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The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI
There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer’s disease and related dementias (ADRD) community, data types and modalities are spread across many organizati...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248126/ https://www.ncbi.nlm.nih.gov/pubmed/37304174 http://dx.doi.org/10.3389/fninf.2023.1175689 |
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author | Toga, Arthur W. Phatak, Mukta Pappas, Ioannis Thompson, Simon McHugh, Caitlin P. Clement, Matthew H. S. Bauermeister, Sarah Maruyama, Tetsuyuki Gallacher, John |
author_facet | Toga, Arthur W. Phatak, Mukta Pappas, Ioannis Thompson, Simon McHugh, Caitlin P. Clement, Matthew H. S. Bauermeister, Sarah Maruyama, Tetsuyuki Gallacher, John |
author_sort | Toga, Arthur W. |
collection | PubMed |
description | There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer’s disease and related dementias (ADRD) community, data types and modalities are spread across many organizations, geographies, and governance structures. The ADRD community is not alone in facing these challenges, however, the problem is even more difficult because of the need to share complex biomarker data from centers around the world. Heavy-handed data sharing mandates have, to date, been met with limited success and often outright resistance. Interest in making data Findable, Accessible, Interoperable, and Reusable (FAIR) has often resulted in centralized platforms. However, when data governance and sovereignty structures do not allow the movement of data, other methods, such as federation, must be pursued. Implementation of fully federated data approaches are not without their challenges. The user experience may become more complicated, and federated analysis of unstructured data types remains challenging. Advancement in federated data sharing should be accompanied by improvement in federated learning methodologies so that federated data sharing becomes functionally equivalent to direct access to record level data. In this article, we discuss federated data sharing approaches implemented by three data platforms in the ADRD field: Dementia’s Platform UK (DPUK) in 2014, the Global Alzheimer’s Association Interactive Network (GAAIN) in 2012, and the Alzheimer’s Disease Data Initiative (ADDI) in 2020. We conclude by addressing open questions that the research community needs to solve together. |
format | Online Article Text |
id | pubmed-10248126 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102481262023-06-09 The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI Toga, Arthur W. Phatak, Mukta Pappas, Ioannis Thompson, Simon McHugh, Caitlin P. Clement, Matthew H. S. Bauermeister, Sarah Maruyama, Tetsuyuki Gallacher, John Front Neuroinform Neuroscience There is common consensus that data sharing accelerates science. Data sharing enhances the utility of data and promotes the creation and competition of scientific ideas. Within the Alzheimer’s disease and related dementias (ADRD) community, data types and modalities are spread across many organizations, geographies, and governance structures. The ADRD community is not alone in facing these challenges, however, the problem is even more difficult because of the need to share complex biomarker data from centers around the world. Heavy-handed data sharing mandates have, to date, been met with limited success and often outright resistance. Interest in making data Findable, Accessible, Interoperable, and Reusable (FAIR) has often resulted in centralized platforms. However, when data governance and sovereignty structures do not allow the movement of data, other methods, such as federation, must be pursued. Implementation of fully federated data approaches are not without their challenges. The user experience may become more complicated, and federated analysis of unstructured data types remains challenging. Advancement in federated data sharing should be accompanied by improvement in federated learning methodologies so that federated data sharing becomes functionally equivalent to direct access to record level data. In this article, we discuss federated data sharing approaches implemented by three data platforms in the ADRD field: Dementia’s Platform UK (DPUK) in 2014, the Global Alzheimer’s Association Interactive Network (GAAIN) in 2012, and the Alzheimer’s Disease Data Initiative (ADDI) in 2020. We conclude by addressing open questions that the research community needs to solve together. Frontiers Media S.A. 2023-05-25 /pmc/articles/PMC10248126/ /pubmed/37304174 http://dx.doi.org/10.3389/fninf.2023.1175689 Text en Copyright © 2023 Toga, Phatak, Pappas, Thompson, McHugh, Clement, Bauermeister, Maruyama and Gallacher. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Toga, Arthur W. Phatak, Mukta Pappas, Ioannis Thompson, Simon McHugh, Caitlin P. Clement, Matthew H. S. Bauermeister, Sarah Maruyama, Tetsuyuki Gallacher, John The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title | The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title_full | The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title_fullStr | The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title_full_unstemmed | The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title_short | The pursuit of approaches to federate data to accelerate Alzheimer’s disease and related dementia research: GAAIN, DPUK, and ADDI |
title_sort | pursuit of approaches to federate data to accelerate alzheimer’s disease and related dementia research: gaain, dpuk, and addi |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248126/ https://www.ncbi.nlm.nih.gov/pubmed/37304174 http://dx.doi.org/10.3389/fninf.2023.1175689 |
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