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Engineering - Computational Intelligence and Complexity | Data-Enabled Discovery and Applications

Data-Enabled Discovery and Applications

Data-Enabled Discovery and Applications

Editor-in-Chief: A.A. Barhorst; G. Qi

ISSN: 2510-1161 (electronic version)

Journal no. 41688

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A forum for methods and best practices for uncovering patterns and associations embedded in data

  • Balances general solution techniques with problem-specific results
  • Offers a forum for sharing methods and best practices that reveal patterns and associations embedded in data
  • Highlights data-centric tools created or cross-utilized to solve discipline-specific problems, and include suggestions for broader applications

The journal is a forum for sharing methods and best practices that reveal patterns and associations embedded in data. It offers research on tools, measurements and methodologies for investigating large data sets which are applicable across a range of fields.

Related subjects » Biomedical Sciences - Business Information Systems - Computational Intelligence and Complexity - Public Health - Theoretical, Mathematical & Computational Physics

Abstracted/Indexed in 

Google Scholar, CNKI, EBSCO Discovery Service, OCLC WorldCat Discovery Service, ProQuest-ExLibris Primo, ProQuest-ExLibris Summon

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For authors and editors

  • Aims and Scope

    Aims and Scope

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    This journal creates new avenues of dissemination for researchers using the increased availability of extensive data sets to quantitatively model real-world systems, and seek new discoveries, innovations and applications, often across the boundaries of traditional disciplines. Especially sought are contributions investigating novel research strategies so as to enable scientific discoveries, engineering innovations, or advances in medical diagnosis, prognosis, and treatment.

    Data-Enabled Discovery and Applications establishes a forum to share methods and best practices–in particular from fields involving large data harvesting such as computational science, engineering and biomedicine–for uncovering patterns and associations embedded in the data. It further provides a common platform for the publication of tools, measurements, and methodologies for investigating large data sets relevant for specific applicability to widely varying fields of endeavor.

    Papers in this journal will typically maintain a balance between general solution techniques and problem-specific results, promote challenges to traditional solutions, and foster a fruitful exchange of ideas among disparate fields. The journal will not be concerned with the information technological aspects of the underlying datasets, i.e. the study of efficient and effective techniques and systems for data collection, management and mining. The journal will also not be a venue for discipline-specific discussion.

    Authors are expected to present results in a manner that highlights the data-centric tools created or cross-utilized to solve discipline-specific problems. Suggestions for broader applications of their work should also be provided.

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