2016
DOI: 10.1007/978-3-319-49157-8_28
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The Metadata Ecosystem of DataID

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Cited by 7 publications
(4 citation statements)
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“…Besides, it provides a common language for connecting and aggregating data coming from diverse platforms. This was also the main reason why Germany decided to use a DCAT-based Application Profile as their national open data standard, contrary to initial thoughts to use CKAN [31].…”
Section: Other Vocabularies and Metadata Standardsmentioning
confidence: 99%
“…Besides, it provides a common language for connecting and aggregating data coming from diverse platforms. This was also the main reason why Germany decided to use a DCAT-based Application Profile as their national open data standard, contrary to initial thoughts to use CKAN [31].…”
Section: Other Vocabularies and Metadata Standardsmentioning
confidence: 99%
“…Similarly dataset contents can be extracted to an ontology for machine understanding of the data . Dataset metadata, including provenance and data quality, will are often represented in a uniform way using standards and well known vocabularies such as DataID [19], Prov-O [20] and DQV [21]. Typically a combination of knowledge extraction from structured sources and information extraction techniques for natural language must be used.…”
Section: Knowledge Interoperability and Managementmentioning
confidence: 99%
“…The W3C data quality vocabulary (DQV) standard [21] will be used to describe datasets quality, whilst data value vocabulary (DaVE) [25] will act as basis for describing the data value metrics and dimensions. The DataID [19] will be used as a metadata specification to describe data assets.…”
Section: A Metamodel For Ai Analytics Ensemblesmentioning
confidence: 99%
“…Existing research for these standard vocabularies has provided best practice for publishing data sets' metadata as linked open data (Brummer et al, 2014). The machine readable nature of metadata can make it easy for an automated system to verify the correctness of the data, or perform other operations such as checking of data formats, completeness of metadata and the provenance of data used (Freudenberg et al, 2016). This approach is amenable to extension with domain-specific experimental metadata, such as the machine learning metadata proposed in the MEX vocabulary (Esteves et al, 2015).…”
Section: Open Data For Open Sciencementioning
confidence: 99%