Experiences in integrated data and research object publishing using GigaDB View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-06

AUTHORS

Scott C Edmunds, Peter Li, Christopher I Hunter, Si Zhe Xiao, Robert L Davidson, Nicole Nogoy, Laurie Goodman

ABSTRACT

In the era of computation and data-driven research, traditional methods of disseminating research are no longer fit-for-purpose. New approaches for disseminating data, methods and results are required to maximize knowledge discovery. The “long tail” of small, unstructured datasets is well catered for by a number of general-purpose repositories, but there has been less support for “big data”. Outlined here are our experiences in attempting to tackle the gaps in publishing large-scale, computationally intensive research. GigaScience is an open-access, open-data journal aiming to revolutionize large-scale biological data dissemination, organization and re-use. Through use of the data handling infrastructure of the genomics centre BGI, GigaScience links standard manuscript publication with an integrated database (GigaDB) that hosts all associated data, and provides additional data analysis tools and computing resources. Furthermore, the supporting workflows and methods are also integrated to make published articles more transparent and open. GigaDB has released many new and previously unpublished datasets and data types, including as urgently needed data to tackle infectious disease outbreaks, cancer and the growing food crisis. Other “executable” research objects, such as workflows, virtual machines and software from several GigaScience articles have been archived and shared in reproducible, transparent and usable formats. With data citation producing evidence of, and credit for, its use in the wider research community, GigaScience demonstrates a move towards more executable publications. Here data analyses can be reproduced and built upon by users without coding backgrounds or heavy computational infrastructure in a more democratized manner. More... »

PAGES

99-111

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00799-016-0174-6

    DOI

    http://dx.doi.org/10.1007/s00799-016-0174-6

    DIMENSIONS

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    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00799-016-0174-6'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00799-016-0174-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00799-016-0174-6'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00799-016-0174-6'


     

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