Importance of scientific research for Achieving Sustainable Development Goals during Covid19 Pandemic: Northern Technical University - A Case Study

Scientific progress in any country is measured by the importance that is given to scientific research. Recently, the interest in scientific research has increased by the local and international universities in various fields of sciences, given its importance in achieving sustainable development goals, where several countries and universities have initiated to increase scientific research funds. Moreover, the reputation of scientific research in the universities strongly depends on the quality and quantity of papers that are published in the international journals. Thus, the main goal of this study is to investigate and identify the reality of the scientific research situation at Northern Technical University during Covid19 Pandemic (i.e., from 2020 till now). This study is based on the descriptive approach and method of analysis for the published papers that related to the sustainable development topics, which is adopted by UI GreenMetric ranking. The data of the published papers have been collected from the Google Scholar website using the same methodology that was approved in the UI GreenMetric ranking. Those publications have been classified based on the keywords, university researchers and the affiliation of the university’s colleges and institutes. The present data has been compared with data of previous years. Keyword: UI Green metric, sustainability, renewable energy, environmen

Ranking institutions within a university based on their scientific performance: A percentile-based approach

Over the recent years, the subject of university rankings has attracted a significant amount of attention and sparked a scientific debate. However, few studies on this topic focus on elaborating the scientific performance of universities’ institutions, such as institutes, schools, and faculties. For this reason, the aim of this study is to design an appropriate framework for evaluating and ranking institutions within a university. The devised methodology ranks institutions based on the number of published papers, mean normalized citation score (MNCS), and four percentile-based indicators using the I-distance method. We applied the proposed framework and scrutinized the University of Belgrade (UB) as the biggest and the best-ranked university in Serbia. Thus, 31 faculties and 11 institutes were compared. Namely, an in-depth percentile-based analysis of the UB papers indexed in the Science Citation Index Expanded (SCIe) and the Social Science Citation Index (SSCI) for the period 2008-2011 is provided. The results clearly show considerable discrepancies in two occasions: first, when it comes to the question of leading author, and second, when it comes to analyzing the percentile rank classes (PRs) of groups of faculties

Diffusion of scientific credits and the ranking of scientists

Recently, the abundance of digital data enabled the implementation of graph based ranking algorithms that provide system level analysis for ranking publications and authors. Here we take advantage of the entire Physical Review publication archive (1893-2006) to construct authors' networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer. On this network we define a ranking method based on a diffusion algorithm that mimics the spreading of scientific credits on the network. We compare the results obtained with our algorithm with those obtained by local measures such as the citation count and provide a statistical analysis of the assignment of major career awards in the area of Physics. A web site where the algorithm is made available to perform customized rank analysis can be found at the address http://www.physauthorsrank.orgComment: Revised version. 11 pages, 10 figures, 1 table. The portal to compute the rankings of scientists is at http://www.physauthorsrank.or

Early identification of important patents through network centrality

One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926-2010) to test our ability to early identify a list of historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers.Comment: 14 page

Centrality Metric for Dynamic Networks

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.Comment: in KDD workshop on Mining and Learning in Graphs (MLG

SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles