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<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
	@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

	## Links

	- Repository: https://github.com/allenai/prescience
	- Paper: https://arxiv.org/abs/2602.20459