Research transparency: using plain language to explain research

Research transparency

Research transparency means openness, clarity and accessibility. This should happen at every stage of the research process, from study design to publishing results.

The HRA’s vision is for information from health and social care research to be publicly available, so everyone can see the outcomes from finished studies.

The HRA’s Make it Public strategy notes that research findings that are well explained and made public translate into better care for patients and service users. Publicly-available research findings also encourage people to take part in research, and the public are more likely to trust research outcomes.

When we talk about transparency at every stage of the research process, we mean:

• registration - making it public that a study has started
• reporting results - making it public what the study has found
• informing participants - letting people who took part know what the study has found, and
• sharing study data - enabling further research

A key part of making research transparent is talking about it in a language that everyone can understand.

See the HRA’s study lifecycle roadmap where researchers can see at a glance where they need to take action to ensure transparency.

Producing lay summaries and using plain language

A lay summary is required when a researcher applies to the Integrated Research Application System, registers their research and makes the results public.

Lay summaries are concise, jargon-free explanations of research projects. They are designed to be understood by people without specialised knowledge of the topic. By distilling complex concepts such as AI into clear and accessible language, lay summaries help people participate more actively in discussions with researchers about the research methods and outcomes.

Explaining AI in plain language can be challenging, but researchers must strive to demystify AI and its functionalities. Research applications and ethical considerations need to be explained in language that is understandable to everyone. This means being able to explain to research ethics committees how AI algorithms are developed and validated, and how they will be integrated into clinical practice. It also means being able to address concerns related to privacy, bias and transparency.

For further guidance on how to write a good plain language summary, see the HRA’s webpage on writing a plain language (lay) summary.