Publication and authorship

Guidance on good practice in publication, authorship and peer review

In publication and authorship, as in all other aspects of research, researchers are expected to follow the principles of good research conduct supported by the University. It is essential that the parties involved in research and publication discuss and agree on:

  • authorship
  • recognition of other contributions
  • acknowledgement of sponsors
  • declaration of any conflicts of interest
  • meeting University and funder requirements for open access

The University expects researchers to follow best practice in publication, such as the guidelines issued by, for example, the Committee on Publication Ethics, the International Committee of Medical Journal Editors (ICMJE),  the Council of Science Editors and the British Sociological Association

Research integrity training is compulsory for all members of the University undertaking research.  The University’s Research Integrity: Core Course (login required) covers good practice in publication, authorship and peer review. Further training on authorship, publication and peer review can be found in the Research Practice Authorship, Publication and Peer Review on-line training module in Canvas.

Authorship

Generally, an author is considered to be someone who has made a substantive intellectual contribution to a published study. The International Committee of Medical Journal Editors (ICMJE) recommends that authorship be based on the following 4 criteria:

  • Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
  • Drafting the work or reviewing it critically for important intellectual content; AND
  • Final approval of the version to be published; AND
  • Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

In addition to being accountable for the parts of the work done, an author should be able to identify which co-authors are responsible for specific other parts of the work. In addition, authors should have confidence in the integrity of the contributions of their co-authors.

There are no universally accepted standards for attributing authorship and there is great variation in practice among different disciplines, research fields and journals.

This places most of the responsibility for decisions about authorship on the researchers who conducted the research reported in the publication. These decisions are best made early in each project and renegotiated regularly as necessary, to avoid misunderstanding and later disputes.

Where the work has more than one author the researchers should also:

  • agree among all authors the contribution each will make to reporting the work and authorship order, reviewing this commitment regularly as the work progresses
  • appoint a lead or corresponding author for communication on the work and keep written records of decisions made regarding authorship
  • report the work fairly according to each author’s contribution, and neither omit, underplay nor overplay a contributor's input
  • comply with the definition of author and co-author given by the journal or by international organisations (for example International Committee of Medical Journal Editors, Committee on Publication Ethics).
  • provide a formal offer of authorship (which should be accepted or declined in writing) to those meeting the agreed definitions
  • maintain a file of all relevant signatures and correspondence (for example exchanges of emails, notes of meetings) in case of disputes.

Authorship and AI

Artificial Intelligence tools (AI), such as large language models (LLMs), do not fulfil the criteria to be listed as authors or co-authors of a research output because they cannot take responsibility for the content and integrity of the output. It is therefore not acceptable to include any AI or LLM (such as ChatGPT) as an author or co-author of a research output. Any use of AI in developing a research output including, for example, the collection, analysis and interpretation of the data, should be cited in the methods or acknowledgements sections of the output as appropriate.  Researchers are responsible for the originality, validity, reliability and integrity of the findings of their research, including research developed using any AI or LLM. They should also refer to the Artificial Intelligence policy of any publisher to which they plan to submit. 

Contributors

Any individual who contributed to the research, but whose input was not sufficient for them to be listed as an author should be recognised in the acknowledgements of the publication, where they can be credited as a contributor rather than an author.

The CRediT – Contributor Roles Taxonomy (niso.org) sets out 14 roles that can be used to represent those typically played by contributors to a scholarly output. This has been widely adopted by a range of publishers to improve accessibility and visibility of the range of contributions made to published research outputs.

Identification of authors and ORCID

To ensure a given author’s publications are accurately attributed to them, all authors should register for an ORCID account and ensure that their ORCID number is referenced in their published research.

Further advice

In general, researchers should seek advice from within their own research field and refer to guidance produced by the appropriate research funder, the journal in question or from their professional society. Such sources include:  

University affiliation

Only staff or students of the collegiate University, or those who have a formal affiliation to the University or a College (or those who were University staff or students, or had a formal affiliation when the research in question was conducted), should state in any journal submission that they are affiliated to the University or a College.

 

Peer review

Researchers should give priority to publishing in publications that employ rigorous standards of peer review.

When acting as peer reviewers, University members should declare all relevant interests as required in the University's conflict of interest policy.

New reviewers are advised to take any available training and follow provided guidance (see Further Resources in the right hand column) to become familiar with good practice, and consult or discuss with colleagues as necessary about the process of peer review (note that the contents of the article being reviewed should be kept confidential). Where appropriate, reviewers should contribute comments that will be attributed, and reviewers are encouraged to sign their reviews.

All reviewers should:

  • apply rigorous objectivity in all assessments
  • complete the review in accordance with the guidance provided and on time
  • respect the confidentiality of any information sent for review (i.e. do not share it with colleagues if/when asking for advice on the process of peer review)
  • report any conflicting interests
  • not allow vested interests or personal bias to influence their impartial assessment
  • only accept assignments for which they have the expertise and no conflicting interests
  • not take advantage of any new data, ideas or privileged information they have had access to during the review process, to further their own research and/or other activities
  • conduct a fair assessment of the work and not deliberately disadvantage a competitor in the field
  • review objectively work that challenges accepted views, crosses traditional boundaries and/or is wholly innovative
  • be aware that the review may identify practice which falls below good conduct (which might be honest error or research misconduct) and which should be reported
  • be vigilant about the use of Artificial Intelligence technologies and Large Language Models (LLMs) in the development of any research output which they review, in particular the collection, analysis and interpretation of data underpinning the output.  They should expect a high degree of transparency in research reporting and for the use of any such technologies or LLMs to be cited in the methods or acknowledgements sections of the output as appropriate.

Reviewers should not use AI or Large Language Models to generate the text of their reviews, as this breaks the confidentiality of the article under review.

If submitting work for peer review, researchers should:

  • not take actions, directly or indirectly, to influence the review of their own work or that of others, positively or negatively
  • accept comments and respond to the factual points made, ignoring any personal remarks made by the reviewer
  • report any suspected infringement of the principles outlined above to the appropriate authority (e.g. journal editor, funder)
  • discuss details with all co-authors before making any revision or appeal
  • ensure all authors approve any appeal or response document and revised publication
Publication

Researchers should seek to publish their results in accordance with current best practice and funders' terms and conditions. They should ensure that they:

  • use the most appropriate means to publish the results of their research, typically as papers in refereed journals, or as research outputs (e.g. data, code, workflows) in a trusted repository
  • comply with University policies and funder requirements in the dissemination of the results of research and, where appropriate, seek guidance and approval to report research to the media (working with Public Affairs and Communications)
  • publish a coherent report of the work and do not report the data more than once (unless in a secondary analysis) or sub-divide the data (unless this was a predefined approach). This is sometimes referred to as "salami-slicing", that is splitting of the data derived from a single research idea into multiple smaller “publishable” units or “slices.”
  • consider negative results of research as important as positive when disseminating research and avoid exaggerating the importance of conclusions obtained
  • analyse the data using appropriate methods of statistical analysis, avoiding practices such as p-hacking and HARKing
  • acknowledge and cite the work of others where appropriate, fully and accurately attributing relevant sources
  • take steps to ensure the accuracy of the data reported and act immediately to correct any genuine errors or misunderstanding prior to and after publishing
  • acknowledge the funding, support, sponsorship and other forms of input (including that of the University) to the work in an appropriate way
  • give notice of intention to publish and seek approval, where appropriate, from all partner organisations
  • openly declare all relevant interests
  • not seek media exposure for research which has not been subject to peer review
  • handle the release of research data which might have high and/or commercial impact with care and sensitivity, consulting the University and other partners as appropriate, keeping in mind rules and regulations such as GDPR, and bearing in mind the University's encouragement of Open Research

Papers presented for publication must be the author’s own work, reflecting their own research and analysis.  Authors should not engage in plagiarism - verbatim or near-verbatim copying, or very close paraphrasing, of text, images, figures or results from another’s work.

If a publication is found to include an honest error (for example a difference in interpretation, errors unrelated to the research), it is advised that authors seek a correction (erratum) or retraction of work as appropriate by getting in contact with the publishing journal’s managing editor, providing the reason for this action. Such errors are not uncommon in research, with The European Code of Conduct for Research Integrity (All European Academies, 2023) stating that authors should be 'given credit for issuing prompt corrections post-publication' if required.

If there is a concern that an error may have constituted research misconduct (including fabrication, falsification and plagiarism), information on formal procedures can be found on the University’s Research Misconduct webpages.

 

Further resources

 

See also

Authorship

Peer review

Publication