Introduction
Asset management, monitoring, and control are some prominent examples of data recorded from aggregate groups of machines/systems - often referred to as fleets, civil engineering infrastructure, etc.
Regularly, in these domains, specific groups or systems will have rich, historical data, while others (typically those recently in operation) will have very sparse or limited data. In turn, emerging methods in data-centric engineering look to extend the value of measurements by considering system populations as a whole, rather than constituent parts. These methods should underpin the data analysis and management of ecosystems of digital twins.
Many tools for conventional engineering practice consider data recorded from groups of systems as independent and distinct. This is a missed opportunity, and modelling the interdependencies (or sharing the data) between groups should greatly quantify the value of measurements, and offer insights that might otherwise be missed.
To share insights of this kind, we encourage authors interested in this topic to submit contributions to this special collection on Representing Populations of Engineering Systems: one of the planned areas of focus in 2023 for Data-Centric Engineering, an open-access journal at Cambridge University Press dedicated to the transformative impact of data science for all areas of engineering.
Areas
We will consider applications of (but limited to):
- Multilevel/hierarchical modelling
- Bayesian Networks
- Transfer and Multitask Learning
- Domain adaptation
- Meta-modelling, Meta-learning
- Decision analysis and experimental design (at the systems level)
- Data management, databases and ontologies
- Edge computing, Embedded data processing
- Sensing: data processing and management, decentralised/distributed learning
Timetable
Data-Centric Engineering publishes on a continuous and open-access basis: articles are made available as soon as possible after acceptance. This collection will therefore be assembled on an ongoing basis, and will be introduced with an editorial and promoted as a distinct entity when all articles have made it through the process.
We encourage submission as soon as possible.
Why Submit to DCE?
✔ A venue dedicated to the potential of data science for all areas of engineering.
✔ Welcoming research and translational articles from authors, whether they are based in academia or industry.
✔ Well-cited (2022 Impact Factor: 3.6; 2022 Cite Score: 3.4) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ #OpenAccess with support for unfunded authors thanks to the Lloyd's Register Foundation - no hard requirement to pay an article processing charge (APC).
✔ Promotes open sharing of data and code through Open Science Badges.
Submission Guidelines
- Article types: DCE encourages the submission of original research papers, translational papers, data descriptors, surveys, tutorial reviews and position papers. See the DCE Instructions for more details about each type of article.
- Templates: DCE LaTeX and Word templates are available and articles should be submitted via DCE ScholarOne. Alternatively, the CUP Data template in the authoring tool Overleaf can be used. Overleaf is particularly useful for co-authored papers - with collaborative features, versioning and a direct submission option into the DCE peer review system.
- Impact Statement: Authors should provide both an abstract that summarises the paper (250 words or less) and beneath it an impact statement (120 words describing the significance of the findings in language that can be understood by a wide audience).
- Open Materials: Authors are encouraged to make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. See the DCE Research Transparency policy for more details. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.
When submitting your contribution please select the ‘Representing Populations of Engineering Systems’ tag in the ‘Special Collection’ drop down menu.
Please contact [email protected] with any queries about article preparation.
Editors
- Lawrence Bull (University of Cambridge);
- Zack Xuereb Conti (The Alan Turing Institute / University of Cambridge);
- Dom Di Francesco (The Alan Turing Institute)
- Keith Worden (University of Sheffield);
- Nikolaos Dervilis (University of Sheffield);
- Andrew Duncan (Imperial College London)