Solution
Collection Registration & Quality Control
Improve the quality, consistency and reliability of collection data. With smart support for registration and quality control, you can identify gaps, inconsistencies and duplicates more effectively and strengthen the basis for management, research and access.
Reliable collection data is essential for collection management, research and public access. In practice, registrations are often built up over many years by different people, systems and methods. This can lead to inconsistencies, incomplete fields, duplicate records or terminology that is used in different ways. With Collection Registration & Quality Control, we help you improve the quality of collection data systematically and at scale, while keeping human expertise in control.
We help you analyse collection registrations, identify inconsistencies and support targeted quality control. This may involve validating provenance information, normalising field values, detecting duplicate object records, preparing migration or strengthening metadata quality.
What it delivers
More reliable collection data
Make registrations more complete, consistent and trustworthy.
More control over collection management
Work with data that better supports registration, management and internal workflows.
Faster quality control
Identify deviations, gaps and duplicates more precisely and efficiently.
A stronger basis for research and access
Make collection data more usable for provenance research, digital presentation and public use.
Better preparation for migration and exchange
Ensure cleaner and better-structured data when systems change or data is linked to other sources.
Control and quality
AI does the preparatory work. Results remain reviewable and are configured around your sources, fields, terminology and quality criteria. Where relevant, the source or passage remains traceable. Your organisation decides what is adjusted, approved and ultimately used.
Applications in this area
Explore concrete applications and choose the task that best fits your sources, data or knowledge question.
Application
Validation
Identify missing, inconsistent or incomplete provenance information and mark it for human review. This improves the quality and reliability of provenance data in your collection.
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Normalisation
Clean up inconsistent field values and bring more unity to your collection data. Registrations become more consistent, easier to search and more useful for management, research and access.
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Deduplication
Detect duplicate object registrations and bring fragmented information together. This improves the quality, coherence and reliability of your collection data.
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Migration
Support data migration by aligning fields and structures between legacy collection systems and new Collection Management Systems. The transition becomes more careful, consistent and controlled.
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