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Library data services are often designed once a dedicated data librarian is hired. But what if a library is receiving data-related requests and questions, but there is no data librarian? You can't ignore these requests! Instead, you gather library staff from multiple service areas with different areas of knowledge and design introductory data-related workshops to support the needs of your campus. You also work with people outside of the library who specialize in a data-related field to help design workshops. These groups create learning outcomes, detailed lesson plans, dynamic presentations, creative activities, summary handouts, and online research guides. The materials allows for multiple library staff to teach the workshop and provide support for the topics afterwards. In addition, you will need a comprehensive marketing plan to connect with faculty and students on campus. Finally, reviews of the workshops' content and assessment of registrants, attendees, and learning outcomes are key to improving the workshops for the future. Attendees will learn how to collaborate and communicate with individuals within and outside the library to create data workshops relevant to the needs of a campus.

We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.