 - Oct 2024 |
| Test | 52,836 | 464,942 | 129,020 | Oct 2024 - Oct 2025 |
| **Total** | **97,826** | **501,866** | **182,727** | Oct 2023 - Oct 2025 |

**arXiv Categories**: cs.CL, cs.LG, cs.AI, cs.ML, cs.CV, cs.IR, cs.NE

**Average Statistics** (computed over target papers):
- Authors per paper: 5.15
- Words in abstract: 187.1
- Key references: 3.08 (median: 3)
- Citations at 12 months: 5.57

## Dataset Structure

### Dataset Construction

PreScience is built from research papers posted to arXiv from October 2023 to October 2025 in seven AI-adjacent categories: **cs.CL, cs.LG, cs.AI, cs.ML, cs.CV, cs.IR, and cs.NE**. These constitute the **target papers** in our benchmark. Papers are represented by their titles and abstracts (full texts are not included).

We include a set of **companion papers** consisting of:
- Key references of target papers
- Prior publications of target authors
- Key references of those prior publications

Together, these form the historical corpus H<sub><t</sub> used to condition all tasks.

### Ensuring Dataset Quality

We apply several design choices to ensure that PreScience supports reliable modeling and evaluation rather than reflecting artifacts of noisy metadata or degenerate task instances:

- **Author disambiguation**: We disambiguate author profiles using the S2AND pipeline (Subramanian et al., 2021), yielding better author clusters than the current Semantic Scholar Academic Graph release
- **Key reference filtering**: We restrict target papers to those with between 1 and 10 key references, excluding instances with zero or unusually large key-reference sets
- **Temporal alignment**: All author- and reference-level metadata (publication counts, citation counts, h-indices) are temporally aligned to each paper's publication date to prevent leakage of future information into task inputs

### Files

This dataset contains:

1. **`train.parquet`**: Training period papers (373,716 papers from Oct 2023 - Oct 2024)
2. **`test.parquet`**: Test period papers (464,942 papers from Oct 2024 - Oct 2025)
3. **`author_disambiguation.jsonl`**: Mapping from S2AND-disambiguated author ID → S2AG author IDs
4. **`author_publications.jsonl`**: Mapping from S2AND-disambiguated author ID → S2AG corpus IDs of their publications

### Paper Schema

#### Roles
Papers in the dataset are each assigned a subset of the following roles:
- `target`: Primary evaluation papers (Oct 2023-2024 for train, Oct 2024-2025 for test)
- `target.key_reference`: Highly influential papers cited by targets
- `target.author.publication_history`: Prior work by target paper authors
- `target.author.publication_history.key_reference`: Key refs of authors' prior work

Each paper record contains:

```python
{
  # Basic metadata (available for all papers)
  "corpus_id": str,                     # S2AG corpus ID
  "arxiv_id": str,                      # arXiv identifier
  "date": str,                          # Publication date (YYYY-MM-DD)
  "categories": list[str],              # arXiv categories
  "title": str,                         # Paper title
  "abstract": str,                      # Paper abstract
  "roles": list[str],                   # Paper roles in dataset

  # Citation data (available for target papers [guaranteed] and target.author.publication_history papers [best-effort])
  "key_references": list[{              # Highly influential references
    "corpus_id": str,
    "num_citations": int                # Citations at target paper date
  }],

  # Author data (availability for target papers [guaranteed] and target.author.publication_history papers [best-effort])
  "authors": list[{                     # Author roster
    "author_id": str,                   # S2AND-disambiguated ID
    "name": str,
    "publication_history": list[str],   # Prior corpus_ids
    "h_index": int,                     # At target paper date
    "num_papers": int,
    "num_citations": int
  }],

  # Impact data (target papers only)
  "citation_trajectory": list[int]      # Monthly cumulative citation counts
}
```


## Usage

### Using with PreScience codebase

The [PreScience codebase](https://github.com/allenai/prescience) includes a helper function to load data from HuggingFace:

```python
import utils

# Load from HuggingFace
all_papers, author_disambiguation, embeddings = utils.load_corpus(
    hf_repo_id="allenai/prescience",
    split="test",
    embeddings_dir="./embeddings",   # Optional: for embedding-based baselines
    embedding_type="grit"            # Optional: gtr, specter2, or grit
)
```

### Adhoc Loading

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("allenai/prescience")

# Access a paper
paper = dataset["test"][0]
print(f"Title: {paper['title']}")
print(f"Authors: {len(paper['authors'])}")
print(f"Roles: {paper['roles']}")
```



## Computing Embeddings

Embeddings are not included in this dataset, but can be computed using the `dataset/embeddings/compute_paper_embeddings.py` script provided with the [PreScience codebase](https://github.com/allenai/prescience).



## Citation

```bibtex
@article{prescience2025,
  title={PreScience: A Benchmark for Forecasting Scientific Contributions},
  author={Anirudh Ajith, Amanpreet Singh, Jay DeYoung, Nadav Kunievsky, Austin C. Kozlowski, Oyvind Tafjord, James Evans, Daniel S Weld, Tom Hope, Doug Downey},
  journal={[TBD]},
  year={2026}
}
```

## License

ODC-BY License

## Links

- **Repository**: https://github.com/allenai/prescience
- **Paper**: [TBD]