Scholarly Data Research Articles

A Handlist of Illustrated Early Solomonic Manuscripts in British Public Collections

As material objects bearing textual and visual information, illustrated Christian manuscripts from the Horn of Africa are among the most valuable sources of data for scholars specializing in this field. This article is the second handlist produced within the framework of the AHRC-DFG project Demarginalizing medieval Africa: Images, texts, and identity in Early Solomonic Ethiopia (1270– 1527). It focuses on illustrated Early Solomonic manuscripts housed in public libraries in the United Kingdom. This is the first time that this body of illuminations has been comprehensively analysed. The resulting work sheds new light on the history of book illustration in Early Solomonic Ethiopia and provides insights into the connected histories of the Christian empire of the Ethiopian-Eritrean highlands and the wider Mediterranean world. Moreover, it showcases the movements of manuscripts and the development of collections of Gǝʿǝz manuscripts in Europe.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential.

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

Analysis of implementation for big data techniques in consumer behavior

Consumer behavior can refer to both the purchasing behavior of consumers towards goods and the decision-making behavior of consumers when choosing goods, the research in this field involves psychology, marketing, and other disciplines. As a popular data mining and processing technology, big data technology has powerful information analysis capabilities. Therefore, with this in mind, applying big data technology to consumer behavior research can greatly improve the efficiency. This study focuses on the implementation for big data techniques in consumer behavior, including application scenarios, application methods, as well as application efficiency. The AIDMA, AISAS, and SOR models are suitable for using big data analysis technology. Big data technology helps enterprises influence external factors in consumer behavior factors, thereby influencing consumer decision-making behavior through internal perception factors. Overall, these results aim to collect and summarize relevant knowledge about consumer behavior and big data for future scholars to investigate and explore the implementation schemes.

Open Science and the impact of Open Access, Open Data, and FAIR publishing principles on data-driven academic research: Towards ever more transparent, accessible, and reproducible academic output?

Contemporary evidence-informed policy-making (EIPM) and societies require openly accessible high-quality knowledge as input into transparent and accountable decision-making and informed societal action. Open Science1 supports this requirement. As both enablers and logical consequences of the paradigm of Open Science, the ideas of Open Access, Open Data, and FAIR publishing principles revolutionise how academic research needs to be conceptualised, conducted, disseminated, published, and used. This ‘academic openness quartet’ is especially relevant for the ways in which research data are created, annotated, curated, managed, shared, reproduced, (re-)used, and further developed in academia. Greater accessibility of scientific output and scholarly data also aims at increasing the transparency and reproducibility of research results and the quality of research itself. In the applied ‘academic openness quartet’ perspective, they also function as remedies for academic malaises, like missing replicability of results or secrecy around research data. Against this backdrop, the present article offers a conceptual discussion on the four academic openness paradigms, their meanings, interrelations, as well as potential benefits and challenges arising from their application in data-driven research.

Contemporary preclinical mouse models for pediatric rhabdomyosarcoma: from bedside to bench to bedside.

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue malignancy, characterized by high clinicalopathological and molecular heterogeneity. Preclinical in vivo models are essential for advancing our understanding of RMS oncobiology and developing novel treatment strategies. However, the diversity of scholarly data on preclinical RMS studies may challenge scientists and clinicians. Hence, we performed a systematic literature survey of contemporary RMS mouse models to characterize their phenotypes and assess their translational relevance. We identified papers published between 01/07/2018 and 01/07/2023 by searching PubMed and Web of Science databases. Out of 713 records screened, 118 studies (26.9%) were included in the qualitative synthesis. Cell line-derived xenografts (CDX) were the most commonly utilized (n = 75, 63.6%), followed by patient-derived xenografts (PDX) and syngeneic models, each accounting for 11.9% (n = 14), and genetically engineered mouse models (GEMM) (n = 7, 5.9%). Combinations of different model categories were reported in 5.9% (n = 7) of studies. One study employed a virus-induced RMS model. Overall, 40.0% (n = 30) of the studies utilizing CDX models established alveolar RMS (aRMS), while 38.7% (n = 29) were embryonal phenotypes (eRMS). There were 20.0% (n = 15) of studies that involved a combination of both aRMS and eRMS subtypes. In one study (1.3%), the RMS phenotype was spindle cell/sclerosing. Subcutaneous xenografts (n = 66, 55.9%) were more frequently used compared to orthotopic models (n = 29, 24.6%). Notably, none of the employed cell lines were derived from primary untreated tumors. Only a minority of studies investigated disseminated RMS phenotypes (n = 16, 13.6%). The utilization areas of RMS models included testing drugs (n = 64, 54.2%), studying tumorigenesis (n = 56, 47.5%), tumor modeling (n = 19, 16.1%), imaging (n = 9, 7.6%), radiotherapy (n = 6, 5.1%), long-term effects related to radiotherapy (n = 3, 2.5%), and investigating biomarkers (n = 1, 0.8%). Notably, no preclinical studies focused on surgery. This up-to-date review highlights the need for mouse models with dissemination phenotypes and cell lines from primary untreated tumors. Furthermore, efforts should be directed towards underexplored areas such as surgery, radiotherapy, and biomarkers.

Allegedly impossible experiences

ABSTRACT In this paper, I will argue for two interrelated theses. First, if we take phenomenological psychopathology seriously, and want to understand what it is like to undergo various psychopathological experiences, we cannot treat madpeople’s testimony as mere data for sane clinicians, philosophers, and other scholars to analyze and interpret. Madpeople must be involved with analysis an interpretation too. Second, sane clinicians and scholars must open their minds to the possibility that there may be experiences that other people have, which they nevertheless cannot conceive of. I look at influential texts in which philosophers attempt to analyze and understand depersonalization and thought insertion. They go astray because they keep using their own powers of conceivability as a guide to what is or is not humanly possible to experience. Several experiences labeled inconceivable and therefore impossible by these philosophers, are experiences I have had myself. Philosophers and others would be less likely to make this mistake if they would converse and collaborate more with the madpeople concerned. When this is not feasible, they should nevertheless strive to keep an open mind. Fantastical fiction may have a role to play here, by showing how bizarre experiences may nevertheless be prima facie conceivable.

Psychological Well-Being and Job Satisfaction of Kenyan Police Officers

Purpose: Police work is a high risk job in terms of stress and low psychological wellbeing. In Kenya, police officers have presented high levels of suicide, homicide, assault and intimate partner abuse and this has become a matter of concern. 
 Methodology: This theoretical paper applies the hermeneutic phenomenological approach to study psychological wellbeing and job satisfaction of police officers as a phenomenon and how it affects police work. Extensive and systematic literature review is done using search engines like google scholar, web of science and Scopus. The search includes historical and contemporary scholarly data which reveals how police work is understood by scholars and the media. This data is then assessed and interpreted to discern the emerging patterns, findings and conclusions.
 Findings: Statistics on the Kenya police service indicate that 20,000 police officers are either depressed or they present with various psychological disorders. Between 2016 and 2020 police have recorded within their ranks across the country 13 murders and 11 suicides per year without a break. Systematic literature view has revealed that high psychological wellbeing of police officers is imperative in order to remove or reduce psychological morbidity in police work. This not only decreases turnover, but also retains talent, and increases productivity. High psychological wellbeing also spills over to work teams and families in terms of happiness and wellbeing. Analysis of literature has also revealed that job satisfaction is one of the facets of psychological wellbeing at the work place.
 Unique Contribution to Theory, Practice and Policy: This paper proposes that police managers, and stakeholders should proactively put in place strategies that enhance job satisfaction and therefore improve the psychological wellbeing of police officers. Researchers in partnership with police management should also address the knowledge gap in explaining how police stressors finally precipitate the high rates of murders and suicide and implement solutions.

Meaningful Review of Existing Trends, Expansion, and Future Directions of Green Bond Research: A Bibliometric Approach

Abstract Green bonds serve as an imperative financial tool for mitigating climate change risk and environmental sustainability. It has received significant attention in the current literature, with increased worldwide efforts to fight against global climate change. Yet, the direction of green bonds' literature is currently unclear. Therefore, this paper aims to deliver a comprehensive vision of the literary landscape of green bond research, in consideration of policymakers, major market players, and investors. To achieve this, bibliometric analysis is performed on 342 filtered articles found in the Scopus database, through VOSviewer and Biblioshiny package. A detailed and descriptive evaluation of scholarly data demonstrates the significance of green bonds in accomplishing the objective of sustainable finance and mitigation of climate change. This study broadens our understanding of the literature and conceptual growth of scholarly discussion of green bonds, which gives insight into developing a strong conceptual foundation and future directions for research.

Deciphering the importance of herbal immunostimulants in aquaculture, using citation network analysis: A futuristic sustainable approach

Aquaculture is the fastest growing food sector round the globe, with huge potential for expansion. The objectives of United Nations Sustainable Development Goals (SDGs) can be met with concerted efforts of stakeholders for circular economy. It plays a critical role in global food production and over half of the aquatic foods for human consumption are farmed. According to several studies bacteria, fungi, viruses, and parasites are the main causative agents of aquaculture diseases. Antibiotics and synthetic chemicals are typically used by fish farmers to overcome these infections and reducing the mortality rate. Medicinal herbs have been used for thousands of years, as immunostimulants. The use of medicinal herbs as natural, risk-free substitutes for antibiotics and immunoprophylactics in aquaculture may prove to be advantageous. In this study, scholarly data was retrieved using Scopus database and were manually curated and approximately 150 scholarly articles were obtained using customized keyword search to be used for citation network analysis. There are a total of 5 clusters based on the relevant keywords consisting of 87 items and 2195 links amongst them. The work related to dietary supplements, immunology and fish foods have gained more relevance since 2016, as revealed by its interactions with relevant terms. In this research article, we aim to provide an overview of current research in this field, identify research hotspots, project future development prospects, and make recommendations for further research in aquaculture using sustainable approaches by exploiting the natural herbs and plants as immunostimulants. This work is related to the sustainable approach exploring medicinal plants as immunostimulants and will definitely find new promising avenues in this area.

Educational Technology Applications for Enhancing Religious and Moral Values in Early Childhood Development: A Bibliometric Analysis

Early childhood refers to the crucial developmental period spanning from 0 to 6 years, often regarded as the golden age due to heightened receptivity to information. During this phase, children rapidly absorb information, influencing their cognitive patterns and behavior. Instilling religious and moral values in children from an early age is imperative for optimal outcomes. This research aims to analyze the evolution of religious and moral values in early childhood and explore the benefits of employing educational technology in enhancing these values. Utilizing bibliometric analysis, the study delves into scholarly data indexed in Scopus, employing the VOSviewer application to visually depict the interconnected data network. Findings reveal that the integration of educational technology positively contributes to the development of religious and moral values in early childhood, offering numerous advantages for fostering these essential attributes.

It is Time to Introduce the Next Generation of Chemists to FAIR and Open Science

Early career scientists are confronted with increasing expectations in sharing scholarly data openly and fair by funding agencies. This manuscript is an appeal to introduce open data management strategies to graduate students in atmospheric chemistry.

Investigating Academic Graph‐Based Factors behind Funding Success in National Institutes of Health

ABSTRACTWhile major funding agencies are striving for diversity and fairness, the mechanisms behind funding success have yet to be fully elucidated. Existing studies reveal valuable evidences about the effect of the applicant's individual attributes, e.g., gender and age, on the funding success. However, the relationship between funding success and academic activities, e.g., collaborator's characteristics, remains underexplored. This work collects massive scholarly data from open academic graphs and public data about National Institutes of Health awards to investigate the effect of various academic graph‐based factors on the “K to R” success. Leveraging a heterogeneous graph model for predicting the “K to R” success, we regard the gain in the model performance of a factor as a proxy variable for the magnitude of its effect on the “K to R” success. Our preliminary results suggest that interest by peers in the applicant's research and the timing of the interest are strongly correlated with the outcome. Meanwhile, the applicant's social connections, e.g., their collaborators, can also contribute to the outcome.

SD^2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this paper, we present an interactive visualization framework, SD^2, to enable flexible data partition and composition to support various analysis requirements within a single system. SD^2 features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD^2 and revise the design and system implementation accordingly. The effectiveness of SD^2 is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.

Personalized literature recommendation based on heterogeneous entity academic network

Although researchers have benefited from big scholarly data, it is still very difficult for them to quickly and accurately find the suitable literature in the massive literature. In recent years, the research on personalized literature recommendation with the help of academic data has gradually attracted the attention of scholars. However, the existing works are mainly based on the similarity of literature content, and ignore the important information of scholars such as research fields and affiliations, which leads to insufficient personalization of recommendation results and there is still room for improvement in accuracy. In this paper, a new personalized literature recommendation method named PR-HeAN is proposed. This method also considers the similarity of literature, and more importantly, introduces a heterogeneous entity academic network. It combines the literature similarity obtained from a K-order literature co-citation network with the literature recommendation probability obtained from the heterogeneous entity academic network to generate the recommendation results. The heterogeneous network is generated from the K-order literature co-citation network, which integrates five types of academic entities, including literature, scholars, research fields, affiliations and publication venues, and enriches the representation information of the literature. The experimental results on two datasets show that the proposed method outperforms four baseline algorithms in terms of recall, accuracy and F1 value.

The economics of forest carbon sequestration: a bibliometric analysis

Carbon sequestration in forests has increasingly captured the attention of scientists as a strategy for climate change mitigation and environmental sustainability. In this era of huge carbon emissions, being a low-carbon and cost-effective technology, the economic analysis of forest carbon sequestration holds higher importance for the successful implementation and intended outcomes. This study elucidates a scientometric view of the research structure and thematic evolution of economic studies on forest carbon sequestration based on 1439 articles over the time slice of 2001–2021. The bibliographic data have been retrieved from the Dimensions database, which accommodates a large coverage of research publications and also provides easy access to essential scholarly data and information. Vosviewer and Biblioshiny software tools have opted for visualisation and evaluation purposes of bibliometric data. This study employs various measures of bibliometric analysis like co-authorship, bibliographic coupling, citation and keyword analysis to find out the principal articles, authors, journals, most frequent keywords and highest publishing countries and institutions in this field, and the results show that the number of publications has escalated substantially in the last five years, the most cited article and most productive author are Popp A, 2017 (305 citations) and André P C Faaij (11 documents), respectively, Bradford’s law calculates 21 core journals out of total 503 journals, among which Forest Policy and Economics is at the top, and the most productive country and institution are the USA and University of Florida, respectively. The study also investigates key publishing subject categories, and the number of publications covered under each of the Sustainable Development Goals. The overall outcome of this bibliometric study confers an in-depth understanding of the various dimensions of economic analysis on forest carbon sequestration and its development pattern in the last 21 years and also provides emerging themes for future reference.

Establishment and Nationalization of Innovative Repository in Kenya: A Collaborative Approach to Scholarly Data Management.

This paper investigates the establishment and nationalization of innovative academic repositories in Kenya. It argues for collaborative efforts of the key stakeholders such as academic institutions, private organizations, and government departments that can be leveraged to create a centralized repository that hosts publications, books, videos, audio, research papers, academic journals, copyrights, innovative ideas, sustainability models, and discoveries. The methodology employed in this research is Literature Review of secondary sources. This involved systematically gathering and synthesizing previous research to identify challenges in accessing scholarly resources and collaborative approaches to establish a comprehensive repository. The study showed that the benefits of establishing a comprehensive repository include enhanced access, data preservation, and increased collaboration. The paper also acknowledges potential challenges such as limited technology access, and financial constraints, and suggests strategies that curb these challenges to ensure the repository’s success and sustainability. In conclusion, a centralized repository in the country will improve the sharing of knowledge, enhance Kenya’s global position in the intellectual space, and attract multinational collaborations, that will stimulate socio-economic development. The key recommendations include promoting collaboration among stakeholders, making the system user-friendly and secure, and formulating regulatory frameworks to ensure security.

Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain

In the last few years, chatbots have become mainstream solutions adopted in a variety of domains for automatizing communication at scale. In the same period, knowledge graphs have attracted significant attention from business and academia as robust and scalable representations of information. In the scientific and academic research domain, they are increasingly used to illustrate the relevant actors (e.g., researchers, institutions), documents (e.g., articles, patents), entities (e.g., concepts, innovations), and other related information. Following the same direction, this paper describes how to integrate conversational agents with knowledge graphs focused on the scholarly domain, a.k.a. Scientific Knowledge Graphs. On top of the proposed architecture, we developed AIDA-Bot, a simple chatbot that leverages a large-scale knowledge graph of scholarly data. AIDA-Bot can answer natural language questions about scientific articles, research concepts, researchers, institutions, and research venues. We have developed four prototypes of AIDA-Bot on Alexa products, web browsers, Telegram clients, and humanoid robots. We performed a user study evaluation with 15 domain experts showing a high level of interest and engagement with the proposed agent.

A Scientific Paper Recommendation Framework Based on Multi-Topic Communities and Modified PageRank

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.

Isidorus versificatus – The Rewriting of an Isidorian Text in the 12th Century

Isidorus versificatus, a 12th-century anonymous Latin poem edited in 1975 by Christian Hünemörder, puts into verse fragments of Book XI (De homine et portentis) and Book XII (De animalibus) of Isidore of Seville’s Etymologies (7th century). Our paper aims to examine how the medieval text modifies the claim of the encyclopedic prose on which it is based. We undertook a contrastive parallel reading of the two texts and found that, in rewriting the text, the medieval versifier reduced the informative dimension of the source by eliminating a critical amount of scholarly data, such as definitions, classifications, etymological approaches, and geographic information. Thus, within the epistemological horizon of the 7th century, it becomes possible to classify the medieval text as a scientific one. Furthermore, the unknown author eliminated the Isidorian theological considerations that helped identify the status of portents and animals within Creation. This has led us to question whether the versified version belongs to the genre of the Lehrgedicht assumed by its editor. Thus, we advance the hypothesis that the literary purpose pursued by the anonymous poet was to create a text that emphasizes the presence of mirabilia.

Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation.

Keeping track of translational research is essential to evaluating the performance of programs on translational medicine. Despite several indicators in previous studies, a consensus measure is still needed to represent the translational features of biomedical research at the article level. In this study, we first trained semantic representations of biomedical entities and documents (i.e., bio-entity2vec and bio-doc2vec) based on over 30 million PubMed articles. With these vectors, we then developed a new measure called Translational Progression (TP) for tracking biomedical articles along the translational continuum. We validated the effectiveness of TP from two perspectives (Clinical trial phase identification and ACH classification), which showed excellent consistency between TP and other indicators. Meanwhile, TP has several advantages. First, it can track the degree of translation of biomedical research dynamically and in real-time. Second, it is straightforward to interpret and operationalize. Third, it doesn't require labor-intensive MeSH labeling and it is suitable for big scholarly data as well as papers that are not indexed in PubMed. In addition, we examined the translational progressions of biomedical research from three dimensions (including overall distribution, time, and research topic), which revealed three significant findings. The proposed measure in this study could be used by policymakers to monitor biomedical research with high translational potential in real-time and make better decisions. It can also be adopted and improved for other domains, such as physics or computer science, to assess the application value of scientific discoveries.