Container-based analysis environments for low-barrier access to research data

Craig Willis, Mike Lambert, Kenton Guadron McHenry, Christine Kirkpatrick

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The growing size of high-value sensor-born or computation-Ally derived scientific datasets are pushing the boundaries of traditional models of data access and discovery. Due to their size, these datasets are often accessible only through the systems on which they were created. Access for scientific exploration and reproducibility is limited to file transfer or by applying for access to the systems used to store or gen-erate the original data, which is often infeasible. There is a growing trend toward providing access to large-scale re-search datasets in-place via container-based analysis envi-ronments. This paper describes the National Data Service (NDS) Labs Workbench platform and DataDNS initiative. The Labs Workbench platform is designed to provide scal-Able and low-barrier access to research data via container-based services. The DataDNS effort is a new initiative de-signed to enable discovery, access, and in-place analysis for large-scale data, providing a suite of interoperable services to enable researchers, as well as the tools they are most fa-miliar with, to access and analyze these datasets where they reside.

Original languageEnglish (US)
Title of host publicationPEARC 2017 - Practice and Experience in Advanced Research Computing 2017
Subtitle of host publicationSustainability, Success and Impact
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450352727
DOIs
StatePublished - Jul 9 2017
Event2017 Practice and Experience in Advanced Research Computing, PEARC 2017 - New Orleans, United States
Duration: Jul 9 2017Jul 13 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F128771

Other

Other2017 Practice and Experience in Advanced Research Computing, PEARC 2017
CountryUnited States
CityNew Orleans
Period7/9/177/13/17

Keywords

  • Container-based analysis environments
  • Research data

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Willis, C., Lambert, M., McHenry, K. G., & Kirkpatrick, C. (2017). Container-based analysis environments for low-barrier access to research data. In PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact [a58] (ACM International Conference Proceeding Series; Vol. Part F128771). Association for Computing Machinery. https://doi.org/10.1145/3093338.3104164