Title
Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution
UMMS Affiliation
Lamar Soutter Library; Eunice Kennedy Shriver Center, Commonwealth Medicine; Child and Adolescent NeuroDevelopment Initiative, Department of Psychiatry
Publication Date
8-12-2016
Document Type
Article
Disciplines
Neurology | Neuroscience and Neurobiology | Psychiatry | Scholarly Communication | Scholarly Publishing
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.
Keywords
credit, data citation, data attribution, data repository, data sharing, neuroimaging community
Rights and Permissions
Copyright: © 2016 Honor, Haselgrove, Frazier and Kennedy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
DOI of Published Version
10.3389/fninf.2016.00034
Source
Honor LB, Haselgrove C, Frazier JA, Kennedy DN. Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution. Front Neuroinform. 2016 Aug 12;10:34. doi: 10.3389/fninf.2016.00034. eCollection 2016. PubMed PMID: 27570508; PubMed Central PMCID: PMC4981598. Link to article on publisher's website
Journal/Book/Conference Title
Frontiers in Neuroinformatics
Related Resources
PubMed ID
27570508
Repository Citation
Honor, Leah B.; Haselgrove, Christian; Frazier, Jean A.; and Kennedy, David N., "Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution" (2016). Psychiatry Publications and Presentations. 737.
https://escholarship.umassmed.edu/psych_pp/737
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
List of Supplementary Figures
Included in
Neurology Commons, Neuroscience and Neurobiology Commons, Psychiatry Commons, Scholarly Communication Commons, Scholarly Publishing Commons
Comments
The proof-of-concept implementation is accessible at http://iaf.virtualbrain.org. The software code for the implementation of the proof-of-concept is available at https://github.com/chaselgrove/doi.
The Supplementary Material for this article is available under Additional Files and can be found online at: http://journal.frontiersin.org/article/10.3389/fninf.2016.00034
A correction to this article has been published: Corrigendum: Data Citation in Neuroimaging: Proposed Best Practices for Data Identification and Attribution.