Assessment of the impact of shared brain imaging data on the scientific literature View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Michael P. Milham, R. Cameron Craddock, Jake J. Son, Michael Fleischmann, Jon Clucas, Helen Xu, Bonhwang Koo, Anirudh Krishnakumar, Bharat B. Biswal, F. Xavier Castellanos, Stan Colcombe, Adriana Di Martino, Xi-Nian Zuo, Arno Klein

ABSTRACT

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice. To justify the significant effort required for sharing data, funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a case study that provides direct evidence of the impact of open sharing on brain imaging data use and resulting peer-reviewed publications. We demonstrate that openly shared data can increase the scale of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings dispel the myth that scientific findings using shared data cannot be published in high-impact journals, suggest the transformative power of data sharing for accelerating science, and underscore the need for implementing data sharing universally. More... »

PAGES

2818

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41467-018-04976-1

DOI

http://dx.doi.org/10.1038/s41467-018-04976-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105584510

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30026557


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HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41467-018-04976-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41467-018-04976-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41467-018-04976-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41467-018-04976-1'


 

This table displays all metadata directly associated to this object as RDF triples.

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