scholarly journals Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses

2021 ◽  
Vol 13 (8) ◽  
pp. 203
Author(s):  
Klaus Kammerer ◽  
Manuel Göster ◽  
Manfred Reichert ◽  
Rüdiger Pryss
Keyword(s):  
Bibliometric Analysis ◽  
Domain Knowledge ◽  
Data Science ◽  
Deep Understanding ◽  
Research Area ◽  
Network Analyses ◽  
Statistical Knowledge ◽  
Academic Publications ◽  
Programming Skills ◽  
Analysis Platform

A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.

scholarly journals Application of Artificial Intelligence in COVID-19 Pandemic: Bibliometric Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 441
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
Belal Alsinglawi ◽  
Li-Fong Lin ◽  
Shuo-Chen Chien ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bibliometric Analysis ◽  
English Language ◽  
R Package ◽  
Research Area ◽  
Republic Of China ◽  
Comprehensive Picture ◽  
The Usa ◽  
The Republic ◽  
Academy Of Sciences

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future.

scholarly journals A Bibliometric Analysis of the Global Trend of Using Alginate, Gelatine, and Hydroxyapatite for Bone Tissue Regeneration Applications

Polymers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 647
Author(s):  
Mohamed Saiful Firdaus Hussin ◽  
Aludin Mohd Serah ◽  
Khairul Azri Azlan ◽  
Hasan Zuhudi Abdullah ◽  
Maizlinda Izwana Idris ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Bone Tissue Engineering ◽  
Bone Tissue ◽  
Bibliometric Analysis ◽  
Research Work ◽  
The United States ◽  
Research Area ◽  
Bone Tissue Regeneration ◽  
Future Research ◽  
Starting Point

Collecting information from previous investigations and expressing it in a scientometrics study can be a priceless guide to getting a complete overview of a specific research area. The aim of this study is to explore the interrelated connection between alginate, gelatine, and hydroxyapatite within the scope of bone tissue and scaffold. A review of traditional literature with data mining procedures using bibliometric analyses was considered to identify the evolution of the selected research area between 2009 and 2019. Bibliometric methods and knowledge visualization technologies were implemented to investigate diverse publications based on the following indicators: year of publication, document type, language, country, institution, author, journal, keyword, and number of citations. An analysis using a bibliometric study found that 7446 papers were located with the keywords “bone tissue” and “scaffold”, and 1767 (alginate), 185 (gelatine), 5658 (hydroxyapatite) papers with those specific sub keywords. The number of publications that relate to “tissue engineering” and bone more than doubled between 2009 (1352) and 2019 (2839). China, the United States and India are the most productive countries, while Sichuan University and the Chinese Academy of Science from China are the most important institutions related to bone tissue scaffold. Materials Science and Engineering C is the most productive journal, followed by the Journal of Biomedical Materials Research Part A. This paper is a starting point, providing the first bibliometric analysis study of bone tissue and scaffold considering alginate, gelatine and hydroxyapatite. A bibliometric analysis would greatly assist in giving a scientific insight to support desired future research work, not only associated with bone tissue engineering applications. It is expected that the analysis of alginate, gelatine and hydroxyapatite in terms of 3D bioprinting, clinical outcomes, scaffold architecture, and the regenerative medicine approach will enhance the research into bone tissue engineering in the near future. Continued studies into these research fields are highly recommended.

scholarly journals Clustering in Wineinformatics with Attribute Selection to Increase Uniqueness of Clusters

Fermentation ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Jared McCune ◽  
Alex Riley ◽  
Bernard Chen
Keyword(s):  
Unsupervised Learning ◽  
Data Science ◽  
Science Research ◽  
Research Area ◽  
Attribute Selection ◽  
Short List ◽  
Wine Quality ◽  
Filtration Process ◽  
Target Region ◽  
Related Data

Wineinformatics is a new data science research area that focuses on large amounts of wine-related data. Most of the current Wineinformatics researches are focused on supervised learning to predict the wine quality, price, region and weather. In this research, unsupervised learning using K-means clustering with optimal K search and filtration process is studied on a Bordeaux-region specific dataset to form clusters and find representative wines in each cluster. 14,349 wines representing the 21st century Bordeaux dataset are clustered into 43 and 13 clusters with detailed analysis on the number of wines, dominant wine characteristics, average wine grades, and representative wines in each cluster. Similar research results are also generated and presented on 435 elite wines (wines that scored 95 points and above on a 100 points scale). The information generated from this research can be beneficial to wine vendors to make a selection given the limited number of wines they can realistically offer, to connoisseurs to study wines in a target region/vintage/price with a representative short list, and to wine consumers to get recommendations. Many possible researches can adopt the same process to analyze and find representative wines in different wine making regions/countries, vintages, or pivot points. This paper opens up a new door for Wineinformatics in unsupervised learning researches.

scholarly journals An intersectional approach to analyse gender productivity and open access: a bibliometric analysis of the Italian National Research Council

2021 ◽  
Author(s):  
Roberta Ruggieri ◽  
Fabrizio Pecoraro ◽  
Daniela Luzi
Keyword(s):  
Open Access ◽  
Bibliometric Analysis ◽  
Gender Gap ◽  
Research Council ◽  
National Research Council ◽  
Research Area ◽  
Scientific Production ◽  
Italian National Research Council ◽  
European Research ◽  
Research And Innovation

AbstractGender equality and Open Access (OA) are priorities within the European Research Area and cross-cutting issues in European research program H2020. Gender and openness are also key elements of responsible research and innovation. However, despite the common underlying targets of fostering an inclusive, transparent and sustainable research environment, both issues are analysed as independent topics. This paper represents a first exploration of the inter-linkages between gender and OA analysing the scientific production of researchers of the Italian National Research Council under a gender perspective integrated with the different OA publications modes. A bibliometric analysis was carried out for articles published in the period 2016–2018 and retrieved from the Web of Science. Results are presented constantly analysing CNR scientific production in relation to gender, disciplinary fields and OA publication modes. These variables are also used when analysing articles that receive financial support. Our results indicate that gender disparities in scientific production still persist particularly in STEM disciplines, while the gender gap is the closest to parity in medical and agricultural sciences. A positive dynamic toward OA publishing and women’s scientific production is shown when disciplines with well-established open practices are related to articles supported by funds. A slightly higher women’s propensity toward OA is shown when considering Gold OA, or authorships with women in the first and last article by-line position. The prevalence of Italian funded articles with women’s contributions published in Gold OA journals seems to confirm this tendency, especially if considering the weak enforcement of the Italian OA policies.

Data Scientists’ Identity Work: Omnivorous Symbolic Boundaries in Skills Acquisition

2021 ◽  
pp. 095001702097730
Author(s):  
Netta Avnoon
Keyword(s):  
Professional Identity ◽  
Domain Knowledge ◽  
Data Science ◽  
Identity Work ◽  
Skills Acquisition ◽  
Sociology Of Culture ◽  
Sociology Of Work ◽  
Discursive Analysis ◽  
Self Learning ◽  
Depth Interviews

Drawing on theories from the sociology of work and the sociology of culture, this article argues that members of nascent technical occupations construct their professional identity and claim status through an omnivorous approach to skills acquisition. Based on a discursive analysis of 56 semi-structured in-depth interviews with data scientists, data science professors and managers in Israel, it was found that data scientists mobilise the following five resources to construct their identity: (1) ability to bridge the gap between scientist’s and engineer’s identities; (2) multiplicity of theories; (3) intensive self-learning; (4) bridging technical and social skills; and (5) acquiring domain knowledge easily. These resources diverge from former generalist-specialist identity tensions described in the literature as they attribute a higher status to the generalist-omnivore and a lower one to the specialist-snob.

Unveiling contemporary dimensions in the internationalisation of family firms through bibliometric analysis and thematic analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonal Thukral ◽  
Apoorva Jain
Keyword(s):  
Bibliometric Analysis ◽  
Thematic Analysis ◽  
Family Firms ◽  
Research Area ◽  
Research Articles ◽  
Future Research ◽  
Content Type ◽  
Holistic Understanding ◽  
The Family ◽  
Research Stream

Purpose For sustaining a competitive advantage in the integrated world economy, it has become imperative for family firms to internationalise their operations in overseas markets. However, despite the growing set of literature, results are still inconclusive with respect to family firms’ internationalisation. Thus, this study aims to address this gap by systematically reviewing 142 articles (1991–2019) to help researchers in identifying and unfolding the unexplored themes in the underlying area. Design/methodology/approach For systematically reviewing articles, the study uses a three-step methodology following PRISMA guidelines, bibliometric analysis and thematic analysis. Descriptive statistics of 142 research articles are obtained through bibliometric analysis while thematic analysis is carried out to create themes or clusters of various factors relating to family firms’ internationalisation. Findings The current review uncovers the evolving trends in the research streams, most productive authors, top journals and articles, co-citation analysis, as well as the major themes surrounding the family firms’ internationalisation literature. Results from bibliometric analysis indicate that family firms’ internationalisation is an upcoming research area. Also, the review indicates an opportunity for scholars from developing nations to make significant contributions in the underlying research stream. Research limitations/implications Results from bibliometric and thematic analysis will help academicians and researchers in accumulating a holistic understanding relating to family firms’ internationalisation and understanding the upcoming trends in family firms’ research, thereby guiding the future research scope. Also, it will assist the family firms’ leaders and managers in understanding the important dynamics in overseas markets and various factors to be considered while planning their internationalisation. Originality/value Undertaking a systematic literature review presents readers with a state-of-the-art understanding of the underlying research topic. To the best of the knowledge, to date, the study is the first to conduct the review of literature through bibliometric analysis with the help of R Studio software in the field of family firms’ internationalisation. Also, the study is the first to review more than 100 research articles in the underlying area. Finally, the study proposes a comprehensive framework integrating the major themes and facets relating to family firms’ internationalisation.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

2021 ◽  
Author(s):  
MUTHU RAM ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Domain Knowledge ◽  
Data Science ◽  
Damage Tolerance ◽  
Electrochemical Impedance ◽  
Research Work ◽  
Rate Of Change ◽  
Data Driven ◽  
Develop Model ◽  
Damage Development ◽  
Fatigue Loads

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.

scholarly journals A Bibliometric Analysis of the Top-Cited Articles on Diabetic Foot Ulcers

2021 ◽  
Author(s):  
Jiasheng You ◽  
Chao Liu ◽  
Yixin Chen ◽  
Weifen Zhu ◽  
Shunwu Fan ◽  
...  
Keyword(s):  
Bibliometric Analysis ◽  
Diabetic Foot ◽  
Median Number ◽  
Scientific Discipline ◽  
Foot Ulcers ◽  
Diabetic Foot Ulcers ◽  
Level Of Evidence ◽  
Bibliometric Method ◽  
Academic Publications ◽  
The Impact

Abstract Background: Citation analysis is a bibliometric method for appraising the impact of academic publications in any given scientific discipline. There is a paucity of literature concerning influential works on diabetic foot ulcers (DFUs). Aims: To determine the top-cited articles in the field of DFU research.Methods: A bibliometric analysis of citations indexed in the Scopus and the Web of Science databases was conducted in January 2021 to determine all publications related to DFU. The 50 top-cited articles that met the inclusion criteria were ranked. Articles were evaluated for several characteristics including year of publication, country of origin, authorship, publishing journal, topic categories, publishing type and level of evidence.Results: The median number of citations per article in the list was 442 (interquartile range [IQR], 320-520), with a median of 21.8 citations (IQR, 16.5-34.5) per year since publication. The publication years ranged from 1986 to 2017, with 1998 accounting for the greatest number of studies (n = 7). The citation classics were published in 20 journals and originated from institutions in nine countries. The majority of the studies were clinical, of which expert-opinion/review with Level V evidence and clinical studies with Levels I and II evidence comprised the greater proportion in the list.Conclusions: This study identified the top-cited articles and provides useful insights into the history and development of DFU research. Our findings may serve as a quick reference for education curriculums and clinical practice, in addition to providing a foundation for further studies on this topic.

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