Objective - To determine which metadata elements best facilitate discovery of digital collections. Design - Case study. Setting - A public research university serving over 32,000 graduate and undergraduate students in the Southwestern United States of America. Subjects - A sample of 22,559 keyword searches leading to the institution's digital repository between August 1, 2013, and July 31, 2014. Methods - The author used Google Analytics to analyze 73,341 visits to the institution's digital repository. He determined that 22,559 of these visits were due to keyword searches. Using Random Integer Generator, the author identified a random sample of 378 keyword searches. The author then matched the keywords with the Dublin Core and VRA Core metadata elements on the landing page in the digital repository to determine which metadata field had drawn the keyword searcher to that particular page. Many of these keywords matched to more than one metadata field, so the author also analyzed the metadata elements that generated unique keyword hits and those fields that were frequently matched together. Main Results - Title was the most matched metadata field with 279 matched keywords from searches. Description and Subject were also significant fields with 208 and 79 matches respectively. Slightly more than half of the results, 195 keywords, matched the institutional repository in one field only. Both Title and Description had significant match rates both independently and in conjunction with other elements, but Subject keywords were the sole match in only three of the sampled cases. Conclusion - The Dublin Core elements of Title, Description, and Subject were the most frequently matched fields in keyword searches. Academic librarians should focus on these elements when creating records in digital repositories to optimize traffic to their site from search engines.
All Science Journal Classification (ASJC) codes
- Library and Information Sciences