Relative Citation Research Articles

Gender inclusivity of ophthalmology journal submission guidelines and associated research metrics.

This cross-sectional study evaluated the prevalence of inclusive author submission guidelines across ophthalmology journals. Journals were identified from the 2021 Journal Citations Report (Clarivate Analytics). Independent reviewers rated each author submission guideline as "inclusive" for satisfying at-least one of six criteria: i) included examples of gender inclusive language; ii) recommended the use of gender-inclusive language; iii) distinguished between sex and gender; iv) provided educational resources on gender-inclusive language; v) provided a policy permitting name changes (e.g., in case of gender and name transition); and/or vi) provided a statement of commitment to inclusivity. The primary objective was to investigate the proportion of journals with "gender-inclusive" author submission guidelines and the elements of the gender-inclusive content within these guidelines. A secondary objective was to review the association between "gender-inclusivity" in author submission guidelines with publisher, origin country, and journal/source/influence metrics (Clarivate Analytics). Across 94 journals, 29.8% journals were rated as inclusive. Inclusive journals had significantly higher relative impact factor, citations, and article influence scores compared to non-inclusive journals. Of the 29.8% of inclusive journals, the three most common domains were inclusion of an inclusivity statement (71.4% of inclusive journals), distinguishing between sex and gender (67.9%), and provision of additional educational resources on gender reporting for authors (60.7%). A minority of ophthalmology journals have gender-inclusive author submission guidelines. Ophthalmology journals should update their submission guidelines to advance gender equity of both authors and study participants and promote the inclusion of gender-diverse communities.

Evaluation of Research Productivity among Academic Glaucoma Specialists Using the Relative Citation Ratio

PurposeTo provide relative citation ratio (RCR) benchmark data for the field of glaucoma. DesignCross-sectional bibliometric analysis. SubjectsFellowship-trained glaucoma faculty at ACGME-accredited institutions. MethodsGlaucoma faculty were individually indexed using the NIH iCite website. Publication count, mean RCR score, and weighted RCR score were collected for each author between May and August 2023 and included PubMed-listed articles from 1980 to 2023. Data were compared by gender, career duration, academic rank, and acquisition of a Doctor of Philosophy (PhD). Main Outcome MeasuresTotal number of publications, mean RCR value, weighted RCR value. Results526 academic glaucoma specialists from 113 institutions were indexed. These physicians produced highly impactful research with a median publication count of 13 (IQR 4-38), median RCR of 1.41 (IQR 0.97-1.98), and median weighted RCR of 16.89 (4.80-63.39). Academic rank, career duration, and having a PhD were associated with increased publication count, mean RCR, and weighted RCR. Publication count and weighted RCR differed significantly by gender, however, no difference was observed with mean RCR. ConclusionsCurrent academic glaucoma specialists have high mean RCR values relative to the NIH standard RCR value of 1. This benchmark data serves as a more accurate gauge of research impact within the glaucoma community and can be used to inform self, institutional, and departmental evaluations. Additionally, the mean RCR may provide an accurate metric for quantifying research productivity among historically underrepresented groups that are disadvantaged by time-dependent factors such as number of publications.

Parataxonomy and diversity of local varieties of taro (Colocasia esculenta (L.) Schott) and macabo (Xanthosoma sagittifolium (L.) Schott) grown in Benin (West Africa)

Taro and macabo are plants with high food, medicinal and economic value, but are under-utilized in Benin. The aim of this study was to identify local varieties of taro and macabo grown in Benin, with their vernacular names, farmers' recognition criteria and distribution. Data were collected between May 2021 and May 2023, through interviews using semi-structured questionnaires. The sample surveyed consisted of 349 taro/macabo producers, distributed in 40 villages belonging to 9 departments. The variables collected concerned producers' socio-demographic characteristics, local appellations, farmers' recognition criteria and the distribution of local varieties of the two species, and were processed by calculating relative citation frequencies and establishing a generalized linear model in R software. In addition, QGIS software was used to represent the distribution of the varieties surveyed. A total of 42 local appellations have been registered to designate all local varieties. Ten local varieties, including six of C. esculenta (V1 to V6) and four of X. sagittifolium (V7 to V10), were identified on the basis of seven (07) farmers' criteria, the main ones being tuber color (41.88 %) and leaf blade color (23.30 %). Local varieties V1, V3, V4 and V5 were the most widespread for C. esculenta, while varieties V7 and V9 were the most widely distributed from the south to the north of the country for X. sagittifolium. The socio-demographic parameters such as age category, occupation and ethnic groups tested had a significant influence (p < 0.05) on the number of local varieties held by growers. Agro-morphological characterization will provide data for the selection of elite varieties.

A scoping review on the use of traditional medicine and oral health in Africa.

This review aimed to chart the landscape of literature concerning the precise applications of traditional medicine in managing specific oral diseases and, in doing so, to pinpoint knowledge gaps surrounding the use of traditional medicine for oral disease management in the African context. A systematic search of the literature was conducted on PubMed, Web of Science, Scopus, and CINAHL. The search was conducted from the inception of the database till September 2023. A search of related citations and references was also carried out. Only English language publications were included. A summary of studies that met the inclusion criteria was conducted. Of the 584 records identified, 11 were duplicates and 12 studies, published between 2006 and 2021, met the inclusion criteria. The studies were published from eight countries located in the five sub-regions on the continent. All the studies were either experimental designs or ethnobotanical surveys and they all utilized plant-based remedies. The five experimental studies aimed to assess the impact of whole plants or plant extracts on the three microorganisms responsible for dental caries and seven responsible for periodontal diseases. The number of plant species identified by the seven ethnobotanical surveys ranged from 29 to 62 while the number of plan families ranged from 15 to 29. The remedies were either topical applied, use as mouth rinses, gargled, or chewed. The systemic routes of administration identified were inhalation and drinking. The remedies were used for the treatment of hard such as dental caries and tooth sensitivity, to soft tissue lesions such as mouth ulcers, gingival bleeding, and mouth thrush. Other oral disorders managed include halitosis, jaw fracture, and oral cancer. Given the increasing prevalence of oral diseases within the region, the shortage of oral healthcare professionals and limited access to financial resources, it becomes imperative to support the generation of empirical evidence to enhance the provision of traditional medicine for oral healthcare in Africa.

Development and trends in research on hypertension and atrial fibrillation: A bibliometric analysis from 2003 to 2022.

This study aimed to comprehensively analyze research related to hypertension and atrial fibrillation, 2 common cardiovascular diseases with significant global public health implications, using bibliometric methods from 2003 to 2022. From the Web of Science Core Collection database, literature on the theme of hypertension and atrial fibrillation was retrieved. Subsequently, comprehensive bibliometric analyses were conducted across multiple dimensions utilizing software tools such as VOSviewer, Citespace, Pajek, Scimago Graphica, and ClusterProfiler. These analyses encompassed examinations of the literature according to country/region, institution, authors, journals, citation relationships, and keywords. It revealed an increasing interest and shifting focus in research over the years. The analysis covered 7936 relevant publications, demonstrating a gradual rise in research activity regarding hypertension combined with atrial fibrillation over the past 2 decades, with a stable growth trend in research outcomes. Geographically, Europe and the Americas, particularly the United States, have shown the most active research in this field, while China has also gained importance in recent years. Regarding institutional contributions, internationally renowned institutions such as the University of Birmingham and the Mayo Clinic have emerged as core forces in this research direction. Additionally, Professor Lip Gregory, with his prolific research output, has stood out among numerous scholars. The American Journal of Cardiology has become a primary platform for publishing research related to hypertension and atrial fibrillation, highlighting its central role in advancing knowledge dissemination in this field. The research focus has shifted from exploring the pathophysiological mechanisms to investigating the treatment of complications and risk factors associated with hypertension and atrial fibrillation. Future research will focus on in-depth exploration of genetic and molecular mechanisms, causal relationship exploration through Mendelian randomization studies, and the application of machine learning techniques in prediction and treatment, aiming to promote the development of precision medicine for cardiovascular diseases. In conclusion, this study provides a comprehensive overview of the developmental trajectory of research on hypertension and atrial fibrillation, presenting novel insights into trends and future research directions, thus offering information support and guidance for research in this crucial field of cardiovascular medicine.

Research on scientific knowledge evolution patterns based on ego-centered fine-granularity citation network

A deep comprehension of the development of scientific knowledge is necessary to properly appreciate technological advancement and the process of knowledge innovation. However, fine-grained citation networks are less frequently utilized to characterize the evolution of scientific knowledge. To bridge this gap, we first constructed the fine-grained citation networks based on the PubMed and Web of Science (WOS) databases, then extracted the ego-centered networks of scientific knowledge, and finally employed Exponential Random Graph Models (ERGMs) to analyze the factors influencing the formation of Ego-centered Fine-granularity Citation Networks (EFCNs), taking into account the endogenous network structure and exogenous knowledge attribute variables. The results reveal that both types of variables play pivotal roles in the evolution of scientific knowledge. Furthermore, we found that (1) the in-degree and the out-degree centrality have a positive effect on knowledge evolution, respectively, in the 98.5 % and 99.9 % sample networks, while the clustering coefficient only has a positive effect on the edge formation of the 6.8 % sample network at the 0.05 significance level. (2) The citation behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 63.2 % and 78.3 % sample networks. (3) There is a tendency to form citation relationships between scientific knowledge units of similar age in 67.1 % of the sample networks. (4) There is a greater possibility of developing a citation relationship between scientific knowledge with the same journal impact rank and knowledge type. Our findings indicate that the evolution of scientific knowledge is influenced not only by the process of scholarly communication but also by self-organizing mechanisms at the fine granularity level.

Study of medicinal plants used in ethnoveterinary medical system in riverine areas of Punjab, Pakistan

BackgroundThe use of medicinal plants to treat various veterinary illnesses has been practiced for millennia in many civilizations. Punjab is home to a diverse ethnic community, the majority of whom work in dairy farming, agriculture, and allied professions and have indigenous practices of treating animal illnesses using native flora. This study was designed to (1) document and preserve information about the applications of medicinal plant species in ethnoveterinary remedies among inhabitants of Punjab, Pakistan, and (2) identify popular plants for disease treatment by quantitative analysis of the obtained data and to assess the pharmacological relevance of these species.MethodsTo collect data from informants (N = 279), questionnaires and semi-structured interviews were used. The ethnoveterinary data were analyzed using principal component analysis, relative frequency citation, fidelity level, relative popularity level, and rank order priority.ResultsA total of 114 plant species utilized in the ethnoveterinary medicinal system were found, which were divided into 56 families and used to treat 16 different illnesses. The Poaceae family, with 16 species, was the most common in the region. The most commonly employed growth form in herbal preparation was herb (49%). The most used part in ethnoveterinary remedies was leaves (35%), while powder was the most commonly used way for preparing ethnoveterinary remedies (51 applications). According to principal component analysis, the most typically used species in the research region were grasses. Five grasses (Arundo donax, Desmostachya bipinnata, Eleusine indica, Hordeum vulgare, and Pennisetum glaucum) showed a 100% FL value when used to treat diuretics, helminthiasis, digestive problems, fever, cough, worm infestation, indigestion, galactagogue, oral infections, and genital prolapse. The maximum value of disease cured level (DCL%) was recorded at 87.6% for endo- and ecto-parasitic ailments in the study area.ConclusionThis study demonstrates that medicinal plants play an important part in satisfying farmers’ animal healthcare demands, making it a feasible practice. The study also provides a wealth of knowledge regarding ethnoveterinary methods for further planning and application, providing an option for farmers who cannot afford allopathic therapy.

A stochastic gradient relational event additive model for modelling US patent citations from 1976 to 2022

Abstract Until 2022, the US patent citation network contained almost 10 million patents and over 100 million citations, presenting a challenge in analysing such expansive, intricate networks. To overcome limitations in analysing this complex citation network, we propose a stochastic gradient relational event additive model (STREAM) that models the citation relationships between patents as time events. While the structure of this model relies on the relational event model, STREAM offers a more comprehensive interpretation by modelling the effect of each predictor non-linearly. Overall, our model identifies key factors driving patent citations and reveals insights in the citation process.

Analysis of neurosurgery resident research activity in the United States.

Evaluation of the demographic and academic characteristics of current neurosurgery residents may provide prospective students with insight into factors that affect research output. Therefore, this study aimed to evaluate the research output among neurosurgery residents. US neurosurgery residency programs were abstracted from the American Association of Neurological Surgeons website. Demographic data on 1690 current residents across 119 programs were collected using publicly available institutional websites, Doximity, and LinkedIn. The h-index of each resident was recorded using Scopus and exported into the NIH iCite tool to determine the weighted relative citation ratio (w-RCR) and mean relative citation ratio (m-RCR). The total number of publications, h-index, and w-RCR were used as a proxy for research output, while m-RCR was used to measure research impact. One-way ANOVA and Kruskal-Wallis H-tests were used to assess the statistical significance of relationships between demographic data and measures of research activity. A total of 1690 residents (25.4% female), representing 119 programs, were evaluated. Neurosurgery residents had an average of 17 publications, h-index of 5.5, m-RCR of 1.4, and w-RCR of 16.9, with an upward trend of research activity by postgraduate year (PGY) class. Male residents on average had a greater total number of publications (p < 0.001), higher h-index (p < 0.001), and higher w-RCR (p = 0.002) compared with their female peers. Significant differences in research activity were also observed by degree (Doctor of Medicine [MD], Doctor of Osteopathy [DO], or other), where those with MD and other degrees had higher metrics than those with DO degrees. International medical graduates (IMGs) also had higher research output than American medical graduates (AMGs) (p < 0.001). Differences in all measures of research activity except impact were also observed in research activity when pre-residency medical school ranks were compared. The authors observed overall high research activity among neurosurgery residents. Factors such as gender, degree, PGY, IMG/AMG status, and medical school rank may therefore be related to the success of matching within neurological surgery. Although large disparities in gender representation have been identified in neurosurgery, newer classes are trending toward shrinking the gap. These data may be used by prospective residents to gauge changes and progress occurring in the neurosurgery match.

Promoting knowledge recommendation in innovative engineering design: a BERT-GAT-based patent representation learning approach

Since innovation in complex product design hinges on thorough engineering knowledge application, high-quality patent recommendations foster innovation in engineering design. However, many patent knowledge recommendation studies perform patent analysis without comprehensive exploration and proper organisation of knowledge, causing a superficial understanding of patents and returning arbitrary results. To mitigate this issue, a deep learning-based approach for patent representation learning and knowledge recommendation is proposed. First, a four-dimensional patent knowledge model is defined to formalise the patent attributes that critically affect the engineering design outcomes, namely patents’ domain(D), function(F), technology(T) and citation(C). Second, to exploit patent knowledge from their content and citation relationships, a representation learning approach integrating Bidirectional Encoder Representations from Transformers(BERT) and Graph Attention Network(GAT) is introduced. Thereafter a patent knowledge space is established in which each patent is characterised by the function, technology, and citation embeddings. Third, a knowledge requirement space is also constructed by vectorising a designer’s search query via BERT model and linking it to a requirement-representing patent based on similarity. Finally, a recommender prototype is developed and showcased by the knowledge recommendation in sealing structure design tasks. Comparative experiments and application cases validate the effectiveness of our method in patent representation learning and knowledge recommendation.

Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy

Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy’s treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node’s representation and adjacency matrix’s construction. For the node’s representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs’ segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN.

The National Institutes of Health Lasker Clinical Research Scholars Program: A Decade of Launching Clinician-Scientist Careers.

In response to the decades-long decrease in U.S. clinician-scientists, the National Institutes of Health (NIH) and the Albert and Mary Lasker Foundation launched the Lasker Clinical Research Scholars Program in academic year 2011 to 2012. The investigators examined the early outcomes of this program. Thirty-nine scholars have matriculated into the program as of May 2023. Productivity was assessed for all scholars who joined the program before October 2020 (n = 31). Extramural early-stage investigators (ESIs) were used as a control group, and coarsened exact matching was used to compare the groups. The scholars were compared with the matched ESIs on 4 productivity metrics: publication count, weighted relative citation ratio, clinical impact, and approximate potential to translate. Publication records for both groups were compiled using the NIH Office of Portfolio Analysis' name disambiguation method and manually curated to ensure integrity of the data set. Of the 39 scholars, 29 were compared with 121 matched extramural ESIs. Five years before matriculation, the 2 groups had comparable numbers of publications, but scholars had a higher median weighted relative citation ratio, clinical impact, and approximate potential to translate score. Five years after matriculation, the scholars had a higher median number of publications than the ESIs, and the gap between scholars and ESIs, with scholars having higher scores, had widened for all metrics except approximate potential to translate scores. Of 10 of the 39 scholars at or approaching tenure eligibility, 6 have attained tenure (3 at NIH and 3 in academic institutions), and 4 are on track to attain tenure at NIH. All the Lasker clinical research scholars are substantially involved in clinical and translational research. Their productivity matches or exceeds that of a matched cohort of ESIs at U.S. academic institutions.

Exploration of traditional Ethno-gynaecological knowledge: advances to ethnobotanical studies from indigenous communities of Neelum Valley in the Himalayan Region

The indigenous communities of the Kashmir region are exclusively dependent on medicinal plants for the treatment of various gynecological disorders. Due to the remoteness and unavailability of modern medications, indigenous communities still use traditional healthcare practices to cure various diseases related to women. The specific objective of this study is to document the traditional ethno-gynecological knowledge in Neelum Alley. Field visits were conducted from April to November 2023 to gather data on medicinal plants used for gynecological problems. Both questionnaires and group discussions were used to collect the data. The gathered data was analyzed using a variety of quantitative metrics, such as use value (UV), and relative frequency citation (RFC). A total of 45 plant species belonging to 22 families were collected from the study area. Herbs made up the majority of the medicinal plants employed by indigenous populations (91.1%), whereas leaves, the most often used plant part, contributed 22.22%. Among medicinal plants, Geranium wallichianum had the highest use value (1.41), whereas Arnebia benthamii had the highest relative frequency citation (0.80). This study emphasizes how important it is to preserve traditional knowledge, pointing out that younger people are losing understanding due to a lack of interest. The documentation of traditional ethno-gynecological knowledge is crucial for the preservation, discovery of novel medications, and sustainable conservation of vulnerable medicinal flora.

112 Flight Tracker: A REDCap Tool to Streamline Career Development Grant Preparation and Reporting

OBJECTIVES/GOALS: Compiling information about characteristics and progress of scholars is required for career development applications and progress reports. The range of information is substantial, and preparation is onerous. We sought to create a tool to facilitate gathering key data about trainees and mentors who participate in programs like NIH K- and T-awards. METHODS/STUDY POPULATION: Using the REDCap platform, we developed forms and surveys to support intake of applicants, updates from scholars, and information about their participation in activities and use of resources. We deployed application programming interfaces (APIs) to automate capture of publicly available data about publications, impact metrics, and federal grant funding. Similar tools capture descriptions of mentor expertise including grant funding, prior trainees, and publications with mentees We also built modules to 1) allow connection to institutional grant and contract data to capture foundation and other funding; 2) pre-populate follow-up surveys to update information about career trajectories with minimal scholar effort; and 3) support mentee-mentor agreements as living documents. RESULTS/ANTICIPATED RESULTS: After a pilot period at our institution, we disseminated Flight Tracker to more than 50 academic institutions, most of whom are CTSA hubs. They track scholars in TL1/T32s, KL2/K12s, MSTP programs, and academic groups. Beyond federal reporting, uses now include publication impact factors (relative citation ratios, Altmetrics scores), grant funding of groups, maps of network relationships among investigators, scholar receipt of internal pilot awards, and statistics about transition to independence and time-to-promotion. Scholars can be separated into smaller cohorts by demographics, training dates, and funding dates. Over 34,000 scholars are tracked nationally among over 260 programs. Having structured data supports program evaluation, continuous improvements, and documents program strengths. DISCUSSION/SIGNIFICANCE: We informally estimate Flight Tracker reduces staff and leadership effort in preparation of program data by 75%, preserving time to focus on service to scholars. Ready access to data over time and within and across institutions creates new opportunities for collaborative data analysis to support evidence-based career development.

Scoping study of research trends on Nili Ravi buffalo applying scientometric analysis and network visualization

The study elucidates scientometric analysis of published scientific communications on Nili Ravi buffalo in journal(s) for having an appraisal of status-quo of the research and scientific activities. Metadata of 383 articles retrieved from Scopus were analysed to identify the most productive author(s), institution(s) vis-a-vis countries and to ascertain their collaboration trends. Keyword based analysis was performed to provide an overview of the strength areas of research on Nili Ravi for better comprehension. The results revealed that the research efforts on Nili Ravi were discernible after the year 2005. All except 1.30% articles have been an outcome of the collaborative authorship. There were only few productive authors with ≥10 records, but others contributed on the subject occasionally. Nearly 90% of the articles have been contributed by Pakistan and its' authors have worked in close collaboration with scientists from United Kingdom, United States of America, China, Canada, and South Korea. They also have conjoint symbiosis on academic/research endavours on Nili Ravi with experts from Austria, Netherlands, India, Germany, Italy, and Australia. Twelve leading institutions contributed to ≥10 articles. Publication outcome of the Animal Sciences Institute, National Agricultural Research Centre, Islamabad, Pakistan; Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan; University of Gujrat, Gujrat, Pakistan and Semen Production Unit, Qadirabad, Sahiwal, Pakistan has higher Relative Citation Impact (RCI), making it obvious that their publication(s) have wider acceptance amongst scientific populace. Most productive vis-à-vis impactful journals publishing articles on Nili Ravi have also been identified.

Ethnobotanical Exploration of Traditional Medicinal Plants Among the Rural Inhabitants of District Muzaffarabad, Kashmir Himalayan Region

Indigenous knowledge provides insights into the therapeutic properties of medicinal plants used in local communities for basic healthcare. This research focuses on documenting the medical knowledge of rural communities that rely on medicinal plants in their traditional healthcare practices in the district of Muzaffarabad, Kashmir Himalayas. A field survey was carried out in 2022 and 2023 to collect data on wild medicinal plants. Informants were selected at random to learn about indigenous medical practices through semi-structured interviews and group discussions with 95 informants. The ethnomedicinal data were quantitatively analyzed using indices of use value (UV) and relative frequency citation (RFC). The study area documented 57 medicinal plants from 34 different families. Polygonaceae was the most dominant family, contributing 14.03% to medicinal plants, followed by Lamiaceae (10.52%) and Rosaceae (7.01%). Among medicinal plants, communities predominantly used herbs (64.91%) and leaves (29.09%) to treat several health problems. Mentha longifolia had the highest reported use value (1.71), and Bergenia ciliata had the highest recorded RFC value (0.72). Rural inhabitants continue to rely on wild medicinal plants as their primary source of medication. The transmission of indigenous knowledge to descendants is steadily declining and is primarily limited to healthcare practitioners and the elderly. These communities have kept important indigenous knowledge, which must be preserved for sustainable resource management and conservation. A more extensive field exploration is required to discover all of the indigenous knowledge in rural areas of Kashmir.

Adaptation to climate change: state of art technologies

PurposeThe world is increasingly threatened by climate change. As the dimensions of this danger grow, it becomes essential to develop the most effective policies to mitigate its impacts and adapt to these new conditions. Technology is one of the most crucial components of this process, and this study focuses on examining climate change adaptation technologies. The aim of the study is to investigate the entire spectrum of technology actors and to concentrate on the technology citation network established from the past to the present, aiming to identify the core actors within this structure and provide a more comprehensive outlook.Design/methodology/approachThe study explores patent citation relationships using social network analysis. It utilizes patent data published between 2000 and 2023 and registered by the US Patent and Trademark Office.FindingsStudy findings reveal that technologies related to greenhouse technologies in agriculture, technologies for combatting vector-borne diseases in the health sector, rainwater harvesting technologies for water management, and urban green infrastructure technologies for infrastructure systems emerge as the most suitable technologies for adaptation. For instance, greenhouse technologies hold significant potential for sustainable agricultural production and coping with the adverse effects of climate change. Additionally, ICTs establish intensive connections with nearly all other technologies, thus supporting our efforts in climate change adaptation. These technologies facilitate data collection, analysis, and management, contributing to a better understanding of the impacts of climate change.Originality/valueExisting patent analysis methods often fall short in detailing the unique contributions of each technology within a technological network. This study addresses this deficiency by comprehensively examining and evaluating each technology within the network, thereby enabling us to better understand how these technologies interact with each other and contribute to the overall technological landscape.

Authorship of Publications Supported by NCI-Funded Grants Involving Low- and Middle-Income Countries

Scientific publication is an important tool for knowledge dissemination and career advancement, but authors affiliated with institutions in low- and middle-income countries (LMICs) are historically underrepresented on publications. To assess the country income level distribution of author affiliations for publications resulting from National Cancer Institute (NCI)-supported extramural grants between 2015 and 2019, with international collaborating institutions exclusively in 1 or more LMICs. This cross-sectional study assessed authorship on publications resulting from NCI-funded grants between October 1, 2015, and September 30, 2019. Grants with collaborators in LMICs were identified in the National Institutes of Health (NIH) Query/View/Report and linked to publications using Dimensions for NIH, published between 2011 and 2020. Statistical analysis was performed from May 2021 to July 2022. Author institutional affiliation was used to classify author country and related income level as defined by the World Bank. Relative citation ratio and Altmetric data from Dimensions for NIH were used to compare citation impact measures using the Wilcoxon rank sum test. In this cross-sectional study, 159 grants were awarded to US institutions with collaborators in LMICs, and 5 grants were awarded directly to foreign institutions. These 164 grants resulted in 2428 publications, of which 1242 (51%) did not include any authors affiliated with an institution in an LMIC. In addition, 1884 (78%) and 2009 (83%) publications had a first or last author, respectively, affiliated with a high-income country (HIC). Publications with HIC-affiliated last authors also demonstrated greater citation impact compared with publications with LMIC-affiliated last authors as measured by relative citation ratios and Altmetric Attention Scores; publications with HIC-affiliated first authors also had higher Altmetric Attention Scores. This cross-sectional study suggests that LMIC-affiliated authors were underrepresented on publications resulting from NCI-funded grants involving LMICs. It is critical to promote equitable scientific participation by LMIC institutions in cancer research, including through current and planned programs led by the NCI.

Evolving Knowledge Graph Representation Learning with Multiple Attention Strategies for Citation Recommendation System

The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proves that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user’s needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model’s accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism.

Trends in authorship in the Journal of Cutaneous Pathology, 1981-2020.

The increase in authors per scientific article in many different medical and scientific disciplines has raised concerns over ethical authorship. Trends in authorship in dermatopathology are unknown. Cross-sectional study of a random sample of 200 articles from the Journal of Cutaneous Pathology (1981-2020). The number of authors per article increased by an estimated 96% between 1981 and 2020 (2.7-5.3), while the relative citation ratio decreased by an estimated 56% during the same period (1.19-0.52). Higher author counts were not associated with higher relative citation ratios (p = 0.2349) or analytic study designs (p = 0.2987). Higher relative citation ratios were associated with analytic study designs (p = 0.0374). There has been significant growth in authorship credit at the journal without a corresponding increase in research impact or study rigor. Remedial measures to stem authorship inflation and promote more impactful studies may be necessary.