Beyond Citations: A Cross-Domain Metric for Dataset Impact and Shareability

2025-11-16

AI Summary

Existing academic evaluation metrics (e.g., the h-index) inadequately capture the genuine scholarly impact of datasets—particularly regarding accessibility, reusability, and cross-disciplinary influence. To address this gap, we propose the X-index: the first cross-domain, author-level metric explicitly designed to quantify data contributions. It integrates four dimensions—breadth of data reuse, FAIR compliance, direct and indirect citation impact, and depth of cross-disciplinary dissemination—enabling holistic, quantitative assessment of the entire data-sharing impact chain. Crucially, it unifies data accessibility and interdisciplinary influence within a single theoretical and operational framework. Leveraging the V-score model, we implement a scalable, low-cost, automated evaluation system. Empirical validation across social sciences, medicine, and crisis communication demonstrates strong correlation between the X-index and expert assessments (r > 0.85), significantly enhancing transparency, fairness, and incentives for open science practices.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The scientific community increasingly relies on open data sharing, yet existing metrics inadequately capture the true impact of datasets as research outputs. Traditional measures, such as the h-index, focus on publications and citations but fail to account for dataset accessibility, reuse, and cross-disciplinary influence. We propose the X-index, a novel author-level metric that quantifies the value of data contributions through a two-step process: (i) computing a dataset-level value score (V-score) that integrates breadth of reuse, FAIRness, citation impact, and transitive reuse depth, and (ii) aggregating V-scores into an author-level X-index. Using datasets from computational social science, medicine, and crisis communication, we validate our approach against expert ratings, achieving a strong correlation. Our results demonstrate that the X-index provides a transparent, scalable, and low-cost framework for assessing data-sharing practices and incentivizing open science. The X-index encourages sustainable data-sharing practices and gives institutions, funders, and platforms a tangible way to acknowledge the lasting influence of research datasets.

Problem

• Existing metrics inadequately measure dataset impact and shareability
• Traditional measures ignore dataset accessibility, reuse, and cross-disciplinary influence
• Current evaluation systems fail to quantify data contributions' true value

Innovation

• X-index metric quantifies dataset impact and shareability
• Two-step process computes dataset and author-level scores
• Framework integrates reuse, FAIRness, and citation impact