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Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis.

ABSTRACT: BACKGROUND:Team science research includes authors from various fields collaborating to publish their work on certain topics. Despite the numerous papers that discussed the ordering of author names and the contributions of authors to an article, no paper evaluatedIn addition, few researchers publish academic articles without co-author collaboration. Whether the bibliometric indexes (eg, h-/x-index) of sole-author researchers are higher than those of other types of multiple authors is required for comparison. We aimed to evaluate a productive author who published 114 sole-author articles with exceptional RA and RD in academics. METHODS:By searching the PubMed database (Pubmed.com), we used the keyword of (Taiwan[affiliation]) from 2016 to 2017 and downloaded 29,356 articles. One physician (Dr. Tseng from the field of Internal Medicine) who published 12 articles as a single author was selected. His articles and citations were searched in PubMed. A comparison of various types of author ordering placements was conducted using sensitivity analysis to inspect whether this sole author earns the highest metrics in RA. Social network analysis (SNA), Gini coefficient (GC), pyramid plot, and the Kano diagram were applied to gather the following data for visualization: RESULTS:: We observed that CONCLUSIONS:: The metrics on RA are high for the sole author studied. The author's RD can be denoted by the MeSH terms and measured by the GC. The author-weighted scheme is required for quantifying author credits in an article to evaluate the author's RA. Social network analysis incorporating the Kano diagrams provided insights into the relationships between actors (eg, coauthors, MeSH terms, or journals). The methods used in this study can be replicated to evaluate other productive studies on RA and RD in the future.

SUBMITTER: Liu MY

PROVIDER: S-EPMC7249850 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis.

Liu Mei-Yuan MY Chou Willy W Chien Tsair-Wei TW Kuo Shu-Chun SC Yeh Yu-Tsen YT Chou Po-Hsin PH

Medicine 20200501 21

<h4>Background</h4>Team science research includes authors from various fields collaborating to publish their work on certain topics. Despite the numerous papers that discussed the ordering of author names and the contributions of authors to an article, no paper evaluatedIn addition, few researchers publish academic articles without co-author collaboration. Whether the bibliometric indexes (eg, h-/x-index) of sole-author researchers are higher than those of other types of multiple authors is requ ...[more]

PMID: 32481321

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Project description:OBJECTIVES:Effective researcher assessment is key to decisions about funding allocations, promotion and tenure. We aimed to identify what is known about methods for assessing researcher achievements, leading to a new composite assessment model. DESIGN:We systematically reviewed the literature via the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols framework. DATA SOURCES:All Web of Science databases (including Core Collection, MEDLINE and BIOSIS Citation Index) to the end of 2017. ELIGIBILITY CRITERIA: (1) English language, (2) published in the last 10 years (2007-2017), (3) full text was available and (4) the article discussed an approach to the assessment of an individual researcher's achievements. DATA EXTRACTION AND SYNTHESIS:Articles were allocated among four pairs of reviewers for screening, with each pair randomly assigned 5% of their allocation to review concurrently against inclusion criteria. Inter-rater reliability was assessed using Cohen's Kappa (ĸ). The ĸ statistic showed agreement ranging from moderate to almost perfect (0.4848-0.9039). Following screening, selected articles underwent full-text review and bias was assessed. RESULTS:Four hundred and seventy-eight articles were included in the final review. Established approaches developed prior to our inclusion period (eg, citations and outputs, h-index and journal impact factor) remained dominant in the literature and in practice. New bibliometric methods and models emerged in the last 10 years including: measures based on PageRank algorithms or 'altmetric' data, methods to apply peer judgement and techniques to assign values to publication quantity and quality. Each assessment method tended to prioritise certain aspects of achievement over others. CONCLUSIONS:All metrics and models focus on an element or elements at the expense of others. A new composite design, the Comprehensive Researcher Achievement Model (CRAM), is presented, which supersedes past anachronistic models. The CRAM is modifiable to a range of applications.

Project description:Background:Deciphering the amount of work provided by different co-authors of a scientific paper has been a recurrent problem in science. Despite the myriad of metrics available, the scientific community still largely relies on the position in the list of authors to evaluate contributions, a metric that attributes subjective and unfounded credit to co-authors. We propose an easy to apply, universally comparable and fair metric to measure and report co-authors contribution in the scientific literature. Methods:The proposed Author Contribution Index (ACI) is based on contribution percentages provided by the authors, preferably at the time of submission. Researchers can use ACI to compare the contributions of different authors, describe the contribution profile of a particular researcher or analyse how contribution changes through time. We provide such an analysis based on contribution percentages provided by 97 scientists from the field of ecology who voluntarily responded to an online anonymous survey. Results:ACI is simple to understand and to implement because it is based solely on percentage contributions and the number of co-authors. It provides a continuous score that reflects the contribution of one author as compared to the average contribution of all other authors. For example, ACI(i) = 3, means that author i contributed three times more than what the other authors contributed on average. Our analysis comprised 836 papers published in 2014-2016 and revealed patterns of ACI values that relate to career advancement. Conclusion:There are many examples of author contribution indices that have been proposed but none has really been adopted by scientific journals. Many of the proposed solutions are either too complicated, not accurate enough or not comparable across articles, authors and disciplines. The author contribution index presented here addresses these three major issues and has the potential to contribute to more transparency in the science literature. If adopted by scientific journals, it could provide job seekers, recruiters and evaluating bodies with a tool to gather information that is essential to them and cannot be easily and accurately obtained otherwise. We also suggest that scientists use the index regardless of whether it is implemented by journals or not.

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Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis.

Publications

Evaluating the research domain and achievement for a productive researcher who published 114 sole-author articles: A bibliometric analysis.

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