All items are ordered by Our Key Outcomes: Actionable Insights, Responsible Innovation and Knowledge Exchange.
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.
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.
A summary and a recording from the event we ran in November 2022: Children's Climate Risk: Their Future, Their Opinion.
UNICEF has generated a new report, supported by our climate change project team, to highlight the unique risks that heatwaves pose for children.
A guide on how to conduct a systematic review on a global scale. Each section will show the steps taken, recommendations, and reflections on each part of the process. The four steps this guide will discuss are recruitment, designing the study, training & engagement of the reviewers, and creating outputs.
A rapid literature review prepared to support UNICEF’s work in assessing the unique risks heat waves have on children globally.
A paper introducing a collaborative, demand-driven methodology for the development of a strategic adolescent mental health research agenda.
A report conducting an investigation into how to collect and map data on 'what matters' to Children and Families in Scotland.
A poster designed for conference presentation by our Population Estimation project team. This highlights key aspects of their work towards sustainable census independent population estimation in Mozambique.
This report is an output from our Poverty team, and it describes the work conducted for the Child Poverty and Access to Services (CPAS) project. The project's ultimate aim was to determine if geographic access to health centres correlated with multidimensional child poverty.
A paper presenting the data and methods used to create more realistic estimates of travel times to health facilities in Uganda, Tanzania, Zimbabwe and Mozambique.
This report is an output from our Mental Health project feasibility phase, aiming to establish foundational knowledge, requirements and frameworks, which will lead to developing replicable research methodologies.
The objective of the focus group discussions was to evaluate the ways that adolescents and young people interact with technology.
