The document summarizes a webinar about the past, present, and future of the Data Information Literacy Project. The project aims to identify data literacy skills for different disciplines, build infrastructure for teaching those skills, and develop a toolkit for librarians. Case studies were conducted at 5 universities to determine data needs of students and faculty. Educational programs were developed and a symposium and toolkit are planned next. The project identifies 12 core data literacy competencies and aims to develop standards in this area.

1. The Data Information
Literacy Project
Supplemental Webinar
Thursday, February 6, 2014
1:00 – 2:30 p.m. EST

2. The Data Information Literacy Project:
Past, Present and Future
Jake Carlson
Associate Professor of Library Science
Purdue University
http://datainfolit.org

3. The Vision
“…science and engineering
digital data are routinely
deposited in well-documented
form, are regularly and easily
consulted and analyzed by
specialists and nonspecialists alike, are openly
accessible while suitably
protected, and are reliably
preserved…”
(NSF 2007)

4. The Challenge
“Small science researchers self report: no specific
person for data management/curation; data is likely
saved to hard drives in the lab and backed up on
CDs, usually by the students. While students have
received “research integrity” training (which focuses
on making data available upon request by funder,
publisher, or FOIA, etc.) it is not likely that anyone
could retrieve usable data easily or quickly.*”
(D. Scott Brandt, Provost Fellowship, 2009)

5. I: Is there a need for education in data
management or curation for graduate students…?
Fac: Absolutely, God yes…
I: So, what would that education program look
like… What kind of things would be taught?
Fac: Um, I don’t really know actually, just how to
do you manage data? Or you know, confidentiality
things, ethics, probably um…I’m just throwing
things out because I hadn’t really thought that out
very well.

6. The Data Information Literacy Project
Goals:
• Identify DIL skills appropriate to disciplinary
•
•
contexts,
Build infrastructure and capacity for teaching DIL
skills,
Develop a toolkit for librarians to articulate DIL
curricula in their research communities.

7. Background
Data Processing and Analysis
Data Curation and Re-Use
Data Management and
Organization
Data Conversion and
Interoperability
Data Preservation
Data Visualization and
Representation
Databases and Data Formats
Discovery and Acquisition
Ethics and Attribution
Metadata and Data Description
Data Quality and Documentation
Cultures of Practice
Carlson, J., Fosmire, M., Miller, C., & Nelson, M. S. (2011). Determining
data information literacy needs: A study of students and research faculty.
portal: Libraries and the Academy, 11, 629-657. doi:10.1353/pla.2011.0022

8. Project Structure
Research Faculty
Data
Librarian
Subject
Librarian
or
Information
Literacy
Librarian
Graduate Students
Post-doc; Research assistant

9. Five Case Studies
Cornell
Minnesota
Natural
Resources
Civil
Engineering
Sara Wright
(DL)
Lisa
Johnston
(DL)
Camille
Jon Jeffreys
Andrews (IL)
(SL)
Oregon
Purdue #1
Purdue #2
Ecology
Electrical &
Computer
Engineering
Agricultural
and
Biological
Engineering
Brian
Jake
Westra (DL) Carlson (DL)
Marianne
Stowell
Bracke (DL)
Dean
Megan Sapp
Walton (SL) Nelson (SL)
Micheal
Fosmire (IL)

10. Project Phases
Literature Review
Interviews
Develop Educational
Programs
Develop DIL
Toolkit
Implement Programs

11. Interview Results

12. Overall Findings
• Overall, the competencies were seen as important for
students to develop.
• Overall, students were seen as lacking in these
competencies.
• Assumption that students have or should have acquired
these competencies earlier.
• Lack of formal training for students in working with data.
• Learning is largely self-directed and through “trial and error.”

13. Overall Findings
• Education / training from advisor tends to occur at the point
of need and is framed in the context of the immediate
issue.
• Students tended to focus on data mechanics over deeper
concepts.
• Faculty were often unsure of best practices or how to
approach these competencies themselves.
• Lack of formal policies in the lab.

14. Background / Audience
Natural resources: long term studies
http://www.papabearoutdoors.com/about/troutfishing/
Robinson, J. M., Josephson, D. C., Weidel, B. C., & Kraft, C. E. (2010).
Influence of variable interannual summer water temperatures on brook trout
growth, consumption, reproduction, and mortality in an unstratified adirondack
lake. Transactions of the American Fisheries Society, 139(3), 685-699.

15. Educational Priorities / Needs
Acquiring the data
management and
organization skills
necessary to work with
databases and data
formats, document data,
and handle accurate data
entry is described as
essential, otherwise, “it’s
[as if] the data set doesn’t
exist.”
• Data management
• Data organization
• Data quality and
•
•
documentation
Data analysis and
visualization
Metadata

16. Response
Six session mini-course:
• Intro to Data Management
• Data Organization
• Data Analysis &
Visualization
• Data Sharing
• Data Quality &
Documentation
• Wrap-up
NTRES 6940 Special Topics Course:
Managing data to facilitate your research

17. Background / Audience
UNIVERSITY OF MINNESOTA – TWIN CITIES
Case Study: Structural Engineering
Lab
Data Types:
1) Real-time bridge sensor readings
2) Experimental structural-integrity tests
Data Management
Issues/Considerations:
• Ownership of data
• Sharing requirements
• Transfer to next student
• Quality concerns/ lack of
documentation

18. Educational Priorities / Needs
“The [data management] skills that they need are many, and they don’t
necessarily have it and they don’t necessarily acquire it in the time of the
project, especially if they’re a Master’s student, because they’re here for such
a short period of time.”
- Faculty Partner at UMN
Data Life Cycle
Educational Needs
Objective
Creation & Collection
Backup and Security
Understand how/where
to store data safely
Organization
Document changes,
shared file/directory
structure
Transition data to next
student in a welldocumented way
Access/Ownership
IP and Rights Issues
List stakeholders
Sharing
Why share data?
Recognize the reuse
value of data
Preservation
Maintaining Access
Consider preservationfriendly file formats

19. Response
(Open) Data Management Course: http://z.umn.edu/datamgmt
Seven Web-Based Modules
1.
2.
3.
4.
5.
6.
7.
Introduction to Data
Management
Data to be Managed
Organization and
Documentation
Data Access and
Ownership
Data Sharing and Re-use
Preservation Techniques
Complete Your DMP
DMP can be shared with
next student!

20. Background / Audience
Discipline – Ecology
Research context –
four-year field study on
impacts of climate
change on prairie ecosystems
Data types – ASCII, tabular (Excel), statistical
analyses (SPSS or R)

21. Educational Priorities / Needs
Best practices promoted by professional
societies
Data management and organization
Documentation and metadata
Data sharing/publishing
Data citation

22. Response
Readings:
• Article: Bulletin of the ESA –
Some Simple Guidelines for Effective Data Mgmnt
• Article: Global Change Biology Global change science requires open data
• Chapter:
lab notebook best practices
Team meeting - seminar format with discussion
on best practices.

23. Background / Audience
Team #1
• Discipline – Electrical &
Computer Engineering
• Data types – Software
Code
• Context – Engineering
Projects in Community
Service (EPICS)

24. Educational Priorities / Needs
Team #1
• Documenting Code
& Project
• Organizing Code &
Project
• Transfer of
Responsibility
• Archiving

25. Response
Team #1
Embedded Librarianship:
• Evaluation Rubric
• Skills Session
• Design Reviews
• Lab Observations &
Consulting

26. Background / Audience
Team #2
• Discipline – Ag & Biological Engineering
• Data types – field data, modeling data,
and remote sensing data
Context – a joint hydrology research group

27. Educational Priorities / Needs
Team #2
• File organization and data completeness
• Adherence to research group standards
• Data description for sharing and re-use
• Data discovery and acquisition

28. Response
Team #2
3 Workshops
• Checklists
• Data Discovery
• Metadata training
• Data deposit in IR

29. Observations
• Need for DIL is strong
• Plenty of room for exploration and action
• Investment
• Understanding the environment
• Building (and rebuilding) the program
• Forging relationships
• Timing of the Program
• Integration of the Program

30. The DIL Symposium
http://docs.lib.purdue.edu/dilsymposium/

31. Next Steps: DIL Toolkit
• A guide for librarians seeking to
develop DIL Programs of their
own
• Developed from the shared
experiences of the 5 project
teams
• Comprised of:
o User Guide
o Case Studies
o Program Materials

32. Next Steps: Publishing the Toolkit
• Static: As a book to
be published by the
Purdue University
Press
• Dynamically: As
a wiki or other
editable website

33. Next Steps: Expansion
Data Literacy Pilot Program – Spring
2014
w/ Librarian: Marianne Stowell Bracke
Sponsored by the College of Ag
• Receive intense, hands-on training using
their own data
• Create a community of students
knowledgeable with data management and
curation issues
• Meet two hours/week, including lecture,
group discussion and exercises
• Students receive a stipend for full
participation
Dr. Karen Plaut
College of Agriculture
Administration
Senior Associate Dean
for Research and
Faculty Affairs

34. Next Steps: Expansion
Data Management Course – Spring
2014
w/ Librarians: Marianne Stowell Bracke
& Pete Pascuzzi (as well as AgIT, Cyber
Center, and faculty from the
Biochemistry department)
An 8 week mini-course on
organizational and technical issues in
managing and working with data.
Dr. Clint Chapple
Head, Biochemistry
Department

35. Data Processing and Analysis
Data Curation and Re-Use
Data Management and
Organization
Data Conversion and
Interoperability
Data Preservation
Data Visualization and
Representation
Databases and Data Formats
Discovery and Acquisition
Ethics and Attribution
Metadata and Data Description
Data Quality and Documentation
Cultures of Practice
How could we move from using the 12 DIL
competencies as touchstones and towards
developing standards in this area?

36. DIL Project Personnel
Principal Investigator:
• Jake Carlson - Purdue University
Co-Principal Investigators:
• Camille Andrews – Cornell University
• Marianne Stowell Bracke – Purdue University
• Michael Fosmire – Purdue University
• Jon Jeffryes – University of Minnesota
• Lisa Johnston – University of Minnesota
• Megan Sapp Nelson – Purdue University
• Dean Walton – University of Oregon
• Brian Westra – University of Oregon
• Sarah Wright – Cornell University

37. Questions?
Jake Carlson
Associate Professor of Library Science
Purdue University
http://datainfolit.org