This report brings together findings from two linked work packages: one exploring data protection officer (DPO) challenges through discovery sessions, surveys, and stakeholder mapping; and the other investigating how existing tools support (or fail to support) practice through interviews and focus groups.  

This report brings together an overall narrative review of what good data sharing looks like in practice, alongside a set of detailed examples from a number of US and European jurisdictions, including recent successes from within Scotland.

This report focuses on exploratory research into workforce training and development for data sharing.

focuses on organisational risk maturity and the communication of risk guidance within the care ‘system’.

This is a summary of a white paper assessing the current state of public service integration across statutory, public, third, and independent sector organisations.

This sandbox provides a practical demonstration environment to illustrate the art of the possible for person-centred, transparent, and secure data sharing

This interim presentation visualises data flows mapped within this project, using existing composite stories to clearly map how data moves across organisations and systems. 

This report addresses the mental health and well-being of adolescents in Kazakhstan and Tajikistan through a comprehensive Digital Ecosystem Analysis. This initiative maps available digital resources that support adolescents through the provision of culturally sensitive and accessible mental health services.

A research report by a postgraduate student, Kyra Fong. This report explores how Scottish secondary school students experience digital data collection and privacy in their education and how they want to learn about these issues in the future.

An extended dataset of individual travel time maps for the 54 countries across the African continent and its island states, generated with the precision of a 100-meter resolution.

Executive summary: We supported UNICEF in exploring innovative ways to understand online mental health and well-being discourse. Our project looked at using AI to identify online discussions of young people on mental health, combining focus group input, annotated datasets, and experiments with large language models (LLMs) in English and Russian.

This presentation, prepared by Dr Clare Llewellyn, a lecturer in Governance, Technology and Data at the University of Edinburgh, outlines the methods and key findings of our project with UNICEF which investigated developing a methodology for using AI to identify social media discussions on mental health and well-being from young people and adolescents.   

Infographic: As part of our project focusing on developing a methodology for using AI to identify social media discussions of mental health and well-being, we ran a few youth engagement sessions together with UNICEF country offices in Kazakhstan and Tajikistan.

An infographic summarizing key findings and recommendations from the final report on the effects of COVID-19 on children's sports in Scotland

In this presentation, Dr Watmough, the project’s principal investigator, details how the team generated novel, more sustainable maps with a five-fold increased resolution for the entire continent of Africa and its island states, including 54 Countries

Literature Review that examines the previously limited studies on the sport and physical activity participation of children in Scotland identifies gaps in the literature and examines the data landscape.

This Gallery of Drawings shows engagement from children and young people in Scotland which played a critical role in understanding what sports and physical activity mean to them and gaining insight into their individual experiences.

Final Report: This project explored the seasonal effects of wasting scores with the goal of establishing if it is possible to answer the following question: “what would the wasting score have been had it been measured in a different month of that year?”

Git Hub code from Phase 1 of the Building Heights project. The aim of this project was to use a convolutional-deconvolutional neural network to predict building height data from satellite images.

Git Hub code from Phase 2 of the Building Heights project. The aim of this project was to use a convolutional-deconvolutional neural network to predict building height data from satellite images.