Ethics and Disclosures

TECS is a COPE member and expects all submissions to meet recognised standards of research and publication ethics. The declarations below must appear in your manuscript after the main text, before the references; state “None” or “Not applicable” where relevant, but do not omit them. The journal’s full policies are available under the Policies menu.

Authorship and contributor roles (CRediT)

Authorship requires a substantial contribution to the work, involvement in drafting or revising it, approval of the final version and accountability for the work. Record each author’s role using the CRediT taxonomy. Contributors who do not meet the criteria should be acknowledged. The corresponding author warrants that all co-authors have approved the submission. Generative-AI tools cannot be authors. The corresponding author must provide an ORCID iD; co-authors are strongly encouraged to do so.

Conflicts of interest and funding

Declare any financial or non-financial interest that could be perceived to influence the work — for example industry funding or consultancy, patents, shareholdings, or advisory roles with organisations that have a stake in a technology or product under study — or state that none exists. Disclose all funding sources, including grant numbers, and the funder’s role (if any) in the study.

Research ethics and human participants

Although much engineering and computing research does not involve human participants, many studies do — for example user studies, surveys, crowdsourced experiments, and research using personal or human-generated data. Where this applies, address the following and state the approving body and reference number:

Security, responsible disclosure and dual use

Research that identifies security vulnerabilities should follow responsible, coordinated disclosure: where feasible, affected parties should be notified and given a reasonable opportunity to respond before publication, and the manuscript should describe the disclosure process. Authors should consider the potential for misuse of their work (dual-use), avoid providing operational detail that would primarily facilitate harm, and describe any risk-mitigation measures. The journal may seek additional review where a manuscript raises security or dual-use concerns.

Responsible and trustworthy AI

For research involving artificial intelligence and machine learning, authors should, where relevant, consider and report on fairness, bias, safety and the societal impact of their systems, describe the provenance and licensing of training data, and avoid overstating capabilities. High-risk or sensitive applications should be discussed responsibly.

Data and code availability

Include a Data and Code Availability Statement describing whether, and how, the data, code and models supporting the results can be accessed. Deposit data and code in a recognised, citable repository (for example Zenodo, figshare, the Open Science Framework, or a version-controlled repository archived for permanence) with a persistent identifier, wherever ethics, law and licensing permit, and explain any restrictions.

Use of generative AI

Disclose any substantive use of generative-AI tools, naming the tool and version and describing how it was used. Authors remain fully responsible for all content, including code and text produced with AI assistance, must verify AI-assisted output, and must not use AI to fabricate or manipulate data, results or images.

Originality and integrity

Manuscripts must be original and properly attributed, and are screened with similarity-detection software. Plagiarism, text recycling, duplicate or redundant publication, and the fabrication, falsification or manipulation of data or results are not permitted and are handled under the journal’s Publication Ethics policy.