A platform for computationally advanced collaborative agroinformatics data discovery and analysis

Andrew Gustafson; Jesse Erdmann; Michael Milligan; Getiria Onsongo; Philip Pardey; Tom Prather; Kevin Silverstein; James Wilgenbusch; Ying Zhang

doi:10.1145/3093338.3093376

A platform for computationally advanced collaborative agroinformatics data discovery and analysis

Andrew Gustafson, Jesse Erdmann, Michael Milligan, Getiria Onsongo, Philip Pardey, Tom Prather, Kevin Silverstein, James Wilgenbusch, Ying Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

The International Agroinformatics Alliance (IAA) is a coalition of public and private institutions that are cooperating to develop a platform for computationally advanced collaborative analysis of agricultural data. By combining large agricultural data sets with advanced analysis techniques, IAA seeks to catalyze agricultural research, leading to improved agricultural productivity and stability. IAA has constructed a platform that combines Jupyterhub web notebooks for interactive data analysis, relational databases for storage of crop genetic and geospatial data, and the Globus file transfer system for efficient data transfer and authentication. The platform uses a data permissions system that allows users to share data with collaborators. The central platform is located at the Minnesota Supercomputing Institute, at the University of Minnesota, which allows access to the large storage and compute resources required for advanced agroinformatics analysis pipelines.

Original language	English (US)
Title of host publication	PEARC 2017 - Practice and Experience in Advanced Research Computing 2017
Subtitle of host publication	Sustainability, Success and Impact
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450352727
DOIs	https://doi.org/10.1145/3093338.3093376
State	Published - Jul 9 2017
Event	2017 Practice and Experience in Advanced Research Computing, PEARC 2017 - New Orleans, United States Duration: Jul 9 2017 → Jul 13 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128771

Other

Other	2017 Practice and Experience in Advanced Research Computing, PEARC 2017
Country/Territory	United States
City	New Orleans
Period	7/9/17 → 7/13/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computing Machinery.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access

10.1145/3093338.3093376

OpenUrl availability

Full text

Cite this

Gustafson, A., Erdmann, J., Milligan, M., Onsongo, G., Pardey, P., Prather, T., Silverstein, K., Wilgenbusch, J., & Zhang, Y. (2017). A platform for computationally advanced collaborative agroinformatics data discovery and analysis. In PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact Article a2 (ACM International Conference Proceeding Series; Vol. Part F128771). Association for Computing Machinery. https://doi.org/10.1145/3093338.3093376

A platform for computationally advanced collaborative agroinformatics data discovery and analysis. / Gustafson, Andrew; Erdmann, Jesse; Milligan, Michael et al.
PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact. Association for Computing Machinery, 2017. a2 (ACM International Conference Proceeding Series; Vol. Part F128771).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gustafson, A, Erdmann, J, Milligan, M, Onsongo, G, Pardey, P, Prather, T, Silverstein, K, Wilgenbusch, J & Zhang, Y 2017, A platform for computationally advanced collaborative agroinformatics data discovery and analysis. in PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact., a2, ACM International Conference Proceeding Series, vol. Part F128771, Association for Computing Machinery, 2017 Practice and Experience in Advanced Research Computing, PEARC 2017, New Orleans, United States, 7/9/17. https://doi.org/10.1145/3093338.3093376

Gustafson A, Erdmann J, Milligan M, Onsongo G, Pardey P, Prather T et al. A platform for computationally advanced collaborative agroinformatics data discovery and analysis. In PEARC 2017 - Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact. Association for Computing Machinery. 2017. a2. (ACM International Conference Proceeding Series). doi: 10.1145/3093338.3093376

@inproceedings{4cac95b3c35f46979ab5d602aa9d6370,

title = "A platform for computationally advanced collaborative agroinformatics data discovery and analysis",

abstract = "The International Agroinformatics Alliance (IAA) is a coalition of public and private institutions that are cooperating to develop a platform for computationally advanced collaborative analysis of agricultural data. By combining large agricultural data sets with advanced analysis techniques, IAA seeks to catalyze agricultural research, leading to improved agricultural productivity and stability. IAA has constructed a platform that combines Jupyterhub web notebooks for interactive data analysis, relational databases for storage of crop genetic and geospatial data, and the Globus file transfer system for efficient data transfer and authentication. The platform uses a data permissions system that allows users to share data with collaborators. The central platform is located at the Minnesota Supercomputing Institute, at the University of Minnesota, which allows access to the large storage and compute resources required for advanced agroinformatics analysis pipelines.",

author = "Andrew Gustafson and Jesse Erdmann and Michael Milligan and Getiria Onsongo and Philip Pardey and Tom Prather and Kevin Silverstein and James Wilgenbusch and Ying Zhang",

note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 Practice and Experience in Advanced Research Computing, PEARC 2017 ; Conference date: 09-07-2017 Through 13-07-2017",

year = "2017",

month = jul,

day = "9",

doi = "10.1145/3093338.3093376",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

booktitle = "PEARC 2017 - Practice and Experience in Advanced Research Computing 2017",

}

TY - GEN

T1 - A platform for computationally advanced collaborative agroinformatics data discovery and analysis

AU - Gustafson, Andrew

AU - Erdmann, Jesse

AU - Milligan, Michael

AU - Onsongo, Getiria

AU - Pardey, Philip

AU - Prather, Tom

AU - Silverstein, Kevin

AU - Wilgenbusch, James

AU - Zhang, Ying

PY - 2017/7/9

Y1 - 2017/7/9

N2 - The International Agroinformatics Alliance (IAA) is a coalition of public and private institutions that are cooperating to develop a platform for computationally advanced collaborative analysis of agricultural data. By combining large agricultural data sets with advanced analysis techniques, IAA seeks to catalyze agricultural research, leading to improved agricultural productivity and stability. IAA has constructed a platform that combines Jupyterhub web notebooks for interactive data analysis, relational databases for storage of crop genetic and geospatial data, and the Globus file transfer system for efficient data transfer and authentication. The platform uses a data permissions system that allows users to share data with collaborators. The central platform is located at the Minnesota Supercomputing Institute, at the University of Minnesota, which allows access to the large storage and compute resources required for advanced agroinformatics analysis pipelines.

AB - The International Agroinformatics Alliance (IAA) is a coalition of public and private institutions that are cooperating to develop a platform for computationally advanced collaborative analysis of agricultural data. By combining large agricultural data sets with advanced analysis techniques, IAA seeks to catalyze agricultural research, leading to improved agricultural productivity and stability. IAA has constructed a platform that combines Jupyterhub web notebooks for interactive data analysis, relational databases for storage of crop genetic and geospatial data, and the Globus file transfer system for efficient data transfer and authentication. The platform uses a data permissions system that allows users to share data with collaborators. The central platform is located at the Minnesota Supercomputing Institute, at the University of Minnesota, which allows access to the large storage and compute resources required for advanced agroinformatics analysis pipelines.

UR - http://www.scopus.com/inward/record.url?scp=85025810539&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85025810539&partnerID=8YFLogxK

U2 - 10.1145/3093338.3093376

DO - 10.1145/3093338.3093376

M3 - Conference contribution

AN - SCOPUS:85025810539

T3 - ACM International Conference Proceeding Series

BT - PEARC 2017 - Practice and Experience in Advanced Research Computing 2017

PB - Association for Computing Machinery

T2 - 2017 Practice and Experience in Advanced Research Computing, PEARC 2017

Y2 - 9 July 2017 through 13 July 2017

ER -

A platform for computationally advanced collaborative agroinformatics data discovery and analysis

Abstract

Publication series

Other

Bibliographical note

UN SDGs

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this