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EN
Production engineering involves the integration of various scientific disciplines and serves as a link between different fields of science. With the advent of new technologies and the emergence of increasingly narrow specializations, it has become necessary to identify where and how modern technologies can provide specific benefits. Despite not being classified as a separate discipline, production engineering is present in various other disciplines and fields of science. This article aims to analyze areas related to production engineering that have the support of scientific and teaching staff. The research conducted examines the areas of support, their consequences, and the expected future direction of development in this field.
EN
The main aim of this work is to provide insight into a bibliometric analysis of Data Journals and Data Papers in terms of research areas, disciplines, publication year and country. In particular, we calculated many bibliometric indicators, especially: the number of publication sand citations. Furthermore, this work also investigated the top 20 journals in which scientists published the largest number of Data Papers. It was found that the first Data Paper in Medical and Health Science was published in 2006. Our results indicate that about 64% of Data Papers are cited and about 30% of Data Papers are articles that have international co-authors. We also found that a significant number of Data Papers were published in top quartile (Q1) Open Access journals. Furthermore, most Data Papers were published in Multidisciplinary Sciences, followed by Biological Sciences, Earth and related Environmental Sciences as well as Computer and Information Sciences. Our analyses show that the largest number of Data Papers were published in journals with an Impact Factor ranging from 5 to 6. Additionally, the largest number of Data Papers were published in the USA, China, the United Kingdom, Italy and France. From the point of view of scientists, this work is needed because scientists can check information about journals in which they may publish research data. Moreover, they can find Data Journals with a high Impact Factor and MEiN points. This analysis is also interesting because scientists/publishers can compare the disciplines and countries in which researchers have published the greatest number of Data Papers. Finally, it is worth noting that this work gives insights into the various bibliometric indicators related to citations and the number of publications. This information can be useful for people associated with bibliometric analyses.
EN
Purpose: The objective of the paper is to use text mining to identify leading research domains concerning the smart city following an analysis of research articles with a high citation index according to the Web of Science. Design/methodology/approach: An original method is proposed for analysing academic texts using the R language, tokenisation, lemmatisation, n-grams and correspondence analysis. The author analysed fifty of the most cited articles indexed in the Web of Science from 2014 to 2019. Findings: The paper presents the advantages and drawbacks of the proposed method of analysing research publications. The assets include automation and repeatability of the analysis of a large number of documents and improved knowledge about links among the articles in terms of research domains. The disadvantage is the loss of information from diagrams and figures. The method identified two leading research domains related to the notion of the smart city, technologies and systems. The analysed publications were categorised by selected keywords. Research limitations/implications: Future work should include further refinement of the assumptions for the method, analyses of a more significant number of research texts and a narrowing down of the domain of the smart city. It is desirable to consider other functional domains of the city, such as energy, public health, environmental protection or transport. Practical implications: The proposed method can complement a standard literature analysis regarding the smart city. The leading research domains related to the smart city in the analysed articles were systems and technologies employed to improve how the city operates. Social implications: Text mining can be employed by various experts focusing on the smart city and constitutes a refreshing complement for other research methods, such as questionnaire surveys, interviews or observations. Originality/value The publication can be useful for researchers from various fields and managers seeking to create and use simple, useful methods and tools for analysing unstructured text documents for decision-making. The paper proposes a separate text mining analysis of abstracts and whole documents using n-grams. This yielded a more precise list of areas relevant to the smart city. The grouping was done using correspondence analysis of the fifty most cited articles indexed in the Web of Science from 2014 to 2019.
PL
Przedstawiono priorytetowe kierunki badań Instytutu oraz główne cele badawcze w perspektywie krótko- i średnioterminowej. Omówiono problemy badawcze w obszarze zaawansowanych materiałów i technologii montażu elektronicznego oraz inteligentnych systemów wspomagania decyzji na potrzeby sterowania złożonych układów elektroenergetycznych.
EN
In the present article there are presented research areas and main short- and middle term research targets. The research problems in the following areas are discussed: advanced materials, soldering technology and intelligent electronic systems used in the very specific area of energetics.
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