ترغب بنشر مسار تعليمي؟ اضغط هنا

Universal and specific features of Ukrainian economic research: publication analysis based on Crossref data

138   0   0.0 ( 0 )
 نشر من قبل Olesya Mryglod
 تاريخ النشر 2021
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Our study is one of the first examples of multidimensional and longitudinal disciplinary analysis at the national level based on Crossref data. We present a large-scale quantitative analysis of Ukrainian economics. This study is not yet another example of research aimed at ranking of local journals, authors or institutions, but rather exploring general tendencies that can be compared to other countries or regions. We study different aspects of Ukrainian economics output. In particular, the collaborative nature, geographic landscape and some peculiarities of citation statistics are investigated. We have found that Ukrainian economics is characterized by a comparably small share of co-authored publications, however, it demonstrates the tendency towards more collaborative output. Based on our analysis, we discuss specific and universal features of Ukrainian economic research. The importance of supporting various initiatives aimed at enriching open scholarly metadata is considered. A comprehensive and high-quality meta description of publications is probably the shortest path to a better understanding of national trends, especially for non-English speaking countries. The results of our analysis can be used to better understand Ukrainian economic research and support research policy decisions.



قيم البحث

اقرأ أيضاً

Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption. In the last years, we saw the emergence of several publicly-available and large-scale Scientific Knowledge Graphs fostering the development of many data-driven approaches for performing quantitative analyses of research trends. This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology. We discuss the advantages of a solution based on a formal representation of topics and describe how it was applied to produce bibliometric studies and innovative tools for analysing and predicting research dynamics.
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get an overview of the state of the art in a cer tain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work. Recently, several initiatives have proposed knowledge graphs (KGs) for organising scientific information as a solution to many of the current issues. The focus of these proposals is, however, usually restricted to very specific use cases. In this paper, we aim to transcend this limited perspective by presenting a comprehensive analysis of requirements for an Open Research Knowledge Graph (ORKG) by (a) collecting daily core tasks of a scientist, (b) establishing their consequential requirements for a KG-based system, (c) identifying overlaps and specificities, and their coverage in current solutions. As a result, we map necessary and desirable requirements for successful KG-based science communication, derive implications and outline possible solutions.
The web application presented in this paper allows for an analysis to reveal centres of excellence in different fields worldwide using publication and citation data. Only specific aspects of institutional performance are taken into account and other aspects such as teaching performance or societal impact of research are not considered. Based on data gathered from Scopus, field-specific excellence can be identified in institutions where highly-cited papers have been frequently published. The web application combines both a list of institutions ordered by different indicator values and a map with circles visualizing indicator values for geocoded institutions. Compared to the mapping and ranking approaches introduced hitherto, our underlying statistics (multi-level models) are analytically oriented by allowing (1) the estimation of values for the number of excellent papers for an institution which are statistically more appropriate than the observed values; (2) the calculation of confidence intervals as measures of accuracy for the institutional citation impact; (3) the comparison of a single institution with an average institution in a subject area, and (4) the direct comparison of at least two institutions.
Many studies in information science have looked at the growth of science. In this study, we re-examine the question of the growth of science. To do this we (i) use current data up to publication year 2012 and (ii) analyse it across all disciplines an d also separately for the natural sciences and for the medical and health sciences. Furthermore, the data are analysed with an advanced statistical technique - segmented regression analysis - which can identify specific segments with similar growth rates in the history of science. The study is based on two different sets of bibliometric data: (1) The number of publications held as source items in the Web of Science (WoS, Thomson Reuters) per publication year and (2) the number of cited references in the publications of the source items per cited reference year. We have looked at the rate at which science has grown since the mid-1600s. In our analysis of cited references we identified three growth phases in the development of science, which each led to growth rates tripling in comparison with the previous phase: from less than 1% up to the middle of the 18th century, to 2 to 3% up to the period between the two world wars and 8 to 9% to 2012.
Without sufficient information about researchers data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental publications are used to analyse the ways in which computer scientists share research data. All journal articles published by researchers in the computer science department of the case studys university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Furthermore, a rudimentary classification of the main study types exhibited in the investigated articles was developed to accommodate the multidisciplinary nature of the case departments research agenda. Human interaction and intervention studies often collected original data, whereas research on novel computational methods and life sciences more frequently used openly available data. Articles that made data available for reuse were most often in life science studies, whereas data sharing was least frequent in human interaction studies. The use of open code was most frequent in life science studies and novel computational methods. The findings highlight that multidisciplinary research organisations may include diverse subfields that have their own cultures of data sharing, and suggest that research information system-based methods may be valuable additions to the questionnaire and interview methodologies eliciting insight into researchers data sharing. The collected data and coding framework are provided as open data to facilitate future research.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا