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

TL;DR

The paper introduces the X-index, a new metric for measuring dataset impact that considers reuse, FAIRness, and cross-disciplinary influence, validated across multiple domains.

Contribution

It proposes the X-index, a novel author-level metric that quantifies dataset impact through a two-step process involving dataset scoring and aggregation.

Findings

X-index correlates strongly with expert ratings

Provides a scalable, transparent framework for dataset impact assessment

Encourages sustainable data-sharing practices

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.