Usage data sources

• OCR-ed bibliographies and page rank

• ILL usage

• Yahoo circ logs

• Web analytics (e.g., DPLA UI analytics, esp. contextual granularity)

• Search terms as form of usage; also as compared to other usage data

• Entities extracted from queries, not simply literal queries themselves

• How often a link is traversed; how many times your link has been reconciled in triple store

• Browsed materials

• Citations; also citation networks as compared to other usage data

• Course-book lists across institutions

StackScore

• Makes data muddy

• Too many metrics mixed together; need to separate out the metrics

• Common metrics needed across institutions

• Computational transparency important: metrics and algorithms

Negative usage data at local institution

• Important to see what users are looking for but local institution doesn’t have

• What doesn’t circulate in-house but is available via ILL

• What isn’t read at Columbia but at Yale

Usage data runs risk of becoming prescriptive

• Blandness of collections when everyone acquires most popular items

Use cases

• Keeping tabs on popularity of colleagues’ publications

• Usage data as diagnostic tool for targeted collections: highly invested-in parts of collection not being used could drive arranging an exhibition to increase awareness

• Scholars doing research on other scholars research and publications

• Look at when items were used: what was checked out in last week, month, year, etc.

• Link traversals and other link metrics could be sent to link’s source

Long tail issue generally and at own institution

• Options: random selection out of tail for exposure, subject-filtered selection

• Important that UI expose long-tail possibilities prominently, above the page-fold

• Usage data from other institutions and ILL balances out local-institution’s biases

Privacy

• Opt-in option for users willing to share their usage data

• Huddersfield University (England): more liberal approach to data exposure, including access to clustering (users who borrowed this also borrowed that) and usage by academic course and school

• IP-based web stats inherently less risky than personal ID-based circulation data

• Anonymization tools important

• Clustering dangerous