A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020

Furstenau, Leonardo Bertolin; Rabaioli, Bruna; Sott, Michele Kremer; Cossul, Danielli; Bender, Mariluza Sott; Farina, Eduardo Moreno Júdice de Mattos; Filho, Fabiano Novaes Barcellos; Severo, Priscilla Paola; Dohan, Michael S.; Bragazzi, Nicola Luigi

doi:10.3390/ijerph18030952

Cited by 65 publications

(49 citation statements)

References 117 publications

(117 reference statements)

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“…As such, we can conclude that the effect of the COVID-19 pandemic is nuanced and complex, on the one hand amplifying already existing inequalities [ 22 , 23 ], and on the other hand paving the way for new opportunities and catalyzing new venues [ 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Pandemic Related Research in Africa: Bibliometric Analysis of Scholarly Output, Collaborations and Scientific Leadership

Fonkou

Bragazzi

Tsinda

et al. 2021

IJERPH

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Scientometrics enables scholars to assess and visualize emerging research trends and hot-spots in the scientific literature from a quantitative standpoint. In the last decades, Africa has nearly doubled its absolute count of scholarly output, even though its share in global knowledge production has dramatically decreased. The still-ongoing COVID-19 pandemic has profoundly impacted the way scholarly research is conducted, published, and disseminated. However, the COVID-19-related research focus, the scientific productivity, and the research collaborative network of African researchers during the ongoing COVID-19 pandemic remain to be elucidated. This study aimed to clarify the COVID-19 research patterns among African researchers and estimate the strength of collaborations and partnerships between African researchers and scholars from the rest of the world during the COVID-19 pandemic, collecting data from electronic scholarly databases such as Web of Science (WoS), PubMed/MEDLINE and African Journals OnLine (AJOL), the largest and prominent platform of African-published scholarly journals. We found that COVID-19-related collaboration patterns varied among African regions. For instance, most of the scholarly partnerships occurred with formerly colonial countries (such as European or North-American countries). In other cases, scholarly ties of North African countries were above all with the Kingdom of Saudi Arabia. In terms of number of publications, South Africa and Egypt were among the most productive countries. Bibliometrics and, in particular, scientometrics can help scholars identify research areas of particular interest, as well as emerging topics, such as the COVID-19 pandemic. With a specific focus on the still-ongoing viral outbreak, they can assist decision- and policy-makers in allocating funding and economic-financial, logistic, organizational, and human resources, based on the specific gaps and needs of a given country or research area.

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Section: Discussionmentioning

confidence: 99%

COVID-19 Pandemic Related Research in Africa: Bibliometric Analysis of Scholarly Output, Collaborations and Scientific Leadership

Fonkou

Bragazzi

Tsinda

et al. 2021

IJERPH

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“…To answer the research questions, studies available in the Web of Science (WoS) Core Collection database were used. All indexes were considered such as Social Science Citation Index (SSCI), Science Citation Index Expanded (SCI-E) and others, since WoS is an indexed database with a large volume of quality research (Cobo et al, 2011;Furstenau et al, 2021aFurstenau et al, , 2021Severo et al, 2021;Sott et al, 2020b). In the population phase of the PICOC protocol, the following search string (('data mining' OR 'knowledge-discovery' OR 'estimation' OR 'prediction') AND ('solid waste')) was used to identify the documents associated with the field of study.…”

Section: Picoc Protocolmentioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

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“…After defining the subperiods, we generate the diagrams based on the keyword co-occurrence matrix with the SciMAT to identify the most important themes and their nodes with other clusters [66][67][68]. Then, to identify the similarity between themes, the data were normalized using the Equivalence Index [69], and then clustered based on the Simple Centers Algorithm to create a network of relationships [70].…”

Section: Quality Assessment Descriptionmentioning

confidence: 99%

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

Mk¹,

LdS²,

Cr³

et al. 2021

Preprint

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: The agriculture sector is one of the backbones of many countries’ economies and its processes have been changing in order to enable technological adoption to increase productivity, quality, and sustainable development. In this research, we present a theoretical reflection through a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, the so-called Precision Agriculture (PA) and Agriculture 4.0 (A4.0). To do this, we used 4,694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis (BPNA) of the literature with the support of the PICOC protocol and the SciMAT software. Our findings present 22 strategic themes related to Digital Agriculture (DA) such as Internet of Things (IoT) and Climate-smart Agriculture (CSA) among others, and the thematic network structures of the motor themes and the thematic evolution structure of the field of the study over time. In addition, our results discuss the main challenges and opportunities of DA. Our findings have the potential to provide insights for practitioners and researchers in decision-making and pave the way for future works.

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A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020

Cited by 65 publications

References 117 publications

COVID-19 Pandemic Related Research in Africa: Bibliometric Analysis of Scholarly Output, Collaborations and Scientific Leadership

COVID-19 Pandemic Related Research in Africa: Bibliometric Analysis of Scholarly Output, Collaborations and Scientific Leadership

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

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