Data Science in Science Journal

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Data Science in Science

An Open Access Journal

Data Science in Science is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science.

Its aim is to advance:

  • new ideas for experimental and observational data-driven learning and discovery that help address fundamental questions at the frontiers of Science and scientific inference

  • quantification and summarization of uncertainty from data-driven theories and complex Data Science models, algorithms, and workflows

  • new practices for scientific reproducibility and replicability enabled through Data Science.

It promotes the intrinsically multidisciplinary nature of the field of Data Science and seeks explicitly science-driven advances in Data Science, and their novel, significant, or transformative applications to Science.

It fosters dedicated collaboration and convergence between the broadly defined fields of Science and Data Science through: (i) deep domain, (ii) broader inter-domain, and (iii) trans-domain collaborative research.

It encourages collective scientific learning through new collaborative and scientific methods and theories that have the potential to inform the knowledge among and strengthen the data practices of domain and data scientists.

 

What will Data Science in Science publish?

Original research articles should encompass scientific and Data Science concepts, techniques, or computational methods and tools for addressing scientific problems in a way that is novel or beyond that of traditional disciplinary research. Review articles should highlight important and timely applications of Data Science within a scientific discipline or insightful applications across disciplines, with comprehensive discussion of any application, specific considerations, or assumptions.

Special issues dedicated to relevant and timely special topics will also be of interest. Proposals should be emailed to the Editor-in-Chief for consideration in the first instance.

We look forward to receiving your submissions and hope you join us to embrace Data Science in Science's Open Access future.

Editor-in-Chief David S. Matteson is Associate Professor of Statistics and Data Science and Social Statistics at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering courses. 

Why publish open access?

  • Increase the visibility and readership of your research by publishing in a fully open access journal.
  • Make an impact beyond the academy by making your article accessible to anyone, anywhere (including readers in industry and even policy-makers).
  • Benefit from format-free submission, saving you more time for your research.
  • Freely share your work with no restrictions or paywall.
  • Retain ownership of your research through our unrestrictive publishing agreements.
  • Discounts and waivers for researchers in developing countries are available. The journal will also consider requests for discretionary APC waivers. Find out if your institution or country has an open access agreement to publish with us.

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Questions about open access?

Find out more about the publishing process for open access journals at our dedicated site for the step-by-guide to publication.
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Ready to submit?

IJSE is welcoming submissions. Find out how to submit your paper by reading the Instructions for Authors.
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