Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution.

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

SUBMITTER: Honor LB 

PROVIDER: S-EPMC4981598 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature