10.1109/BigData.2016.7840618
https://zenodo.org/records/820878
oai:zenodo.org:820878
Chard, Kyle
Kyle
Chard
The University of Chicago and Argonne National Laboratory, Chicago IL, USA
D'Arcy, Mike
Mike
D'Arcy
University of Southern California, Los Angeles, CA, USA
Heavner, Ben
Ben
Heavner
Institute for Systems Biology, Seattle, WA, USA
Foster, Ian
Ian
Foster
The University of Chicago and Argonne National Laboratory, Chicago IL, USA
Kesselman, Carl
Carl
Kesselman
University of Southern California, Los Angeles, CA, USA
Madduri, Ravi
Ravi
Madduri
The University of Chicago and Argonne National Laboratory, Chicago IL, USA
Rodriguez, Alexis
Alexis
Rodriguez
The University of Chicago and Argonne National Laboratory, Chicago IL, USA
Soiland-Reyes, Stian
Stian
Soiland-Reyes
The University of Manchester, Manchester, UK
Goble, Carole
Carole
Goble
The University of Manchester, Manchester, UK
Clark, Kristi
Kristi
Clark
University of Southern California, Los Angeles, CA, USA
Deutsch, Eric W.
Eric W.
Deutsch
Institute for Systems Biology, Seattle, WA, USA
Dinov, Ivo
Ivo
Dinov
The University of Michigan, Ann Arbor, MI, USA
Price, Nathan
Nathan
Price
Institute for Systems Biology, Seattle, WA, USA
Toga, Arthur
Arthur
Toga
University of Southern California, Los Angeles, CA, USA
I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets
IEEE
2016
Big Data
data analysis
BDBags
Big Data analysis
Big Data bags
Big Data sharing
Minid
data assembling
data collections
data descriptions
datasets
identifiers
research objects
Encoding
Metadata
Payloads
Robustness
Software
Uniform resource locators
bdbag
Jung, Segun
Segun
Jung
The University of Chicago
2016-12-05
https://static.aminer.org/pdf/fa/bigdata2016/BigD418.pdf
https://www.research.manchester.ac.uk/portal/files/45989205/bagminid.pdf
http://bd2k.ini.usc.edu/tools/
https://github.com/ini-bdds/bdbag
https://www.research.manchester.ac.uk/portal/en/publications/ill-take-that-to-go(8335e672-1d85-4649-a245-56fbdb1bd423).html
https://w3id.org/ro/bagit
https://zenodo.org/communities/linkeddata
https://zenodo.org/communities/eu
https://zenodo.org/communities/bioexcel
Creative Commons Attribution 4.0 International
Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified.
We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (BDBags), data descriptions (Research Objects), and simple persistent identifiers (Minids) to create a powerful ecosystem of tools and services for big data analysis and sharing.
We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.
European Commission
10.13039/501100000780
675728
Centre of Excellence for Biomolecular Research