CiteGraph: a citation network system for MEDLINE articles and analysis.

Studies of bibliographic data suggest a strong correlation between the growth of citation networks and their corresponding co-authorship networks. We explore the interdependence between evolving citation and co-authorship networks focused on the publications, by Indian authors, in American Physical Society journals between 1970 and 2013. We record interactions between each possible pair of authors in two ways: first, by tracing the change in citations they exchanged and, second, by tracing the shortest path between authors in the co-authorship network. We create these data for every year of the period of our analysis. We use probability methods to quantify the correlation between citations and shortest paths, and the effect on the dynamics of the citation-co-authorship system. We find that author pairs who have a co-authorship distance $d \leq 3$ significantly affect each others citations, but that this effect falls off rapidly for longer distances in the co-authorship network. The exchange of citation between pairs with $d=1$ exhibits a sudden increase at the time of first co-authorship events and decays thereafter, indicating an aging effect in collaboration. This suggests that the dynamics of the co-authorship network appear to be driving those of the citation network rather than vice versa. Moreover, the majority of citations received by most authors are due to reciprocal citations from current, or past, co-authors. We conclude that, in order to answer questions on nature and dynamics of scientific collaboration, it is necessary to study both co-authorship and citation network simultaneously.

As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks — dependency parse construction, coreference resolution, and ontology concept identification — over full-text biomedical articles. The structural annotation task requires the automatic generation of dependency parses for each sentence of an article given only the article text. The coreference resolution task focuses on linking coreferring base noun phrase mentions into chains using the symmetrical and transitive identity relation. The ontology concept annotation task involves the identification of concept mentions within text using the classes of ten distinct ontologies in the biomedical domain, both unmodified and augmented with extension classes. This paper provides an overview of each task, including descriptions of the data provided to participants and the evaluation metrics used, and discusses participant results relative to baseline performances for each of the three tasks.

Biomedical texts can be typically represented by four rhetorical categories: introduction, methods, results and discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied approaches to automatically classify sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We explored different approaches to automatically classify a sentence in a full-text biomedical article into the IMRAD categories. Our best system is a support vector machine classifier that achieved 81.30% accuracy, which is significantly higher than baseline systems.

PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.

BackgroundThe 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs).MethodsWe used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR.ResultsThis method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field.ConclusionsNetwork analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990–2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.

To date, many studies of scientific citation, collaboration and coauthorship networks have focused on the concept of cross-community ties. In this article we explore how Semantometrics can help to characterise the types of research collaboration in scholarly publication networks and the nature of the cross-community ties, and how this information can be utilised in aiding research evaluation. In contrast to the existing research evaluation metrics such as Bibliometrics, Altmetrics or Webometrics, which are based on measuring the number of interactions in the scholarly network, Semantometrics build on the premise that fulltext is needed to understand the value of publications. Using the CORE dataset as a case study, this paper looks at the relation between the semantic distance of authors and their research endogamy value. We identify four potential types of collaboration in a coauthorship network. The results suggest similar measures can be used to provide meaningful information about the nature of collaboration in scholarly publication networks.

In today's dynamic manufacturing market, quality management is critical to competitiveness, efficiency, and customer happiness. This study includes a bibliometric analysis from 2020 to 2024 to illuminate trends, patterns, and developing quality management areas in the industrial sector. Using bibliometric methodologies, this study examines a wide range of scholarly outputs, including journal articles, conference proceedings, and other relevant material, to determine the intellectual structure of this field. This methodology finds crucial contributors, seminal works, research focal points, and emerging themes in the field by quantitatively examining publication outputs, citation networks, collaboration patterns, and keyword co-occurrences. By synthesizing and visualizing the bibliographic data, this study provides a comprehensive picture of the knowledge landscape, allowing researchers, practitioners, and policymakers to navigate the complexities of quality management in manufacturing and foster continuous improvement initiatives. Furthermore, this bibliometric research provides important insights into the evolution of quality management techniques, emphasising the adaptation of methodologies and frameworks in response to growing difficulties and technological advancements. This study provides a road map for future investigations to improve quality management techniques in the industrial industry, indicating significant research gaps and areas ripe for discovery. Furthermore, the analysis gives information on the global distribution of research efforts, allowing for international collaborations and knowledge exchange to propel collective growth in quality management. Finally, this thorough review of the scholarly literature broadens our understanding of manufacturing quality management. It informs strategic decision-making processes for businesses looking to optimise their operations and provide superior products and services to customers.

This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.

Finding associations between itemsets within two categories (e.g., drugs and adverse effects, genes and diseases) are very important in many domains. However, these association mining tasks often involve computation-intensive algorithms and a large amount of data. This paper investigates how to leverage MapReduce to effectively mine the associations between itemsets within two categories using a large set of unstructured data. While existing MapReduce-based association mining algorithms focus on frequent itemset mining (i.e., finding itemsets whose frequencies are higher than a threshold), we proposed a MapReduce algorithm that could be used to compute all the interestingness measures defined on the basis of a 2 × 2 contingency table. The algorithm was applied to mine the associations between drugs and diseases using 33,959 full-text biomedical articles on the Amazon Elastic MapReduce (EMR) platform. Experiment results indicate that the proposed algorithm exhibits linear scalability.

• In economics journals, the share of theoretical work declined between 2000 and 2024, while empirical research, especially in development, labor, and public economics, expanded and received substantially higher citation counts. • Network analysis of economics journals reveals persistent concentrations of scholarly influence. • In the economics discipline, a small set of elite U.S. universities occupies central positions in citation and co-authorship networks. • Despite changes in research topics and methods, the structure of scholarly knowledge production in economics remains hierarchical, suggesting that intellectual innovation occurs within a stable institutional core. This paper examines patterns in knowledge production within the top-five economics journals from 2000 to 2024. Drawing on the full set of published articles and associated bibliometric details, citations, co-authorship links, and institutional affiliations, I document notable shifts in the orientation of research. The share of theoretical work has declined, while empirical fields, particularly development, labor, and public economics, have expanded. Network analysis shows that although the substantive questions and analytical approaches in top-five journals are changing, a small group of U.S. universities continues to account for a disproportionate share of highly cited work and occupies central positions in citation networks, a core that at the same time facilitates communication and exchange across fields.

Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars’ publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE® abstracts. We apply a gene and protein name tagger trained on Medline abstracts (ABGene) to a randomly selected set of full text journal articles in the biomedical domain. We show the effect of adaptations made in response to the greater heterogeneity of full text.

This diffusion of knowledge through co-authorship, organizational collaboration and country collaboration networks from 2004 to 2023 has been studied in this paper. Utilizing a dataset of 1,349 documents from 830 sources, the research investigates the structure and dynamics of knowledge dissemination. Key metrics such as total link strength, citation counts, and co-authorship patterns are used to identify influential authors, organizations and countries. Thematic focus and trends are explored through author and index keyword analyses, while citation relationships among various entities are examined to map the intellectual structure and collaborative interactions within the field. The results highlight significant contributors like Yi Bu, Ying Ding and Wei Lu in the co-authorship network, with the United States, the United Kingdom, and China leading in international collaborations. The study also identifies prevalent themes such as knowledge diffusion and innovation and emphasizes influential sources like Scientometrics and Journal of Informetrics. Through bibliographic coupling and co-citation analysis, the research uncovers emerging areas and potential interdisciplinary intersections, enhancing the understanding of global knowledge flow. This comprehensive network analysis highlights the crucial role of knowledge diffusion efforts in fostering research, education and informed decision-making within society.

Introduction: Eye gaze tracking is essential in understanding non verbal communication, human-computer interaction and cognitive responses. Its applications range from healthcare to consumer behaviour analysis and gaming. The evolution of eye gaze tracking technology and its increasing adoption highlight its significance across diverse domains. Aim: To conduct a bibliometric analysis of eye gaze tracking research over the past two decades (2005-2024), exploring publication trends, collaboration patterns, key contributors and emerging research themes. Materials and Methods: The present review was a bibliometric review in which data were extracted from the Web of Science Core Collection, amounting to 9,773 peer-reviewed articles. The bibliometric analysis tools used were VOSviewer and Biblioshiny for the investigation of co-authorship, citation and co-occurrence network analysis. The critical conceptual and intellectual trends in research on eye gaze tracking were identified, focusing on publication output and global collaborations in research. Results: Findings showed that more than 62% of publications were published within the period 2018-2024. The United States of America (USA) accounted for the majority of research contributions, followed by England and Germany. Applications in healthcare, marketing and cognitive sciences were evident, with “autism” being a focus of critical importance. Conclusion: Eye gaze tracking has seen rapid growth since 2018, with an increasing focus on Artificial Intelligence (AI)- assisted applications in healthcare, assistive technology and marketing. Emerging trends such as deep learning-based eye movement prediction and gaze-driven user experiences are shaping future developments. Upcoming research is expected to integrate AI, neuroscience and human-computer interaction to advance diagnostics and gaze-based security solutions.

How does a body of scholarship emerge, develop, and evolve? Research is the product of a community of scholars and their collaboration over time builds and disseminates knowledge. One way to examine a scholarly community and scholarship evolution is to consider patterns of collaboration through coauthorship networks. This article conducts a social network analysis of coauthorship between public service motivation (PSM) scholars from 1990 to 2016. This analysis depicts the social structure of the field as it evolved and offers implications both for its theoretical progress and for individual scholars. In general, we find that the PSM coauthorship network has grown increasingly since 1990 but it is not a cohesive network of scholars. It consists of many disconnected subgroups that actually represent opportunities for individual scholars to build social capital and influence. We conclude with implications of our findings and we offer suggestions for further analysis.