Web indicators for research evaluation. Part 1: Citations and links to academic articles from the Web

Thelwall, Mike; Kousha, Kayvan

doi:10.3145/epi.2015.sep.08

Cited by 57 publications

(48 citation statements)

References 110 publications

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“…Science can be improved by broadening the number and range of performance indicators. The development of alternative metrics covering web (88) and social media (89) activity and societal impact (90) is critical in this regard. Other measurable dimensions include the information (e.g., data) that scientists share with competitors (91), the help that they offer to their peers (92), and their reliability as reviewers of their peers’ works (93).…”

Section: Discussionmentioning

confidence: 99%

Science of science

Fortunato

Bergstrom

Börner

et al. 2018

Science

1,052

709

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BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as sciento-metrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for un...

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Section: Discussionmentioning

confidence: 99%

Science of science

Fortunato

Bergstrom

Börner

et al. 2018

Science

1,052

709

View full text Add to dashboard Cite

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“…To date, the main method to validate measurements made with data from GS has been to calculate their correlation with other well-established indicators. Many studies, which have been recently compiled by Thelwall y Kousha [70], have analysed correlations between the number of citations according to GS and other databases (mainly WoS and Scopus), either at the level of journals or at the level of authors, as a way to evaluate its suitability as a source of data bibliometric studies.…”

Section: Figure 11 Google Scholar Versus Traditional Bibliographic Dmentioning

confidence: 99%

Google Scholar as a Data Source for Research Assessment

2019

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The launch of Google Scholar (GS) marked the beginning of a revolution in the scientific information market. This search engine, unlike traditional databases, automatically indexes information from the academic web. Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search. Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products which leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses. The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation. First, we present a general overview of how GS works. Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate). Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance. Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data. We conclude that Google Scholar presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.

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“…Those were the first of an extensive corpus of scientific literature on link analysis (Thelwall, 2004;Thelwall and Kousha, 2015), which can be further separated into the studies that focus on web impact (links received), on the one hand, and the ones focused on connectivity (graphs), on the other hand. Therefore, the beginning of Altmetrics (understood as alternative metrics for the study of academic impact) dates back to the last decade of the twentieth century, before even the term Altmetrics was coined.…”

Section: Introductionmentioning

confidence: 99%