The pursuit of approaches to federate data to accelerate Alzheimer's disease and related dementia research: GAAIN, DPUK, and ADDI.

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Neuroinformatics Pub Date : 2023-05-25 eCollection Date: 2023-01-01 DOI:10.3389/fninf.2023.1175689
Arthur W Toga, Mukta Phatak, Ioannis Pappas, Simon Thompson, Caitlin P McHugh, Matthew H S Clement, Sarah Bauermeister, Tetsuyuki Maruyama, John Gallacher
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Abstract

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.

Abstract Image

寻求数据联合的方法,以加速阿尔茨海默病和相关痴呆症的研究:GAAIN、DPUK 和 ADDI。
数据共享加速科学发展已成为共识。数据共享提高了数据的实用性,促进了科学思想的创造和竞争。在阿尔茨海默病及相关痴呆症(ADRD)社区,数据类型和模式分散在许多组织、地域和管理结构中。阿尔茨海默病及相关痴呆症(ADRD)界并非唯一面临这些挑战的群体,然而,由于需要共享来自世界各地中心的复杂生物标记物数据,问题变得更加棘手。迄今为止,强硬的数据共享规定所取得的成效有限,而且经常遭到公然抵制。人们对使数据可查找、可访问、可互操作和可重复使用(FAIR)的兴趣往往导致集中式平台的出现。然而,当数据管理和主权结构不允许数据移动时,就必须采用其他方法,如联盟。实施完全联合的数据方法并非没有挑战。用户体验可能会变得更加复杂,对非结构化数据类型的联合分析仍然具有挑战性。在推进联合数据共享的同时,还应改进联合学习方法,使联合数据共享在功能上等同于直接访问记录级数据。在本文中,我们将讨论由 ADRD 领域的三个数据平台实施的联合数据共享方法:英国痴呆症平台(DPUK)(2014 年)、全球阿尔茨海默氏症协会互动网络(GAAIN)(2012 年)和阿尔茨海默氏症数据倡议(ADDI)(2020 年)。最后,我们探讨了研究界需要共同解决的开放性问题。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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