Introduction

Now open for submissions, the Cambridge University Press Data-Centric Engineering journal is a cutting edge, cross-disciplinary and open access journal focusing on research at the intersection of data science and a broad range of engineering subjects. It covers the use of data science methods to model systems downstream from the lab in order to build prototypes and engineering solutions that are safer, more resilient and fitter for purpose.

Mark Girolami, Programme Director for Data-Centric Engineering, is Data-Centric Engineering's Editor-in-Chief.

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About the journal

The journal will publish the following types of peer-reviewed paper:

  • Research articles using data science methods and models for improving the reliability, resilience, safety, efficiency and usability of engineered systems
  • Translational papers and case studies showing how data-centric methods can be successfully translated into downstream applications
  • Systematic reviews providing a detailed, balanced and authoritative current account of the existing literature concerning data-intensive methods in a particular facet of engineering sciences
  • Tutorial reviews providing an introduction and overview of an important topic of relevance to the journal readership
  • Position papers that describe and promote new standards and benefits, in terms of ethics, policy, regulation, dissemination and usability, for the role of data in engineering

Data-Centric Engineering welcomes contributions from researchers in academia and industry that explore:

  • Data science, artificial intelligence and machine learning as applied across all areas of engineering – for example, civil, mechanical, aeronautical, materials, electrical, industrial, chemical -- tackling real, consequential problems 
  • Data-heavy approaches to engineering, e.g. machine learning, inverse models 
  • Data collection, e.g., sensors and sensor-intensive engineering 
  • Algorithmic engineering; that is, optimal use of data science algorithms tuned for particular application type 
  • Data representation, data preservation, knowledge recovery
  • Product development

Contact

Further information about the journal and instructions for authors can be found on the Cambridge University Press website.

Contact: [email protected]

Twitter: @dce_journal

 

Published by:

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Supported by:

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Organisers