March 4, 2022 Dataset Open Access

Pospiech, Solveig

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  <dc:creator>Pospiech, Solveig</dc:creator>
  <dc:date>2022-03-04</dc:date>
  <dc:description>These data are supplementary material for the publication "Uncertainty Estimation for Measurement Data - A Practical Guide for Earth Scientists" in the journal Geostandards and Geoanalytical Research.

Data have uncertainties. Including a good estimate of the uncertainties for data analysis might significantly change the data interpretation. Therefore, high data quality is characterized by good accuracy of measurement results, but equally important by a good estimation of data uncertainty, which includes all relevant sources of dispersion of a measurement procedure. These data are example data sets for two case studies using uncertainty models based on replicated measurements. The case studies demonstrate how the models can be parameterized by using measurement data. The case studies are accompanied by code examples for the statistical programming language R.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/1469</dc:identifier>
  <dc:identifier>10.14278/rodare.1469</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:1469</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/776804/</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-34342</dc:relation>
  <dc:relation>doi:10.14278/rodare.1468</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/ecfunded</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Data for case studies about estimating measurement uncertainties</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
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