Maurier Ramirez
Utah State University | Computer Scientist
| Subject Areas: | Computer Science, Web Development |
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ABSTRACT:
This workshop introduces HydroServer, an open-source platform developed at Utah State University for collecting, managing, and sharing time series data from hydrologic and environmental monitoring sites. Built on the OGC SensorThings standard and extended with environmental metadata attributes, HydroServer provides a web application for site registration and data visualization, a Python client package (hydroserverpy) for programmatic data loading and retrieval, and APIs supporting both real-time sensor streaming and scheduled ETL workflows. Participants work through hands-on Jupyter notebooks covering site setup, data ingestion from CSV files, and metadata and data retrieval using hydroserverpy, with an overview of additional platform capabilities including automated data loading and a data quality control application currently in development.
Acknowledgements:
This research was supported by the Cooperative Institute for Research to Operations in Hydrology (CIROH) with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.
ABSTRACT:
world’s
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Created: March 19, 2025, 11:26 a.m.
Authors: Black, Scott · Jamy
ABSTRACT:
world’s
Created: June 25, 2026, 9:29 p.m.
Authors: Lippold, Kenneth · Horsburgh, Jeffery S. · Slaugh, Daniel · Ramirez, Maurier
ABSTRACT:
This workshop introduces HydroServer, an open-source platform developed at Utah State University for collecting, managing, and sharing time series data from hydrologic and environmental monitoring sites. Built on the OGC SensorThings standard and extended with environmental metadata attributes, HydroServer provides a web application for site registration and data visualization, a Python client package (hydroserverpy) for programmatic data loading and retrieval, and APIs supporting both real-time sensor streaming and scheduled ETL workflows. Participants work through hands-on Jupyter notebooks covering site setup, data ingestion from CSV files, and metadata and data retrieval using hydroserverpy, with an overview of additional platform capabilities including automated data loading and a data quality control application currently in development.
Acknowledgements:
This research was supported by the Cooperative Institute for Research to Operations in Hydrology (CIROH) with funding under award NA22NWS4320003 from the NOAA Cooperative Institute Program. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the opinions of NOAA.