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An author keyword analysis for mapping Sport Sciences.

ABSTRACT: Scientific production has increased exponentially in recent years. It is necessary to find methodological strategies for understanding holistic or macro views of the major research trends developed in specific fields. Data mining is a useful technique to address this task. In particular, our study presents a global analysis of the information generated during last decades in the Sport Sciences Category (SSC) included in the Web of Science database. An analysis of the frequency of appearance and the dynamics of the Author Keywords (AKs) has been made for the last thirty years. Likewise, the network of co-occurrences established between words and the survival time of new words that have appeared since 2001 has also been analysed. One of the main findings of our research is the identification of six large thematic clusters in the SSC. There are also two major terms that coexist ('REHABILITATION' and 'EXERCISE') and show a high frequency of appearance, as well as a key behaviour in the calculated co-occurrence networks. Another significant finding is that AKs are mostly accepted in the SSC since there has been high percentage of new terms during 2001-2006, although they have a low survival period. These results support a multidisciplinary perspective within the Sport Sciences field of study and a colonization of the field by rehabilitation according to our AK analysis.

SUBMITTER: Gonzalez LM

PROVIDER: S-EPMC6070281 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An author keyword analysis for mapping Sport Sciences.

González Luis-Millán LM García-Massó Xavier X Pardo-Ibañez Alberto A Peset Fernanda F Devís-Devís José J

PloS one 20180801 8

Scientific production has increased exponentially in recent years. It is necessary to find methodological strategies for understanding holistic or macro views of the major research trends developed in specific fields. Data mining is a useful technique to address this task. In particular, our study presents a global analysis of the information generated during last decades in the Sport Sciences Category (SSC) included in the Web of Science database. An analysis of the frequency of appearance and ...[more]

PMID: 30067822

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Project description:Analyses of author collaborations and keyword co-occurrences are frequently used in bibliographic research. However, no studies have introduced a straightforward yet effective approach, such as utilizing ChatGPT with Code Interpreter (ChatGPT_CI) or the R language, for creating cluster-oriented networks. This research aims to compare cluster analysis methods in ChatGPT_CI and R, visualize country-specific author collaborations, and then demonstrate the most effective approach. The research focused on articles and review pieces from Medicine (Baltimore) published in 2023. By August 20, 2023, we had gathered metadata for 1976 articles using the Web of Science core collections. The efficiency and effectiveness of cluster displays between ChatGPT_CI and R were compared by evaluating their time consumption. The best method was then employed to present a series of visualizations of country-specific author collaborations, rooted in social network and cluster analyses. Visualization techniques incorporating network charts, chord diagrams, circle bar plots, circle packing plots, heat dendrograms, dendrograms, and word clouds were demonstrated. We further highlighted the research profiles of 2 prolific authors using timeline visuals. The research findings include that (1) the most active contributors were China, Nanjing Medical University (China), the Medical School Department, and Dr Chou from Taiwan when considering countries, institutions, departments, and individual authors, respectively; (2) the highest cited articles originated from Medicine (Baltimore) accounting for 4.53%: New England Journal of Medicine, PLOS ONE, LANCET, and The Journal of the American Medical Association, with respective contributions of 3.25%, 2.7%, 2.52%, and 1.54%; (3) visual cluster analysis in R proved to be more efficient and effective than ChatGPT_CI, reducing the time taken from 1 hour to just 3 minutes; (4) 7 cluster-focused networks were crafted using R on a custom platform; and (5) the research trajectories of 2 prominent authors (Dr Brin from the United States and Dr Chow from Taiwan) and articles themes in Medicine 2023 were depicted using timeline visuals. This research highlighted the efficient and effective methods for conducting cluster analyses of author collaborations using R. For future related studies, such as keyword co-occurrence analysis, R is recommended as a viable alternative for bibliographic research.

Project description:In the elite domain of interactive sports, athletes who demonstrate a left preference (e.g., holding a weapon with the left hand in fencing or boxing in a 'southpaw' stance) seem overrepresented. Such excess indicates a performance advantage and was also interpreted as evidence in favour of frequency-dependent selection mechanisms to explain the maintenance of left-handedness in humans. To test for an overrepresentation, the incidence of athletes' lateral preferences is typically compared with an expected ratio of left- to right-handedness in the normal population. However, the normal population reference values did not always relate to the sport-specific tasks of interest, which may limit the validity of reports of an excess of 'left-oriented' athletes. Here we sought to determine lateral preferences for various sport-specific tasks (e.g., baseball batting, boxing) in the normal population and to examine the relationship between these preferences and handedness. To this end, we asked 903 participants to indicate their lateral preferences for sport-specific and common tasks using a paper-based questionnaire. Lateral preferences varied considerably across the different sport tasks and we found high variation in the relationship between those preferences and handedness. In contrast to unimanual tasks (e.g., fencing or throwing), for bimanually controlled actions such as baseball batting, shooting in ice hockey or boxing the incidence of left preferences was considerably higher than expected from the proportion of left-handedness in the normal population and the relationship with handedness was relatively low. We conclude that (i) task-specific reference values are mandatory for reliably testing for an excess of athletes with a left preference, (ii) the term 'handedness' should be more cautiously used within the context of sport-related laterality research and (iii) observation of lateral preferences in sports may be of limited suitability for the verification of evolutionary theories of handedness.

Project description:BackgroundThe tool kits of bibliometrics and science mapping provide a standard methodology to map the knowledge base of specific fields of science. The aim of the present research is the analysis of the recent international trends of sport nutrition science, as well as the primary identification of the research topics and results of sport nutrition science via enhanced bibliometric methods for the 2000-2018 time period.MethodsAltogether, 3889 publications were included in this study. We identified the most relevant sport nutrition topics by running a community detection algorithm on the proximity network constructed via network text analysis. The key issues and key concepts of sport nutrition topics as well as their relations were evaluated via network analysis. Besides, we carried a chronological analysis of topics out and a scientometric evaluative analysis was also created.ResultsWe identified the four main basic groups from which the 18 most characteristics topics were analyzed. The 18 topics are the following: 'soccer and physiology', 'carbohydrate metabolism', 'muscle physiology: alkalosis and acidosis', 'muscle mass gain and dietary supplementation', 'fluid balance and hydration', 'dietary intake and nutrition knowledge', 'determination of energy need of athletes', 'bone health and female athlete triad', 'hydration strategy', 'body weight management', 'nutritional strategies and human skeletal muscle', 'dietary supplementation of nitrates', 'oxidative stress and dietary supplement use', 'dietary supplement use and doping', 'oxidative stress and inflammation and dietary antioxidants', 'exercise adaptation and nutritional strategies', 'gut microbiota', 'celiac disease'. Regarding the size of the topic, researches on sport nutrition science have put the focus on the following three groups: 'muscle mass gain and dietary supplementation', 'carbohydrate metabolism', 'oxidative stress and dietary supplement use'. The greatest scientific impact can be ascribed to the following topics: 'nutritional strategies and human skeletal muscle', 'dietary supplementation of nitrates', 'body weight management', and 'gut microbiota'.ConclusionsScientific output on sport nutrition has continuously been rising between 2000 and 2018. The ratio of topics related to sport nutrition but predominantly connected to basic research has decreased significantly within all publications. The results of this study confirm the role of science mapping in the identification of specific research topics and primary research directions in the field of sport nutrition science.

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An author keyword analysis for mapping Sport Sciences.

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An author keyword analysis for mapping Sport Sciences.

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