Our Work

Work Packages


Defining best practice for dataset curation and use


Mapping dataset deficiencies in priority disease areas


Identifying and overcoming barriers to curating datasets



The STANDING Together Working Group, Nature Medicine

To launch the first round of the STANDING Together Delphi Study, an announcement paper was published in Nature Medicine in September 2022. 

The full paper can be found at:  https://doi.org/10.1038/s41591-022-01987-w

Close-up photo of an eye

The availability of health datasets has accelerated digital health research. Ophthalmology has been one of the leading areas of innovation, where several public datasets for ophthalmic imaging have been use in machine learning research. Datasets are a critical component for machine learning algorithm development, hence these need careful scrutiny prior to use. Prior to our review, it was previously unknown how many ophthalmic datasets existed, their degree of accessibility...  Read more

Close-up photo of skin

Freely available (open access) datasets containing skin images are frequently used to develop deep learning algorithms for skin cancer diagnosis. As these algorithms are heavily influenced by the images that they are trained on, it is important that the composition and characteristics of datasets are outlined, such as which populations images are taken from. This information is often...   Read more

STANDING Together: STANdards for Data Diversity, Inclusivity and Generalisability

Presented by Dr Joe Alderman at Machine Learning for Healthcare, 2022

Click here to view the poster

Click here to watch the presentation

Insights from the STANDING Together Consensus Study

Presented by Dr Elinor Laws at The Symposium on Artificial Intelligence for Learning Health Systems, 2023 

Click here to view the poster

ML4HC poster reference list


25 April 2022   |   NHS Transformation Directorate

21 April 2022   |   STANDING Together 

10 November 2021   |   The Lancet Digital Health

20 October 2021   |   NHSX