Data-Centric Engineering: a new open access now open to submissions
The advent and accelerated rate of development of new methods of sensing, measurement, and data capture, from the macro to the nano-scale, open up opportunities for new scientific and technological advances. These data-led methods are having a transformative impact across all of engineering with the discovery of new materials, new methods of manufacture, new methods of operation, control and construction being made possible.
Now open for submissions Data-Centric Engineering is a cutting edge, cross-disciplinary journal focussing 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.
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.
Articles should be accompanied by an impact statement (200 words) that summarises the significance of the research problem, so that the paper's contribution can be quickly grasped by a wider and multidisciplinary audience (industry, policy, wider academia) and a data availability statement that explains how to access data and other resources necessary to support the findings.
More details about the article types in DCE and requirements are in the instructions for authors.
DCE 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
Mark Girolami (University Cambridge & The Alan Turing Institute) is Data-Centric Engineering's Editor-in-Chief. He oversees a team of Executive Editors with expertise in different areas of engineering, also responsible for commissioning and handling papers.
As an open access journal making all content freely available to read and redistribute, we ask authors of accepted research articles with access to grant or institutional open access funds to contribute an article processing charge (APC) to cover the costs of publishing. However, thanks to the support of the Lloyd's Register Foundation, we are able to waive the APC for those authors who do not have funding, and for translational papers, case studies and position papers.