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Data Quality: Thinking about Quality and DDI Metadata

Authors: Molloy, Laura; Gregory, Arofan; Kulla, Kaia; Danciu, Alina;

Data Quality: Thinking about Quality and DDI Metadata

Abstract

Data quality is a topic which is often discussed and attracts a lot of interest, but the way in which it is approached varies widely. This, our fourth webinar of the 2021 series took place on 18 November 2021 and examined different approaches in the use of metadata for describing data quality from the perspective of data producers in official statistics and in the scientific and research domains, and how DDI fits into this picture. Some data providers will attempt to assert their data quality as a function of certification for the services they provide, and the richness of their metadata. In the official statistics world, additional descriptive metadata is provided according to agreed quality frameworks. DDI supports both approaches, and the details of each are explored in this webinar. Presenters were: Arofan Gregory, Chair, CDI Working Group, DDI Alliance / CODATA Kaia Kulla, Statistics Estonia The session was introduced by Laura Molloy, CODATA; and the question and answer session was moderated by Alina Danciu, Sciences Po. Data quality is a topic which is often discussed and attracts a lot of interest, but the way in which it is approached varies widely. This, our fourth webinar of the 2021 series took place on 18 November and examined different approaches in the use of metadata for describing data quality from the perspective of data producers in official statistics and in the scientific and research domains, and how DDI fits into this picture. Some data providers will attempt to assert their data quality as a function of certification for the services they provide, and the richness of their metadata. In the official statistics world, additional descriptive metadata is provided according to agreed quality frameworks. DDI supports both approaches, and the details of each are explored in this webinar. Presenters were: Arofan Gregory, Chair, CDI Working Group, DDI Alliance / CODATA Kaia Kulla, Statistics Estonia The session was introduced by Laura Molloy, CODATA; and the question and answer session was moderated by Alina Danciu, Sciences Po. Data Privacy Statement for participants. About DDI DDI Alliance: https://ddialliance.org/ Current products: https://ddialliance.org/products/overview-of-current-products About DDI-CDI Introduction: https://ddi-alliance.atlassian.net/wiki/download/attachments/860815393/Part_1_DDI-CDI_Intro_PR_1.pdf Public review page: https://ddi-alliance.atlassian.net/wiki/x/IQBPMw Complete download package: https://ddi-alliance.bitbucket.io/DDI-CDI/DDI-CDI_Public_Review_1.zip Announcement at DDI Alliance website: https://ddialliance.org/announcement/public-review-ddi-cross-domain-integration-ddi-cdi

Keywords

DDI, metadata, data quality, [SHS] Humanities and Social Sciences, CODATA, research data

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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