Atlas of Longitudinal Datasets
The Atlas of Longitudinal Datasets is a free longitudinal data discoverability platform developed in partnership with the Wellcome Trust, MQ Mental Health Research and lived experience experts. The platform allows researchers, governments, funders and members of the public to find thousands of longitudinal datasets from across the world using a designated search engine.
The Atlas allows users to explore thousands of longitudinal datasets on a map, compare the features of multiple datasets, and apply filters to identify datasets most relevant to specific research questions.
The Atlas provides key information about the sample size, participant demographics, data-sharing policies, data collected in each dataset, the mental health topics studied, and engagement with people who have lived experience of the condition being studied. The Atlas team are continuing to expand the platform by adding new datasets and information about additional types of data included in the datasets.
The Atlas follows the extensive Landscaping International Longitudinal Datasets project, commissioned by the Wellcome Trust in partnership with MQ, the Open Data Institute and lived experience experts. The research team conducted a global search and review of large-scale longitudinal datasets with the potential to facilitate mental health research, identifying over 3,000 unique datasets. The Atlas hosts this information to make these valuable datasets discoverable to researchers around the world.
Aims
- Increase discoverability
- Maximise the use of longitudinal datasets and the value of prior investments
- Foster new collaborations and partnerships
- Facilitate innovative mental health research
- Support research validation and predictive modelling
- Generate new knowledge
- Identify gaps in longitudinal data
Methods
Our team systematically identifies and records information about longitudinal datasets from around the world and shares this information on the Atlas.
How we find longitudinal datasets
We searched for and identified longitudinal datasets on mental health and other topics from across the world and various sectors. In this process, we identified richness in the datasets by reviewing each dataset individually and collecting information about the dataset’s sample, geographical coverage, data sharing policies, data linkage, and more.
We conducted this work in partnership with charities (MQ Mental Health Research), non-profit organisations (the Open Data Institute), and lived experience expert (LEE) groups, and worked with a range of national and international collaborators throughout. We shared our findings in a report for the Wellcome Trust in July 2023, which details our search strategy and methodology.
Throughout this process, we identified over 3,000 longitudinal datasets from across the world. The Atlas of Longitudinal Datasets shares information about these datasets to make them more discoverable to researchers worldwide. This list of datasets includes a range of longitudinal observational studies designed to capture diverse health outcomes, life events, and social factors.
Key study designs include cohort studies, such as birth cohorts, twin cohorts, caregiver and child cohorts, household and community panel studies, and registry studies using linked administrative records and biobanks. We continue work towards adding all of these datasets to the Atlas and have so far prioritised datasets focused on mental health, large-scale datasets, and datasets from low- and middle-income countries.
How we gather information about datasets
For each longitudinal dataset that we have identified, we search for and then extract information (metadata) about the datasets of interest to researchers in a process we call reviewing.
Reviewing refers to searching for and identifying metadata from various sources, such as journal articles, study websites, aggregate websites, repositories, and reports.
We collect information about these datasets using manual methods enhanced by artificial intelligence (AI) processes.
Our search methods include:
- Manually extracting information from documentation that is available on the internet, such as research papers, study websites, and data repositories.
- Using advanced web searches to help narrow down results when searching for documentation.* Using AI to search for and extract information from documentation.
- Contacting data custodians when we are unable to find key information or if documentation is unclear.
Our core approach is manual, focusing on accuracy, completeness, and verification for each review. This process allows us to gain valuable insight into the landscape of available data and understand how best to make it discoverable. While AI is a useful tool, it cannot replace the depth of insight we achieve through hands-on, manual work.
For more information on our methods, head to the How We Work page on our website.
Impact
The Atlas platform has welcomed over 5,000 users from 116 countries across the world. The Atlas is used as a source of information by news articles and AI chatbots such as ChatGPT. The Atlas is listed as a useful resource, metadata catalogue or tool on several websites, including The Wellcome Trust, Datamind, and the International Alliance of Mental Health Research Funders (IAMHRF).
Principal Investigator
Professor of Developmental Psychology