Can we measure the impact of a database?

TL;DR

This paper explores extending the h-index to hierarchical structures within databases to measure their impact, enabling comparison and attribution similar to traditional scholarly metrics.

Contribution

It introduces a method to compute an extended h-index for hierarchical databases, facilitating impact measurement and contributor recognition.

Findings

Extended h-index can be efficiently computed for hierarchical databases.

Hierarchical interpretation allows impact measurement similar to scholarly metrics.

Analysis of three widely used databases demonstrates the method's applicability.

Abstract

In disseminating scientific and statistical data, on-line databases have almost completely replaced traditional paper-based media such as journals and reference works. Given this, can we measure the impact of a database in the same way that we measure an author's or journal's impact? To do this, we need somehow to represent a database as a set of publications, and databases typically allow a large number of possible decompositions into parts, any of which could be treated as a publication. We show that the definition of the h-index naturally extends to hierarchies, so that if a database admits some kind of hierarchical interpretation we can use this as one measure of the importance of a database; moreover, this can be computed as efficiently as one can compute the normal h-index. This also gives us a decomposition of the database that might be used for other purposes such as giving credit to the curators or contributors to the database. We illustrate the process by analyzing three widely used databases.