Libraries keep a lot of data. As David Weinberger, a senior researcher at the Berkman Center for Internet and Society at Harvard Law School, notes, libraries should leverage this data to create search applications that include context to evaluate sources.
But privacy concerns and the lack of interoperability of the data makes leveraging the data across libraries a challenge.
By leveraging the data, "[l]ibrary search engines [can] be tuned to what ... is relevant to the community. Researchers could explore usage patterns over time and across disciplines, schools, geographies, and economies. Libraries could be guided in their acquisitions by what they’ve learned from the behavior of communities around the corner and around the globe."
As Weinberger notes, "[t]here are many types of relevant data: Check-ins. Usage broken down by class of patron (faculty? grad student? undergrad?). Renewals. Number of copies in the collection. Whether an item has been put on reserve for a course. Inclusion in a librarian-created guide. Ratings by users on the library’s website. Early call-backs from loans. Citations. Being listed on a syllabus. Being added to a user-created list."
To this end, Weinberger promotes the use of a stackscore to bring all of the data together. "Any library that would like to make its usage data public is encouraged to create a 'stackscore' for each item in its collection. A stackscore is a number from 1 to 100 that represents how relevant an item is to the library’s patrons as measured by how they’ve used it." Each library would be responsible for creating a methodology for computing a stackscore, and "[i]n the interest of transparency, libraries should publish their formulae, but they are not beholden to any other library’s idea of relevance."
And the Harvard Innovation Lab is a great example of leveraging library data to browse Harvard Library's 13 million items. HIL uses a system called StackLife, which "always shows items in a context of other items [displayed] as spines on a shelf. [HIL] use stackscore to 'heat map' each work: the deeper the blue, the higher the stackscore. And [they] generally sort the shelves in stackscore order." When a user is browsing "an unfamiliar area, being guided by a metric of community relevance often turns out to be extraordinarily helpful."