7,600 research outputs found
What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature
In this article, we conduct data mining to discover the countries,
universities and companies, produced or collaborated the most research on
Covid-19 since the pandemic started. We present some interesting findings, but
despite analysing all available records on COVID-19 from the Web of Science
Core Collection, we failed to reach any significant conclusions on how the
world responded to the COVID-19 pandemic. Therefore, we increased our analysis
to include all available data records on pandemics and epidemics from 1900 to
2020. We discover some interesting results on countries, universities and
companies, that produced collaborated most the most in research on pandemic and
epidemics. Then we compared the results with the analysing on COVID-19 data
records. This has created some interesting findings that are explained and
graphically visualised in the article
The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis
Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)
Bibliometric Mapping of Big Data (BD) in Higher Education (HE): Towards a comprehensive framework
Although big data (BD) has emerged rapidly over the last decade, its importance within higher education (HE) has remained scarce in academic literature. This research aims to develop a comprehensive framework for using big data in HE. We achieved our research objective by conducting a bibliometric analysis of the available literature on BD in HE published in the English language between 2013 -2021. A total of 4312 articles were considered for analysis. Our results showed that most studies focused on the technical specifications of BD, such as data mining and Hadoop. There was a slight reference to the operationality and management
functions. However, it is pertinent to note that data privacy, advanced analytics, and machine learning were highlighted as emerging topics. It, therefore, suggests the importance of advanced data analytics and data privacy in establishing a comprehensive framework for managing BD in HE
Big Data in Sports: A Bibliometric and Topic Study
Background: The development of the sports industry was impacted by the era of Big Data due to the rapid growth of information technology. Unfortunately, that has become an increasingly challenging Issue. Objectives: The purpose of the research was to analyze the scientific production of Big Data in sports and sports-related activities in two databases, Web of Science and Scopus. Methods/Approach: Bibliometric analysis and topic mining were done on 51 articles selected after four exclusion criteria (written in English, journal articles, the final stage of publication, and a detailed review of all full texts). The software tool used was Statistica Data Miner. Results: We found that the first articles appeared in Scopus in 2013 and WoS in 2014. USA and China are countries which produced the most articles. The most common research areas in WoS and Scopus are Public environmental and occupational health, Medicine, Environmental science ecology, and Engineering. Conclusions: We conducted that further research and literature review will be required as this is a broad and new topic
Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998â2018)
Opinion mining and sentiment analysis has become ubiquitous in our society, with
applications in online searching, computer vision, image understanding, artificial intelligence and
marketing communications (MarCom). Within this context, opinion mining and sentiment analysis
in marketing communications (OMSAMC) has a strong role in the development of the field by
allowing us to understand whether people are satisfied or dissatisfied with our service or product
in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To
the best of our knowledge, there is no science mapping analysis covering the research about opinion
mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science
mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work
during the last two decades in this interdisciplinary area and to show trends that could be the basis
for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer
and InCites based on results from Web of Science (WoS). The results of this analysis show the
evolution of the field, by highlighting the most notable authors, institutions, keywords,
publications, countries, categories and journals.The research was funded by Programa Operativo FEDER AndalucĂa 2014â2020, grant number âLa
reputaciĂłn de las organizaciones en una sociedad digital. ElaboraciĂłn de una Plataforma Inteligente para la
LocalizaciĂłn, IdentificaciĂłn y ClasificaciĂłn de Influenciadores en los Medios Sociales Digitales (UMA18â
FEDERJAâ148)â and The APC was funded by the same research gran
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?
Given the many technical, social, and policy shifts in access to scholarly
content since the early days of text data mining, it is time to expand the
conversation about text data mining from concerns of the researcher wishing to
mine data to include concerns of researcher-authors about how their data are
mined, by whom, for what purposes, and to whose benefits.Comment: Forum Statement: Data Mining with Limited Access Text: National
Forum. April 5-6, 2018. https://publish.illinois.edu/limitedaccess-tdm
Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better?)
Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies.
Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications
Quantifying the consistency of scientific databases
Science is a social process with far-reaching impact on our modern society.
In the recent years, for the first time we are able to scientifically study the
science itself. This is enabled by massive amounts of data on scientific
publications that is increasingly becoming available. The data is contained in
several databases such as Web of Science or PubMed, maintained by various
public and private entities. Unfortunately, these databases are not always
consistent, which considerably hinders this study. Relying on the powerful
framework of complex networks, we conduct a systematic analysis of the
consistency among six major scientific databases. We found that identifying a
single "best" database is far from easy. Nevertheless, our results indicate
appreciable differences in mutual consistency of different databases, which we
interpret as recipes for future bibliometric studies.Comment: 20 pages, 5 figures, 4 table
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