International Journal of Population Data Science - Swansea University | 2024 Cite Score:1.6 | Q1

Cite Score and Journal Rank of International Journal of Population Data Science

• About: The International Journal of Population Data Science is a peer-reviewed academic journal dedicated to the advancement of population data science. It focuses on the use of large-scale data to understand and address population health, demographic trends, and social phenomena. The journal publishes high-quality research articles, reviews, and technical notes that contribute to the development and application of data science methodologies in population studies.
• Objective:
  The primary objective of the International Journal of Population Data Science is to promote and disseminate research that leverages population data to improve understanding and decision-making related to population health and social issues. The journal aims to provide a platform for innovative research that uses data science techniques to address challenges in population health, policy, and social science. It seeks to foster collaboration and knowledge exchange among researchers, policymakers, and practitioners in the field.
• Interdisciplinary Approach:
  The International Journal of Population Data Science adopts an interdisciplinary approach by integrating research from various domains related to population data science. This includes epidemiology, biostatistics, social science, data analytics, and public health. The journal encourages contributions that combine insights from these diverse fields to develop comprehensive and impactful solutions to population-related issues.
• Impact:
  The journal has a significant impact on the field of population data science, influencing both academic research and practical applications. International Journal of Population Data Science is widely cited by researchers, policymakers, and data scientists working on population health and social issues. The research published in the journal contributes to the development of new methodologies and insights that drive progress in understanding and addressing population-related challenges.
• Significance:
  The International Journal of Population Data Science plays a crucial role in advancing the use of data science for understanding and improving population health and social outcomes. By providing a platform for high-quality research and innovative applications, the journal supports the development of new methods and approaches in population data science. Its focus on interdisciplinary research makes it a valuable resource for scholars, practitioners, and policymakers aiming to leverage data science in addressing complex population issues.

• Journal Home:  Journal Homepage

• Editor-in-Chief:  Professor Kim McGrail

• Scope: The International Journal of Population Data Science focuses on the application and development of data science techniques and methodologies to population-based research. The journal publishes high-quality articles that explore the use of large-scale data and statistical methods to address various issues related to population health, social science, and public policy. The scope of the journal includes, but is not limited to, the following areas:
• Population Health Analytics: Research on methods and applications of data science in analyzing health data from populations, including epidemiological studies, disease surveillance, and health outcomes assessment.
• Big Data in Population Studies: Exploration of the use of big data sources and technologies in understanding population dynamics, including data from electronic health records, social media, and national surveys.
• Statistical Methods and Models: Development and application of advanced statistical techniques and models for analyzing population data, including regression models, machine learning algorithms, and causal inference methods.
• Social Determinants of Health: Studies on the impact of social, economic, and environmental factors on health outcomes and disparities, using population data and analytical techniques.
• Population Dynamics and Trends: Research on demographic trends, population growth, migration patterns, and their implications for public policy and planning.
• Public Policy and Decision-Making: Application of data science to inform and evaluate public policy decisions and interventions, including policy impact analysis and cost-effectiveness studies.
• Data Privacy and Ethics: Examination of ethical issues related to the collection, use, and sharing of population data, including privacy concerns, informed consent, and data governance.
• Health Inequalities and Disparities: Research on identifying and addressing health inequalities and disparities within populations, using data-driven approaches to inform targeted interventions.
• Population Data Integration: Studies on methods for integrating data from diverse sources to provide comprehensive insights into population health and social issues.
• Data Visualization and Interpretation: Development of techniques for effectively visualizing and interpreting population data, including graphical representations, interactive dashboards, and data storytelling.
• Longitudinal and Cohort Studies: Research involving the analysis of longitudinal and cohort data to track changes in populations over time and identify trends and patterns.
• Health Informatics: Exploration of informatics tools and systems used in the management and analysis of health data, including electronic health records and health information exchanges.
• Methodological Innovations: Presentation of new methodologies and innovations in data science applied to population research, including advancements in computational techniques and analytical frameworks.
• Latest Research Topics for PhD in Computer Science

• Print ISSN:  2399-4908

  Electronic ISSN:  

• Abstracting and Indexing:  Scopus

• Imapct Factor :  

• Subject Area and Category:  Computer Science, Information Systems, Decision Sciences Information Systems and Management, Medicine Health Informatics, Social Sciences, Demography

• Publication Frequency:  

• H Index:  20

• Best Quartile:

  Q1:  Demography

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• Cite Score:  1.6

• SNIP:  0.780

• Journal Rank(SJR):  0.687

• Latest Articles:   Latest Articles in International Journal of Population Data Science

• Guidelines for Authors: International Journal of Population Data Science Author Guidelines

• Paper Submissions: Paper Submissions in International Journal of Population Data Science

• Publisher:  Swansea University

  Country:  United Kingdom

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