Science models as value-added services for scholarly information systems

Relevance assessments, bibliometrics, and altmetrics: a quantitative study on PubMed and arXiv

Relevance is a key element for analyzing bibliometrics and information retrieval (IR). In both domains, relevance decisions are discussed theoretically and sometimes evaluated in empirical studies. IR research is often based on test collections for which explicit relevance judgments are made, while bibliometrics is based on implicit relevance signals like citations or other non-traditional quantifiers like altmetrics. While both types of relevance decisions share common concepts, it has not been empirically investigated how they relate to each other on a larger scale. In this work, we compile a new dataset that aligns IR relevance judgments with traditional bibliometric relevance signals (and altmetrics) for life sciences and physics publications. The dataset covers PubMed and arXiv articles, for which relevance judgments are taken from TREC Precision Medicine and iSearch, respectively. It is augmented with bibliometric data from the Web of Science and Altmetrics. Based on the reviewed literature, we outline a mental framework supporting the answers to our research questions. Our empirical analysis shows that bibliometric (implicit) and IR (explicit) relevance signals are correlated. Likewise, there is a high correlation between biblio- and altmetrics, especially for documents with explicit positive relevance judgments. Furthermore, our cross-domain analysis demonstrates the presence of these relations in both research fields.

Open innovation: past, present and future trends

Purpose

The purpose of this paper is to provide interested parties with the means of grasping how the literature on open innovation has evolved over the course of time. In this way, the authors furthermore contribute towards a better understanding, scaling and positioning of this field of research.

Design/methodology/approach

This study applies a combination of bibliometric techniques, such as citations, co-citations and social network analysis in order to map the scientific domain of open innovation. Currently, bibliometric analysis represents a methodology in effect on a global scale to evaluate the existing state of fields of research (Mutschke et al., 2011). This spans the application of quantitative and statistical analysis to publications such as articles and their respective citations and serving to evaluate the performance of research through returning data on all of the activities ongoing in a scientific field with summaries of these data generating a broad perspective on the research activities and impacts, especially as regards the researchers, journals, countries and universities (Hawkins, 1977; Osareh, 1996; Thomsom Reuters, 2008).

Findings

This research aims to map and analyse the intellectual knowledge held on open innovation. To this end, the authors carried out a bibliometric study with recourse to co-citations. Based on cluster and factorial analyses, it is possible identify and classify the several theoretical perspectives on open innovation across six areas: open innovation concept, open innovation and networks, open innovation and knowledge, open Innovation, and innovation spillovers, open innovation management and open innovation and technology.

Originality/value

This paper aims to map and analyse the intellectual knowledge held on open innovation.