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February 24 LD4L Workshop breakout session: Usage Data

facilitator: Paul Deschner

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
  • No labels