Citation Data Research Articles

Patterns and trends of university-industry research collaboration in Ghana between 2011 and 2020

Purpose The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales. Design/methodology/approach This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020. Findings Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities. Originality/value This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.

Mapping Emotional Intelligence and Psychological Capital Research: A Bibliometric Analysis and Future Research Agenda

This study explores the scholarly literature on emotional intelligence (EI) and psychological capital (PsyCap) using a comprehensive bibliometric analysis to map the research landscape and propose future directions. Data was collected from the Scopus database using specific search queries related to EI and PsyCap, with inclusion and exclusion criteria established to refine the dataset. A total of 39 articles published between 2007 and 2023 were selected for analysis. Utilizing bibliometric techniques through VOSviewer, patterns of citation and collaboration within the field were quantified, capturing descriptive statistics related to citation data and performing network analysis on authors, countries, publications, citations, and keywords. Key documents, sources, authors, organizations, and countries contributing to the discourse on PsyCap and EI were identified, providing insights into the evolution and trends within this field. The analysis revealed longitudinal trends in research output and explored the intersectionality of EI and PsyCap with other individual differences and personality traits. The findings contribute to a deeper understanding of knowledge growth in positive organizational psychology, informing evidence-based practices, and guiding future research endeavors.

A statistical approach to law school citation rankings

AbstractCitation rankings have emerged as a popular approach to ranking the scholarly impact of law faculties. This paper develops a statistical approach for inferring faculty quality from citation counts and determining when differences among law schools are significant. Statistical tests demonstrate that the distribution of citations within faculties closely follows the lognormal distribution, subject to small adjustments. This suggests a simple test for comparing faculties: whether they could be drawn from lognormal distributions with the same log mean. Under this approach, the geometric mean of citations is the most efficient measure for summarizing faculty quality. Using citation data collected from HeinOnline, this article provides a citation ranking for 195 law schools in the United States. Most differences between peer schools are statistically insignificant, and confidence intervals on citation ranks are extremely wide. Except for the highest‐ranked faculties, citation rankings provide little information on the relative quality of faculties.

An academic recommender system on large citation data based on clustering, graph modeling and deep learning

Recommendation (recommender) systems (RS) have played a significant role in both research and industry in recent years. In the area of academia, there is a need to help researchers discover the most appropriate and relevant scientific information through recommendations. Nevertheless, we argue that there is a major gap between academic state-of-the-art RS and real-world problems. In this paper, we present a novel multi-staged RS based on clustering, graph modeling and deep learning that manages to run on a full dataset (scientific digital library) in the magnitude of millions users and items (papers). We run several tests (experiments/evaluation) as a means to find the best approach regarding the tuning of our system; so, we present and compare three versions of our RS regarding recall and NDCG metrics. The results show that a multi-staged RS that utilizes a variety of techniques and algorithms is able to face real-world problems and large academic datasets. In this way, we suggest a way to close or minimize the gap between research and industry value RS.

State of open science in cancer research

PurposeThis study has been focused on assessing the Open Science scenario of cancer research during the period 2011–2021, in terms of the derived scientific publications and raw data dissemination.MethodsA cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion.Results50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository.ConclusionsThere has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations.

Learning by exporting: Evidence from patent citations in China

Using matched firm-level data on operations, trade, and patents in China, we empirically investigate the impact of exporting on firms’ innovation and learning from destinations. We first show that access to export markets improves both the quantity and quality of innovation. We then construct a measure of knowledge flows based on Chinese patent citation data and demonstrate that there are actual knowledge transfers from destination markets to exporting firms, which help firms improve their innovation. These findings support the learning-by-exporting effect. Moreover, we show that this learning effect remains significant and unchanged, even after controlling for the spillover effect of FDI. To investigate how the learning process occurs, we also explore whether the learning-by-exporting effect varies with different export products or export modes. We find that vertical linkages and direct exporting facilitate Chinese firms’ learning from destination markets. Finally, we show that the learning effect is particularly salient at the extensive margin compared to the intensive margin.

Bibliometric analysis and survey on electronic nose used in agriculture

Work carried out at the beginning of this century on improvements in semiconductor materials, transducers, sensors and artificial intelligence algorithms led to the rapid expansion of research topics related to the electronic nose, with diverse applications. Agriculture is a recent application field that needs a review of the usage of electronic noses in this field and the future challenges. The paper provided a bibliometric analysis and review of electronic noses used in agriculture. A search of published works on the e-nose and its applications in agriculture was carried out in the Web of Science and Scopus databases, which provide comprehensive citation data for academic disciplines worldwide. In the end, 2,953 documents were identified, and the data collected was analyzed mainly using the bibliometric toolbox, and then a deep study was carried out. The study results show that in the agricultural field, some works were achieved on different varieties of plants to detect disease or plant damage with very good results using electronic noses. However, less research was carried out to directly identify animals in crops like pests or environmental monitoring using electronic noses in agriculture. Some recommendations for future research efforts are finally provided.

A new approach to computing the distances between research disciplines based on researcher collaborations and similarity measurement techniques

The measurement of distance between research disciplines involves various approaches, with a focus on publication citation analysis. However, calculating discipline distance requires more than just selecting relevant information; it also involves choosing suitable quantification methods and similarity measures. In this paper, we introduce a novel approach to measuring the distance between research disciplines, referred to as a distance matrix. This approach is particularly useful when there is limited availability of citation data, providing an alternative method for quantifying the distance between disciplines. Our method counts co-occurrences of disciplines based on researcher collaborations in projects and evaluates various similarity measures to convert the co-occurrence matrix into a similarity matrix. We analyze the behavior of different similarity measures and propose functions to transform the similarity matrix into a distance matrix, capturing research discipline dissimilarity effectively. Additionally, we establish evaluation criteria for distance matrix quality. We implement our approach on the Flanders Research Information Space dataset, showing promising results. The distance matrix demonstrates satisfactory density scores, outperforming traditional approaches in skewness and deviation. The probability density functions of distances remain consistent over time, indicating stability. Furthermore, the distance matrix proves valuable for visualizing discipline profiles associated with the dataset, providing valuable insights.

Targeted therapy for immune mediated skin diseases. What should a dermatologist know?

BackgroundMolecularly targeted therapies, such as monoclonal antibodies (mAbs) and Janus Kinase inhibitors (JAKis), have emerged as essential tools in the treatment of dermatological diseases. These therapies modulate the immune system through specific signaling pathways, providing effective alternatives to traditional systemic immunosuppressive agents. This review aims to provide an updated summary of targeted immune therapies for inflammatory skin diseases, considering their pathophysiology, efficacy, dosage, and safety profiles. MethodsThe review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A systematic search was conducted on PubMed over the past 10 years, focusing on randomized clinical trials, case reports, and case series related to targeted immune therapies in dermatology. Eligibility criteria were applied, and data were extracted from each study, including citation data, study design, and results. ResultsWe identified 1360 non-duplicate articles with the initial search strategy. Title and abstract review excluded 1150, while a full-text review excluded an additional 50 articles. The review included 143 studies published between 2012 and 2022, highlighting 39 drugs currently under investigation or in use for managing inflammatory skin diseases. Study limitationsThe heterogeneity of summarized information limits this review. Some recommendations originated from data from clinical trials, while others relied on retrospective analyses and small case series. Recommendations will likely be updated as new results emerge. ConclusionTargeted therapies have revolutionized the treatment of chronic skin diseases, offering new options for patients unresponsive to standard treatments. Paradoxical reactions are rarely observed. Further studies are needed to fully understand the mechanisms and nature of these therapies. Overall, targeted immune therapies in dermatology represent a promising development, significantly improving the quality of life for patients with chronic inflammatory skin diseases.

Variations in Citations Across Databases: Implications for Journal Impact Factors

ABSTRACT The Journal Impact Factor (JIF) is a widely used metric for ranking journals based on the number of citations garnered by papers published over a specific timeframe. To assess the accuracy of JIF values, I compared citation counts for 30 of my own publications across six major bibliography databases: CrossRef, Web of Science, Publisher records, Google Scholar, PubMed and Scopus. The analysis revealed noteworthy variations in citation counts, ranging from 10% to over 50% between the lowest and highest citation counts. Google Scholar records the highest citation numbers, while PubMed reported the lowest. Notably, Web of Science, whose citation data are used in JIF calculations, tend to underestimate citation counts compared to other databases. These observations raise concerns about the accuracy of JIF calculation based on Web of Science’s citation data. The real JIF values for most journals would differ from those annually reported by Clarivate’s journal citation reports (JCR). These citation discrepancies underscore the importance of comprehensive data collection and the necessity to include additional citation sources. Not because a paper is cited in one journal rather than another should it have a less or more citation weight. Ultimately, one citation remains one citation, regardless of its origin. Clarivate Analytics may thus need to consider integrating all citation sources for more accurate JIF values. Alternatively, Google Scholar could potentially develop its own journal or citation impact based on its extensive journal citation records. However, while making adjustments to how the Journal Impact Factor is calculated can make it more mathematically precise, it doesn’t address the fundamental biases built into the metric. Even with refinements, the Journal Impact Factor will remain skewed due to how it’s defined and used.

Anthocyanins - dietary natural products with a variety of bioactivities for the promotion of human and animal health

Abstract Anthocyanins are water-soluble pigments contained in numerous food plants. This study provides an overall quantitative literature analysis on anthocyanins. Bibliometric data were collected through the search string TOPIC = anthocyan*, which yielded 44,121 publications indexed within the Web of Science Core Collection. Term maps were generated using VOSviewer software to visualize frequently mentioned terms alongside their citation data. The literature has been growing fast since the 2000s and predominantly consists of original articles, with a ratio of 13.8:1 compared to reviews. The most productive author was Professor Victor de Freitas from the University of Porto, whereas the most productive countries were China and the United States. Many publications were published in food science & technology and plant sciences journals. Frequently mentioned chemicals/chemical classes included anthocyanin, flavonoid, cyanidin, phenolic compound, and polyphenol. Recurring food items of the anthocyanin papers were grapes, many berries, and specific varieties of rice, maize, potato, and tomato.

Analysis on open data as a foundation for data-driven research

AbstractOpen Data, one of the key elements of Open Science, serves as a foundation for “data-driven research” and has been promoted in many countries. However, the current status of the use of publicly available data consisting of Open Data in new research styles and the impact of such use remains unclear. Following a comparative analysis in terms of the coverage with the OpenAIRE Graph, we analyzed the Data Citation Index, a comprehensive collection of research datasets and repositories with information of citation from articles. The results reveal that different countries and disciplines tend to show different trends in Open Data. In recent years, the number of data sets in repositories where researchers publish their data, regardless of the discipline, has increased dramatically, and researchers are publishing more data. Furthermore, there are some disciplines where data citation rates are not high, but the databases used are diverse.

Scholar Metrics Scraper (SMS): automated retrieval of citation and author data.

Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Linking crash and breathalyzer data in Connecticut

Objectives To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information. Methods Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables. Results Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record. Conclusions Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

Information effects of high-speed rail: Evidence from patent citations in China

Using high-speed rail (HSR) connection information and patent citation data, we find robust patterns linking HSR connection with increased patent citations between Chinese cities. Furthermore, HSR connection is shown to slow down the decay in knowledge diffusion over distance. The main results are not only robust to alternative choices of city sample, HSR connection measure, citation measure, and citation windows, as well as other tests, but also remain when the endogeneity concern is addressed. And evidence has been found that academic conferences and the establishment of non-local subsidiaries serve as mechanisms through which HSR connection affects cross-regional knowledge spillovers. The heterogeneity effects of HSR connection are also in line with expectations, where cities with larger innovation capacity gaps, industries with higher delivery costs and faster technological upgrading, and patents with more technological innovation content experience greater impacts of HSR connection. These findings thus provide consistent support for the beneficial effects of transportation infrastructure upgrading on knowledge transfer and technological innovation.

Unveiling the gender gap in research: a bibliometric analysis of the 100 most-cited articles on food-borne pathogen outbreaks from 1990 to 2020.

Despite a recent increase in the representation of female authors in scientific literature, a significant gap persists concerning the inclusion of women in research. This necessitates the analysis of published literature from a gender perspective. This study aimed to provide gender distribution in authorship in the 100 most-cited articles on food-borne pathogen outbreaks from 1990 to 2020. Bibliometric analysis was conducted using the Scopus database. Two reviewers were selected to search the database. We included the 100 most-cited articles on foodborne outbreak investigations. The analysis was conducted using Statistical Package for Social Sciences (SPSS) version 26 and Microsoft Excel version 2016. The citation data, including total citations, citations per year, and representation of women as first and senior authors, was analyzed in terms of frequencies, mean, median, and interquartile range. The correlation between journal impact factor and the representation of women in high-impact factor journals was determined. A p-value of <0.05 was considered significant. Most of the top-cited articles were published between 2001 and 2010 (n=47). The top 3 most-cited articles were from the USA. Of the total 100 articles, women were the first and last authors in 46% and 28% of the articles, respectively, reflecting a significant gender gap. However, the proportion of females as principal investigators gradually increased from 25% (n=10/30) to 52% (n=24/47) during the period 2001-2010 and to 92% (n=12/13) during 2011-2020. The USA had the highest number of included articles (n=48), and women were principal authors in 56% (n=27) of them. The lowest representation of women was observed in Austria, Denmark, Japan, Netherlands, New Zealand, Nigeria, Portugal, and the United Kingdom. Women are under-represented in published literature on food-borne pathogen outbreaks. Although the representation of women as principal authors has recently increased, disparities still exist at the senior-author level, calling for women's advancement in academic science.

How is Tencent Catching up with Facebook: Empirical Analysis using Patent Citation Data

This paper examines Tencent’s economic performances over time in terms of its technological catch-up and business strategies, with Facebook as a benchmark. Patent and citation data from 2010 to 2019, extracted from the Worldwide Patent Statistical Database (PATSTAT), was used to analyze the technological catching up made by Tencent and Facebook. Results indicate that Tencent has overtaken Facebook in terms of market performances, and that the overtaking in the number of patents preceded that of the market performances, but Tencent has not yet overtaken Facebook in terms of the quality of patents. Tencent has surpassed Facebook in terms of technological independence from external sources and further widened the gap in this aspect over the years. Patterns in the cycle time of the patented technology and citations of non-patent scientific literatures illustrate Tencent’s strategies to seek a niche using recent technologies and avoid issues related to intellectual property rights. Overall, there is some evidence that technological prowess contributes to Tencent’s overtake of Facebook in market performances.

Extracting Sentiments from Youtube Comments Using Deep Learning and Transformers

Abstract: YouTube is the most used social media platform, and it has been the most popular website where users can post the video. The public generally does comment, like or dislike, video-sharing on a YouTube video. Comment plays a vital role in expressing opinions and mindset, and it is used as an expression of public opinion. The massive amount of comment is generated mainly on famous channels where challenges arise to analyse public opinion or behaviour regarding that particular video. This project proposes sentiment analysis on YouTube video by Natural Language Processing (NLP) technique along with Deep Learning techniques Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) models. Sentiment analysis is when comprehension, citation, and processing of text-based data is done, and it directly converts it into sentiment information. This analysis help users to get the report of their YouTube video. The output of this analysis gives the classification of sentiment analysis, as Positive, negative, or neutral.

Publication and impact: a bibliometric survey of Megataxa 1–10

Megataxa was founded in January 2020 as a sister journal of the two most important journals in taxonomy, Zootaxa and Phytotaxa, with similar goals to accelerate the documentation of undescribed species and promote the development of global taxonomy, but aims to be a premium journal for most important works in taxonomy. Megataxa has grown steadily since January 2020 and has published 44 papers of various types in 3329 pages, 14 issues and 10 volumes, averaging 76 pages per paper and including six large monographs. These were contributed by 99 authors from 27 countries in six regions. Analysis of citation data in the Web of Science Core Collection showed that papers in Megataxa were cited three to nine times as often as those in Zootaxa published in the same year. The estimated (non-official) journal impact factor of 2023 for Megataxa is 6.8. These indicate that although Megataxa is still in its infancy, it has great promise to become a journal of high impact in taxonomy.