Developing indicators on Open Access by combining evidence from diverse data sources

TL;DR

This paper introduces a methodology to systematically identify open access publications by combining multiple data sources, addressing the challenge of accurately tracking OA progress in research policy and management.

Contribution

It presents a novel approach to label open access publications in large datasets by integrating diverse evidence sources, improving over previous bibliometric methods.

Findings

Developed a systematic OA labeling methodology

Enhanced accuracy of OA identification in large datasets

Facilitated better tracking of OA progress for policy analysis

Abstract

In the last couple of years, the role of Open Access (OA) publishing has become central in science management and research policy. In the UK and the Netherlands, national OA mandates require the scientific community to seriously consider publishing research outputs in OA forms. At the same time, other elements of Open Science are becoming also part of the debate, thus including not only publishing research outputs but also other related aspects of the chain of scientific knowledge production such as open peer review and open data. From a research management point of view, it is important to keep track of the progress made in the OA publishing debate. Until now, this has been quite problematic, given the fact that OA as a topic is hard to grasp by bibliometric methods, as most databases supporting bibliometric data lack exhaustive and accurate open access labelling of scientific publications. In this study, we present a methodology that systematically creates OA labels for large sets of publications processed in the Web of Science database. The methodology is based on the combination of diverse data sources that provide evidence of publications being OA