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Several initiatives have been taken to promote the openly availability of bibliographic metadata of scholarly publications in Crossref. We present an up-to-date overview of the availability of six metadata elements in Crossref: reference lists, abstracts, ORCIDs, author affiliations, funding information, and license information. Our analysis shows that the availability of these metadata elements has improved over time. However, it also shows that many publishers need to make additional efforts to realize full openness of bibliographic metadata. To illustrate the value of open metadata, we use the metadata in Crossref to construct and visualize a large citation network of scholarly journals.

The main purpose of this article is to reveal the effect of self-archiving on the citation impact of preprints in Information Science & Library Science (LIS). The arXiv and WoS database are used and all citations from WoS are collected to address two key questions—one is the share of papers published in the core LIS journals that also deposited in arXiv and time lags in publication process, the other is the differences in aging characteristics and citation impact of journal articles deposited in arXiv compared to those not deposited—through statistical and bibliometric analysis. The results show that the proportion of journal articles deposited in arXiv is low but there is an overall increasing trend, nearly 80% of all arXiv papers are preprints and authors are more likely to post the paper to arXiv when it is received or accepted by a journal, the preprints enjoy a boost in early citations and the citation advantage decrease with the increasing author productivity, the citations to arXiv version papers indicate an OA effect of arXiv.

Science studies are persistently challenged by the elusive structures of their subject matter, be it scientific knowledge or the various collectivities of researchers engaged with its production. Bibliometrics has responded by developing a strong and growing structural bibliometrics, which is concerned with delineating fields and identifying thematic structures. In the course of these developments, a concern emerged and is steadily growing. Do the sets of publications, authors or institutions we identify and visualise with our methods indeed represent thematic structures? To what extent are results of topic identification exercises determined by properties of knowledge structures, and to what extent are they determined by the approaches we use? Do we produce more than artefacts? These questions triggered the collective process of comparative topic identification reported in this special issue. The introduction traces the history of bibliometric approaches to topic identification, identifies the major challenges involved in these exercises, and introduces the contributions to the special issue.

Referenced Publication Year Spectroscopy (RPYS) was recently introduced as a method to analyze the historical roots of research fields and groups or institutions. RPYS maps the distribution of the publication years of the cited referencesin a document set. In this study, we apply this methodology to the oeuvre of an individual researcher on the occasion of aFestschrift for András Schubert’s 70th birthday. We discuss the different options of RPYS in relation to one another (e.g. Multi-RPYS), and in relation to the longer-term research program of algorithmic historiography (e.g., HistCite™) based on Schubert’spublications (n=172) and cited references therein as a bibliographic domain in scientometrics. Main path analysis and Multi-RPYS of the citation network are used to show the changes and continuities in Schubert’s intellectual career. Diachronic and static decomposition of a document set can lead to different results, while the analytically distinguishable lines of research may overlap and interact over time, and intermittently.

The hybrid clustering approach combining lexical and link-based similarities suffered for a long time from the different properties of the underlying networks. We propose a method based on noun phrase extraction using natural language processing to improve the measurement of the lexical component. Term shingles of different length are created form each of the extracted noun phrases. Hybrid networks are built based on weighted combination of the two types of similarities with seven different weights. We conclude that removing all single term shingles provides the best results at the level of computational feasibility, comparability with bibliographic coupling and also in a community detection application.