Lessons from a Simple Tool for Neuroimaging Data Sharing
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1
University of Massachusetts Medical School, United States
Data sharing is becoming increasingly common, but despite encouragement and facilitation by funding agencies, journals [1], and some labs and larger research efforts, there remain political, financial, social, and technical barriers to sharing data [2]. In particular, technical solutions are few for researchers that are not a part of larger efforts with dedicated sharing infrastructures, and social excuses such as the time commitment required to share or the lack of motivation to share can keep data from becoming public [3].
We present a system for sharing neuroimaging data, designed to be simple to use and to provide benefit to the data provider. The system consists of a server at the International Neuroinformatics Coordinating Facility (INCF) and client tools for uploading data to the server. The primary design principle for the client side is ease of use: the user identifies a directory containing DICOM data and provides his INCF Portal user name and (public) identifiers for the subject and imaging session. The client probes the data for metadata and prompts the user for additional or missing information, then anonymizes the data and sends it to the server. The server first checks anonymization of incoming data, deleting data that is not property anonymized. The server then runs quality control routines on the data, and the data and the quality control reports are made public. The user is notified by e-mail when this is complete, and retains control of the data and may delete it from the server if necessary. The result is that in the time required for upload and quality control processing, including a scant minute or two of the user's time, the data is anonymized, made publicly available, and quality control is performed.
At this point, the system is in place and working as specified. Users need only to start using the system, but we have not seen the system adopted as we anticipated. We not see this as a failure of the system, but rather as a form of progress in ongoing data sharing efforts: technical barriers have been removed, throwing into relief some of the social issues standing in the way of effective data sharing, and exposing these issues will allow us to better understand and focus on them. With "we can't share" out of the way, we can better attack "we won't share." And as further barriers are removed, we have in place an infrastructure for sharing and archiving.
The client tools and access to the public image database are available at http://xnat.incf.org/.
Acknowledgements
This work was conducted within the Neuroimaging Datasharing Task Force of the INCF Program on Standards for Datasharing.
References
1. Kennedy, DN, et al. Next Steps in Data Publishing. Neuroinformatics. 2011; 9: 317-320. 10.1007/s12021-011-9131-0.
2. Poline JB, et al. Data sharing in neuroimaging research. Frontiers in Neuroinformatics. April 2012; 6: 9. 10.3389/fninf.2012.00009.
3. Ascoli GA. The Ups and Downs of Neuroscience Shares. Neuroinformatics. 2006; 4: 213-6. 10.1385/NI:4:3:213.
Keywords:
Neuroimaging,
data sharing,
database,
Quality Assurance,
usability
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Demo, to be considered for oral presentation
Topic:
Neuroimaging
Citation:
Haselgrove
C
(2014). Lessons from a Simple Tool for Neuroimaging Data Sharing.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00050
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Received:
25 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Mr. Christian Haselgrove, University of Massachusetts Medical School, Worcester, United States, christian.haselgrove@umassmed.edu