Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,174 @@
+---
+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. We develop baselines and evaluations for each task, including LACERScore, a novel LLM-based measure of contribution similarity that outperforms previous metrics and nears inter-annotator agreement. We find substantial headroom remains in each task---e.g. in contribution generation, frontier LLMs achieve only moderate similarity to the ground-truth (GPT-5, averages 5.6 on a 1-10 scale). When composed into a 12-month end-to-end simulation of scientific production, the resulting synthetic corpus is systematically less diverse and less novel than human-authored research from the same period.
+
+## 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]