Measuring completeness as metadata quality metric in Europeana

Király, Péter; Büchler, Marco

doi:10.1109/bigdata.2018.8622487

Cited by 23 publications

(16 citation statements)

References 6 publications

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“…Metadata completeness (Bruce and Hillmann, 2004; Király and Büchler, 2018; Margaritopoulos et al , 2012) reflects the need of supplementing all data items with the corresponding quality metadata. We define it as a degree of completeness with respect to the metadata connected to the data values.…”

Section: Data Qualitymentioning

confidence: 99%

Data quality for federated medical data lakes

Eder

Shekhovtsov

2021

IJWIS

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Purpose Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules. Design/methodology/approach Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research. Findings This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies. Originality/value This novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.

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Section: Data Qualitymentioning

confidence: 99%

Data quality for federated medical data lakes

Eder

Shekhovtsov

2021

IJWIS

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“…Additionally, specific aspects of metadata quality have been evaluated using computational metrics or operationalized within systems, such as the analysis of metadata quality over time in the UNT Libraries Digital Collections (Tarver et al , 2014) using a locally-calculated “completeness” score to review record changes. On a larger scale, Király and Büchler also evaluated completeness as a metric to study the quality in the Europeana metadata aggregation (Király and Büchler, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the use of metadata record graphs to analyze subject headings in the digital public library of America

Phillips

Tarver

2021

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Purpose This study furthers metadata quality research by providing complementary network-based metrics and insights to analyze metadata records and identify areas for improvement. Design/methodology/approach Metadata record graphs apply network analysis to metadata field values; this study evaluates the interconnectedness of subjects within each Hub aggregated into the Digital Public Library of America. It also reviews the effects of NACO normalization – simulating revision of values for consistency – and breaking up pre-coordinated subject headings – to simulate applying the Faceted Application of Subject Terminology to Library of Congress Subject Headings. Findings Network statistics complement count- or value-based metrics by providing context related to the number of records a user might actually find starting from one item and moving to others via shared subject values. Additionally, connectivity increases through the normalization of values to correct or adjust for formatting differences or by breaking pre-coordinated subject strings into separate topics. Research limitations/implications This analysis focuses on exact-string matches, which is the lowest-common denominator for searching, although many search engines and digital library indexes may use less stringent matching methods. In terms of practical implications for evaluating or improving subjects in metadata, the normalization components demonstrate where resources may be most effectively allocated for these activities (depending on a collection). Originality/value Although the individual components of this research are not particularly novel, network analysis has not generally been applied to metadata analysis. This research furthers previous studies related to metadata quality analysis of aggregations and digital collections in general.

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“…Besides the framework by Bruce and Hillmann, few other approaches have been proposed to automatically compute quality metrics. The ones that are more related to our work are the Framework for Information Quality Assessment by Stivlia [36], the Metadata Quality Framework by Ochoa and Duvall [28] and the Metdatata Quality Assurance Framework by Péter Király [18,19]. Other frameworks (e.g.…”

Section: Metadata Quality Frameworkmentioning

confidence: 99%

“…The third metadata quality framework we consider has been developed in collaboration with the Data Quality Committee (DQC) from the European Digital Library "Europeana" 4 by Péter Király [19]. The Metadata Quality Assurance Framework is an ongoing project tailored to measure the metadata quality of the Europeana digital library and based on Europeana Data Model (EDM) metadata profile.…”

Section: Metadata Quality Frameworkmentioning

confidence: 99%

Automatically evaluating the quality of textual descriptions in cultural heritage records

2021

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Metadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library “Cultura Italia” and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 $$\sim $$ ∼ 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance.

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Measuring completeness as metadata quality metric in Europeana

Cited by 23 publications

References 6 publications

Data quality for federated medical data lakes

Data quality for federated medical data lakes

Investigating the use of metadata record graphs to analyze subject headings in the digital public library of America

Automatically evaluating the quality of textual descriptions in cultural heritage records

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