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CrossDI: A comprehensive dataset crossing three databases for calculating disruption indexes.

Shuo Xu¹, Congcong Wang¹, Xin An²

¹College of Economics and Management, Beijing University of Technology, Beijing, 100124, P.R. China.

Scientific Data

|December 10, 2025

Summary

The Disruption Index (DI) measures scientific impact by analyzing citation patterns. We introduce the Cross-source Disruption Indexes (CrossDI) dataset to benchmark various DI metrics across multiple databases and fields.

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Area of Science:

Bibliometrics
Scientometrics
Research evaluation

Background:

The Disruption Index (DI) is a key metric for identifying groundbreaking research.
Current DI studies are limited by single-indicator, discipline-specific, or database-confined approaches.
A comprehensive benchmark is needed to evaluate diverse DI metrics and their temporal dynamics.

Purpose of the Study:

To introduce the Cross-source Disruption Indexes (CrossDI) dataset.
To provide a benchmark resource for evaluating and comparing disruption indexes.
To facilitate systematic analysis of DI metrics across different sources and fields.

Main Methods:

Integrated multiple established DI metrics for key articles.
Curated data from Web of Science (WoS), Dimensions, and OpenCitations.
Included articles from four diverse scientific fields.

Main Results:

Developed the CrossDI dataset, a novel benchmark resource.
Enabled systematic evaluation and comparison of DI metrics.
Facilitated understanding of the intrinsic properties and temporal dynamics of DI.

Conclusions:

The CrossDI dataset addresses the critical gap in DI research.
It serves as a reusable benchmark for advancing bibliometric and scientometric studies.
This resource supports more robust assessment of scientific impact and research frontiers.