Publikation: The Three Most Common Needs for Training on Measurement Uncertainty
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Sammlungen
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Measurement uncertainty is essential for assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities, including national metrology institutes, accreditation bodies, calibration and testing laboratories, and legal metrology, universities and different metrology fields. An important cornerstone to convey an understanding of measurement uncertainty is to provide training. This article identifies the status and needs for training on measurement uncertainty in each of the above communities and among those who teach uncertainty. It is the first study to do so across many different disciplines, and it merges many different sources of information with a focus on Europe. As a result, awareness of the training needs of different communities is raised and teachers of uncertainty are supported in addressing their audiences’ needs as well as in improving their uncertainty-specific pedagogical and technology-related knowledge. The three needs that are most commonly encountered in the communities requiring an understanding of measurement uncertainty, are 1) to address a general lack of training on measurement uncertainty, 2) to gain a better overview of existing training on measurement uncertainty in several communities, and 3) to deliver more training on specific technical topics, including the use of a Monte Carlo method for propagating probability distributions and treating multivariate measurands and measurement models. These needs will serve to guide future developments in uncertainty training and will, ultimately, contribute to increasing the understanding of uncertainty.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
KLAUENBERG, Katy, Peter HARRIS, Philipp MÖHRKE, Francesca PENNECCHI, 2025. The Three Most Common Needs for Training on Measurement Uncertainty. In: Measurement Science Review. De Gruyter. 2025, 25(5), S. 257-275. eISSN 1335-8871. Verfügbar unter: doi: 10.2478/msr-2025-0029BibTex
@article{Klauenberg2025-10-01Three-75029,
title={The Three Most Common Needs for Training on Measurement Uncertainty},
year={2025},
doi={10.2478/msr-2025-0029},
number={5},
volume={25},
journal={Measurement Science Review},
pages={257--275},
author={Klauenberg, Katy and Harris, Peter and Möhrke, Philipp and Pennecchi, Francesca}
}RDF
<rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/75029">
<dc:contributor>Möhrke, Philipp</dc:contributor>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41"/>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 International</dc:rights>
<bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/75029"/>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-10-30T11:00:46Z</dcterms:available>
<dc:creator>Möhrke, Philipp</dc:creator>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2025-10-30T11:00:46Z</dc:date>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:contributor>Klauenberg, Katy</dc:contributor>
<dc:creator>Klauenberg, Katy</dc:creator>
<dcterms:issued>2025-10-01</dcterms:issued>
<dc:contributor>Harris, Peter</dc:contributor>
<dcterms:title>The Three Most Common Needs for Training on Measurement Uncertainty</dcterms:title>
<dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/4.0/"/>
<dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/75029/1/Klauenberg_2-snnevzrg7n5t5.pdf"/>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41"/>
<dcterms:abstract>Measurement uncertainty is essential for assessing, stating and improving the reliability of measurements. An understanding of measurement uncertainty is the basis for confidence in measurements and is required by many communities, including national metrology institutes, accreditation bodies, calibration and testing laboratories, and legal metrology, universities and different metrology fields. An important cornerstone to convey an understanding of measurement uncertainty is to provide training.
This article identifies the status and needs for training on measurement uncertainty in each of the above communities and among those who teach uncertainty. It is the first study to do so across many different disciplines, and it merges many different sources of information with a focus on Europe. As a result, awareness of the training needs of different communities is raised and teachers of uncertainty are supported in addressing their audiences’ needs as well as in improving their uncertainty-specific pedagogical and technology-related knowledge.
The three needs that are most commonly encountered in the communities requiring an understanding of measurement uncertainty, are 1) to address a general lack of training on measurement uncertainty, 2) to gain a better overview of existing training on measurement uncertainty in several communities, and 3) to deliver more training on specific technical topics, including the use of a Monte Carlo method for propagating probability distributions and treating multivariate measurands and measurement models. These needs will serve to guide future developments in uncertainty training and will, ultimately, contribute to increasing the understanding of uncertainty.</dcterms:abstract>
<dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/75029/1/Klauenberg_2-snnevzrg7n5t5.pdf"/>
<dc:language>eng</dc:language>
<dc:contributor>Pennecchi, Francesca</dc:contributor>
<dc:creator>Harris, Peter</dc:creator>
<dc:creator>Pennecchi, Francesca</dc:creator>
</rdf:Description>
</rdf:RDF>