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| Titel |
QualityML: a dictionary for quality metadata encoding |
| VerfasserIn |
Miquel Ninyerola, Eva Sevillano, Ivette Serral, Xavier Pons, Alaitz Zabala, Lucy Bastin, Joan Masó |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250095013
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| Publikation (Nr.) |
 EGU/EGU2014-10452.pdf |
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| Zusammenfassung |
| The scenario of rapidly growing geodata catalogues requires tools focused on facilitate users
the choice of products. Having quality fields populated in metadata allow the users to rank
and then select the best fit-for-purpose products. In this direction, we have developed the
QualityML (http://qualityml.geoviqua.org), a dictionary that contains hierarchically
structured concepts to precisely define and relate quality levels: from quality classes to
quality measurements. Generically, a quality element is the path that goes from the higher
level (quality class) to the lowest levels (statistics or quality metrics). This path is used to
encode quality of datasets in the corresponding metadata schemas. The benefits of having
encoded quality, in the case of data producers, are related with improvements in their product
discovery and better transmission of their characteristics. In the case of data users,
particularly decision-makers, they would find quality and uncertainty measures to take the
best decisions as well as perform dataset intercomparison. Also it allows other components
(such as visualization, discovery, or comparison tools) to be quality-aware and
interoperable.
On one hand, the QualityML is a profile of the ISO geospatial metadata standards
providing a set of rules for precisely documenting quality indicator parameters that is
structured in 6 levels. On the other hand, QualityML includes semantics and vocabularies
for the quality concepts. Whenever possible, if uses statistic expressions from the
UncertML dictionary (http://www.uncertml.org) encoding. However it also extends
UncertML to provide list of alternative metrics that are commonly used to quantify
quality.
A specific example, based on a temperature dataset, is shown below. The annual mean
temperature map has been validated with independent in-situ measurements to obtain a global
error of 0.5 °C.
Level 0: Quality class (e.g., Thematic accuracy)
Level 1: Quality indicator (e.g., Quantitative attribute correctness)
Level 2: Measurement field (e.g., DifferentialErrors1D)
Level 3: Statistic or Metric (e.g., Half-lengthConfidenceInterval)
Level 4: Units (e.g. Celsius degrees)
Level 5: Value (e.g.0.5)
Level 6: Specifications. Additional information on how the measurement took
place, citation of the reference data, the traceability of the process and a publication
describing the validation process encoded using new 19157 elements or the GeoViQua
(http://www.geoviqua.org) Quality Model (PQM-UQM) extensions to the ISO models.
Finally, keep in mind, that QualityML is not just suitable for encoding dataset level but
also considers pixel and object level uncertainties. This is done by link the metadata quality
descriptions with layers representing not just the data but the uncertainty values associated
with each geospatial element. |
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