Data Science

Aims & Scope

Data Science is an interdisciplinary journal that addresses the development that data has become a crucial factor for a large number and variety of scientific fields.

This journal covers aspects around scientific data over the whole range from data creation, mining, discovery, curation, modeling, processing, and management to analysis, prediction, visualization, user interaction, communication, sharing, and re-use.

We are interested in general methods and concepts, as well as specific tools, infrastructures, and applications.

The ultimate goal is to unleash the power of scientific data to deepen our understanding of physical, biological, and digital systems, gain insight into human social and economic behaviour, and design new solutions for the future.

The rising importance of scientific data, both big and small, brings with it a wealth of challenges to combine structured, but often siloed data with messy, incomplete, and unstructured data from text, audio, visual content such as sensor and weblog data.

New methods to extract, transport, pool, refine, store, analyze, and visualize data are needed to unleash their power while simultaneously making tools and workflows easier to use by the public at large.

The journal invites contributions ranging from theoretical and foundational research, platforms, methods, applications, and tools in all areas.

We welcome papers which add a social, geographical, and temporal dimension to Data Science research, as well as application-oriented papers that prepare and use data in discovery research.

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
• Preservation 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 requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 1,600.00 USD. Learn more.

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.

• Towards FAIR principles for research software

  Citation: 205

  Authors: Anna-Lena, Leyla, Mateusz, Carlos, Ricardo, Eva, Victoria, Stephanie, Jon, Paula Andrea, Peter, Alfonso, Jennifer, Fotis, Josep Ll., Neil, Carole, Salvador

• Packaging research artefacts with RO-Crate

  Citation: 113

  Authors: Stian, Peter, Mercè, Leyla Jael, Frederik, José M., Daniel, Björn, Marco, Simone, Eoghan, Marc, Ana, Paul, Carole

• Stream reasoning: A survey and outlook

  Citation: 91

  Authors: Daniele, Emanuele, Frank, Abraham

• The knowledge graph as the default data model for learning on heterogeneous knowledge

  Citation: 53

  Authors: Xander, Peter, Victor

• Reinforcement learning for personalization: A systematic literature review

  Citation: 31

  Authors: Floris, Eoin Martino, Ali, Mark

• Data Science and symbolic AI: Synergies, challenges and opportunities

  Citation: 30

  Authors: Robert, Núria

• BioVenn – an R and Python package for the comparison and visualization of biological lists using area-proportional Venn diagrams

  Citation: 28

  Authors: Tim

• Conflict forecasting and its limits

  Citation: 25

  Authors: Thomas

• WORCS: A workflow for open reproducible code in science

  Citation: 24

  Authors: Caspar J., Andreas M., Loek, Anna-Lena, Aaron, Marijn E., Barbara M.I.

• Genuine semantic publishing

  Citation: 21

  Authors: Tobias, Michel