How DPUK’s dementia global Data Landscape Report will make repurposing datasets better, easier and quicker
The DPUK's Data Landscape Report is an ambitious project to create a publicly available tool to identify dementia-related datasets worldwide. Here researcher Amelia Morgan describes how it has been created and is now available for researchers to use.
Repurposing existing datasets and maximising existing data assets is vital for efficient resource allocation and accelerating translational research. The DPUK Data Landscape Report aims to create a publicly accessible metadata tool to facilitate new research by comprehensively identifying all dementia-related cohorts, registers, and clinical trials around the world.
Utilising relevant search algorithms, we are systematically reviewing dementia-related publications in PubMed to identify dementia cohorts and research registers. We have been able to widen our sources to find additional datasets from major international dementia-related data platforms, including DPUK’s own Data Portal and the AD Workbench. Clinical trials data were identified and extracted from ClinicalTrials.gov.
These datasets are categorised to the C-Surv data model (Bauermeister et al., 2023), covering 18 data themes, to indicate availability of specific measurements, administrative information, and data accessibility.
These categorised datasets are accessible in the Data Landscape Report, an interactive tool, on the DPUK Data Portal. This allows users to undertake searches and to filter datasets based on variables such as geographic location, study size, and design. It provides a valuable resource for researchers seeking insights into the dementia data landscape.
The scope of this will continue to grow as more datasets are identified and added to the Data Landscape Report. It has already grown to contain:
- 860 population and clinical cohorts
- 46 research registers
- over 9,600 clinical trials
Take a look at the Data Landscape Report here.
This project was developed with funding from Gates Ventures and the AD Data Enablement Fund.
References: Bauermeister S, Bauermeister JR, Bridgman R, Felici C, Newbury M, North L, Orton C, Squires E, Thompson S, Young S, Gallacher JE. (2023). Research-ready data: the C-Surv data model. Eur J Epidemiol. Feb;38(2):179-187. doi: 10.1007/s10654-022-00916-y.