Resources available for autism research in the big data era: a systematic review.
Resources available for autism research in the big data era: a systematic review.
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作者：
Al-Jawahiri R;Milne E
最近，许多利益相关者鼓励采取行动，在许多研究领域生成大的、开放的数据。大而开放的数据特别有价值的一个领域是与自闭症谱系障碍 (ASD) 等复杂异质性疾病相关的研究。研究结果的不一致和自闭症谱系障碍的巨大异质性需要使用大数据和开放数据来应对重要挑战，例如理解和定义自闭症谱系障碍的异质性和潜在亚型。为此，已经制定了许多举措，旨在为自闭症研究开发大型和/或开放数据资源。为了给自闭症研究人员提供有用的数据参考，我们利用Scopus数据库、Google搜索引擎以及重点期刊的“推荐库”页面对ASD数据资源进行了系统检索，并将结果转化为针对ASD数据的综合列表。本次综述的目的是系统地搜索所有可用的 ASD 数据资源，提供以下数据类型：表型、神经影像、人脑连接矩阵、人脑统计图、生物样本和 ASD 参与者招募。总共发现了 33 个资源，其中包含来自不同数量的参与者的不同类型的数据。提供了每个数据资源中可用数据的描述以及每个资源的链接。此外，还解决了关键影响并确定了代表性不足的数据领域。
Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.