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31 Research products, page 1 of 4

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  • Open Access English
    Authors: 
    Arash Hajikhani; Arho Suominen;
    Country: Finland

    AbstractThe sustainable development goals (SDGs) are a blueprint for achieving a better and more sustainable future for all by defining priorities and aspirations for 2030. This paper attempts to expand on the United Nations SDGs definition by leveraging the interrelationship between science and technology. We utilize SDG classification of scientific publications to compile a machine learning (ML) model to classify the SDG relevancy in patent documents, used as a proxy of technology development. The ML model was used to classify a sample of patent families registered in the European Patent Office (EPO). The analysis revealed the extent to which SDGs were addressed in patents. We also performed a case study to identify the offered extension of ML model detection regarding the SDG orientation of patents. In response to global goals and sustainable development initiatives, the findings can advance the identification challenges of science and technology artefacts. Furthermore, we offer input towards the alignment of R&D efforts and patenting strategies as well as measurement and management of their contribution to the realization of SDGs.

  • Open Access English
    Authors: 
    Enara Zarrabeitia; Izaskun Alvarez-Meaza; Rosa M Rio-Belver; Gaizka Garechana;
    Publisher: Springer
    Country: Spain

    AbstractThis paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.

  • Open Access English
    Authors: 
    Ivan Kodvanj; Jan Homolak; Davor Virag; Vladimir Trkulja;
    Publisher: Springer International Publishing
    Country: Croatia

    ABSTRACTIntroductionCOVID-19-related (vs. non-related) articles appear to be more expeditiously processed and published in peer-reviewed journals. We aimed to evaluate: (i) whether COVID-19-related preprints were favored for publication, (ii) preprinting trends and public discussion of the preprints, and (iii) the relationship between the publication topic (COVID-19-related or not) and quality issues.MethodsManuscripts deposited at bioRxiv and medRxiv between January 1 and September 27 were assessed for the probability of publishing in peer-reviewed journals, and those published were evaluated for submission-to-acceptance time. The extent of public discussion was assessed based on Altmetric and Disqus data. The Retraction Watch Database and PubMed were used to explore the retraction of COVID-19 and non-COVID-19 articles and preprints.ResultsWith adjustment for the preprinting server and number of deposited versions, COVID-19-related preprints were more likely to be published within 120 days since the deposition of the first version (OR=2.04, 95%CI 1.87-2.23) as well as over the entire observed period (OR=1.42, 95%CI 1.33-1.52). Submission-to-acceptance was by 38.67 days (95%CI 34.96-42.39) shorter for COVID-19 articles. Public discussion of preprints was modest and COVID-19 articles were overrepresented in the pool of retracted articles in 2020.ConclusionCurrent data suggest a preference for publication of COVID-19-related preprints over the observed period.

  • Open Access English
    Authors: 
    Ali Ghorbi; Mohsen Fazeli-Varzaneh; Erfan Ghaderi-Azad; Marcel Ausloos; Marcin Kozak;

    This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001-2019, related to scientific articles (co-)written by at least one author affiliated with an Iranian institution. In order to determine reasons for retractions, we merged this database with the database from Retraction Watch. The data were analyzed using Excel 2016 and IBM-SPSS version 24.0, and visualized using VOSviewer software. Most of the retractions were due to fake peer review (95 retractions) and plagiarism (90). The average time between a publication and its retraction was 591 days. The maximum time-lag (about 3,000 days) occurred for papers retracted due to duplicate publications; the minimum time-lag (fewer than 100 days) was for papers retracted due to ''unspecified cause'' (most of these were conference papers). As many as 48 (14%) of the retracted papers were published in two medical journals: Tumor Biology (25 papers) and Diagnostic Pathology (23 papers). From the institutional point of view, Islamic Azad University was the inglorious leader, contributing to over one-half (53.1%) of retracted papers. Among the 343 retraction notices, 64 papers pertained to international collaborations with researchers from mainly Asian and European countries; Malaysia having the most retractions (22 papers). Since most retractions were due to fake peer review and plagiarism, the peer review system appears to be a weak point of the submission/publication process; if improved, the number of retractions would likely drop because of increased editorial control. 29 pages, 7 figures, 5 tables, 41 references

  • Open Access English
    Authors: 
    Milad Haghani; Pegah Varamini;
    Country: Australia
    Project: ARC | Discovery Early Career Re... (DE210101175)

    Following the outbreak of SARS-CoV-2 disease, within less than 8 months, the 50 years-old scholarly literature of coronaviruses grew to nearly three times larger than its size prior to 2020. Here, temporal evolution of the coronavirus literature over the last 30 years (N = 43,769) is analysed along with its subdomain of SARS-CoV-2 articles (N = 27,460) and the subdomain of reviews and meta-analytic studies (N = 1027). The analyses are conducted through the lenses of co-citation and bibliographic coupling of documents. (1) Of the N = 1204 review and meta-analytical articles of the coronavirus literature, nearly 88% have been published and indexed during the first 8 months of 2020, marking an unprecedented attention to reviews and meta-analyses in this domain, prompted by the SARS-CoV-2 pandemic. (2) The subset of 2020 SARS-CoV-2 articles is bibliographically distant from the rest of this literature published prior to 2020. Individual articles of the SARS-CoV-2 segment with a bridging role between the two bodies of articles (i.e., before and after 2020) are identifiable. (3) Furthermore, the degree of bibliographic coupling within the 2020 SARS-CoV-2 cluster is much poorer compared to the cluster of articles published prior to 2020. This could, in part, be explained by the higher diversity of topics that are studied in relation to SARS-CoV-2 compared to the literature of coronaviruses published prior to the SARS-CoV-2 disease. (4) The analyses on the subset of SARS-CoV-2 literature identified studies published prior to 2020 that have now proven highly instrumental in the development of various clusters of publications linked to SARS-CoV-2. In particular, the so-called “sleeping beauties” of the coronavirus literature with an awakening in 2020 were identified, i.e., previously published studies of this literature that had remained relatively unnoticed for several years but gained sudden traction in 2020 in the wake of the SARS-CoV-2 outbreak. This work documents the historical development of the literature on coronaviruses as an event-driven literature and as a domain that exhibited, arguably, the most exceptional case of publication burst in the history of science. It also demonstrates how scholarly efforts undertaken during peace time or prior to a disease outbreak could suddenly play a critical role in prevention and mitigation of health disasters caused by new diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s11192-021-04036-4.

  • Open Access English
    Authors: 
    Adrian G. Barnett; Zoë A. Doubleday;
    Publisher: Springer International Publishing
    Country: Australia
    Project: ARC | ARC Future Fellowships - ... (FT190100244)

    “COVID” which stands for corona virus disease, has become the world’s most infamous acronym. Previous analysis of acronyms in health and medical journals found a growing use of acronyms over time in titles and abstracts, with “DNA” as the most common. Here we examine acronyms in the pandemic year of 2020 to show the dramatic rise of COVID-related research. “COVID” was over five times more frequently used than “DNA” in 2020, and in just one year it has become the sixth most popular acronym of all time, surpassing “AIDS”, “PCR” and “MRI”. Refereed/Peer-reviewed

  • Open Access English
    Authors: 
    Bakthavachalam Elango;
    Publisher: Springer International Publishing

    The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

  • Open Access English
    Authors: 
    Zhiqi Wang; Ronald Rousseau;
    Country: Belgium

    The Yule-Simpson paradox refers to the fact that outcomes of comparisons between groups are reversed when groups are combined. Using Essential Sciences Indicators, a part of InCites (Clarivate), data for countries, it is shown that although the Yule-Simpson phenomenon in citation analysis and research evaluation is not common, it isn't extremely rare either. The Yule-Simpson paradox is a phenomenon one should be aware of, otherwise one may encounter unforeseen surprises in scientometric studies. ispartof: SCIENTOMETRICS vol:126 issue:4 pages:3501-3511 ispartof: location:Switzerland status: published

  • Open Access English
    Authors: 
    Mei Hsiu-Ching Ho; John S. Liu;
    Publisher: Springer Science and Business Media LLC

    Scholars all over the world have produced a large body of COVID-19 literature in an exceptionally short period after the outbreak of this rapidly-spreading virus. An analysis of the literature accumulated in the first 150 days hints that the rapid knowledge accumulation in its early-stage development was expedited through a wide variety of journal platforms, a sense and pressure of national urgency, and inspiration from journal editorials.

  • Open Access English
    Authors: 
    Ozcan Saritas; Pavel Bakhtin; Ilya Kuzminov; Elena Khabirova;
    Publisher: Springer Science and Business Media LLC

    Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
31 Research products, page 1 of 4
  • Open Access English
    Authors: 
    Arash Hajikhani; Arho Suominen;
    Country: Finland

    AbstractThe sustainable development goals (SDGs) are a blueprint for achieving a better and more sustainable future for all by defining priorities and aspirations for 2030. This paper attempts to expand on the United Nations SDGs definition by leveraging the interrelationship between science and technology. We utilize SDG classification of scientific publications to compile a machine learning (ML) model to classify the SDG relevancy in patent documents, used as a proxy of technology development. The ML model was used to classify a sample of patent families registered in the European Patent Office (EPO). The analysis revealed the extent to which SDGs were addressed in patents. We also performed a case study to identify the offered extension of ML model detection regarding the SDG orientation of patents. In response to global goals and sustainable development initiatives, the findings can advance the identification challenges of science and technology artefacts. Furthermore, we offer input towards the alignment of R&D efforts and patenting strategies as well as measurement and management of their contribution to the realization of SDGs.

  • Open Access English
    Authors: 
    Enara Zarrabeitia; Izaskun Alvarez-Meaza; Rosa M Rio-Belver; Gaizka Garechana;
    Publisher: Springer
    Country: Spain

    AbstractThis paper presents TeknoAssistant, a domain-specific tech mining method for building a problem–solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem. We evaluate our approach using Natural Language Processing field, and propose a 2-g text mining process adapted for analyzing scientific publications. We rely on a combination of custom indicators with Stanford OpenIE SAO extractor to build a Bernoulli Naïve Bayes classifier which is trained by using domain-specific vocabulary provided by the TeknoAssistant user. The 2-g contained in the abstracts of a scientific publication dataset are classified in either “problem”, “solution” or “none” categories, and a problem–solution network is built, based on the co-occurrence of problems and solutions in the abstracts. We propose a combination of clustering technique, visualization and Social Network Analysis indicators for guiding a hypothetical user in a domain-specific problem solving process.

  • Open Access English
    Authors: 
    Ivan Kodvanj; Jan Homolak; Davor Virag; Vladimir Trkulja;
    Publisher: Springer International Publishing
    Country: Croatia

    ABSTRACTIntroductionCOVID-19-related (vs. non-related) articles appear to be more expeditiously processed and published in peer-reviewed journals. We aimed to evaluate: (i) whether COVID-19-related preprints were favored for publication, (ii) preprinting trends and public discussion of the preprints, and (iii) the relationship between the publication topic (COVID-19-related or not) and quality issues.MethodsManuscripts deposited at bioRxiv and medRxiv between January 1 and September 27 were assessed for the probability of publishing in peer-reviewed journals, and those published were evaluated for submission-to-acceptance time. The extent of public discussion was assessed based on Altmetric and Disqus data. The Retraction Watch Database and PubMed were used to explore the retraction of COVID-19 and non-COVID-19 articles and preprints.ResultsWith adjustment for the preprinting server and number of deposited versions, COVID-19-related preprints were more likely to be published within 120 days since the deposition of the first version (OR=2.04, 95%CI 1.87-2.23) as well as over the entire observed period (OR=1.42, 95%CI 1.33-1.52). Submission-to-acceptance was by 38.67 days (95%CI 34.96-42.39) shorter for COVID-19 articles. Public discussion of preprints was modest and COVID-19 articles were overrepresented in the pool of retracted articles in 2020.ConclusionCurrent data suggest a preference for publication of COVID-19-related preprints over the observed period.

  • Open Access English
    Authors: 
    Ali Ghorbi; Mohsen Fazeli-Varzaneh; Erfan Ghaderi-Azad; Marcel Ausloos; Marcin Kozak;

    This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001-2019, related to scientific articles (co-)written by at least one author affiliated with an Iranian institution. In order to determine reasons for retractions, we merged this database with the database from Retraction Watch. The data were analyzed using Excel 2016 and IBM-SPSS version 24.0, and visualized using VOSviewer software. Most of the retractions were due to fake peer review (95 retractions) and plagiarism (90). The average time between a publication and its retraction was 591 days. The maximum time-lag (about 3,000 days) occurred for papers retracted due to duplicate publications; the minimum time-lag (fewer than 100 days) was for papers retracted due to ''unspecified cause'' (most of these were conference papers). As many as 48 (14%) of the retracted papers were published in two medical journals: Tumor Biology (25 papers) and Diagnostic Pathology (23 papers). From the institutional point of view, Islamic Azad University was the inglorious leader, contributing to over one-half (53.1%) of retracted papers. Among the 343 retraction notices, 64 papers pertained to international collaborations with researchers from mainly Asian and European countries; Malaysia having the most retractions (22 papers). Since most retractions were due to fake peer review and plagiarism, the peer review system appears to be a weak point of the submission/publication process; if improved, the number of retractions would likely drop because of increased editorial control. 29 pages, 7 figures, 5 tables, 41 references

  • Open Access English
    Authors: 
    Milad Haghani; Pegah Varamini;
    Country: Australia
    Project: ARC | Discovery Early Career Re... (DE210101175)

    Following the outbreak of SARS-CoV-2 disease, within less than 8 months, the 50 years-old scholarly literature of coronaviruses grew to nearly three times larger than its size prior to 2020. Here, temporal evolution of the coronavirus literature over the last 30 years (N = 43,769) is analysed along with its subdomain of SARS-CoV-2 articles (N = 27,460) and the subdomain of reviews and meta-analytic studies (N = 1027). The analyses are conducted through the lenses of co-citation and bibliographic coupling of documents. (1) Of the N = 1204 review and meta-analytical articles of the coronavirus literature, nearly 88% have been published and indexed during the first 8 months of 2020, marking an unprecedented attention to reviews and meta-analyses in this domain, prompted by the SARS-CoV-2 pandemic. (2) The subset of 2020 SARS-CoV-2 articles is bibliographically distant from the rest of this literature published prior to 2020. Individual articles of the SARS-CoV-2 segment with a bridging role between the two bodies of articles (i.e., before and after 2020) are identifiable. (3) Furthermore, the degree of bibliographic coupling within the 2020 SARS-CoV-2 cluster is much poorer compared to the cluster of articles published prior to 2020. This could, in part, be explained by the higher diversity of topics that are studied in relation to SARS-CoV-2 compared to the literature of coronaviruses published prior to the SARS-CoV-2 disease. (4) The analyses on the subset of SARS-CoV-2 literature identified studies published prior to 2020 that have now proven highly instrumental in the development of various clusters of publications linked to SARS-CoV-2. In particular, the so-called “sleeping beauties” of the coronavirus literature with an awakening in 2020 were identified, i.e., previously published studies of this literature that had remained relatively unnoticed for several years but gained sudden traction in 2020 in the wake of the SARS-CoV-2 outbreak. This work documents the historical development of the literature on coronaviruses as an event-driven literature and as a domain that exhibited, arguably, the most exceptional case of publication burst in the history of science. It also demonstrates how scholarly efforts undertaken during peace time or prior to a disease outbreak could suddenly play a critical role in prevention and mitigation of health disasters caused by new diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s11192-021-04036-4.

  • Open Access English
    Authors: 
    Adrian G. Barnett; Zoë A. Doubleday;
    Publisher: Springer International Publishing
    Country: Australia
    Project: ARC | ARC Future Fellowships - ... (FT190100244)

    “COVID” which stands for corona virus disease, has become the world’s most infamous acronym. Previous analysis of acronyms in health and medical journals found a growing use of acronyms over time in titles and abstracts, with “DNA” as the most common. Here we examine acronyms in the pandemic year of 2020 to show the dramatic rise of COVID-related research. “COVID” was over five times more frequently used than “DNA” in 2020, and in just one year it has become the sixth most popular acronym of all time, surpassing “AIDS”, “PCR” and “MRI”. Refereed/Peer-reviewed

  • Open Access English
    Authors: 
    Bakthavachalam Elango;
    Publisher: Springer International Publishing

    The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

  • Open Access English
    Authors: 
    Zhiqi Wang; Ronald Rousseau;
    Country: Belgium

    The Yule-Simpson paradox refers to the fact that outcomes of comparisons between groups are reversed when groups are combined. Using Essential Sciences Indicators, a part of InCites (Clarivate), data for countries, it is shown that although the Yule-Simpson phenomenon in citation analysis and research evaluation is not common, it isn't extremely rare either. The Yule-Simpson paradox is a phenomenon one should be aware of, otherwise one may encounter unforeseen surprises in scientometric studies. ispartof: SCIENTOMETRICS vol:126 issue:4 pages:3501-3511 ispartof: location:Switzerland status: published

  • Open Access English
    Authors: 
    Mei Hsiu-Ching Ho; John S. Liu;
    Publisher: Springer Science and Business Media LLC

    Scholars all over the world have produced a large body of COVID-19 literature in an exceptionally short period after the outbreak of this rapidly-spreading virus. An analysis of the literature accumulated in the first 150 days hints that the rapid knowledge accumulation in its early-stage development was expedited through a wide variety of journal platforms, a sense and pressure of national urgency, and inspiration from journal editorials.

  • Open Access English
    Authors: 
    Ozcan Saritas; Pavel Bakhtin; Ilya Kuzminov; Elena Khabirova;
    Publisher: Springer Science and Business Media LLC

    Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.