Abstract
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.
Highlights
There has been a move towards generating ‘big’ and ‘open’ data in many areas of science such as psychology, neuroscience, genetics, and omics
The first criterion is whether they specialize in only Autism Spectrum Disorder (ASD) data as opposed to providing data from other populations in addition to the ASD data
The second criterion is based on the similarity in the general ASD data-type they handle and provide
Summary
There has been a move towards generating ‘big’ and ‘open’ data in many areas of science such as psychology, neuroscience, genetics, and omics. Funding bodies (including the Medical Research Council, the Wellcome Trust, and the US National Institutes of Health (NIH), among others) are increasingly funding large studies and initiatives that facilitate data sharing. Their data-policies are recommending and, in certain cases, requiring the studies that they fund to submit a data-sharing plan and to share their data. Publishers are playing a role in driving big data and data sharing efforts through their data policies, data-oriented special issues and call for papers (e.g., Focus on Big Data, 2014; Eickhoff et al, 2016), and the launch of machine-readable data journals (e.g., Scientific Data and GigaScience). Depending on the data type and the journal, researchers are expected to
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