AData(Viewer)
Exploring the Alzheimer's Disease Data Landscape

ABOUT

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.. License

Cohort Comparison

Cohorts

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.

CONTENT
20 Cohorts
66,807 Patients
13 Compared Modalities
Over 1,000 Variables Mapped
StudyPicker

StudyPicker

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.

Biomarker Overview

Explore and compare variable distributions across cohorts.

Heatmap Screenshot
Total of All Studies

Ethnoracial Diversity

The displayed chart visualizes the ethnoracial diversity across the investigated cohorts. Following the link, you can find individual charts per cohort illustrating their diversity.

Modality Overview

In this section, you can investigate which of the curated data modalities are available in each cohort dataset.

Heatmap Screenshot
Feature Mapping

Feature Mappings

Following the link, you will find semantic mappings between variables of the investigated datasets. Equivalent features have been connected and highlight semantic interoperability.

Longitudinal Follow-up

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.

Cohort Comparison

PROJECTS

Projects in which AData(Viewer) is currently used.

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Virtual Brain Cloud

Personalized Recommendations
for Neurodegenerative Diseases
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AETIONOMY

Organising Knowledge about Neurodegenerative Disease Mechanisms
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EPAD

European prevention of Alzheimer’s dementia consortium
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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).


Contact Us

Prof. Dr. Martin Hofmann-Apitius

Head of the Bioinformatics department.

Prof. Dr. Martin Hofmann-Apitius

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 Birkenbihl

PhD Candidate.

Colin Birkenbihl

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

PhD Candidate.

Yasamin Salimi

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.