BIP! NDR (NoDoiRefs): A Dataset of Citations From Papers Without DOIs in Computer Science Conferences and Workshops

TL;DR

This paper introduces BIP! NDR, a new dataset of over 510,000 citations from nearly 60,000 open access CS conference papers without DOIs, addressing gaps in citation data and supporting research assessment.

Contribution

It presents a workflow to identify and extract citation data from papers lacking DOIs, filling a critical gap left by previous datasets like MAG.

Findings

Dataset contains over 510,000 citations.

Includes approximately 60,000 papers without DOIs.

Enhances citation analysis for CS conferences.

Abstract

In the field of Computer Science, conference and workshop papers serve as important contributions, carrying substantial weight in research assessment processes, compared to other disciplines. However, a considerable number of these papers are not assigned a Digital Object Identifier (DOI), hence their citations are not reported in widely used citation datasets like OpenCitations and Crossref, raising limitations to citation analysis. While the Microsoft Academic Graph (MAG) previously addressed this issue by providing substantial coverage, its discontinuation has created a void in available data. BIP! NDR aims to alleviate this issue and enhance the research assessment processes within the field of Computer Science. To accomplish this, it leverages a workflow that identifies and retrieves Open Science papers lacking DOIs from the DBLP Corpus, and by performing text analysis, it extracts citation information directly from their full text. The current version of the dataset contains more than 510K citations made by approximately 60K open access Computer Science conference or workshop papers that, according to DBLP, do not have a DOI.