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Preprint Open Access

DOIBoost - Boosting CrossRef for Research

La Bruzzo, Sandro; Manghi, Paolo; Mannocci, Andrea

Research in information science and scholarly communication strongly relies on the availability of openly accessible datasets of scholarly entities metadata and, where possible, their relative payloads. Since such metadata information is scattered across diverse, freely accessible, online resources (e.g. CrossRef, ORCID), researchers in this domain are doomed to struggle with metadata integration problems, in order to produce custom datasets of undocumented and rather obscure provenance. This practice leads to waste of time, duplication of efforts, and typically infringes open science best practices of transparency and reproducibility of science. In this article, we describe how to generate DOIBoost, a metadata collection that enriches CrossRef with inputs from Microsoft Academic Graph, ORCID, and Unpaywall for the purpose of supporting high-quality and robust research experiments, saving times to researchers and enabling their comparison. To this aim, we describe the dataset value and its schema, analyse its actual content, and share the software Toolkit and experimental workflow required to reproduce it. The DOIBoost dataset and Software Toolkit are made openly available via

This is the pre-print of a data paper submitted to the IRCDL 2019 conference:
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