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Metrics, Volume 1, Issue 1 (December 2024) – 5 articles

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11 pages, 2382 KiB  
Article
Bibliometric Studies as a Publication Strategy
by Libor Ansorge
Metrics 2024, 1(1), 5; https://doi.org/10.3390/metrics1010005 - 21 Nov 2024
Viewed by 990
Abstract
The number of bibliometric studies published in the scientific literature has been increasing in recent years. Some authors publish more bibliometric studies than others. The aim of this study is to (i) identify authors who focus on bibliometric studies and their publication strategy [...] Read more.
The number of bibliometric studies published in the scientific literature has been increasing in recent years. Some authors publish more bibliometric studies than others. The aim of this study is to (i) identify authors who focus on bibliometric studies and their publication strategy based on these studies, and to (ii) determine whether the focus of the bibliometric studies can be considered a successful publication strategy. Bibliometric analysis, including citation analysis, was used to determine the results. The Scopus database was selected as the source of bibliometric data. A total of 100 authors who frequently publish bibliometric studies were identified. For almost half of them, bibliometric studies is considered the main or significant part of their publication portfolio. A relatively small group of authors widely publish bibliometric studies. The bibliometric indicators of these authors point out that the specialization of bibliometric studies is quite successful. Full article
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2 pages, 174 KiB  
Editorial
Welcome to Metrics: A New Open-Access Journal
by Manuel Pedro Rodríguez Bolívar
Metrics 2024, 1(1), 4; https://doi.org/10.3390/metrics1010004 - 6 Nov 2024
Viewed by 501
Abstract
As science has expanded in size and recognition as the dominant driver of innovation and economic growth, the need for studies to analyze and characterize the contributions made by research in each field of knowledge and related research areas from a critical standpoint [...] Read more.
As science has expanded in size and recognition as the dominant driver of innovation and economic growth, the need for studies to analyze and characterize the contributions made by research in each field of knowledge and related research areas from a critical standpoint has become increasingly prominent in international journals [...] Full article
15 pages, 1549 KiB  
Article
Topic Modeling as a Tool to Identify Research Diversity: A Study Across Dental Disciplines
by Maria Teresa Colangelo, Stefano Guizzardi and Carlo Galli
Metrics 2024, 1(1), 3; https://doi.org/10.3390/metrics1010003 - 13 Oct 2024
Viewed by 1223
Abstract
This study investigates the diversity and evolution of research topics within the dental sciences from 1994 to 2023, using Topic modeling and Shannon’s entropy as a measure of research diversity. We analyzed a dataset of 412,036 scientific articles across six dental disciplines: Orthodontics, [...] Read more.
This study investigates the diversity and evolution of research topics within the dental sciences from 1994 to 2023, using Topic modeling and Shannon’s entropy as a measure of research diversity. We analyzed a dataset of 412,036 scientific articles across six dental disciplines: Orthodontics, Prosthodontics, Periodontics, Implant Dentistry, Oral Surgery, and Restorative Dentistry. This research relies on BERTopic to identify distinct topics within each field. The study revealed significant shifts in research focus over time, with some disciplines exhibiting robust growth in article numbers, such as Periodontics and Prosthodontics. However, despite the overall increase in publications, the number of topics per discipline varied, with Restorative Dentistry increasing at a faster rate and exceeding 50 topics over the last 15 years. We observed an increasing diversification of research efforts in disciplines such as Restorative Dentistry, with entropy levels consistently above 2 and progressively increasing. In contrast, fields such as Prosthodontics, despite high publication output, maintained a more specialized research focus, reflected in entropy levels remaining below 1.5. Oral Surgery showed a steep increase in research diversification until 2000, after which it stabilized. Taken together, our findings describe the dynamic nature of dental research and highlight the balance shifts in research focus across several key areas of Dentistry. Full article
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24 pages, 2561 KiB  
Article
Topic Modeling for Faster Literature Screening Using Transformer-Based Embeddings
by Carlo Galli, Claudio Cusano, Marco Meleti, Nikolaos Donos and Elena Calciolari
Metrics 2024, 1(1), 2; https://doi.org/10.3390/metrics1010002 - 8 Oct 2024
Viewed by 1419
Abstract
Systematic reviews are a powerful tool to summarize the existing evidence in medical literature. However, identifying relevant articles is difficult, and this typically involves structured searches with keyword-based strategies, followed by the painstaking manual selection of relevant evidence. A.I. may help investigators, for [...] Read more.
Systematic reviews are a powerful tool to summarize the existing evidence in medical literature. However, identifying relevant articles is difficult, and this typically involves structured searches with keyword-based strategies, followed by the painstaking manual selection of relevant evidence. A.I. may help investigators, for example, through topic modeling, i.e., algorithms that can understand the content of a text. We applied BERTopic, a transformer-based topic-modeling algorithm, to two datasets consisting of 6137 and 5309 articles, respectively, used in recently published systematic reviews on peri-implantitis and bone regeneration. We extracted the title of each article, encoded it into embeddings, and input it into BERTopic, which then rapidly identified 14 and 22 topic clusters, respectively, and it automatically created labels describing the content of these groups based on their semantics. For both datasets, BERTopic uncovered a variable number of articles unrelated to the query, which accounted for up to 30% of the dataset—achieving a sensitivity of up to 0.79 and a specificity of at least 0.99. These articles could have been discarded from the screening, reducing the workload of investigators. Our results suggest that adding a topic-modeling step to the screening process could potentially save working hours for researchers involved in systematic reviews of the literature. Full article
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14 pages, 880 KiB  
Review
Embeddings for Efficient Literature Screening: A Primer for Life Science Investigators
by Carlo Galli, Claudio Cusano, Stefano Guizzardi, Nikolaos Donos and Elena Calciolari
Metrics 2024, 1(1), 1; https://doi.org/10.3390/metrics1010001 - 30 Sep 2024
Viewed by 1044
Abstract
As the number of publications is quickly growing in any area of science, the need to efficiently find relevant information amidst a large number of similarly themed articles becomes very important. Semantic searching through text documents has the potential to overcome the limits [...] Read more.
As the number of publications is quickly growing in any area of science, the need to efficiently find relevant information amidst a large number of similarly themed articles becomes very important. Semantic searching through text documents has the potential to overcome the limits of keyword-based searches, especially since the introduction of attention-based transformers, which can capture contextual nuances of meaning in single words, sentences, or whole documents. The deployment of these computational tools has been made simpler and accessible to investigators in every field of research thanks to a growing number of dedicated libraries, but knowledge of how meaning representation strategies work is crucial to making the most out of these instruments. The present work aims at introducing the technical evolution of the meaning representation systems, from vectors to embeddings and transformers tailored to life science investigators with no previous knowledge of natural language processing. Full article
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