Reconciling Legacy Archival Metadata

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Presentation transcript:

Reconciling Legacy Archival Metadata Greer Martin Paul V. Galvin Library, Illinois Institute of Technology Mashcat 2017

The Project Migrate 5,000 records from Re:Discovery Proficio to ArchivesSpace Over 1,700 are donor/creator records (Agents) Complete in 1 year Staff of 1 librarian + 1 volunteer (Alison) The catch: No authorities were used! And names are not linked

The Plan Batch export names (100)

LC Authorities reconciliation The Plan Batch export names (100) LC Authorities reconciliation

LC Authorities reconciliation The Plan Batch export names (100) LC Authorities reconciliation Add local names

LC Authorities Reconciliation Matches a spreadsheet of names against id.loc.gov 23 matches, 22 suggested matches: 45% Avg. 1 hour per 100 records Suggested matches link out to the LC record Original metadata intact, serves as check for Alison and for me Identifiers can be pulled in too

Matches Suggested matches Unmatched Post- Reconciliation

Adding Authority IDs [screenshot of adding the GREL thing, and added column of URIs] *Maybe two slides*

LC Authorities reconciliation The Plan: local names Batch export names (100) LC Authorities reconciliation Add local names

Local Names Authority [screenshot of local name authority]

CSV Reconciliation reconcile-csv matches one dataset against another Found at: http://okfnlabs.org/reconcile-csv/ Need CSV file + java + OpenRefine

LC Authorities reconciliation The Plan Batch export names (100) LC Authorities reconciliation Add local names LC-reconciled names Local name authority

LC Authorities reconciliation The Plan Batch export names (100) LC Authorities reconciliation Add local names LC-reconciled names Local name authority Master CSV of all names

LC Authorities reconciliation The Plan Batch export names (100) LC Authorities reconciliation Add local names LC-reconciled names Local name authority Master CSV of all names

CSV Reconciliation OpenRefine’s reconcile-csv matches one dataset against another 56 matches, 44 suggested matches: 100% 15 minutes per 100 records Can also pull in IDs (or any other column) from spreadsheet

Subject Heading Reconciliation Did I use reconcile-csv for this too?

Conclusion Took the most time = Evaluating LOC matches + creating local names Took the least time = CSV reconciliation Next time: concentrate volunteer time on authorities reconciliation, not CSV matching.