---
license: apache-2.0
language:
- en
pretty_name: BEIR SciDocs (citation prediction retrieval)
size_categories:
- "1K<n<10K"
tags:
- retrieval
- text-retrieval
- beir
- scientific
- citation-prediction
- biomedical
- rag
---

# BEIR SciDocs (`orgrctera/beir_scidocs`)

## Overview

**SciDocs** is a **scientific document** evaluation resource originally introduced with **SPECTER** (Cohan et al., ACL 2020) as part of a broader **SciDocs** benchmark of document-level tasks (citation-related prediction, classification, and recommendation). The **Benchmarking-IR (BEIR)** project (Thakur et al., NeurIPS 2021 Datasets & Benchmarks) repackages the **citation-prediction** slice of SciDocs into the standard BEIR retrieval format: a corpus of papers, a set of queries, and **qrels** (query–document relevance judgments).

This Hub dataset is the **CTERA-formatted** release of that **BEIR SciDocs** test collection: each row is one **retrieval query** with **gold relevant document IDs** (and binary relevance scores) aligned to the BEIR **scidocs** split.

**Source lineage:** [Allen AI SciDocs](https://allenai.org/data/scidocs) → [BEIR `scidocs`](https://github.com/beir-cellar/beir) → `orgrctera/beir_scidocs`.

## Background

### SciDocs and SPECTER

**SPECTER** trains a Transformer on **citation graphs** so that embeddings reflect **inter-document relatedness**—a signal that token- or sentence-level pretraining often misses. The authors introduce **SciDocs**, a suite of **seven document-level tasks** for scientific NLP. The portion used in BEIR is the **citation prediction** setting: given a **query paper** (represented by its **title** as the query text), retrieve **papers it should cite** (or that are citation-relevant in the benchmark construction)—i.e. **citation-oriented retrieval** over a **scientific paper corpus** (papers are typically drawn from **Semantic Scholar**–indexed literature; BEIR uses a fixed **corpus** and **1,000 test queries**).

### BEIR

**BEIR** aggregates **18 heterogeneous IR datasets** across **nine task types** to study **zero-shot** retrieval: models are evaluated **without** task-specific training on each dataset. SciDocs contributes the **citation prediction** task type. Reported statistics for the BEIR SciDocs configuration include on the order of **~25K corpus documents**, **1,000 queries**, and **~4–5 relevant documents per query** on average (see the BEIR paper / dataset tables).

## Task

- **Task type:** **Retrieval** — specifically the **BEIR SciDocs / citation-prediction** setting (retrieval over a scientific paper corpus).
- **Input (`input`):** Query text (in BEIR, the **title** of a **query paper**).
- **Reference output (`expected_output`):** JSON string: list of objects `{"id": "<corpus_doc_id>", "score": 1}` for **relevant** corpus documents (binary relevance).
- **Metadata:** `metadata.query_id` is the BEIR/query identifier; `metadata.split` is **`test`** (BEIR SciDocs is **test-only** in the benchmark).

Evaluators typically embed the corpus and queries, retrieve top-*k* documents per query, and measure **nDCG@k**, **MAP**, **Recall@k**, etc., against the gold ID lists—consistent with [BEIR evaluation](https://github.com/beir-cellar/beir).

## Data fields

| Column | Type | Description |
|--------|------|-------------|
| `id` | `string` | Unique row identifier (UUID) for this Hub release. |
| `input` | `string` | Query string (paper title) for retrieval. |
| `expected_output` | `string` | JSON array of `{ "id": "...", "score": 1 }` gold relevant documents. |
| `metadata.query_id` | `string` | Original query / paper id in the BEIR SciDocs pipeline. |
| `metadata.split` | `string` | Split name (`test`). |

**Splits:** `test` — **1,000** query rows.

## Examples

Illustrative rows from this dataset (document IDs shortened for display).

### Example 1

- **`input`:** `Time-Agnostic Prediction: Predicting Predictable Video Frames`
- **`metadata.query_id`:** `b4c80fc4b140eb08a717a446824cacb77f319166`
- **`expected_output` (structure):**
```json
[
  {"id": "0d8a5addbd17d2c7c8043d8877234675da19938a", "score": 1},
  {"id": "10b987b076fe56e08c89693cdb7207c13b870540", "score": 1},
  {"id": "385750bcf95036c808d63db0e0b14768463ff4c6", "score": 1}
]
```
*(Additional relevant IDs appear in the full row.)*

### Example 2

- **`input`:** `Autonomous Vehicle Navigation by Building 3 D Map and by Detecting Human Trajectory using LIDAR`
- **`metadata.query_id`:** `81b14341e3e063d819d032b6ce0bc0be0917c867`
- **`expected_output`:** JSON array of five `{ "id": "...", "score": 1 }` objects (same schema as Example 1).

## References

### BEIR (benchmark packaging and zero-shot IR evaluation)

Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych. **BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.** *NeurIPS 2021 Datasets and Benchmarks Track.*

> Abstract (excerpt): *We introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems…*

- Paper: [arXiv:2104.08663](https://arxiv.org/abs/2104.08663)  
- Code / data hub: [beir-cellar/beir](https://github.com/beir-cellar/beir)  
- Original BEIR dataset page: [`BeIR/scidocs` on the Hub](https://huggingface.co/datasets/BeIR/scidocs)

### SPECTER & SciDocs (original scientific benchmark)

Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel Weld. **SPECTER: Document-level Representation Learning using Citation-informed Transformers.** *ACL 2020.*

> Abstract (excerpt): *We propose SPECTER… based on pretraining a Transformer language model on… the citation graph… we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation.*

- Paper: [ACL Anthology](https://aclanthology.org/2020.acl-main.207/)  
- SciDocs data: [allenai.org/data/scidocs](https://allenai.org/data/scidocs) · [allenai/scidocs (GitHub)](https://github.com/allenai/scidocs)

### Citation (BEIR)

```bibtex
@inproceedings{thakur2021beir,
  title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
  author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021},
  url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```

### Citation (SPECTER / SciDocs)

```bibtex
@inproceedings{cohan-etal-2020-specter,
  title={SPECTER: Document-level Representation Learning using Citation-informed Transformers},
  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel Weld},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year={2020},
  pages={2270--2282},
  url={https://aclanthology.org/2020.acl-main.207/}
}
```

## Notes

- This repository format (`input` / `expected_output` / `metadata`) is adapted for **benchmark and RAG tooling**; the underlying judgments follow **BEIR SciDocs** / **SciDocs** citation-prediction retrieval.
- For the **raw BEIR JSONL** layout (corpus / queries / `qrels.tsv`), see the [BEIR documentation](https://github.com/beir-cellar/beir) and [`BeIR/scidocs`](https://huggingface.co/datasets/BeIR/scidocs).