TeamTree analysis: A new approach to evaluate scientific production

Abstract

Advances in science and technology depend on the work of research teams and the publication of results through peer-reviewed articles representing a growing socio-economic resource. Current methods to mine the scientific literature regarding a field of interest focus on content, but the workforce credited by authorship remains largely unexplored. Notably, appropriate measures of scientific production are debated. Here, a new bibliometric approach named TeamTree analysis is introduced that visualizes the development and composition of the workforce driving a field. A new citation-independent measure that scales with the H index estimates impact based on publication record, genealogical ties and collaborative connections. This author-centered approach complements existing tools to mine the scientific literature and to evaluate research across disciplines.

Neurodegenerative Diseases and Cholesterol: Seeing the Field Through the Players

“…The development of the field with respect to these contributors is shown in Figure 2A using TeamTree graphs. In this type of scatterplot, the years of publication are plotted against a chronologic index assigned to each author (Pfrieger, 2021 ). The number of last authors entering the field per year has grown steadily during the last two decades ( Figure 2B ).…”

“…Genealogical relations in a field can be derived from the last and first authors on article bylines representing ancestor and offspring, respectively (Pfrieger, 2021 ). Figure 3A shows family relations among authors highlighting those with the largest offspring counts.…”

“…Collaborative connections can be delineated based on middle and last byline positions (Newman, 2001 ; Pfrieger, 2021 ). Figure 4 exposes collaborations between authors contributing to the field.…”

“…Original articles represent an accessible primary basis to estimate the contribution of an author. A new approach takes into account publication record, offspring generation, and collaborative connections, and delivers a new citation-independent parameter named TeamTree product (TTP; Pfrieger, 2021 ). Based on this parameter, key players studying neurodegenerative diseases and cholesterol are exposed in Figure 5 .…”

“…Possible implications of cholesterol and derived molecules in neurodegenerative diseases have been reviewed elsewhere (Martín et al, 2014 ; Zarrouk et al, 2014 ; Leoni and Caccia, 2015 ; Doria et al, 2016 ; Arenas et al, 2017 ; Chang et al, 2017 ; Testa et al, 2018 ; Zarrouk et al, 2018 ; Adorni et al, 2019 ; Griffiths and Wang, 2019 ; Hussain et al, 2019 ; Jeong et al, 2019 ; Jin et al, 2019 ; Loera-Valencia et al, 2019 ; Petrov and Pikuleva, 2019 ; Segatto et al, 2019 ; Blauwendraat et al, 2020 ; González-Guevara et al, 2020 ; McFarlane and Kędziora-Kornatowska, 2020 ; Sáiz-Vazquez et al, 2020 ; Dai et al, 2021 ; Duong et al, 2021 ; Feringa and van der Kant, 2021 ; García-Sanz et al, 2021 ; Pikuleva and Cartier, 2021 ; Samant and Gupta, 2021 ). This article shows the workforce driving research in the field using original research articles obtained from MEDLINE ( Table 1 ) and a new bibliometric approach (Pfrieger, 2021 ; https://github.com/fw-pfrieger/TeamTree ). Bibliometric analyses of other aspects can be found elsewhere (Guido et al, 2015 ; Barboza and Ghisi, 2018 ; Zhang et al, 2020 ; Du et al, 2021 ; Li et al, 2021 ; Rizzi et al, 2021 ).…”