Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data

Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BioScience Oxford University Press

Loading next page...
 
/lp/oxford-university-press/quantity-is-nothing-without-quality-automated-qa-qc-for-streaming-vmGLSzpr0Z

References (46)

Publisher
Oxford University Press
Copyright
© Published by Oxford University Press.
Subject
Biologist's Toolbox
ISSN
0006-3568
eISSN
1525-3244
DOI
10.1525/bio.2013.63.7.10
Publisher site
See Article on Publisher Site

Abstract

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the demands of real-time processing. Automated methods for rapidly identifying and (ideally) correcting problematic data are essential. However, advances in sensor hardware have outpaced those in software, creating a need for tools to implement automated quality assurance and quality control procedures, produce graphical and statistical summaries for review, and track the provenance of the data. Use of automated tools would enhance data integrity and reliability and would reduce delays in releasing data products. Development of community-wide standards for quality assurance and quality control would instill confidence in sensor data and would improve interoperability across environmental sensor networks.

Journal

BioScienceOxford University Press

Published: Jul 1, 2013

Keywords: Keywords: computers in biology informatics instrumentation environmental science

There are no references for this article.