✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Museums manage collection databases that span thousands of objects cataloged over decades by different curators, registrars, and volunteers. The same artwork or artifact can appear under different accession numbers when it was re-cataloged during a system migration, listed in both a departmental database and the institution-wide CMS, or entered by a volunteer who did not find the existing record. Artist names vary between 'Pablo Picasso', 'Picasso, Pablo', and 'P. Picasso' across records. dataclean.to identifies these overlapping entries by matching object descriptions, artist names, accession numbers, and provenance details.
The Problem
Duplicate collection records undermine a museum's ability to manage and present its holdings. A curator planning an exhibition may not realize the institution owns two works by the same artist because they are filed under different name spellings. Insurance valuations based on inflated object counts lead to overpayment on premiums. Provenance research becomes unreliable when the history of an object is split across two records with contradictory acquisition information. Public-facing collection search tools frustrate researchers when the same object appears multiple times with inconsistent metadata. Loan requests from other institutions fail when staff cannot locate the definitive record for a requested object. Collections Trust SPECTRUM collection management standard
How to Fix It
1
Export collection records
Pull records from your collection management system into CSV format. Include accession number, object title, artist/maker name, date created, medium, dimensions, department, location, and acquisition source.
2
Upload to dataclean.to
Upload the CSV. The tool compares object titles, artist names, dimensions, and media to identify collection items entered more than once under different accession numbers or catalog formats.
3
Review duplicate object clusters
Examine flagged groups. Typical duplicates include objects cataloged in both legacy and current systems, works by the same artist listed under different name formats, and items entered by different departments that overlap in scope.
4
Merge into authoritative catalog records
Consolidate confirmed duplicates into single records. Retain the primary accession number, the most complete provenance narrative, current conservation status, and all historical catalog references as cross-references.
5
Export the clean collection database
Download the deduplicated catalog for import into your collection management system. Clean records support accurate insurance valuations, reliable provenance research, and trustworthy public search tools.
Frequently Asked Questions
How does the tool handle artworks with generic titles like 'Untitled'?
Generic titles are common in museum collections. For objects titled 'Untitled', the tool relies on artist name, medium, dimensions, and date to determine whether two records describe the same work. Objects matching on these secondary fields are flagged for review.
Can it match objects cataloged in different classification systems?
Yes. Museums that migrated between systems often have objects classified differently in legacy and current records. The tool matches on physical attributes and artist information rather than classification codes, so objects are detected as duplicates regardless of which taxonomy was used.
What about sets or multi-part objects cataloged individually?
A set of six chairs might be cataloged as one group record or six individual records. The tool flags entries with similar titles and matching artist/medium fields for review, but does not auto-merge items where the title indicates distinct parts of a set.
Example: Input → Output
name
email
phone
city
status
Alice Johnson
alice@example.com
+1-555-0101
New York
active
alice johnson
ALICE@EXAMPLE.COM
5550101
new york
Active
Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.