Indiana Memory Dublin Core Metadata Guide (2021)
DPLA Metadata Quality Guidelines
Professional Development Resources for Technical Services LibGuide Metadata Page
This list of resources is part of a LibGuide created by Janice Gustaferro at Butler University and Andrea Morrison at Indiana University in 2019 for the Indiana Library Federation annual conference and updated in 2022 for the Ohio Valley Group of Technical Services Librarians annual conference. The guide also includes resources related to digitization, copyright, digital preservation, controlled vocabularies, and other related topics.
University of Louisville Hyku Cookbook by Rachel Howard
Please note that while this guide has helpful examples, it is geared towards the instance of Hyku used by the University of Louisville University Libraries and it has different worktypes and requirements.
Minnesota Digital Library Metadata Entry Guidelines
University of Illinois Metadata Best Practices for Digital Collections
Atla Digital Library Metadata Guidelines & Best Practices
University of Illinois Library Metadata Best Practices
Metadata guidelines for dissertations, theses, and other scholarship for IDEALS, the U of I institutional repository.
Bulk term replacement using Find and Replace or INDEX and MATCH with lookup tables
Normalize names and dates using CONCAT
Preserve legacy metadata field labels using CONCAT
Combine legacy metadata field values into a new field using TEXTJOIN
Filter large datasets by sorting multiple fields at the same time
Color-code progress tracking using conditional formatting
Practice version control by working in duplicate worksheets, e.g. create a new copy after each metadata field is remediated, so any mistakes can be reverted
Avoid metadata shifting by applying A-Z sorting and leveraging alphanumerical identifiers for related items (e.g. sequential pages of a compound object)
Keep a library of frequently used formulas for reference
Georgieva, Marina. “Metadata Remediation of Legacy Digital Collections: Efficient Large-Scale Metadata Clean-Up with a Sleek Workflow and a Handy Tool.” Against the Grain, vol. 33, 2021), pp. 1-4.
Fields used incorrectly/inconsistently (including using the same fields to describe different things)
Incorrect formats
Inconsistent controlled vocabulary
Misspellings
Differing use of punctuation
Trailing/Leading white spaces
Lack of/Improper delimited content in fields (e.g. “Bob Jill Stanley Steve” in separate fields vs. “Bob| Jill|Stanley|Steve” or "Bob | Jill | Stanley | Steve")
A controlled vocabulary is important to improve search in a system both locally and in aggregate (e.g. DPLA). It also allows for easier training, both with researchers and individuals working on the back-end of the system
Verify that new systems have easy-to-use and working export options and accepted metadata standards
Spend time creating a detailed project design
Recognize that there is continued maintenance required even after the completed migration process
Ensure that there isn’t a set-in-stone contract end date for the old system, as this pushes the process to quicken and leads to lost information or materials
Take time to reflect on the overall organization and structure of the new system, its collections, and how users could use and search it more easily
Dealt with old metadata early at the beginning of the process rather than migrating it to a new system and then having to deal with it