Journal of Data Science

Aims & Scope

Established in 2003, the Journal of Data Science aims to advance and promote data science methods, computing, and applications in all scientific fields where knowledge and insights are to be extracted from data.

The journal publishes research works on the full spectrum of data science including statistics, computer science, and domain applications.

The topics can be about any aspect of the life cycle (collecting, processing, analyzing, communicating, etc.) of data science projects from any field that involves understanding and making effective use of data.

The emphasis is on applications, case studies, statistical methods, computational tools, and reviews of data science.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Apc url
• License

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science and Mathematics, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Singular Spectrum Analysis: Methodology and Comparison

  Citation: 434

  Authors: Hossein

• The Weibull-G Family of Probability Distributions

  Citation: 357

  Authors: Marcelo, Rodrigo B., Gauss M.

• Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data

  Citation: 165

  Authors: Felix, Karan P.

• The Exponentiated Generalized Class of Distributions

  Citation: 162

  Authors: Gauss M., Edwin M. M., Daniel C. C. da

• Contrast Coding in Multiple Regression Analysis: Strengths, Weaknesses, and Utility of Popular Coding Structures

  Citation: 137

  Authors: Matthew J.

• Comparison of Distance Measures in Cluster Analysis with Dichotomous Data

  Citation: 128

  Authors: Holmes

• Skew-normal Linear Mixed Models

  Citation: 126

  Authors: R. B., H., V. H.

• A Comparison of Propensity Score and Linear Regression Analysis of Complex Survey Data

  Citation: 111

  Authors: Elaine L.

• Identifying Groups: A Comparison of Methodologies

  Citation: 81

  Authors: Abdolreza, Dominique, Pascal

• Stochastic Diffusion Modeling of Degradation Data

  Citation: 73

  Authors: Sheng-Tsaing, Chien-Yu