Abstract
Data sharing and reuse, while widely accepted as good ideas, have been slow to catch on in any concrete and consistent way. One major hurdle within the scientific community has been the lack of widely accepted standards for citing that data, making it difficult to track usage and measure impact. Within the neuroimaging community, there is a need for a way to not only clearly identify and cite datasets, but also to derive new aggregate sets from multiple sources while clearly maintaining lines of attribution. This work presents a functional prototype of a system to integrate Digital Object Identifiers (DOI) and a standardized metadata schema into a XNAT-based repository workflow, allowing for identification of data at both the project and image level. These item and source level identifiers allow any newly defined combination of images, from any number of projects, to be tagged with a new group-level DOI that automatically inherits the individual attributes and provenance information of its constituent parts. This system enables the tracking of data reuse down to the level of individual images. The implementation of this type of data identification system would impact researchers and data creators, data hosting facilities, and data publishers, but the benefit of having widely accepted standards for data identification and attribution would go far toward making data citation practical and advantageous.
Highlights
Amid growing institutional, national, and international pressure, data sharing is becoming an accepted and increasingly mandated component of the research process
We review the data citation and attribution problem and propose a set of “best practices” for the identification and citation of neuroimaging data in a context that will ensure proper attribution and credit is maintained when data is reused in subsequent studies
The EZID API was integrated in order to allow Digital Object Identifiers (DOI) to be assigned upon dataset upload, meaning that both an overarching identifier is attached to the set as a whole, and each image within that set had its own DOI which reflects its individual attributes, as well as inheriting provenance
Summary
National, and international pressure, data sharing is becoming an accepted and increasingly mandated component of the research process. The “impact” of a publication is inferred from factors like the number of citations the publication receives, and the perceived impact of the source journal of the publication, etc., Citations are the method by which the authors of a paper acknowledge credit to another publication for supporting (or contrasting) ideas, concepts, or observations. These impact factors are monitored by accepted indexing services. In contrast to “credit” for a publication, there currently lacks an efficient and accepted means of deriving credit for shared data This is due, in part, to a lack of standards for identifying and attributing the use of shared data. The following presents a vision of this credit and identification system, and the possibilities for further development of this effort
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