Questions and responses to USGS-wide poll on quality assurance practices for timeseries data, 2021
Dates
Publication Date
2023-01-23
Start Date
2021-09-28
End Date
2021-10-08
Citation
Katoski, M.P., Cashman, M.J., and Lester T., 2023, Questions and responses to USGS-wide poll on quality assurance practices for timeseries data, 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9C8Q9XE.
Summary
This data record contains questions and responses to a USGS-wide survey conducted to identify issues and needs associated with quality assurance and quality control (QA/QC) of USGS timeseries data streams. This research was funded by the USGS Community for Data Integration as part of a project titled “From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making”. The poll targeted monitoring network managers and technicians and asked questions about operational data streams and timeseries data collection in order to identity opportunities to streamline data access, expedite the response to data quality issues, improve QA/QC procedures, reduce operations costs, and uncover [...]
Summary
This data record contains questions and responses to a USGS-wide survey conducted to identify issues and needs associated with quality assurance and quality control (QA/QC) of USGS timeseries data streams. This research was funded by the USGS Community for Data Integration as part of a project titled “From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making”. The poll targeted monitoring network managers and technicians and asked questions about operational data streams and timeseries data collection in order to identity opportunities to streamline data access, expedite the response to data quality issues, improve QA/QC procedures, reduce operations costs, and uncover other maintenance needs. The poll was created using an online survey platform. It was sent to 2326 systematically selected USGS email addresses and received 175 responses in 11 days before it was closed to respondents. The poll contained 48 questions of various types including long answer, multiple choice, and ranking questions. The survey contained a mix of mandatory and optional questions. These distinctions as well as full descriptions of survey questions are noted on the metadata.
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USGS_QAQC_POLL_METADATA.xml Original FGDC Metadata
View
127.03 KB
application/fgdc+xml
qaqc_ideas.csv
1.24 KB
text/csv
USGS_QA_poll_responses.csv
268.8 KB
text/csv
Purpose
This survey was conducted to identify issues and needs associated with quality assurance and quality control (QA/QC) of USGS timeseries data streams, as part of a project funded by the USGS Community for Data Integration (CDI) titled, “From reactive- to condition-based maintenance: Artificial intelligence for anomaly predictions and operational decision-making”. This project set out to build a pilot machine-learning application that produces early-warning signals upon detection of sensor abnormalities. This project will increase the capacity of the USGS to build “always-on” artificial-intelligence applications that constantly scan data-streams for issues and predict problems before they occur. The goal of this survey was to uncover opportunities to: improve real-time data access, expedite the response to data quality issues, improve QA/QC procedures, reduce operations and maintenance costs, reduce the time and effort it takes to review and approve data records, and uncover other unmet needs.
