Scientific Research Data Research Articles

A psychometric data science approach to study latent variables: a case of class quality and student satisfaction

Data science follows a data-driven approach focused on non-hypothesised pattern discovery from data in an automatic manner (or semi-automatic). Social science areas frequently address latent variables, whose study is mainly guided by a deductive paradigm and requires a confirmatory scope. Both areas, data science and latent variable research, have ample development, but their combination is not mature. The objective is to develop a practical and reproducible framework for data science projects from a psychometric perspective for the study of latent variables. The framework is practical and integrative, and consists of nine steps, which guide from the construct definition, discovery and confirmed patterns (structural equation modelling and considering goodness-of-fit, reliability, validity, equity and subjects’ segmentation) to the use of patterns. This last step includes three sub-steps: one for structural hypotheses contrast and two computational approaches (supervised: machine learning; and non-supervised: principal component analysis). Additionally, the framework is efficient and accessible due to its semi-automation using the R language. The framework is applied to study the relationship between class quality and student satisfaction and is consistent with the reproducible research paradigm, widely used in computational areas and recently demanded by social science.

Sharing Research Data: An Analysis of the Interest of Social Scientists in the Context of a Mexican University

Open Science and open research data have the potential to speed up the processes of science and to generate benefits to society. However, the openness of research data and science cannot be taken for granted since there is a trend toward the capitalization of knowledge. In addition, each area of knowledge differs in terms of the data used and the rules that govern each scientific community. The aim of this article is to analyze social researchers’ interest in sharing research data within the context of a Mexican university. Based on the constructivist grounded theory approach, 12 interviews were conducted with social scientists from a higher education institution in Mexico. From the analysis, four categories associated with the researchers’ attitudes of sharing their data emerged. The findings exhibit that researchers’ interest in sharing their scientific data is prone to (1) selectively sharing, (2) perpetuating the system, (3) protecting privacy and (4) considering resources. These results show that the scientists interviewed show an opposite inclination to Open Science, since they are not willing to share their data openly, including the fact that within the Mexican context, the practice of sharing data openly is not encouraged.

Cost-effectiveness analysis of screening for first-degree relatives of patients with bicuspid aortic valve

Abstract Introduction Bicuspid aortic valve (BAV) is the commonest congenital heart valve defect, found in 1% to 2% of the general population and associated with life-threatening complications. Given the high heritability index of BAV, many experts recommend echocardiography for first-degree relatives (FDRs) of an index patient. However, the cost-effectiveness of such cascade screening for BAV has not been fully evaluated. Materials and methods Using a decision-analytic model, we performed a cost-effectiveness analysis of echocardiographic screening of FDRs of BAV index cases. Data on BAV probabilities and BAV complications among FDRs were derived from our institution's BAV familial cohort and from the relevant literature on population-based BAV cohorts with long-term follow-up. Health gain was measured as quality-adjusted life years (QALYs). Cost inputs were based on list prices and literature data. One-way and probabilistic sensitivity analyses were performed to account for uncertainty in the model's variables. Results and disscusion Screening of FDRs was found to be the dominant strategy, being more effective and less costly than no screening, with savings of €208 and gains of 1.6 QALYs. Results were sensitive to the full range of reported BAV rates among FDRs across the literature, with the benefit gradually decreasing from the screening age of 55 years, with trend shifting at the age of 69. Conclusions This economic evaluation model revealed that echocardiographic screening of FDRs of BAV index case is not only clinically important but also highly cost effective and cost-saving. Health gains could be achieved from initiating screening program, along with costs saving. Sensitivity analysis supported the model's robustness, suggesting its generalization. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Center for Interdisciplinary Data Science Research fellowships grant

Research data management practice in higher education institutions in Ethiopia

There is increasing pressure from funders, publishers, the public and other research organizations on researchers to improve their research data management. The main objective of the study was to examine the practices of research data management at higher education institutions in Ethiopia. The study was employed multi-stage sampling technique to select the actual sample unit of respondents. Eight first generation Universities and one Science and Technology University were selected based on purposive sampling technique. 390 faculty member and 119 stakeholder respondents were selected based on simple random and purposive sampling techniques. The findings revealed that majority of faculty members were used and produced spreadsheet and structured scientific and statistical research data formats. Majority of faculty members were used their personal device to hold their research data. The findings indicated that majority of faculty members were identified their needs of research data management services such as, data management template, storage facilities, research data management policy, guidelines and providing training. The level of awareness on research data management among faculty members was very low. The study concludes that proper research data management is not being practiced. Further research can be done on research data management framework at higher education institutions.

Russian School in the Socio-Political Space of Latvia (1991 – 2021)

Russian-language teaching schools, or bilingual schools, are the most important institution for the reproduction of the ethno-cultural collective identity of the Russian and Russian-speaking population in modern Latvia. The present and future of these schools are included in a very complex legal and socio-political context. On the one hand, Latvian legislation proclaims the right of ethnic minorities to preserve their identity, on the other hand, the dominant political discourse is focused on reducing teaching in Russian in these schools to a minimum. The article shows that it is the political context constructed by the leading political parties, and not the rights guaranteed by the constitution to ethnic minorities, that is the main factor that leads to a sharp weakening of Russian (bilingual) education in Latvia. The research material is based on the Latvian legislation in the field of education and language, the programs of political parties, the data of scientific research of Latvian sociologists, including the author of the article, expert assessments of scientists and public figures of the process of education of ethnic minorities.

PENGARUH TEKNOLOGI TERHADAP PERTUMBUHAN EKONOMI

The Effect of Technology on Economic Growth. The research objective was to qualitatively analyze the effect of technological progress on economic growth, as well as the positive and negative impacts of technological developments on the economy. The research model used is a qualitative research method, namely research based on theories contained in the literature, more in the nature of descriptive and narrative descriptions. The theoretical foundation is used as a guide.
 Data collection uses literature studies or library research (Library Research, which is a technique of gathering information through data collection by tracing important documents that are related and relevant to the object under study. Data and information are obtained from literature books, articles scientific research, theses, dissertations, encyclopedias, internet, and other sources Data and information obtained from library studies in the form of academic texts, photos, graphics, journals, policy briefs, or pamphlets, banners, and journalistic reports. data reduction, data presentation and drawing conclusions / verification
 The research results show that technology is very influential on the economy of a country. The economy is measured by Economic Growth or the value of GDP and GDP per capita. Developed countries (America, China, Japan, South Korea, Germany, Singapore) with their modern technology, their economic growth rate is always positive and stable, although it can experience negative growth, the growth gap is not too big. GDP per capita is very high. Likewise, poor and underdeveloped countries (African continent) have started to catch up by starting to make business innovations using modern technologies. The impact felt by their economy began to move up, marked by high economic growth (YoY GDP), GDP per capita also started to move up.
 
 Keywords: technology, per capita income, economic growth, GDP

Demographic and Economic Potential of Migration from Latin America

The article examines the demographic and economic potential of organizing a new migration flow from LatinAmerica to Russia. In the context of the demographic crisis in which Russia is located, and the low fertility of Russian women, scientists see the only way to preserve and increase the population of the Russian Federation – migration. However, the quality and level of migration that exists in Russia today is problematic. Migrants from the former Soviet republics form ethnic enclaves, do not want to assimilate, and damage the Russian economy by withdrawing funds outside the Russian Federation. Under these conditions, the author proposes to organize an irrevocable migration from Latin American countries, which will solve the demographic problem by naturalizing Latinos who are mentally close to Russians, and stabilize the Russian economy due to the influx of workers, as well as a significant reduction in money transfers abroad due to the low exchange rate of the ruble against the dollar, and, in this regard, the unattractiveness of Russia as a donor of material resources. At the same time, the organization of this migration flow will “unload” the region. Russia will accept migrants who want to leave their countries that are in political and economic crisis, but whose entry into the territory of neighboring states and the United States is difficult. The organization of this migration flow will also help the Russian Federation to gain geopolitical partners in the problematic region. The author examines all the positive and negative aspects of the organization of this migration flow, presents applied technologies for organizing migration from Latin America. The author uses statistical materials, scientific research data and publications in the media that reflect the content of the studied problems. The material of this article is a project, the organization of which will require further study and significant methodological efforts. The proposed concept of organizing irrevocable migration from Latin American countries can be a way out of the complex crisis that has developed in Russia and in Latin American countries.

Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions

AbstractDesign-by-analogy (DbA) is a design methodology wherein new solutions, opportunities, or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence (AI) technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications into four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state-of-the-art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

Modeling Of The Dependence Of The Produced Amount Of Hydrogen Gas (Y) On The Amperage Of Cell (X) Of A Marine Diesel Engine Skl 3Nvd24

The introduction of new fuels in the field of maritime transport is considered a serious challenge due to the harsh environmental conditions in which the motor ship must operate. Modern marine engines are designed to improve the overall efficiency of the marine system so any attempt to exploit a new fuel must be accompanied by scientific research and experimental data to provide engineers and ship operators with sufficient information on its efficiency and safety.

Observing Data-Driven Approaches to Covid-19: Reflections from a Distributed, Remote, Interdisciplinary Research Project

The Observatory for Monitoring Data-Driven Approaches to Covid-19 (OMDDAC) is an Arts and Humanities Research Council funded research project investigating data-driven approaches to Covid-19, focused upon legal, ethical, policy and operational challenges. The project is a collaboration between Northumbria University (Law School, Department of Computing and Information Sciences, Department of Mathematics) and the Royal United Services Institute, a defence and security think-tank, and aims to carry out integrated interdisciplinary research, regarded as the most challenging type of interdisciplinarity but where the outputs can be the most impactful. Due to the constraints of the pandemic, the project has been carried out in a fully distributed and remote manner, with some team members never having met in person. The subject of the research is continually changing and developing, creating unique project management issues, with the impact of the pandemic pervasive in the lives of the researchers. This article takes the form of a series of reflections from the points of view of individual project researchers – the specialist legal researcher, the think-tank Co-Investigator, the post-doctoral researcher, statistical and data science researchers, and the Principal Investigator – and organised under two main themes - project management and internal communication; and methodologies/interdisciplinary research. We thus draw out lessons for future remote and distributed research, focused upon interdisciplinarity, the benefits and challenges of remote research methodologies, and issues of collegiality. Finally, we warn that it will be a false economy for universities and funders to assume that research projects can continue to be conducted in a mainly remote manner and therefore, that budgetary savings can be made by reducing time allocations, travel and academic networking.

BRIEF DESCRIPTION OF THE BIOLOGICAL PROPERTIES OF CORONAVIRUS (COVID-19)

The article provides morphological and functional characterization of the new coronavirus (COVID-19) based on the latest scientific research data, study of biological properties, epidemiology and the course of the infectious process in humans, some issues of treatment and prevention.

Assessing the Static and Dynamic Efficiency of Scientific Research of HEIs China: Three Stage DEA–Malmquist Index Approach

Higher education institutions (HEIs) are the key to the economic and social development of a country. However, the recent advancements of higher education institutions’ universities in China have become a pivotal factor contributing to their swift growth. Considering the impact of the external environment, applying a three-stage data-envelopment analysis (DEA) and the Malmquist index method, we evaluated the static and dynamic efficiency of input–output data of scientific research produced by universities directly under the Ministry of Education in the period of 2010 to 2017. Results showed that the three stage DEA model is more accurate than the traditional DEA method for measuring the efficiency of scientific research input and universities’ output. The overall efficiency of universities’ scientific research activities increased at an average annual rate of 3.7% from 2010 to 2017. Further analysis showed that the optimization of the scale was the primary internal factor able to promote the efficiency of scientific research in universities. Technological progress slightly diminishes the efficiency of scientific research in universities, which should, therefore, take measures to improve pure technical efficiency to enlarge the scale of their scientific research according to the characteristics of scientific research efficiency. Such steps will strengthen internal motivation towards scientific research efficiency in universities.

Non-negative Matrix Factorization: A Survey

AbstractNon-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.

Therapeutic Benefit of Salvigenin Against Various forms of Human Disorders Including Cancerous Disorders: Medicinal Properties and Biological Application in the Modern Medicine

: Natural products have always been used in medicine for the discovery of new biological molecules against different types of human disorders. Crude drugs have been used in different systems of medicine, including Traditional Chinese Medicine and the Ayurvedic System of Medicine. From ancient time to this modern age, special emphasis has been given to the traditional knowledge of plants based products for the development of newer and better modern drugs. Traditional medicines are always better choices in the medicine and healthcare system by the people of developing countries for the treatment of human disorders. Polyphenolic compounds have been used in medicine due to their vast medicinal application and pharmacological activities. Flavonoidal class chemicals are the better examples of polyphenolic compounds, which have a 15- carbon skeleton chemical structure. Salvigenin is a pure flavonoid class phytochemical found to be present in the Salvia lachnocalyx, Salvia hydrangea and Plectranthus amboinicus and other medicinal plants and vegetables. Pharmacologically salvigenin has been found to have cytotoxic effects on various forms of cancer cells, such as colon adenocarcinoma, breast adenocarcinoma and glioblastoma. Further salvigenin has beneficial potential in the medicine for the treatment of diabetes and protects cells against oxidative stress. In order to know the therapeutic benefit of salvigenin in the medicine, here in the present study, all the scientific research data of salvigenin have been collected and analyzed for their biological significance in medicine. Extensive pharmacological investigation has been performed to know the better medicinal properties of the salvigenin in modern medicine. Analytical techniques used in the separation, isolation and identification of salvigenin have also been searched and presented in the present work. From the above-presented data, it was found that salvigenin has significant biological potential in medicine and could be used for the development of newer and better molecule or drugs against various forms of human disorders, including cancerous disorders.

P-MN005. Pharmacological importance and biological potential of apigetrin for the treatment of neurodegenerative disorders: investigation through numerous scientific research data analysis in the medicine

Introduction . Medicinal plants have been used as remedies and pharmaceutical products since very ancient time and demand of herbal based drugs have been increasing in the modern age due to their beneficial pharmacological activities of plants and their derived products such as extracts, fractions and pure isolated phytocompounds. Flavonoidal compounds have been known as a better natural phytoconstituents over a century and are found to be present in the foods and plant derived natural product. Apigetrin, also called as apigenin-7-O-glucoside is a natural flavonoid glucoside found to be present in the nature for their anticancer activity. Methods . In order to know the effectiveness of the apigetrin in the medicine for the treatment of neurodegenerative disorders, here in the present investigation numerous scientific research work data have been collected and analyzed. Detailed pharmacological activities of apigetrin have been searched through numerous scientific data analysis in the present investigation. Biological importance of cytokines, interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) in the treatment of neurodegenerative disorders have been also investigated through scientific data analysis of different research works. Results . From the analysis of various scientific researches work of different field of science revealed the biological potential of apigetrin in the medicine. Detailed pharmacological studied have proven the biological importance of apigetrin in the medicine for the treatment of neurodegenerative diseases of human being. In some scientific research work apigetrin was found to be effective against neurodegenerative diseases as it significantly reduced the secretion of cytokines, TNF-α and interleukin-6 in BV-2 mouse microglia. In the scientific study apigetrin significantly enhanced expression of antioxidant enzymes and increased radical scavenging activity. Apigetrin has been found to inhibit neuroinflammation through antioxidant and neuroprotection properties. Conclusion . and biological investigations have proven the effectiveness of apigetrin for the treatment of neurodegenerative disorders in the modern medicine.

Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle.

Relying on the Biomedical Big Data Center of West China Hospital, this paper makes an in-depth research on the construction method and application of breast cancer-specific database system based on full data lifecycle, including the establishment of data standards, data fusion and governance, multi-modal knowledge graph, data security sharing and value application of breast cancer-specific database. The research was developed by establishing the breast cancer master data and metadata standards, then collecting, mapping and governing the structured and unstructured clinical data, and parsing and processing the electronic medical records with NLP natural language processing method or other applicable methods, as well as constructing the breast cancer-specific database system to support the application of data in clinical practices, scientific research, and teaching in hospitals, giving full play to the value of medical big data of the Biomedical Big Data Center of West China Hospital.

An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication.

Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual's disease course unfolds.

Biological potential of swertisin in the medicine: Therapeutic benefit and biological potential in the health sectors through current scientific research work

Swertisin is a pure phytochemical found to be present in the Iris tectorum which is used in the traditional system of medicine for the treatment of numerous health complications. In order to know the effectiveness of swertisin in the medicine against Hepatitis B virus, here in the present investigation numerous scientific research work data have been collected and analyzed from the literature work. Effect of swertisin in different cells was studied in some scientific research work and data have been collected in the present investigation to know their effectiveness for potential anti- Hepatitis B virus activity. Literature data analysis revealed the biological potential of swertisin in the modern system of medicine for the treatment of numerous health complications due to their therapeutic benefit and important pharmacological activities. Scientific data analysis of different research work of the literature revealed the biological potential in the medicine for the treatment of numerous human disorders. Literature data analysis of different research work revealed the biological importance of swertisin against Hepatitis B virus as it showed significant inhibitory activity on Hepatitis B virus replication. Some other literature data analysis also signified the biological importance of swertisin in the medicine due to their effectiveness in the Hepatitis B virus transgenic mice model. Literature data analysis of scientific research works signified the biological potential of swertisin in the medicine for the treatment of Hepatitis B virus infection and could be used for the development of better medicine in the future.

DEVELOPING QUESTIONNAIRE FOR POST GRADUATE STUDIES IN CONSTRUCTION MANAGEMENT

Although questionnaires are widely used in construction management studies, designing the instrument has proven to be a difficult task among research students. This article explored the process of developing and testing questionnaires for construction management studies focusing on managing risks in Public Private Partnership (PPP) housing projects. The processes of developing questionnaire were grouped into four stages consisting of the background, conceptualization, design and instrument testing and administration. Using the processes identified, a flowchart indicating step-by-step stages for developing questionnaires was designed. Results of validity and reliability tests conducted on certain aspects of the questionnaire indicated that the instrument can be relied upon to generate acceptable data for scientific research. The main contribution of this paper is expanding the scope and simplifying the design of questionnaire among upcoming researchers in the field of construction management. This study recommends the design for students undertaking construction management related studies.

Social Media and Research Publication Activity During Early Stages of the COVID-19 Pandemic: Longitudinal Trend Analysis.

BackgroundThe COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discoveries. Current platforms available for the distribution of scientific and clinical research data and information include preprint repositories and traditional peer-reviewed journals. In recent times, social media has emerged as a helpful platform to share scientific and medical discoveries.ObjectiveThis study aimed to comparatively analyze activity on social media (specifically, Twitter) and that related to publications in the form of preprint and peer-reviewed journal articles in the context of COVID-19 and gastroenterology during the early stages of the COVID-19 pandemic.MethodsCOVID-19–related data from Twitter (tweets and user data) and articles published in preprint servers (bioRxiv and medRxiv) as well as in the PubMed database were collected and analyzed during the first 6 months of the pandemic, from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ–specific social media trends were compared to preprint and publication activity. Any relationship between Twitter activity and preprint articles published and that between Twitter activity and PubMed articles published overall, by organ system, and by geographic location were identified using Spearman’s rank-order correlation.ResultsOver the 6-month period, 73,079 tweets from 44,609 users, 7164 journal publications, and 4702 preprint publications were retrieved. Twitter activity (ie, number of tweets) peaked in March 2020, whereas preprint and publication activity (ie, number of articles published) peaked in April 2020. Overall, strong correlations were identified between trends in Twitter activity and preprint and publication activity (P<.001 for both). COVID-19 data across the three platforms mainly concentrated on pulmonology or critical care, but when analyzing the field of gastroenterology specifically, most tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal gastroenterology. Furthermore, there were significant positive associations between trends in Twitter and publication activity for all gastroenterology topics (luminal gastroenterology: P=.009; hepatology and inflammatory bowel disease: P=.006; gastrointestinal endoscopy: P=.007), except pancreatology (P=.20), suggesting that Twitter activity did not correlate with publication activity for this topic. Finally, Twitter activity was the highest in the United States (7331 tweets), whereas PubMed activity was the highest in China (1768 publications).ConclusionsThe COVID-19 pandemic has highlighted the potential of social media as a vehicle for disseminating scientific information during a public health crisis. Sharing and spreading information on COVID-19 in a timely manner during the pandemic has been paramount; this was achieved at a much faster pace on social media, particularly on Twitter. Future investigation could demonstrate how social media can be used to augment and promote scholarly activity, especially as the world begins to increasingly rely on digital or virtual platforms. Scientists and clinicians should consider the use of social media in augmenting public awareness regarding their scholarly pursuits.