Mapping the highly collaborative stem cell research field: Adding last-author-based analysis to the author co-citation analysis family

Zhao, Dangzhi; Strotmann, Andreas

doi:10.1002/meet.14504701284

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Multidimensional scaling and factor analysis have been employed to describe the scientific structure of information science authors. Since then, a number of studies have been conducted using variations of the ACA method, including normalization (Ahlgren, Jarneving, & Rousseau, ; Leydesdorff & Vaughan, ; White, ; van Eck & Waltman, ), author counts (Zhao & Strotmann, ), and last‐author ACA (Zhao & Strotmann, ). One disadvantage of cocitation analysis is the lack of cognitive interpretation of the relatedness of the co‐cited units.…”

Section: Related Workmentioning

confidence: 99%

Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

Lü

Wolfram

2012

J Am Soc Inf Sci Tec

101

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Relationships between authors based on characteristics of published literature have been studied for decades.Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic wordbased approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map.This literature review section covers two parts. The first section reviews existing techniques used for mapping bibliometric units. The second section briefly reviews the relevant models used in the study. It includes an introduction to the essential ideas of the vector space model, how it applies to the current study, and provides a short introduction to the LDA or topic model. Bibliometric Relatedness MeasuresMany bibliometric studies have formulated quantitative measures to map scientific structure at different levels of granularity including authors, articles, and journals. In reviewing visualization studies for knowledge domains, Börner, Chen, and Boyack (2005) categorized relatedness measures into two broad categories: citation linkages and co-occurrence similarities. Within the relatedness measures, five basic approaches were identified: direct citation, cocitation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis.Direct citation. Direct citation accounts for the relatedness between a citing work and a cited work based on citing behavior. This measure is usually asymmetric. Shibata, Kajikawa, Takeda, and Matsushima (2008) explored citation networks for two research domains and divided the networks into clusters in order to identify research fronts. Direct citation has not attracted wide attention. One possible reason may be its requirement for a very long time window to obtain a sufficient linking signal for clustering (

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Section: Related Workmentioning

confidence: 99%

Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

Lü

Wolfram

2012

J Am Soc Inf Sci Tec

101

View full text Add to dashboard Cite

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“…Here, recall denotes the fraction of the total number of investigators who are correctly identified. The algorithm has been used subsequently in [55], [56] and [57]. Although it is a simple rule-based approach, some of the issues are as follows: (1) the rationale for the various decision rules (like merging two author names that have at least one collaborator) is apparently not based on strong evidence and may not generalise well; (2) affiliation information is apparently not used and (3) the evaluation is done on a very limited dataset.…”

Section: Detailed Reviewmentioning

confidence: 99%

A review of author name disambiguation techniques for the PubMed bibliographic database

Sanyal

Bhowmick

Das

2019

Journal of Information Science

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Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work.

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Mapping the highly collaborative stem cell research field: Adding last-author-based analysis to the author co-citation analysis family

Cited by 2 publications

References 30 publications

Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

A review of author name disambiguation techniques for the PubMed bibliographic database

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