6. Data Information Literacy Symposium Purdue University, West Lafayette, IN, September 22-24, 2013

7. Data Information Literacy Project DIL Definition DIL Competencies Implementation Strategies Challenges Symposium Highlights

8. IMLS (Institute of Museum and Library Services) funded Purdue (Project lead with four members) Cornell, University of Minnesota, University of Oregon (two members each) Five initial projects based on ongoing relationships with faculty Conducted interviews with faculty and others about their data management issues Trained graduate students and other project members to work on concerns brought up during interviews. Focused on researchers as “data producers” rather than “data consumers.” Data Information Literacy Projects

9. Use courtesy of Jake Carlson, Associate Professor of Library Science and Data Services Specialist with the Purdue University Libraries.

10. “Data information literacy, then, merges the concepts of researcher-as-producer and researcher-as-consumer of data products. As such it builds upon and reintegrates data, statistical, information and science data literacy into an emerging skill set.” Carlson, Jake R.; Fosmire, Michael; Miller, Chris; and Sapp Nelson, Megan, “Determining Data Information Literacy Needs: A Study of Students and Research Faculty.” (2011). Libraries Faculty and Staff Scholarship and Research. Paper 23. http://docs.lib.purdue.edu/lib_fsdocs/23 http://docs.lib.purdue.edu/lib_fsdocs/23 (Quote & source courtesy of Hinchliffe, L., Hogenboom, K., Wiley, C., and Williams, S. “Data Information Literacy,” September 2013, University of Illinois Libraries) DIL Definition

11. Faculty Processing and Analysis Visualization and Representation Quality and Documentation Metadata and Description Ethics and Attribution Curation and Re-use Databases and Formats Conversion and Interoperability Management and Organization Cultures of Practice Preservation Discovery and Acquisition DIL Competencies in Order of Importance Students Management and Organization Processing and Analysis Visualization and Representation Ethics and Attribution Conversion and Interoperability Quality and Documentation Discovery and Acquisition Curation and Re-use Metadata and Description Cultures of Practice Preservation Databases and Formats

12. Highest ranking of importance by faculty Students’ use of process and analysis tools in the lab and within their own discipline Workshops and classes may help students learn to use these tools more efficiently as most learn on their own Tool examples: o R, SPSS, SAS, Excel, GIS, Data loggers, plus coding languages such as Python, C++, and writing on paper Data Processing and Analysis

13. Students learn to use visualization tools for their discipline by: Avoiding unclear or erroneous representations when using the following tools to present their data: o tables o charts o diagrams, etc. Selecting the right visualization tool - such as maps, graphs, animations, or videos based on their understanding of the reasons behind the visualization or display of the data Visualization and Representation

14. Students’ ability to: Document steps that led to producing the data in addition to the end result or conclusion Do quality control of the data, and recognize when data has been corrupted and/or is incomplete Use metadata systematically for consistent quality control Provide adequate documentation so research results can be reproduced if needed Keep track of versions of the data produced Quality and Documentation

15. Embedded Data Services Consultant Training Sessions o Course-integration o Stand-alone: workshop, workshop series, course o Lab meetings o Online modules Partnerships o Subject liaison/Data specialist o Data specialist/Information literacy librarian o Subject liaison/Data specialist/Information literacy librarian (Content courtesy of Hinchliffe, L., Hogenboom, K., Wiley, C., and Williams, S. “Data Information Literacy,” September 2013, University of Illinois Libraries) Implementation Strategies

16. Courtesy of Carolyn Mills, Life Sciences Liaison and eScience Team Leader, University of Connecticut Library.

17. Connecting with faculty Developing data librarian skills Scalability (Content courtesy of Hinchliffe, L., Hogenboom, K., Wiley, C., and Williams, S. “Data Information Literacy,” September 2013, University of Illinois Libraries) Challenges

18. Who have you worked with in the past? o Build on past instruction sessions Address the local (often internal) needs first o Do environmental scans and literature reviews o Does the discipline have standards, repositories, in place already? o Do your background research first! Do an interview to gather information about research project o Listen and look for “gaps” in data management o Take a digital voice recorder or a scribe so you can concentrate on communicating well o Don’t excessively push library agenda o Open access is a key to some, but not all Approaching Faculty

19. Connecting with Faculty Faculty Values and Benefits Sharing data in the lab/group Sharing data externally Increasing citing of their work More collaboration opportunities Accelerating research and discovery

20. Connecting with Faculty Faculty Concerns Making it easier to locate and reuse your own and your team’s data Reducing risk of data loss, errors and mismanagement Meeting funding mandates Future verification of data Lack of time to teach students about data management Lack of awareness about the landscape of tools, repositories, etc. for data management - assistance is appreciated!

21. Required Qualifications (Data-related) Experience working with data sets and knowledge of research practices related to data. Experience with using statistical software (SPSS, Stata, SAS). Demonstrated subject knowledge and experience in social sciences. Familiar with data management requirements of Federal agencies, and of national and international trends in data management. Job Example: Data Research Librarian Preferred Qualifications Experience writing, obtaining, and managing grants. Experience developing data management plans. Second advanced degree, preferably in a data- oriented social science field. Experience with text mining or analysis. Experience with institutional or subject repository systems. Experience with Geographic Information Systems (GIS). (Job description from Florida State University Libraries --as of 11/13/2013)

22. Courtesy of Jon Jeffryes, Engineering Librarian, University of Minnesota

23. Build on Existing Relationships with Faculty FYI - Official Faculty-Staff Online Newsletter http://www.scu.edu/fyi/ Sponsored Projects Office http://www.scu.edu/sponsoredprojects/index.cf m Research and Faculty Affairs Office http://www.scu.edu/provost/research/ Sources for Starting a DIL Project at SCU

24. For additional information Research Guide http://libguides.scu.edu/datainfolit Slide presentation in Scholar Commons https://scholarcommons.scu.edu Questions? Photo taken by Kore Chan http://kcphoto.photoshelter.com/