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

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 publisher

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

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

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