The October 2015 version of GreenGlass, described in the previous post, incorporates robust support for shared print projects. More than 70% of the libraries OCLC/SCS works with are part of shared print monographs projects, in groups such as the Eastern Academic Scholars’ Trust (EAST), the Michigan Shared Print Initiative (MI-SPI), the Washington Research Library Consortium (WRLC), Virtual Library of Virginia (VIVA) and others. For that reason, group functionality has become a priority for GreenGlass. Its group functionality includes both visualisation and retention modelling.
Group Analysis: Visualisation
With GreenGlass group functionality, groups of any size can gain a deeper understanding of their shared collection at both full-group and sub-group levels. Participating libraries can toggle back and forth between the individual library view and the group view, to better understand both the shared collection and their own library’s contributions. Users can visualise the group or sub-group collection with respect to geography, size, overlap, age, subject distribution, usage and other criteria. The tool employs a variety of contemporary visualisation techniques, including stacked bar charts, tree maps, heat maps, and scatterplots, and uses colour and size to further clarify distinctions.
Some examples of those visualisations follow, highlighting a number of the many pathways that GreenGlass gives into the shared collection. More examples can be seen in this short video.
Group Analysis: Modelling Retention Scenarios
In working on shared print projects over the past four years, it has become clear that an interactive—and iterative—approach to modelling group retention scenarios would be very useful. For that reason, we’ve introduced a shared print Model Builder into the GreenGlass group functionality.
The Model Builder supports broader collaboration in developing retention criteria, allows real-time adjustments and iterations, and works for both the full group and user-created sub-groups. It makes the implications and outcomes of any retention scenario immediately visible, both at the group level and across participating libraries. Models are easy to save and compare. This allows group-wide conversations on retention policies to be informed and efficient, and for participants to fully understand all options and dependencies. It also means that policy decisions can be made with confidence.
The simple example below posits a group of nine libraries that seek to retain two holdings of any title currently held in the group for which holdings are relatively light elsewhere. Their initial definition of “lightly-held” is titles for which there are fewer than 50 holdings in the US and fewer than five holdings in their state. These lightly-held titles represent 28 percent of the overall group collection, but that percentage—as well as the corresponding retention commitments—varies by library. The Model Builder shows an estimate of the impact on the group as a whole and on each participating library. This very simple scenario can be refined by incorporating holdings among designated comparator libraries, aggregate uses, publication or acquisition date, HathiTrust status and other factors. Models can be built and iterated in real time.
Executive Director, Sustainable Collection Services/OCLC
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