Technological research in the EU is less efficient than the world average. EU research policy risks Europeans' future

We have studied the efficiency of research in the EU by a percentile-based citation approach that analyzes the distribution of country papers among the world papers. Going up in the citation scale, the frequency of papers from efficient countries increases while the frequency from inefficient countries decreases. In the percentile-based approach, this trend, which is permanent at any citation level, is measured by the ep index that equals the Ptop 1%/Ptop 10% ratio. By using the ep index we demonstrate that EU research on fast-evolving technological topics is less efficient than the world average and that the EU is far from being able to compete with the most advanced countries. The ep index also shows that the USA is well ahead of the EU in both fast- and slow-evolving technologies, which suggests that the advantage of the USA over the EU in innovation is due to low research efficiency in the EU. In accord with some previous studies, our results show that the European Commission's ongoing claims about the excellence of EU research are based on a wrong diagnosis. The EU must focus its research policy on the improvement of its inefficient research. Otherwise, the future of Europeans is at risk.Comment: 30 pages, 3 figures, 7 tables, in one single file. Version accepted in Journal of Informetric

Evaluating patterns of national and international collaboration in Cuban science using bibliometric tools

Purpose -- The purpose of this paper is to explore the hypothesis that collaboration was a key characteristic of Cuban science to maintain their scientific capacity during a period of economic restrictions and an important feature of Cuban science policy and practice for the benefit of society. Design/methodology/approach -- Collaboration was studied through Cuban scientific publications listed in PubMed for the period 1990-2010. The search was carried out using the advanced search engine of PubMed indicating oCubaW in the affiliation field. To identify participating institutions a second search was performed to find the affiliations of all authors per article through the link to the electronic journal. A data set was created to identify institutional publication patterns for the surveyed period. Institutions were classified in three categories according to their scientific production as Central, Middle or Distal: the pattern of collaboration between these categories was analysed. Findings -- Results indicate that collaboration between scientifically advanced institutions (Central) and a wide range of national institutions is a consequence of the social character of science in Cuba in which cooperation prevails. Although this finding comes from a limited field of biomedical science it is likely to reflect Cuban science policy in general. Originality/value -- Using bibliometric tools the study suggests that Cuban science policy and practice ensure the application of science for social needs by harnessing human resources through national and international collaboration, building in this way stronger scientific capacity

Pre-research Study based on Bibliometrics, Deep Learning Research for Aspect-Based Sentiment Analysis

 Background: Massive publications on deep learning research for aspect-based sentiment analysis are challenging for interested researchers who want to research this area. Purpose: to provide an overview and comprehensive analysis on the research trend, which include the growth of publications, the most used keywords, the most popular publication sources to publish and find literature, the most cited publication, the most productive researcher, the most productive institution and country affiliation. Method: This study used a bibliometric method to analyze Scopus's indexed publications from 2014 (the year when the first publication was first indexed) to 2020. A total of 222 publications were analyzed and visualized using the VosViewer software. Result: In general, there is an increase in the number of publications from year to year. Keyword visualization shows keywords related to text-based processing, deep learning architectures, the research object and media, and the application of the method. The most popular sources to publish and to find literature are the “Lecture Notes in Computer Science” and the “Expert Systems with Applications''. The most cited publication is “Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review”, written by Do, Prasad (cited 81 times). The most productive researcher is Zhang Y from China. The most productive institution is Nanyang Technological University (6 publications), and China has the highest number of publications (76 documents). Conclusion: The bibliometric method can provide a conclusive and comprehensive preliminary overview of research trends for interested researchers who want to start research about deep learning for aspect-based sentiment analysis.   Keywords: Bibliometrics; Deep learning; Aspect-based sentiment analysis; VosViewer    Abstrak  Latar Balakang: Banyaknya publikasi mengenai penelitian deep learning untuk aspect-based sentiment analysis menjadi tantangan tersendiri bagi peneliti yang tertarik dan ingin memulai penelitian terkait topik ini. Tujuan: memberikan gambaran umum serta analisis komprehensif tren penelitian meliputi pertumbuhan jumlah publikasi, kata kunci yang banyak digunakan, sumber publikasi populer yang dapat dimanfaatkan untuk tujuan publikasi maupun menemukan literatur, publikasi utama yang paling banyak disitir, peneliti paling produktif dan pola kolaborasi peneliti, serta afiliasi institusi dan negara paling produktif. Metode: Kajian ini menggunakan metode bibliometrik untuk menganalisis publikasi terindeks Scopus dari tahun 2014 (tahun pertama kali publikasi terindeks) hingga tahun 2020. Sebanyak 222 judul publikasi dianalisis, kemudian divisualisasikan dengan software VosViewer. Hasil: Secara umum jumlah publikasi mengalami peningkatan dari tahun ke tahun. Visualisasi kata kunci menggambarkan kata kunci yang berkaitan dengan pemrosesan berbasis teks, arsitektur deep learning, obyek dan media penelitian, serta aplikasi aspect-based sentiment analysis dengan metode deep learning. Sumber publikasi terpopuler untuk tujuan publikasi dan sumber literatur utama berturut-turut adalah Lecture notes in Computer Science dan Expert Systems with Applications. Publikasi yang paling banyak disitir adalah Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review oleh Do, Prasad (disitir 81 kali). Peneliti paling produktif adalah Zhang Y dari Cina. Institusi yang paling produktif adalah Nanyang Technological University (6 publikasi), dan Cina menjadi negara paling produktif dengan jumlah publikasi sebanyak 76 dokumen. Kesimpulan: Kajian menggunakan metode bibliometrik dapat memberikan gambaran awal tren penelitian yang konklusif dan komprehensif bagi peneliti yang tertarik dan ingin memulai penelitian tentang topik deep learning untuk aspect-based sentiment analysis.   Kata kunci: Bibliometrika; Deep learning; Aspect-based sentiment analysis; VosViewer&nbsp

Assessing the influence of R&D institutions by mapping international scientific networks: the case of INESC Porto

Although scientometric and bibliometric studies embrace a much wider perspective of the linkages/networks of R&D institutions than standard economic studies, to the best of our knowledge, these studies have not yet made use of scientometric tools to analyse the influence and impact of R&D institutions. Moreover, the international perspective has so far been neglected both in standard and bibliometric studies. Based on networks of 1239 foreign co-authorships and 13035 foreign citation linkages, we demonstrate that INESC Porto international influence has considerably expanded since 2003, a year that coincided with the implementation of an internal policy of granting monetary prizes to publications in scientific international journals. In terms of co-authorship, the network of INESC Porto more than duplicated (13 countries in the initial period to 27 in 2004-07). In terms of citations, INESC Porto’s network encompassed almost 40 countries during the whole period (1996-2007). Its more prolific units (optoelectronics, energy and multimedia) presented a rather distinct pattern both in terms of size and evolution of the corresponding network boundaries. The network size of foreign co-authorships was not much different between the three units by the beginning of the 2000s (around 10 countries) but it evolved quite distinctly. The most remarkable pattern was registered by the multimedia (UTM) unit, whose network size rose exponentially to 21 countries in 2004-07. This contrasted with the decline (down to 8 countries) of the energy (USE) unit. The citation network of the optoelectronic unit (UOSE) was by far the largest, until 2003, involving 34 distinct countries, which contrasted with the size of USE (12 countries) and UTM (1 country). But again, after 2003, the size of the citation network of USE and UTM converged spectacularly to that of UOSE’s, reaching in the last period 21 and 16, respectively. The influence of INESC Porto reaches all five continents, especially when we consider citation networks. Indeed, excluding the citations from authors affiliated in Portuguese institutions, those that most cite INESC Porto’s (and UOSE’s) works are affiliated in institutions located in China, the UK and the US. The scientific works produced by USE influences mostly authors affiliated in institutions located in India, China and Spain, whereas for UTM the corresponding countries are the US, Germany and Italy. We infer from the evidence analysed that not only did the boundaries of INESC Porto’s scientific network substantially enlarge in the period of analysis (1996-2007) but its ‘quality’ also evidenced a positive evolution, with authors affiliated in institutions located in the scientific frontier countries citing works of INESC Porto (and its units).Bibliometrics, Knowledge networks; R&D Institutions

Intellectual Structure of the Belt and Road Initiative Research: A Scientometric Analysis and Suggestions for a Future Research Agenda

publishedVersio

Coronavirus mapping in scientific publications: When science advances rapidly and collectively, is access to this knowledge open to society?

The COVID-19 pandemic is creating a global health emergency. Mapping this health emergency in scientific publications demands multiple approaches to obtain a picture as complete as possible. To progress in the knowledge of this pandemic and to control its effects, international collaborations between researchers are essentials, as well as having open and immediate access to scientific publications, what we called 'coopetition'. Our main objectives are to identify the most productive countries in coronavirus publications, to analyse the international scientific collaboration on this topic, and to study the proportion and typology of open accessibility to these publications. We have analyzed 18,875 articles indexed in Web of Science. We performed the descriptive statistical analysis in order to explore the performance of the more prolific countries and organizations, as well as paying attention to the last 2 years. Registers have been analyzed separately via the VOSviewer software, drawing a network of links among countries and organizations to identify the starred countries and organizations, and the strongest links of the net. We have explored the capacity of researchers to generate scientific knowledge about a health crisis emergency, and their global capacity to collaborate among them in a global emergency. We consider that science is moving rapidly to find solutions to international health problems but access to this knowledge by society is not so quick due to several limitations (open access policies, corporate interests, etc.). We have observed that papers from China in the last 3 months (from January 2020 to March 2020) have a strong impact compared with papers published in years before. The United States and China are the major producers of documents of our sample, followed by all European countries, especially the United Kingdom, Germany, the Netherlands, and France. At the same time, the leading role of Saudi Arabia, Canada or South Korea should be noted, with a significant number of documents submitted but very different dynamics of international collaboration. The proportion of international collaboration is growing in all countries in 2019-2020, which contrasts with the situation of the last two decades. The organizations providing the most documents to the sample are mostly Chinese. The percentage of open access articles on coronavirus for the period 2001-2020 is 59.2% but if we focus in 2020 the figures increase up to 91.4%, due to the commitment of commercial publishers with the emergency

Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020