TY - GEN
T1 - Monitoring water quality in the great lakes leveraging geo-temporal cyber infrastructure
AU - Gutierrez-Polo, Indira
AU - Zhao, Yan
AU - Bradley, Shannon
AU - Roeder, Eugene
AU - Pitcel, Michelle
AU - Tepas, Kristin
AU - Collingsworth, Paris
AU - Marini, Luigi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/14
Y1 - 2017/11/14
N2 - When hydrologic scientists are looking for data to use in their data models relevant to their research area, they encounter a bottleneck when trying to collect relevant information, sometimes they might not know of all the available sources. Great Lakes Monitoring (GLM) collects data from 5 different state and federal agencies that take measurements on nutrients, geochemicals, contaminants, among other water quality data that directly affect the water health in the Great Lakes. In this paper, we describe the cyberinfrastructure that has been implemented in order to address this issue. To accomplish this, it cleans and organizes the data into a semi-standardized schema, ingests the data to a database, and takes advantage of a user interface for data visualizations. On top of this, trends for selected parameters can be observed within a map, data can be searched, downloaded in various formats and compared across different locations within the Great Lakes.
AB - When hydrologic scientists are looking for data to use in their data models relevant to their research area, they encounter a bottleneck when trying to collect relevant information, sometimes they might not know of all the available sources. Great Lakes Monitoring (GLM) collects data from 5 different state and federal agencies that take measurements on nutrients, geochemicals, contaminants, among other water quality data that directly affect the water health in the Great Lakes. In this paper, we describe the cyberinfrastructure that has been implemented in order to address this issue. To accomplish this, it cleans and organizes the data into a semi-standardized schema, ingests the data to a database, and takes advantage of a user interface for data visualizations. On top of this, trends for selected parameters can be observed within a map, data can be searched, downloaded in various formats and compared across different locations within the Great Lakes.
KW - data access
KW - data discovery
KW - data preservation
KW - data visualization
KW - geographic information systems
KW - hydrologic information systems
KW - web services
UR - http://www.scopus.com/inward/record.url?scp=85043768458&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043768458&partnerID=8YFLogxK
U2 - 10.1109/eScience.2017.50
DO - 10.1109/eScience.2017.50
M3 - Conference contribution
AN - SCOPUS:85043768458
T3 - Proceedings - 13th IEEE International Conference on eScience, eScience 2017
SP - 364
EP - 373
BT - Proceedings - 13th IEEE International Conference on eScience, eScience 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th IEEE International Conference on eScience, eScience 2017
Y2 - 24 October 2017 through 27 October 2017
ER -