See 2013 Library Trends, and More Trends in Libraries 2014, and Trends and new pursuits Fall 2014 on Kimberly Hoffman wiki
Class presentation – February, 10, 2015
Affelt, A. L. 2015. The accidental data scientist: big data applications and opportunities for librarians and information professionals.
Bo?rner, Katy, and David E. Polley. 2014. Visual insights: a practical guide to making sense of data.
Caldwell, Sally. 2013. Statistics unplugged. Australia: Wadsworth Cengage Learning.
Ray, Joyce M. 2014. Research data management: practical strategies for information professionals.
Tufte, Edward R. 2006. Beautiful evidence. Cheshire, Conn: Graphics Press.
Class presentation – September 2014
Bonn, Maria. 2014. Tooling up: Scholarly communication education and training. College & Research Libraries News 75 (3): 132-5.
Johnson, L., S. Adams Becker, V. Estrada, and A. Freeman. “NMC Horizon Report: 2014 Library Edition.” (2014). http://privacytools.seas.harvard.edu/files/privacytools/files/2014-nmc-horizon-report-library-en.pdf
Willinsky, John, and Juan Pablo Alperin. 2011. The academic ethics of open access to research and scholarship. Ethics and Education 6 (3): 217-23.
Davis-Kahl, Stephanie, and Merinda Kaye Hensley. 2013. Common ground at the nexus of information literacy and scholarly communication. Chicago: Association of College and Research Libraries, a division of the American Library Association.
Class presentation – February 2014
Recommended Reading – About Data
Research Data Management: Practical Strategies for Information Professionals [2014 9781557536648 ]
Follow up notes from class presentation:
JISC? Historically, JISC stood for Joint Information Systems Committee but over the last decade we have evolved and as a company we are now known as Jisc.
For more on HDF Hierarchical Data Format (Used with MatLab – for engineering, physics)
CDF vs. HDF5 CDF is a scientific data management software package and format based on a multidimensional (array) model. HDF is a Hierarchical Data Format developed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois. The data model of CDF is very similar to HDF5’s data model. They both have 2 basic objects: data and attribute. Data is an entity that represents data while attribute is a mechanism used to denote data. HDF5 allows to group similar objects into a group, but CDF doesn’t have a grouping mechanism.