Geospatial Librarian

Issue Capitalization and pluralization of ingested keywords vary.  Our keyword list for in GeoBlacklight is somewhat messy and contains near duplicates. Challenge Our instance of Solr for GeoBlacklight indexes Dog, dog, dogs as separate keywords. Solution Use OpenRefine to normalize keywords before importing to Solr. Description As we aggregate metadata records from multiple sources, we found that the keywords need attention. The GIS records have keyword groups that may or may not come from a thesaurus, but frequently are coming from the TAGS field in ArcGIS Open Data Portals.  As a result, the keywords are frequently just regional acronyms or abbreviations and often have many spelling variants. We also anticipate combining our metadata records with those made at other institutions outside of the Big Ten Academic Alliance Geospatial Data Project.  After reviewing records from other universities and consulting the RDA rules on capitalization , we decided to...

We are planning to perform some keyword remediation on the Big Ten Academic Alliance Geoportal records starting in 2017.  This process includes normalizing values by spelling, capitalization, and pluralization. ISSUE: Duplicate keywords in metadata SOLUTION: Use a GREL expression in OpenRefine One of the challenges our project has encountered is duplicated keywords.  Thanks to the code provided on the  Free Your Metadata site , fixing this is an easy process with OpenRefine.  Here are the steps: 1. Create a Project in OpenRefine from a csv file 2. The keywords should be combined in one cell per row.  Take note of the separator character (usually a comma, but our csv files often use triple hash marks- ###) 3. Click the dropdown arrow next to the column name and select Edit Cells-Transform 4. Enter this Grel expression:  value.split(", ").uniques().join(", "). Note: The character between the quotes in the expression needs to match the...