UCL Research


Research Transparency

Research transparency covers how we ensure our research is responsible, reproducible, open and evidence-based.

UCL is committed to transparency and rigour in research across all disciplines, and to continue to improve the ways in which we conduct research. This is part of our broader commitment to responsible and open research, as set out in UCL’s Research Strategy and webpages on research integrity.

Find out more below about:

UCL Statement on Transparency in Research

UCL has developed a Statement on Transparency in Research, which sets out the expectations we have for researchers relating to transparency and reproducibility at UCL.  

We recognise that behaviours in support of transparency vary considerably across disciplines and methodologies, and expect researchers to adopt those actions most appropriate to their field. This will usually include making research open and, in relevant contexts, taking actions to support reproducibility.

We are conscious that data protection requirements, in particular under the General Data Protection Regulation, must be considered alongside efforts to promote open data. We will seek to ensure that UCL policies on these respective topics are coordinated and in alignment.

Online training course: Transparency and reproducibility in research

This training course is open to UCL staff. To sign up for UCL’s online training course on transparency and reproducibility in research, follow the instructions below:

Please note: You will need to be logged in to UCLeXtend with your UCL credentials before clicking on the course link

1.       Log in to UCLeXtend: https://extend.ucl.ac.uk

2.       Click on course link, you will need to click ‘purchase’, but the course is free: https://extendstore.ucl.ac.uk/product?catalog=UCLX_TRANS_RES

What’s the course about?

This self-paced course covers what transparency and reproducibility in research are, why they are important, what steps you can take to make your research transparent and reproducible, and the limitations on doing so. It consists of a series of videos – mostly animated videos and some video talks from experts – with short exercises for enhancing your learning and an assignment. In total, it will take 3-5 hours to complete. The course is aimed at early-career researchers but is relevant to researchers at all stages. 

Why should I take the course? 

Making your research transparent will improve its credibility and maximise its potential for impact. As funders, publishers and institutions increasingly expect research to be practised openly – from study preregistration to open data – this course will arm you with the knowledge you need to make your research transparent and (where appropriate) reproducible, in line with the expectations for researchers set out in UCL’s Statement on Transparency in Research.

Why are we addressing ‘transparency’ as a whole, rather than ‘reproducibility’ on its own?

While reproducibility is crucial in certain research contexts, including quantitative and experimental sciences, it is less relevant to certain other disciplines and methodologies. To ensure our work is inclusive and sensitive to disciplinary variations, we are promoting reproducibility alongside open research and other relevant initiatives, in the context of our broader commitment to transparency in all of our research.


Scroll up for more information on UCL’s online training course on transparency and reproducibility in research.

Other upcoming training opportunities are circulated via the UCL research transparency mailing list.


ReproducibiliTea journal club at UCL – sign up to the ReproducibiliTea mailing list

Summary of initiatives that support reproducibility – from the ‘manifesto for reproducible science’ by Munafò et al. (2017)

Further UCL resources and information

Research integrity

Research ethics

2019 Research Strategy

Office for Open Science and Scholarship 

Open access

Research data management

External resources and information


-          Michel et al. 2020: Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology

-          Aczel et al. 2019: A consensus-based transparency checklist (social and behavioural research)

-          Munafò et al. 2017: A manifesto for reproducible science (particularly Table 1, which sets out suggested measures to promote reproducibility)