An Assessment of Metadata Quality: A Case Study of the National Science Digital Library Metadata Repository

Bui, Yen; Park, Jung‐ran

doi:10.29173/cais166

Cited by 16 publications

(17 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used metadata from the NCBI BioSamples database to analyze the performance of the system for the 6 selected fields versus all the fields in the template (26 fields). 3 The results show that adding more fields to the evaluation considerable increased the number of rules generated and the time needed to generate the rules. The difference in the accuracy of the suggestions and in the time needed to generate them was minimal.…”

Section: Step 7: Analysis Of Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

et al. 2019

View full text Add to dashboard Cite

Metadata—the machine-readable descriptions of the data—are increasingly seen as crucial for describing the vast array of biomedical datasets that are currently being deposited in public repositories. While most public repositories have firm requirements that metadata must accompany submitted datasets, the quality of those metadata is generally very poor. A key problem is that the typical metadata acquisition process is onerous and time consuming, with little interactive guidance or assistance provided to users. Secondary problems include the lack of validation and sparse use of standardized terms or ontologies when authoring metadata. There is a pressing need for improvements to the metadata acquisition process that will help users to enter metadata quickly and accurately. In this paper, we outline a recommendation system for metadata that aims to address this challenge. Our approach uses association rule mining to uncover hidden associations among metadata values and to represent them in the form of association rules. These rules are then used to present users with real-time recommendations when authoring metadata. The novelties of our method are that it is able to combine analyses of metadata from multiple repositories when generating recommendations and can enhance those recommendations by aligning them with ontology terms. We implemented our approach as a service integrated into the CEDAR Workbench metadata authoring platform, and evaluated it using metadata from two public biomedical repositories: US-based National Center for Biotechnology Information BioSample and European Bioinformatics Institute BioSamples. The results show that our approach is able to use analyses of previously entered metadata coupled with ontology-based mappings to present users with accurate recommendations when authoring metadata.

show abstract

Section: Step 7: Analysis Of Resultsmentioning

confidence: 99%

“…Despite the strong focus on requiring rich metadata for dataset submissions, the quality of the submitted metadata tends to be extremely poor (3,4). A significant problem is that creating wellspecified metadata takes time and effort, and scientists view metadata authoring as a burden that does not benefit them (5).…”

Section: Introductionmentioning

confidence: 99%

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Such protocols enable researchers to aggregate large quantities of records programmatically, without curatorial controls. Bui and Park (2006) harvested over one million records from the National Science Digital Library and used spreadsheets to tally the elements that were most commonly utilized by contributors. Kapidakis (2012) performed a similar analysis on millions of metadata records in Europeana by collection but also counted controlled vocabulary usage and the length of descriptive fields.…”

Section: Metadata Quality Control For Consortial Metadata Collectionsmentioning

confidence: 99%

Consortial Geospatial Data Collection: Toward Standards and Processes for Shared GeoBlacklight Metadata

Battista

Majewicz

Balogh

et al. 2017

Journal of Library Metadata

View full text Add to dashboard Cite

Consortial geospatial data communities, such as the OpenGeo-Portal federation and the GeoBlacklight initiative, facilitate contextualized discovery and promote metadata sharing to disperse hosting and preservation responsibilities across institutions. However, the challenges of communal metadata are manifold; they include proliferating standards, varying levels of completeness, mutable technology infrastructures, and uneven availability of human labor. Drawing from literature on metadata quality control, we outline a procedure for "scoring" GeoBlacklight records to establish a Domain Specific Language for metadata best practices. We propose strategies for authorship and management conducive to functionally interoperable geospatial metadata, that is versioned and enhanceable by the collective.

show abstract

“…In a study to assess the quality of metadata records in the National Science Digital Library (NSDL) repository, Bui and Park (2006) harvested over 1 million Dublin Core metadata records and assessed metadata quality in terms of metadata uses in frequency, consistency, completeness, and accuracy. Completeness is given a percentage measure based on the presence or absence of values in the 15 elements of the simple Dublin Core standard (Dublin Core Metadata Element Set).…”

Section: Related Workmentioning

confidence: 99%

“…As a consequence, such variations are not reflected to the measure of completeness of the field's record, yielding degraded results. The need to consider the multiple instances of multivalued fields is recognized in Bui and Park (2006), where the instances of the Subject field are recorded separately, since the authors recognize the need to analyze the richness of the various subject areas in a repository.…”

Section: Motivationmentioning

confidence: 99%

Quantifying and measuring metadata completeness

Margaritopoulos

Mavridis

et al. 2011

Acta Anaesthesiol Scand

View full text Add to dashboard Cite

Completeness of metadata is one of the most essential characteristics of their quality. An incomplete metadata record is a record of degraded quality. Existing approaches to measure metadata completeness limit their scope in counting the existence of values in fields, regardless of the metadata hierarchy as defined in international standards. Such a traditional approach overlooks several issues that need to be taken into account. This paper presents a fine‐grained metrics system for measuring metadata completeness, based on field completeness. A metadata field is considered to be a container of multiple pieces of information. In this regard, the proposed system is capable of following the hierarchy of metadata as it is set by the metadata schema and admeasuring the effect of multiple values of multivalued fields. An application of the proposed metrics system, after being configured according to specific user requirements, to measure completeness of a real‐world set of metadata is demonstrated. The results prove its ability to assess the sufficiency of metadata to describe a resource and provide targeted measures of completeness throughout the metadata hierarchy.

show abstract

An Assessment of Metadata Quality: A Case Study of the National Science Digital Library Metadata Repository

Cited by 16 publications

References 4 publications

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases

Consortial Geospatial Data Collection: Toward Standards and Processes for Shared GeoBlacklight Metadata

Quantifying and measuring metadata completeness

Contact Info

Product

Resources

About