ALBERT

Beschreibung: How do the level of usage of an article, the timeframe of its usage and its subject area relate to the number of citations it accrues? This paper aims to answer this question through an observational study of usage and citation data collected about the multidisciplinary, open access mega-journal Scientific Reports. This observational study answers these questions using the following methods: an overlap analysis of most read and top-cited articles; Spearman correlation tests between total citation counts over two years and usage over various timeframes; a comparison of first months of citation for most read and all articles; a Wilcoxon test on the distribution of total citations of early cited articles and the distribution of total citations of all other articles. All analyses were performed in using the programming language R. As Scientific Reports is a multidisciplinary journal covering all natural and clinical sciences, we also looked at the differences across subjects. We found a moderate correlation between usage in the first year and citations in the first two years since publication (Spearman correlation coefficient of 0.49, α = 0.05), and that articles with high usage in the first six months are more likely to have their first citation earlier (Wilcoxon = 1,811,500, p 

Beschreibung: Studying research fronts enables researchers to understand how their academic fields emerged, how they are currently developing and their changes over time. While topic modelling tools help discover themes in documents, they employ a “bag-of-words” approach and require researchers to manually label categories, specify the number of topics a priori, and make assumptions about word distributions in documents. This paper proposes an alternative approach based on entity linking, which links word strings to entities from a knowledge base, to help solve issues associated with “bag-of-words” approaches by automatically identifying topics based on entity mentions. To study topic trends and popularity, we use four indicators—Mann–Kendall’s test, Sen’s slope analysis, z-score values and Kleinberg’s burst detection algorithm. The combination of these indicators helps us understand which topics are particularly active (“hot” topics), which are decreasing (“cold” topics or past “bursty” topics) and which are maturely developed. We apply the approach and indicators to the fields of Information Science and Accounting.

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The peer review system aims to be effective in separating unacceptable from acceptable manuscripts. However, a reviewer can distinguish them or not. If reviewers distinguish unacceptable from acceptable manuscripts they use a fine partition of categories. But, if reviewers do not distinguish them they use a coarse partition in the evaluation of manuscripts. Most reviewers learned how to evaluate a manuscript from good and bad experiences, and they have been characterized as zealots (who uncritically favor a manuscript), assassins (who advise rejection much more frequently than the norm), and mainstream referees. In this paper we use the quasi-species model to describe the evolution of recommendation profiles in peer review. A recommendation profile is composed of a reviewer recommendation for each manuscript category under a particular categorization of manuscripts (fine or coarse). We see the reviewer mind as being built up with recommendation profiles. Assassins, zealots and mainstream reviewers are “ecologically” interrelated species whose progeny tend to mutate through errors made in the process of reviewer training. We define the recommendation profile as replicator, and selection arises because different types of recommendation profiles tend to replicate at different rates. Our results help to explain why assassins and zealots evolutionary appear in peer review because of the evolutionary success of reviewers who do not distinguish acceptable and unacceptable manuscripts.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Academic journals play a significant role in the dissemination of new research insights and knowledge among scientists. The number of such journals has recently increased significantly. Scientists prefer to publish their scholarly work at reputed venues. Speed of publication is also an import factor considered by many while selecting a publication venue. To evaluate a journal’s quality, few of the key indicators include impact factor, Source Normalized Impact per Paper (SNIP), and Hirsch index (〈em〉h〈/em〉-index). Journals’ ranking is an indication of their impact and quality with respect to other venues in a specific discipline. Various measures can be utilized for ranking, like, field specific statistics, intra discipline ranking, or a combination of both. Earlier, the journals’ ranking was done through a manual process by providing an institutional list created by academic leaders. Factors like politicization, biases, and personal interests were the key issues with such categorization. Later, the process evolved to a database system based on impact factor, SNIP (Source Normalized Impact per Paper), 〈em〉h〈/em〉-index, or any combination of these. All this demanded an external source of categorizing academic journals. This work presents a data science-based framework that evaluates journals based on their key bibliometric indicators and presents an automated approach to categorize them. For this, the current proposal is restricted to the journals published in the computer science domain. The journal’s features considered in the proposed framework include: publisher, impact factor, website, CiteScore, SJR (SCImago Journal & Country Rank), SNIP, 〈em〉h〈/em〉-index, country, age, cited half-life, immediacy factor/index, Eigenfactor score, article influence score, open access, percentile, citations, acceptance rate, peer review, and the number of articles published yearly. A dataset is collected for 660 journals consisting of these 19 features. The dataset is preprocessed to fill-in the missing values and perform scaling. Three feature selection techniques, namely, Mutual Information (MI), minimum Redundancy Maximum Relevance (mRMR), and Statistical Dependency (SD) are used to rank the aforementioned features. The dataset is then vertically divided into three sets, all features, top nine features, and bottom ten features. Later, two clustering techniques, namely, 〈em〉k〈/em〉-means and 〈em〉k〈/em〉-medoids are employed to find the optimum number of coherent groups in the dataset. Based on a rigorous evaluation, four groups of journals are identified. It is followed by training two classifiers, i.e., 〈em〉k〈/em〉-NN (Nearest Neighbor) and Artificial Neural Network (ANN) to predict the category of an unknown journal. Where, the ANN shows an average accuracy of 82.85%. A descriptive analysis of the clusters formed is also presented to gain insights about the four journal categories. The proposed framework provides an opportunity to independently categorize academic journals based on data science methods using multiple significant bibliometric indicators.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Balance (or evenness) as used in conventional approaches to measuring diversity needs careful definition. Nijssen et al. (Coenoses 13(1):33–38, 〈span〉1998〈/span〉) used a mathematical approach to show that the Lorenz curve is an adequate representation of evenness and proposed that the Gini coefficient is a perfect indicator of balance. In this paper, we take an alternative thermodynamic perspective that leads to a very simple dimensionless measure of balance that ranges from 0 (perfect balance or evenness) to 1 (perfect unevenness or absolute concentration). It has the expected permutation invariance, scaling invariance, and replication invariance properties of a good evenness indicator and also has sensitivity to the transfer requirements (i.e. sensing the entropy changes correctly). It also has a naturalness property.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The characteristics of the 〈em〉h〈/em〉-index in the field of condensed matter physics are studied using high-quality data from ResearcherID. The results are examined in terms of theoretical descriptions of the 〈em〉h〈/em〉-index’ overall dependence on a researcher’s total number of published papers, and total number of citations. In particular, the models by Hirsch, Egghe and Rousseau, as well as by Glänzel and Schubert are examined. Special emphasis is placed on the deviations from such statistical descriptions, and it is argued that the deviation of a particular researcher’s 〈em〉h〈/em〉 value from the Egghe–Rousseau model’s prediction can be used as a supplementary measure of impact. A corresponding analysis with similar results is performed using the multi-author 〈em〉h〈/em〉〈sub〉〈em〉m〈/em〉〈/sub〉-index.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉We assess knowledge development and knowledge diffusion for orthopaedic device innovation in South Africa over the period 2000–2015. A structural network analysis is performed on bibliometric data using co-authorship on scientific publications as an indicator of collaboration between different organisations. We apply a technological innovation system (TIS) framework, quantitatively assessing the TIS functions ‘knowledge creation’ and ‘knowledge diffusion’ in their spatial and sectoral contexts. Network metrics (degree and betweenness centralities), and empirical TIS analyses are used to describe the knowledge functions of the TIS. Our results show that scientific knowledge development has increased as time has progressed, and that university and healthcare sectors have largely been responsible. Results further indicate that, for the national healthcare and national industry sector actors, ties to university and science council actors support scientific knowledge creation. The collaboration networks were found to be sparse, and disjointed, with many actors largely unreachable, indicating barriers to knowledge exchange between actors. Initially the network displayed spatial elements of an internationalised TIS, but over time, the spatial typology changed to that of a nationalised TIS. This shift may be a positive one, as South African research and development activity shifts towards being driven by local actors and towards medical devices which address the South African burden of disease.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉A disaster is a crisis situation that causes significant harm to humans, the environment, the economy, and impacts upon society’s ability to cope both during the event and with the aftermath. A disaster may escalate rapidly to reach unpredicted consequences. They can be naturally occurring, or man-made, or a combination of the two. In this study we have focused on the Fukushima Daiichi nuclear power plant accident of 2011 that subsequently unleashed a floodgate of scientific activities, including basic and applied research, technology development, policy reform, inter-disciplinary collaboration, citizen science and re-strategization of research networks. In this context, a bibliometric study has been conducted on 5455 publications on the Fukushima disaster indexed by Scopus for the 2011–2017 period. The aim is to identify key papers in the field, the most substantive contributors to the literature, and to begin to map out the emergence of a body of knowledge resulting from the disaster. The findings present main national and international collaborators on Fukushima disaster related research, as well as an indication of the major research themes evolving over the immediate years following the event. This allows for a deeper understanding of the longitudinal impact of a disaster on society and upon the international research community and its deliberations.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The antioxidant nature and the significant role played by phenolic compounds in wine organoleptic properties place these molecules in the core of a significant amount of viticulture and oenology research. Despite the large amount of grape and wine phenolic research, the understanding of the phenolic chemistry and its role played on wine quality still remains inconclusive. Bibliometric studies are a valuable tool to provide a critical evaluation of the research status of a specified topic. The evaluation of quantitative methods is used to process measurable outcomes of research activity from articles published in scientific journals and included in databases. The main aim of this study was thus to provide a bibliometric approach on phenolic compounds with an exclusive focus on grape and wine phenolics from a strictly winegrowing and winemaking perspective. To evaluate the scientific structure in phenolic compounds a bibliometric search using the Web of Science Core Collection platform from Clarivate Analytics was evaluated. The critical evaluation of the results showed a significant effort made towards the understanding of the organoleptic role played by phenolic compounds. Despite a considerable amount of research currently available, it is foreseen that the research efforts will move towards a more complete interpretation of the astringency and bitterness phenomena. Furthermore, it can also be speculated that future research topics will point towards the comprehension of wine flavour compounds in which phenolic substances are also expected to play a crucial role.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Recently, tremendous advances have been observed in information retrieval systems designed to search for relevant knowledge in scientific publications. Although these techniques are quite powerful, there is still room for improvement in the area of searching for metadata relating to algorithms in full-text publication datasets—for instance, efficiency-related metrics such as precision, recall, 〈em〉f〈/em〉-measure and accuracy, and other useful metadata such as the datasets deployed and the algorithmic run-time complexity. In this study, we proposed a novel deep learning-based feature engineering approach that improves search capabilities by mining algorithmic-specific metadata from full-text scientific publications. Typically, traditional term frequency-inverse document frequency (TF-IDF)-based approaches function like a ‘bag of words’ model and thus fail to capture either the text’s semantics or the word sequence. In this work, we designed a semantically enriched synopsis of each full-text document by adding algorithmic-specific deep metadata text lines to enhance the search mechanism of algorithm search systems. These text lines are classified by our deployed deep learning-based bi-directional long short term memory (LSTM) model. The designed bi-directional LSTM model outperformed the support vector machine by 9.46%, with a 0.81 〈em〉f〈/em〉1-score on a dataset of 37,000 algorithm-specific deep metadata text lines that had been tagged by four human experts. Lastly, we present a case study on 21,940 full-text publications downloaded from ACL (〈a href="https://aclweb.org/"〉https://aclweb.org/〈/a〉) to show the effectiveness of deep learning-based advanced feature engineering search compared to the conventional TF-IDF-based (Lucene) search.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The last few years have seen a growing interest in main path analysis among scholars across a wide spectrum of disciplines. Hummon and Doreian first introduced this method, and it has since become an effective technique for mapping technological trajectories, exploring scientific knowledge flows, and conducting literature reviews. Nevertheless, there are issues not broadly discussed in applying the method, including the handling of citation data, choosing a proper traversal weight scheme, search options, and interpretation of the resulting paths. This note aims to deepen the discussions and concludes with several suggestions and strategies in applying main path analysis.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉This paper attempts to provide an overview of scientific publication trends over the period of a century (1900–2017). It then drills down to three important explorations that depict world trends and the development of science, namely: the growth behaviors of different time periods; fields and research orientations; and cumulative capability patterns of different countries by size of population. To study growth behaviors, we employed logistic growth function to model the world publication trajectory. We observed that the carrying capacity of the world publication trajectory has increased, attaining a much higher level of production. The rate of publication for the period of 1990–2017 has elongated the cycle and witnessed a longer trend towards saturation. For research activities in recent decades, we noted that they were performed by coordinated entities that are endowed to chart a multidisciplinary/interdisciplinary research path. Publications were found to be oriented towards medical and industrial applications, which can be attributed to the attempts of emerging countries which aspire to develop science-based industries. The analyses and observations in this paper have enabled us to corroborate our previous findings on growth behaviors of scientific publications. We believe that these findings will allow us to theorize further on the world development of science, and realize a strong theoretical narrative for a century of publication in the near future.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉In research policy, effective measures that lead to improvements in the generation of knowledge must be based on reliable methods of research assessment, but for many countries and institutions this is not the case. Publication and citation analyses can be used to estimate the part played by countries and institutions in the global progress of knowledge, but a concrete method of estimation is far from evident. The challenge arises because publications that report real progress of knowledge form an extremely low proportion of all publications; in most countries and institutions such contributions appear less than once per year. One way to overcome this difficulty is to calculate probabilities instead of counting the rare events on which scientific progress is based. This study reviews and summarizes several recent publications, and adds new results that demonstrate that the citation distribution of normal publications allows the probability of the infrequent events that support the progress of knowledge to be calculated.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Science can be examined from several standpoints, such as through a bibliometric analysis of the scientific output of researchers, research groups or institutions. However, there is little information about the advisor–advisee relationships or the academic supervision of researchers or between teachers and students. In this paper, we examine the results of the academic genealogy of PhD and Master’s students working in Brazil, which was obtained from 737,919 curriculum vitae extracted from the Lattes Platform. Our findings bring to light three main sources of evidence related to the Brazilian academic genealogy: (1) the degree of interdisciplinarity between main areas of knowledge, (2) the structural features and evolving patterns with regard to both areas of knowledge and researchers, and (3) the patterns in the levels of training that affect the topological metrics. We conclude that academic genealogy offers a great opportunity to assess researchers and their areas of research from the perspective of human resource training.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉This study describes the increase of research productivity of latecomer countries (latecomers) in the high-energy physics (HEP) community by research strategies based on a national system and international collaboration (IC). The INSPIRE system, a bibliographic database for HEP researchers was used to obtain the number of publications and citations as indicators of research productivity. Our bibliometric estimates highlight two main results. First, latecomers’ national systems of public research institutes play a major role, and initially produced a large proportion of the research output, but this influence declined as IC increased. Second, IC greatly increased both the quantity and quality (number of citations) of research output in all latecomers. In most countries, the IC strategy has shown a strong correlation with the research output. The findings highlight the importance of a national research-support system and development of IC as strategies for new states that are entering the HEP field, and provide comparison of the two strategies. Further bibliometric research, such as examination of the strategic patterns of the leading countries will broaden the understanding of the national units of the HEP academic community.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Organizing scientific conferences requires the execution of a series of activities which aim to ensure quality in terms of published papers, completed reviews, information forwarded to participants on time, to putting together an exceptional event as a whole. Essentially, this is far from an easy task and requires the participation of a large number of people who will carry out organizational obligations daily, and often all day long. The quality, or rather satisfaction of the conference participants can be viewed through five aspects: Reliability, Responsiveness, Assurance, Empathy, and Tangibility–dimensions that comprise the SERVQUAL model. In this paper, a new hybrid model that uses the advantages of rough set theory, multi-criteria decision making, and quality assessment models has been developed. The proposed model uses the Rough BWM (Best–Worst Method) in order to determine the significance of five aspects, while the modified SERVQUAL model, based on 28 items, is used to determine expectations and observations. The model was applied for the evaluation of quality of the New Horizons Conference held on November 17–18, 2017, in Doboj. The conference was attended by authors from all six continents, representing a total of 58 different institutions. Therefore, it was necessary to fulfill the wishes and demands of numerous authors, which naturally differ due to the different geographical areas from which they hail. The total sample, on the basis of which the quality of the scientific conference was assessed, includes 104 authors who completed the questionnaire. A total of six hypotheses were set up, which were then tested using the Signum test.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Assessing and evaluating the academic impact and its results produced by researchers is necessary to promote the academic progress. A diverse and varied set of parameters have been proposed by the scientific community to find the most influential researchers, including citation count, the total number of publications, hybrid approaches, 〈em〉h〈/em〉-index, extensions and variants of 〈em〉h〈/em〉-index. Current state-of-the-art depicts that there is no standard benchmark available to determine the optimum parameter to find the most influential author of a specific domain. Furthermore, it has been observed that such indices are assessed on a small dataset and ingenious scenarios. The small dataset can never truly help to analyze the nature of these indices and it is very difficult to determine the significance and influence of every index over the others. Hence, it’s necessary to assess them on a large dataset. The following research would help in scrutinizing the 〈em〉h〈/em〉-index along with its citation intensity based variants to rank the authors by using a large dataset of Mathematics domain that consist of 57,533 authors and 62,033 total numbers of publications. These indices make use of the total published papers, citation count, along with the 〈em〉h〈/em〉-index and the five of its citation intensity based variants. The esteemed awards that are won nationally and internationally in the field of mathematics serve as a benchmark. This study would deal and help to recognize the most influential authors by concluding the results gained after evaluation of these indices. For this purpose, firstly, we calculated the correlation among different indices. The strong correlation was found between the 〈em〉h〈/em〉-index and its five citation intensity based variants. The occurrence of the award winners is examined according to the rank lists. 〈em〉H〈/em〉-index brought around 30.88% awardees in the top 10% of the ranked list. In a bird’s eye view, no index could succeed in elevating a 50% of award winners in the top-ranking. Our benchmark dataset is composed of 68 awardees. In the ranking lists, the maximum number of awardees belongs to American Mathematics Society (AMS) which are 29.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉To provide new evidence on the empirically challenging aspect of measuring self-esteem, we collect written CV content from over 500 influential economics scholars and use such self-presentation modes as absolute and relative font sizes and typographic emphasis of author name on the CV’s front page to derive proxies for self-symbolizing behavior. Our analysis of these factors provides empirical support for the symbolic self-completion proposition that goal accomplishment reduces the need to self-symbolize. In particular, we find that receiving an education from a top 20 ranked university reduces the need to self-symbolize even when the scholars work for a non-top 20 university after earning the doctorate. On the other hand, major academic awards or honors do not seem to affect self-symbolizing behavior, which is also negatively correlated with activities having an external (e.g., policy-focused) social impact. Scholars with a higher 〈em〉h〈/em〉-index are also less inclined to self-symbolize, while being ranked in the Coupé top 1000 has no impact. Hence, despite some evidence of non-linearity, academic maturity shows a tendency (albeit not a robust one) to reduce the need to self-symbolize.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Following the ‘black–white swan’ interaction metaphor introduced in an earlier study, we now trace and observe a new ‘swan groups’ pattern. Our motivation for introducing the ‘swan groups’ is based on the fact that ‘black–white swan’ interactions are observed primarily in physics, which belongs to science. We extend a newer model called ‘swan groups’ model and test its applicability to the field of economics, belonging to social sciences. The primary feature of this model is that the ‘black swan’ represents an important scientific discovery or contribution that has been awarded Nobel Prize, while the ‘white swans’ are highly cited publications by the ‘black swan’. Together the two types of swans form a group, though unlike the original ‘black–white swan’ interaction pattern, the ‘swan groups’ do not necessarily interact in a way where we see a marked decrease in citations to white swans. Our findings show that the new ‘swan groups’ pattern covers about 50% of key Nobel prize-winning physics papers and about 40% of key Nobel prize-winning economic papers. This allows us to identify important academic achievements both qualitatively and quantitatively, not only in science where major breakthroughs can cause paradigm shifts, but also in the social sciences where progress often remains open to multiple discoveries and doctrines.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Interdisciplinary research is vital in addressing complex real-world problems. To understand how the scientific workforce is being engaged in the interdisciplinary research, it is important to track the involvement of different research fields over time and the grants that drive the research endeavour. Unfortunately, there has been very little work in this understanding of interdisciplinary research and grant success. In this paper, we analysed the contribution of different disciplines within multidisciplinary research that secured grants. We tracked these contributions over a 10-year period to understand how different research fields evolved over time and played roles in interdisciplinary grant success. We followed a basic statistical approach and proposed a network-based approach to understand relative participation of different disciplines. We found disparities within different disciplines which showed that only few research fields contributed more in the interdisciplinary research grant success.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉We present a bibliometric study, from the perspective of the mode 2 knowledge production, from a transdisciplinary point of view (Gibbons et al. in The new production of knowledge: the dynamics of science and research in contemporary societies, Sage, London, 〈span〉1994〈/span〉), offering empirical evidence by means of a detailed approach to identify, record and characterize the participation of Mexican Civil Society Organizations (CSO) in scientific articles. Our findings show that this sector has been growing over the past decade along with its participation in the scientific sphere. This shows a differentiated pattern from the habitual patterns of scientific publications by Mexican researchers (ACM in Atlas de la Ciencia Mexicana, 〈a href="http://atlasdelacienciamexicana.org/"〉http://atlasdelacienciamexicana.org〈/a〉, 〈span〉2014〈/span〉; Gonzalez-Brambila et al. in PLoS One 11(3):e0151328, 〈span〉2016〈/span〉; Rodriguez et al. in Investigacion Bibliotecologica 2017:187–219, 〈span〉2017〈/span〉; Lopez-Olmedo et al. in Scientometrics 112(3):1343–1366, 〈span〉2017〈/span〉). CSO publications show a greater participation in foreign co-authoring, a broad diversification of the actors involved, a complex framework of relationships, and an overwhelming tendency toward social issues such as healthcare and environment-ecology. Based on our findings, it is concluded that the increment in their participation by them, as secondary effect, is related to the implementation of public policies that encourages the activities carried out by this sector since 2004, even though, its goal was not to encourage specifically the participation of CSO in research processes.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The relationship between influential tweeters and highly cited articles in the field of information sciences was analysed using Twitter data gathered by Altmetric.com from July 2011 through February 2017. The dataset consists of more than 10,000 tweets, and these mentions, retweets and followers were used to generate a connected, undirected graph. This graph reveals the most influential tweeters by identifying the largest drop in the eigenvalue of adjacency or affinity matrix of a graph when certain nodes are removed; those which, when deleted, cause the greatest drop in the eigenvalue of the graph are considered to be the most influential. The machine-learning model applied in this work utilizes a feature vector containing the accumulated sum of the rank scores of those influential users who tweet a given article, along with known altmetric features such as the user type and post counts for various social media. Finally, the supervised-learning model was trained using Random Forest and Support Vector Machine classifiers with 11 features, including the sum of the ranks of influential users who tweet a given article in our dataset. The results were analysed using Receiver Operating Characteristic (ROC) curves and Precision Recall (PR) curves, which give the commendable outcomes compared to the baseline model. We found that, for the classification of highly cited articles, Twitter users’ score for influence is the most important feature. Finally, we show that our model—which was trained by taking the score for influence into consideration—outperforms the baseline, at 79% for ROC and 90% for PR with the Random Forest Model, effectively identifying the highly cited articles.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉We propose a framework to evaluate, in relative terms, author-level publishing performance. To that end we introduce the publishing performance index (PPI) and the publishing performance box (PPB), and discuss the associated publishing profiles. We illustrate our approach conducting an extensive empirical application covering 472 top economists and developing several robustness tests. Instead of using a pre-designed measure without flexibility to adjust to the circumstances of each specific case, our approach accommodates alternative evaluation criteria, as defined by the evaluators. Beyond this key characteristic, our approach has some other important advantages: (1) it is easy to apply; (2) it is sensitive to the full list of publications and citations; (3) it is able to include additional dimensions of scientific performance beyond papers and citations; (4) it is a high granularity measure, providing a complete ranking of the authors under analysis.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The understanding of the inner workings of a research community is essential to evaluate the impact of an author as well as to decide where and how to publish results. One of the key metrics is the number of citations that a publication receives. In parallel, information security is now a key and strategic area, partially fueled by the advent of the Internet of Things (IoT) and the need of pursuing cybercriminals by using digital forensics techniques. Therefore, this paper analyzes several factors influencing the number of citations in the domain of information security, such as differences between journal and conference publications, or the impact of the number of pages and the length of the abstract. To obtain quantitative results, we investigated papers of six sub-disciplines, i.e., anonymity and privacy, cryptography, information hiding, IoT and Cyber-Physical System security, digital forensics and incident response, and network security. For each sub-domain, we used metadata of 5000 publications collected from IEEE-Xplore. Results indicate some clear behaviors, for instance, papers tend to receive more citations when their abstract is longer and the number of references positively influences the performance of the work.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Marine shrimp fishing is an economic activity of global importance due to its high profitability, but it also presents several environmental and socioeconomic problems. In a context of increasing need for fishery sustainability, scientific basis supporting fishery resources management is essential. However, evidence-based information is frequently scarce or generated by developed countries, even when resources are most abundant in areas of developing economy. Here we present a bibliometric analysis to map each country’s scientific production in relation to its marine shrimp fishery yield, along with a hurdle model with socioeconomic factors that could influence publication of articles on this subject. We observed a geographic mismatch between research needs and the places that produce them, once tropical and subtropical regions account for most of fishery yield while knowledge is produced in temperate regions where the most developed countries are concentrated. Accordingly, our model reveled that GDP was the most influential factor in number of articles, while population density had a negative effect. Concurrently, key research interests about marine shrimp fisheries tend to be basic biology topics, despite the need for conservation solutions.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Scientometric methods have long been used to identify technological trajectories, but we have seldom seen reproducible methods that allow for the identification of a technological emergence in a set of documents. This study evaluates the use of three different reproducible approaches for identifying the emergence of technological novelties in scientific publications. The selected approaches are term counting technique, the emergence score (EScore) and Latent Dirichlet Allocation (LDA). We found that the methods provide somewhat distinct perspectives on technological. The term count based method identifies detailed emergence patterns. EScore is a complex bibliometric indicator that provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community. LDA traces emergence at the thematic level and provides insights on the linkages between emerging research topics. The results suggest that term counting produces results practical for operational purposes, while LDA offers insight at a strategic level.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉U.S. university engagement with Africa in various areas including global health, education, environment, and agriculture has grown in the last 15 years. However, there is limited literature that acknowledges the scholarly output and impact of U.S.-Africa university research collaboration in support of their respective institutions’ comprehensive internationalization goals. Using Michigan State University (MSU)—a public, research-intensive, land grant university with long history of engagement with Africa, as a case study, this paper aims to measure the research output, describe the collaboration patterns and research trends, and assess the impact of MSU’s research engagement on Africa for the last 10 years (2006–2015). It also attempts to determine the gender dimension of MSU’s research focus on Africa. Our scientometric and social network analysis provide strong quantitative evidence of scholarly success, research focus and diversification, collaboration, and impact on MSU’s engagement on Africa, especially in science, technology, engineering, and math-related disciplines. The co-authorship data provides evidence of greater collaboration of MSU with non-African countries on African-focused research but a growing number of partner institutions from African countries. This growing collaboration provides significant benefits on strategic research conducted and its impact, as well as increasing the recognition of African researchers as contributors to impactful international research collaborations and improving the ability of MSU and its global and African partners to co-generate knowledge and innovation that can help solve global problems more effectively. Finally, this study provides additional evidence of the increasing involvement of women in advancing knowledge and global innovation to address Africa’s socio-economic development challenges. The paper, overall, contributes to the science, research management, and gender perspectives of university internationalization goals and initiatives.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Artificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. The recent literature focuses on AI’s application to cyber security but lacks visual analysis of AI applications. Structural changes have been observed in cyber security since the emergence of AI. This study promotes the development of theory about AI in cyber security, helps researchers establish research directions, and provides a reference that enterprises and governments can use to plan AI applications in the cyber security industry. Many countries, institutions and authors are densely connected through collaboration and citation networks. Artificial neural networks, an AI technique, gave birth to today’s research on cloud cyber security. Many research hotspots such as those on face recognition and deep neural networks for speech recognition may create future hotspots on emerging technology, such as on artificial intelligence systems for security. This study visualizes the structural changes, hotspots and emerging trends in AI studies. Five evaluation factors are used to judge the hotspots and trends of this domain and a heat map is used to identify the areas of the world that are generating research on AI applications in cyber security. This study is the first to provide an overall perspective of hotspots and trends in the research on AI in the cyber security domain.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉770 million motorcycles are estimated on the roads. Motorcyclists represent more than 380,000 annual deaths worldwide. 28% of the global fatalities in the roads in 2016. With the increase of the accident rate, studies have been developed within the scientific literature. Bibliometric analysis is applied in the field of motorcycle safety in order to identify relevant publications on risk factors of road crashes and their implications. The information in this research was extracted from Web of Science and Scopus databases between 1947 and May 31, 2018. The study identified the key bibliometric indicators such as publications, authors, journals, countries, institutions, citation and co-citation analysis, subject categories, and co-occurrence of terms. EndNote, Microsoft Excel, Statgraphics Centurion and VOS-viewer software were used for the analysis. In total, 1813 articles were considered. The publications from 2000 to 2017 exhibits an average growth of 9%. The journal “Accident Analysis and Prevention” was the key issue in the publication and citation. The top institutions were the University of California, Universiti Putra Malaysia, and Monash University. The average citation of the top 10 articles was 134. A network visualization map showed that ‘vehicle’, ‘model’, ‘system’, ‘road’, ‘safety’, and ‘behavior’ were the most commons key terms. Bibliometric analysis demonstrates a high collaboration between authors and institutions. Two growing trends were identified. First, studies on the protection of the motorcyclist and the safe design considering the performance. Second, studies in analysis, characterization, and prevention of accidents. These studies are more related to the generation of strategies for the protection of road safety for motorcyclists.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The focus of this paper is the question “Can scale-invariant properties of collaborative research activities of a complex innovation system be quantified, modeled and used to inform decision makers about the effect that cooperation has on the impact of published peer-reviewed research?” Over the past few decades cooperative research activities have been extensively studied. Presently, encouragement and support for collaborative research and training is a cornerstone of many innovation policies and programs. Concurrently, the study of complex systems has produced tools and techniques that can be applied to the study of innovation systems. They have been shown to be complex systems with scale-invariant properties that can be measured and modeled providing novel insights to decision makers. An important factor contributing to the emergence of scale-invariant properties is the inseparable tension between competitive and cooperative activities among actors within a complex system. Peer-reviewed papers index in the 1990–2010 Web of Science and citations to these papers are used as a partial measure of size and impact, respectively. Documents are classified into 14 natural, health and applied sciences fields. Numbers of authors and country information from each paper are used to classify documents into various types of cooperation. Scale-invariant correlations between impact and sizes where prepared to provide measures and models used to explore the effects of cooperation types. It is shown that collaborative research tends to have greater impact and for a longer period of time that non-collaborative research. Cooperation in the more applied fields show higher growth of impact when compared to the growth of their sizes than cooperation in fields closer to the basic or ‘blue sky’ end of the R&D spectrum. Cooperation in a complex innovation system can have significant effects on the relative growth of impact with respect to growth of size and it enhances the sustainability of the Matthew Effect over time. Cooperative activities appear to sustain self-organization in a complex innovation system.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Nowadays, one of the most discussed topics in the technology industry is related to the new industrial revolution, called Industry 4.0. Industry 4.0 will transform entire production systems and products. However, the subject still lacks systematic study in its state of the art. This study seeks to identify relations or associations among emerging technologies in Industry 4.0. Through publications on its theme and keywords, a data mining technique was applied to help identify the network of associations with a new bibliometric approach. In order to reach the objective of the study, we utilized the Apriori algorithm in the Waikato Environment for Knowledge Analysis software. In this process, 15 association rules were found that met the input metrics: support, confidence, and lift. The rules point to two main technologies, internet of things and cyber-physical systems. This research points out that these technologies are key elements of Industry 4.0, and are related to others, such as cloud, big data, automation, virtualization, and robotics. Through data mining, the best associations and relations of the technologies in Industry 4.0 were identified. Moreover, this study pointed out the most important technologies for the new industrial revolution and the complementary technologies of each identified group. Thus, this network of technologies provides a basic guide for future works, which seek to deepen the characteristics of these relations.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉The pattern and impact of scientific collaborations depends on a country’s or region’s development stage. Prior to promoting scientific collaborations, it is crucial to understand what types of collaboration are more beneficial. By taking the stage of regional scientific development into consideration, this paper investigates the effect of various collaborations on regional scientific output. Our results highlight the differences between collaboration effects across regions at various capability levels. Scientifically weak regions benefit from more concentrated collaboration, in particular with advanced domestic regions. However, scientifically strong regions can benefit from a centrally located position in a broader collaboration network. Findings from this analysis indicate that international collaborations might not be beneficial a priori. Whether or not one region benefits from international collaborations depends on this region’s local capacity. Being embedded in a broad network structure can help increase one region’s publication output. However, such benefit is limited only to regions that have relatively higher research capacity.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Digital libraries containing scholarly publications are common today. They are an invaluable source of information to students, researchers, and practitioners. However, many digital libraries expose only the article metadata like title, author names, publication date, and the abstract for free; access to full-text requires access toll. Given that journal subscription charges are sometimes prohibitive, many important publications remain beyond the access of researchers, especially in developing countries. While open access publication solves this issue, the hard reality is that many research papers are not currently available for free reading or download. In this paper, we present a novel approach to alleviate this problem. We present a technique to retrieve open access surrogates of a scholarly article when the latter is unavailable freely in a digital library. Surrogates are articles semantically close to the original articles, written by the same author(s) and give valuable insights into the paper being searched for; they address the same or a very similar problem using the same or very similar techniques. Our focus on approximate matches of scholarly articles distinguishes our application from many academic search engines. We run it on a large corpus of computer science papers and compare the results with human judgment. Experimental results show that our tool can indeed identify relevant OA surrogates of access-restricted papers.〈/p〉

Beschreibung: 〈h3〉Abstract〈/h3〉 〈p〉Due to the development of academic, more and more attentions are paid to citation recommendation. To solve the citation recommendation problem, researchers begin to focus on the network representation, because it fuses semantic information and structural information well. It is a big challenge that how to map articles in a heterogeneous information network into a low-dimensional space while preserving the potential associations between articles. We propose a novel citation recommendation algorithm based on citation tendency, named CIRec which learns more about the potential relationship of articles in the process of network embedding. Citation tendency means if an article can be selected as a reference, it probability satisfies some kinds of conditions. In our algorithm, five weight matrices which represent the probability of entity-to-entity migration based on citation tendency are defined to build weighted heterogeneous network first. Second, we design a biased random walk procedure which efficiently explores articles’ characteristics and citations information. Finally, the skip-gram model is used to learn the neighborhood relationship of the nodes in the walk sequence and map the nodes to the vector space. Comparing with existing state-of-the-art technique, experiment results show that our algorithm CIRec has better recall, precision, NDCG on AAN and DBLP dataset.〈/p〉