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.

Bespoke Workbook for Designing Responsible Youth Participation, developed while supporting The Data Tank’s NextGenData initiative, which focuses on pioneering a new approach to youth engagement in data and service provision.

Phase 2 Report: As a part of phase 2 of the project, which explored the seasonal effects on wasting scores, this report proposes a robust, data-driven protocol to correct observed wasting prevalence for seasonal variation at a global scale.

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.

This case study outlines how Data for Children Collaborative’s processes allowed for a reimagining of the End of Violence multi-country study to bring in the voices of youth.

This case study explains how the Data for Children Collaborative Facilitated a Collaborative Solution for UNICEF to Support them in Exploring Ways to Better Understand Access to Child Services.

This case study shows how the Data for Children Collaborative helped The Promise Scotland build the right team to map diverse care system data.

This case study explains how the Data for Children Collaborative supported UNICEF in better understanding the risks so that countries have more evidence to build their capacity and resilience to help children around the globe and into the future.

This case study explains how the Data for Children Collaborative helped build a multidisciplinary team of experts spanning academia, the private, and public sectors to gain data-driven insights.

This case study explains how the Data for Children Collaborative helped the Northern Alliance uncover a deeper understanding of the mechanisms by which educational attainment can be negatively affected by poverty, frame the conversation and layout logical project steps. 

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

Final Report: A robust, evidence-based set of recommendations for Scotland's children’s sports strategy to improve access and support every child's right to sport. 

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.