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1.
Int Endod J ; 53(3): 308-316, 2020 Mar.
Article in English | MEDLINE | ID: mdl-31566775

ABSTRACT

AIM: To analyse and visualize the knowledge structure of scientific articles in the field of Endodontology with high altmetric attention scores to discover hot topics, active researchers and the journals involved. METHODOLOGY: On 5 June 2019, the altmetric database (Altmetric LLP, London, UK) was searched using the titles of 11 endodontic journals. Bibliometric data from endodontic articles and journals with an altmetric score >5 (top 5%) were retrieved from PubMed and analysed using the VOSviewer. Science mapping of articles with an altmetric score >5 at two levels was created: author keywords co-occurrence and co-authorship network analysis. RESULTS: Of the 2197 articles in the field of Endodontology identified with altmetrics, 192 had altmetric scores >5 (top 5%). Considering the total mentions amongst all altmetric resources, the Journal of Endodontics had the highest rank followed by the International Endodontic Journal and Australian Endodontic Journal. Twitter was the most popular altmetric data resource followed by patents and Facebook. Meta-analysis, systematic review and pulpitis were the hot topics. At the author level, Dummer P.M.H had the greatest influence on the network. There was no significant correlation between altmetric score and citations count (P > 0.05). Mendeley mentions correlated with citations (P < 0.05). CONCLUSIONS: Overall, the altmetric scores of topics within Endodontology were low, possibly due to the specific and specialized nature of the specialty, as well as the difficulty members of the public probably have in understanding endodontic research. Journals and researchers with a focus on Endodontology would have more influence if they were to set-up their own social media profiles and thus enhance their visibility and social impact by immediately sharing research findings and communicating with their network and audience.


Subject(s)
Endodontics , Social Media , Australia , Bibliometrics , Journal Impact Factor , Meta-Analysis as Topic
2.
Br Dent J ; 222(9): 695-699, 2017 May 12.
Article in English | MEDLINE | ID: mdl-28496225

ABSTRACT

Introduction To report and analyse Altmetric data of all dental articles and journals in 2015.Methods To identify all 2015 dental articles, PubMed was searched via Altmetric platform using the following query: ("2015/1/1"[PDAT]: "2015/12/31"[PDAT]) AND jsubsetd[text] NOT 2016[PDAT] on November 12, 2016. Altmetric data of all 2015 dental articles and journals were extracted and analysed by Microsoft Office Excel 2016 using descriptive statistics, graphs and trend-line analysis. To find the most important and influential Altmetric factors, multi-layered perceptron artificial neural network was employed using SPSS 22.Results A total of 14,884 dental articles published in 2015 using PubMed database were found, from which 5,153 (34.62%) articles had an Altmetric score. The mean Altmetric score was 2.94 ± 9.2 (95% C.I:2.703.22). Mendeley readers (73.19%), Twitter (21.48%), Facebook walls (3.67%), news outlets (0.69%) and bloggers (0.57%) were the most popular Altmetric data resources. At journal level, 147 dental journals with valid Altmetric data were included in the study. The British Dental Journal had the first rank, followed by Journal of Dental Research, Journal of Clinical Periodontology and Journal of the American Dental Association. Sensitivity analysis showed news outlets, tweeters and scientific bloggers were the most important and influential Altmetric data resources.Discussion In comparison with all science subjects and medical and health sciences, 2015 Altmetric scores in dentistry were very low. Uses of new and emerging scholarly tools such as social media, scientific blogs and post-publication peer-review were not common in the dental science. This negligence may be due to lack of knowledge and attitude. An Altmetric score is dynamic and may fluctuate over time.


Subject(s)
Bibliometrics , Dentistry/statistics & numerical data , Journal Impact Factor , Cross-Sectional Studies , Humans , Neural Networks, Computer , Periodicals as Topic/statistics & numerical data
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