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

Data Information Literacy Symposium

Purdue University, West Lafayette, IN, September 22-24,

2013

Data Information Literacy Project

DIL Definition

DIL Competencies

Implementation Strategies

Challenges

Symposium Highlights

IMLS (Institute of Museum and Library Services) fundedPurdue (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 theirdata 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

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

“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

(Quote & source courtesy of Hinchliffe, L., Hogenboom, K., Wiley, C., and Williams, S. “Data Information Literacy,” September 2013, University of Illinois Libraries)

DIL Definition

Faculty

Processing and AnalysisVisualization and

RepresentationQuality and DocumentationMetadata and DescriptionEthics and AttributionCuration and Re-useDatabases and FormatsConversion and

InteroperabilityManagement and

OrganizationCultures of PracticePreservationDiscovery and Acquisition

DIL Competencies in Order of Importance

Students

Management and Organization

Processing and AnalysisVisualization and

RepresentationEthics and AttributionConversion and

InteroperabilityQuality and DocumentationDiscovery and AcquisitionCuration and Re-useMetadata and DescriptionCultures of PracticePreservationDatabases and Formats

• 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

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 tableso chartso 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

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

• Embedded Data Services Consultant

• Training Sessionso Course-integrationo Stand-alone: workshop, workshop series, courseo Lab meetingso Online modules

• Partnershipso Subject liaison/Data specialisto Data specialist/Information literacy librariano 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

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

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)

(Content courtesy of Hinchliffe, L., Hogenboom, K., Wiley, C., and Williams, S. “Data Information Literacy,” September 2013, University of Illinois Libraries)

Challenges

• 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

Connecting with FacultyFaculty Values and Benefits

• Sharing data in the lab/group• Sharing data externally• Increasing citing of their work• More collaboration opportunities• Accelerating research and discovery

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!

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)

Courtesy of Jon Jeffryes, Engineering Librarian, University of Minnesota

Build on Existing Relationships with Faculty

FYI - Official Faculty-Staff Online Newsletterhttp://www.scu.edu/fyi/

Sponsored Projects Officehttp://www.scu.edu/sponsoredprojects/index.cfm

Research and Faculty Affairs Officehttp://www.scu.edu/provost/research/

Sources for Starting a DIL Project at SCU

For additional information

Research Guidehttp://libguides.scu.edu/datainfolit

Slide presentation in Scholar Commonshttps://scholarcommons.scu.edu

Questions?

Photo taken by Kore Chan http://kcphoto.photoshelter.com/