Datasets:

allenai
/

prescience

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_<t 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:

train.parquet: Training period papers (373,716 papers from Oct 2023 - Oct 2024)
test.parquet: Test period papers (464,942 papers from Oct 2024 - Oct 2025)
author_disambiguation.jsonl: Mapping from S2AND-disambiguated author ID → S2AG author IDs
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:

{
  # 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 includes a helper function to load data from HuggingFace:

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

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.

Citation

@misc{ajith2026presciencebenchmarkforecastingscientific,
      title={PreScience: A Benchmark for Forecasting Scientific Contributions}, 
      author={Anirudh Ajith and Amanpreet Singh and Jay DeYoung and Nadav Kunievsky and Austin C. Kozlowski and Oyvind Tafjord and James Evans and Daniel S. Weld and Tom Hope and Doug Downey},
      year={2026},
      eprint={2602.20459},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.20459}, 
}

License

ODC-BY License