DOI: 10.5281/zenodo.18410049
title: MDCite: A Large-Scale Multi disciplinary Citation Context Dataset

description: MDCite

MDCite is a large-scale, multi-disciplinary citation context dataset designed to support research in citation-aware scholarly information retrieval.The dataset treats citation contexts local textual spans surrounding in-text citations as the primary unit of retrieval and analysis, enabling fine-grained evaluation of intent-aware retrieval, ranking, and candidate generation methods.

This Zenodo record contains all data artifacts required to reproduce the MDCite dataset and its construction pipeline, as described in the accompanying SIGIR 2026 resource paper.

Data Sources

MDCite is constructed by integrating multiple large-scale scholarly data sources:

🔹 Scopus bibliographic records (2000–2024)

Scopus bibliographic records are collected via the Scopus API, which provides programmatic access to journal articles, citation counts, and rich publication metadata.The Scopus API is used to retrieve article-level information for selected journals, including publication year, venue, citation counts, and persistent identifiers.These records form the foundation of the dataset and are used to identify influential papers based on citation statistics, enabling reproducible large-scale data collection over a long temporal span (2000–2024).

🔹 Web of Science (WoS) 2024 Subject Categories

Web of Science (WoS) 2024 subject categories are used to group journals by scientific field and to select Top-5 Q1 journals per field.This field-aware categorization enables journal-stratified selection and controls for substantial differences in citation practices across disciplines, supporting fair and interpretable cross-field analysis.

🔹 OpenAlex API

The OpenAlex API is used for:





DOI resolution




Citation link retrieval (i.e., identifying papers that cite selected influential works)




Structured bibliographic metadata access



OpenAlex provides large-scale, open citation graph infrastructure enabling efficient identification of citing papers.

🔹 Semantic Scholar Graph API

Citation context spans and citation intent labels are retrieved via the Semantic Scholar Graph API.

For each citing paper, the API provides:





Structured citation metadata




Textual spans surrounding in-text citation markers




Automatically assigned citation intent labels



These structured reference-linked context spans form the core citation context units in MDCite.

Dataset Construction Pipeline

The MDCite construction process follows a transparent and reproducible pipeline:

Journal selection





Journals are grouped by WoS subject categories




Top-5 Q1 journals are selected per scientific field



Citation-based filtering





Within each selected journal, the Top 5% most-cited papers are identified independently




This journal-stratified strategy avoids overrepresentation of citation-intensive fields



Citation context extraction





Citation contexts are extracted from papers citing the selected influential papers




Each context corresponds to a textual span surrounding an in-text citation marker



Citation intent classification





Each citation context is automatically assigned one of seven functional citation intents:background, uses, similarities, differences, motivation, extends, future_work



Dataset variants





A multi-intent intermediate variant is retained for provenance




A single-intent benchmark variant is released for standard IR and classification evaluation



File Overview

🔹 Core Dataset Files





dataset_context_intent_single.csv (1.45 GB)Single-intent benchmark version of MDCite in CSV formatEach row corresponds to one citation context



Provenance: The single-intent labels were derived from the intermediate JSON artifacts in citation_context_intent_data.zip (2.29 GB), where probabilistic/multi-intent model outputs were generated. The final label field corresponds to the primary intent selected per context.





dataset_context_intent_single.parquet (620 MB)Same content as above, stored in Parquet formatRecommended for large-scale indexing, retrieval, and analytics workflows




citation_context_intent_data.zip (2.29 GB)Full citation context data prior to single-label filtering.Includes intermediate JSON artifacts containing probabilistic/multi-intent model outputs and structured citation context records used during dataset construction.These files represent the complete multi-intent version of MDCite before benchmark conversion.



🔹 Construction and Provenance Files





Scopus_(Year_2000–2024).zip (120 MB)Bibliographic metadata collected via the Scopus API




WOS_2024_Subject_categories_(Top-5_Q1_Journals_per_field)_data.zip (51 MB)Journal lists grouped by scientific field




Top_5%_cited_papers_per_journal_dataset.zip (8.05 MB)Lists of Top-5% cited papers selected independently within each journal



Schema of MDCite Dataset

Each citation context instance includes the following fields:





text — citation context text




label — functional citation intent




field — scientific field




group_id — citing paper identifier (for document-level evaluation splits)




paperId — Semantic scholar work identifier




doi — Digital Object Identifier




venue — publication venue




year — publication year




source_file — provenance identifier



Dataset Statistics





Citation contexts: 2,057,196




Citing papers: ~100,000




Scientific fields: 21




Publication venues: >1,000




Citation intent classes: 7



The intent distribution is highly imbalanced, reflecting real-world citation behavior rather than artificially balanced labels.

Intended Use Cases

MDCite is intended for:





Citation-aware information retrieval




Intent-aware ranking and re-ranking




Candidate generation analysis




Large-scale citation intent classification




Scholarly search and citation analysis



Baseline BM25 retrieval experiments demonstrate that MDCite naturally supports deep-retrieval evaluation, where recall improves only at larger cutoffs due to large and distributed relevance sets.

Reproducibility & Licensing

All files are released for research and educational use.

Reproducing the full construction pipeline requires:





Access to the Scopus API (institutional entitlement may be required)




Access to the OpenAlex API (publicly available)




Access to the Semantic Scholar Graph API (publicly available; subject to rate limits)



API keys are not included in this repository and must be supplied via environment variables.

The dataset is designed to support end-to-end reproducible experimentation through clear provenance tracking, intermediate artifacts, and document-level identifiers.