2015-02-24 breakout: Usage Data

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

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

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

Blandness of collections when everyone acquires most popular items

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

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

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