Data collected in cohort studies lay the groundwork for a plethora of Alzheimer’s disease (AD) research endeavors. ADataViewer lets you explore this AD data landscape and identify cohort datasets that suit your research needs.
We accessed and curated major AD cohort datasets in a purely data-driven manner with the aim of 1) characterizing their underlying data, 2) assessing the quantity and availability of data, and 3) evaluating the interoperability across these distinct cohort datasets. All displayed results are based on the data that were shared and made accessible to the curators.
Our tool and content is licensed under CC BY-SA 4.0 facilitating Open Access as long as the appropriate credit is provided..
Here you can find an overview on the investigated cohorts, references to the study design and links for data access application. Additionally, there are summary statistics on key Alzheimer's disease biomarkers displayed.
The StudyPicker assists you in identifying suited AD data resources for your research plans. Rank cohorts based on their available variables and investigate further properties.
The displayed chart visualizes the ethnoracial diversity across the investigated cohorts. Following the link, you can find individual charts per cohort illustrating their diversity.
Following the link, you will find semantic mappings between variables of the investigated datasets. Equivalent features have been connected and highlight semantic interoperability.
This section lets you explore interactive visualizations of the longitudinal follow-up and participant drop-out encountered in the included studies. Additionally, you can visualize the longitudinal coverage of individual variables per study.
Projects in which AData(Viewer) is currently used.
This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking 'AETIONOMY' (grant n° 115568), and 'EPAD' (grant n° 115736), and additionally from the H2020 project 'Virtual Brain Cloud' (grant n° 826421).
Martin is leading the Department of Bioinformatics at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Sankt Augustin (Germany), a governmental not-for-profit, applied research institute. He is also Professor for Applied Life Science Informatics at Bonn-Aachen International Center for Information Technology (B-IT). He is (co-) author of more than 150 scientific publications.
Colin is currently pursuing his PhD. He is involved in multiple international EU projects (AETIONOMY, EPAD, RADAR-AD) and is deputy lead of a work package in the Horizon2020 project "TheVirtualBrain-Cloud". His main focus is data mining, machine learning, and statistics applied to research neurodegenerative diseases in a data-driven manner.
Yasamin Salimi joined Fraunhofer SCAI as a student research assistant in August 2019. She successfully finished her master’s thesis on “Comparison of Alzheimer’s Disease Progression Patterns Across Multiple Cohort Studies.” She started her PhD studies in November 2020 with a focus in evaluating the Data Landscape in Neurodegenerative Diseases.
