The DCE Journal at Cambridge University Press is delighted to partner with The Alan Turing Institute's Data-Centric Engineering programme on a workshop accepted at the IEEE Conference on Artificial Intelligence (IEEE CAI’2024) in Singapore, in June 2024. See the full details and the abstract submission process on The Alan Turing Institute website.
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Introduction
From autonomous vehicles and 3D printing through to smart cities and digital twins, the gap between our physical and digital worlds is growing ever smaller. At the interface of these two worlds is Data-Centric Engineering, a rapidly emerging new branch of science for the 21st century which combines the power and insight available from large-scale data sources with the tools and technology that shape our real-world environment. Bridging the gap between the digital and physical realms requires the development of new machine learning algorithms that are capable of processing large volumes of data from satellites, cell phones, distributed sensors, etc. and making robust and transparent decisions that improve our daily lives and protect our natural environment.
As Data-Centric Engineering is an emerging discipline it needs to define its boundaries, determine the techniques of importance and nurture a diverse community of ECRs to take these ideas forward. This workshop also welcomes contributions in the form of advanced techniques and/or applications on topics relating to Physics-informed machine learning. This is a rapidly emerging subfield, whereby scientific knowledge of systems and phenomena is incorporated into machine learning workflows.
The focus of this workshop - leading to a special collection in the open-access DCE Journal at Cambridge University Press - is to bring the engineering and machine learning communities together to define Data-Centric Engineering, through keynote talks and contributed sessions, panel discussions, poster presentations and networking.
Topics
Topics on DCE including (but not limited to):
- Manufacturing, Civil Engineering, Mechanical Engineering, Aeronautical Engineering, Materials Engineering, Electrical Engineering, Industrial Engineering, and Chemical Engineering.
Topics on Physics-informed Machine Learning topics including (but not limited to):
- Neural ODEs, Domain adaptation, Geometry-aware approaches, Probabilistic approaches to Physics-informed Machine Learning, Scientific Machine Learning (PDEs, ODEs, etc.), Reduced order modelling.
Key dates
Note that the early bird registration deadline has been extended to the 9th May.
- Extended Abstract Submission Deadline:
April 24, 2024May 6, 2024 (DEADLINE EXTENDED) - Acceptance Notification (for workshop presentation):
MAY 1, 2024May 12, 2024 - Workshop Date: June 25, 2024
- Full Paper Submission via DCE journal (8-12 pages): October 5, 2024
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.
How to submit
Abstract submissions
Submissions should be made through Microsoft CMT.
Full paper submissions
Selected abstracts will be invited to submit through the DCE Journal's ScholarOne site.
Key considerations for submitting are below, with full details available in the DCE Instructions for Authors.
Article Types
DCE encourages the submission of:
- Research articles using data science methods and models for improving the reliability, resilience, safety, efficiency and usability of engineered systems.
- Translational Articles from contributors based in industry or academic-industry collaborations and we believe this special issue holds a lot of potential for this type of contribution.
- Data papers that describe in a structured way, with a narrative and accompanying metadata, important and re-usable data sets in open repositories with potential for re-use in engineering research and practice. These papers promote data transparency and data re-use
- Survey papers 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. The topic should be of relevance to both students and researchers who are new to the field as well as experts and provide a good introduction to the development of a subject, its current state and indications of future directions the field is expected to take
- Position papers providing an overview of an important issue for this emerging field. (Typically 6,000 words or less).
Templates
Authors have the option but are not required to use the following templates:
- DCE LaTeX template files (for research articles)
- DCE LaTeX template files (for Translational Articles)
- Use Overleaf (a LaTeX-based collaborative authoring tool; read about benefits of this tool)
- DCE Word template
Articles should be submitted through the DCE ScholarOne Manuscript Central system, but note that if you use the Overleaf tool you can submit directly into the system without having to reupload files.
Open access
Any author can publish on an open access basis in DCE if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles are covered through the Transformative Agreements that Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers open access publishing costs. Authors not affiliated with these agreements who have grants that budget for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived without question. DCE is supported by a grant from the Lloyd’s Register Foundation, which helps subsidise the publishing costs of unfunded authors.
Organising Committee
- Prof Adam Sobey, The Alan Turing Institute, UK.
- Dr Gabin Kayumbi, The Alan Turing Institute, UK
- Prof Jiangyan Shao,Wuhan University of Technology, China
- William Cooper, The Alan Turing Institute, UK
- Dr Zack Xuereb Conti, The Alan Turing Institute, UK
- Dr Lawrence Bull, University of Cambridge, UK