Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
Highlights
The word “data” is the plural form of the Latin word datum, meaning “a thing given.” This definition is very appropriate in human subjects research, as participants are giving researchers something of themselves, which researchers in turn collect and store to be used to address important questions in science
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science and the ability to ask important research questions that could benefit others
For researchers who are not keen on data sharing, recent and emerging regulations regarding human subjects data can be used as a barrier, or excuse, for not taking part in data sharing initiatives
Summary
The collaborative informatics and neuroimaging suite toolkit for anonymous computation (COINSTAC [Plis et al, 2016]; https://github.com/trendscenter/ coinstac) tool and approach goes a step further in offering fully decentralized (and potentially privatized analysis), allowing the data to remain local at the site of collection, by leveraging local compute resources for each site's data This allows researchers to draw conclusions from large scale data without the need to have full control over the samples or aggregating them in a central place. At the other end of the spectrum are fully open approaches mentioned earlier that share the preprocessed data, NIfTI files (avoiding potential privacy issues included in the DICOM file headers) or the DICOM files This is the best option for research groups that focus on creating novel neuroimaging methodologies and require the raw DICOM or NIfTI neuroimaging data, as they will need software and computational power (i.e., GPUs) to run their algorithms).
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