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Assessing Metadata Quality Across Helmholtz Data Providers: A Practical Approach for Harmonization

Haghiri, Hamideh; Brendike-Mannix, Oonagh; Casas, Santiago; Lamparter, Lucas Philip; Martens, Fabia ORCID iD icon 1
1 Institut für Automation und angewandte Informatik (IAI), Karlsruher Institut für Technologie (KIT)

Abstract:

This poster presents results from the TF Harmony kick-off workshop and an accompanying large-scale metadata gap analysis conducted within the Helmholtz Metadata Collaboration (HMC). The work explores how publication metadata is currently structured across Helmholtz infrastructures and identifies both technical patterns and underlying contextual factors that influence metadata quality.

 

The quantitative analysis is based on approximately 3 million metadata records harvested via OAI-PMH and focuses on three priority fields central to interoperability: identifier (dc:identifier), publication date (dc:date), and resource type (dc:type). Across these fields, simple and reproducible assessment criteria were applied to evaluate presence, representation, and standardization. The results show that while field availability is generally high, important inconsistencies persist, particularly in the use of persistent identifiers, controlled vocabularies, and standardized date formats.

 

To better understand the reasons behind these patterns, a community workshop with representatives from 17 Helmholtz centres complemented the analysis. ... mehr


Volltext §
DOI: 10.5445/IR/1000192993
Veröffentlicht am 06.05.2026
Originalveröffentlichung
DOI: 10.5281/zenodo.20024630
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Poster
Publikationsmonat/-jahr 05.2026
Sprache Englisch
Identifikator KITopen-ID: 1000192993
HGF-Programm 46.21.05 (POF IV, LK 01) HMC
Veranstaltung Helmholtz Metadaten Collaboration : Metadata in Action (HMC 2026), Heidelberg, Deutschland, 28.04.2026 – 30.04.2026
Schlagwörter Metadata, Information Storage and Retrieval, Data Collection/statistics & numerical data, Databases as Topic, Data Accuracy, Semantics
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