Presented here are example assessment questions that can be answered with some of the data elements recommended in the previous sections. For some of the more complex scenarios, guidance on how to analyze the data is also provided.
General collections management
What collections do we have and how are they described?
Data needed:
- List of collections (titles, identifiers, etc)
- Description status
What percentage of our collections need to be rehoused?
Data needed:
- Total number of collections
- Physical condition
Onsite storage space is at a premium; what collections are currently stored onsite that could be candidates for moving off site, and likewise, what collections should be stored onsite that aren’t currently?
Data needed:
- List of collections (titles, identifiers, etc)
- Frequency of use
- Physical condition
- (optional) Research value
Processing Planning
What collections should be our next processing priorities?
Data needed for all collections:
- List of collections (titles and/or identifiers)
- Processing status
- Description status
- Extent
- Research value
- Subject matter
- Physical condition
- Number of use requests (for unprocessed but discoverable collections)
How to analyze: weigh any or all of the above factors depending on local needs and available staffing.
How long will it take to process this new collection/our backlog/this set of collections for this grant?
Data needed for selected collections:
- Processing status
- Description status
- Extent
- Anticipated processing level
- Collection complexity
- Average processing rate (time spent and extent for past processing projects, ideally with similar complexity and processing levels as the selected collections)
Processing practices
What percentage of our new collections (yearly accessions) are made available for research immediately upon accession?
Data needed:
- Number of collections accessioned each year
- Processing status
- Access status
Are we are acquiring collections at a greater rate than we are processing them? (The answer may inform local processing practices as well as collection development plans and appraisal practices).
Data needed:
- List of acquisitions (Collection title, accession number, etc)
- Date of acquisition
- Extent
- List of processed collections (titles and/or identifiers)
- Date processing completed
- Extent
How to analyze: Compare the total volume of collections acquired in a given year to the total volume of processed collections.
We’re experimenting with opening collections with minimal (collection-level only) description created at the point of accession. How does this approach impact discoverability and use?
Data needed for each collection:
- Level of processing
- Date processing completed
- Research value
- Number of requests/uses
How to analyze: Compare the number of uses of fully processed collections to minimally described collections with similar research value within a given year.
Is team processing more efficient than archival processing?
Data needed for completed processing projects:
- Collection extent
- Collection complexity level
- Processing level
- Staffing configuration
- Total hours spent processing
How to analyze: Compare the average processing rate (hours/cubic foot) for collections processed by an individual vs. by a team, ideally comparing collections of similar complexity and processing level.
Access & discovery
We have some collections with extensive paper finding aids. We’re interested in scanning these to create PDFs and link to them from their respective MARC records. How much time will we save by not encoding them?
Data needed for previously processed collections:
- Extent
- Average time spent encoding finding aids
How to analyze: Calculate an approximate encoding rate (# hours per cubic foot, for example) and apply it to the extent of the collections for which scanned paper finding aids will be made available.
What collections are high priorities for digitization?
Data needed:
- Number of requests/uses (onsite and offsite)
-
Number of reproduction requests