The Data Information Literacy Project

Supplemental Webinar

Thursday, February 6, 2014 1:00 – 2:30 p.m. EST

The Data Information Literacy Project: Past, Present and Future

Jake Carlson

Associate Professor of Library Science Purdue University

http://datainfolit.org

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

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)

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.

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.

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

Background

Project Structure

Data Librarian

Research Faculty

Graduate Students

Post-doc; Research assistant

Subject Librarian

or Information

Literacy Librarian

Five Case Studies

Cornell

Minnesota

Oregon

Purdue #1

Purdue #2

Natural Resources

Civil Engineering Ecology

Electrical & Computer

Engineering

Agricultural and

Biological Engineering

Sara Wright (DL)

Camille

Andrews (IL)

Lisa Johnston

(DL)

Jon Jeffreys (SL)

Brian Westra (DL)

Dean

Walton (SL)

Jake Carlson (DL)

Megan Sapp Nelson (SL)

Marianne Stowell

Bracke (DL)

Micheal Fosmire (IL)

Project Phases

Literature Review Interviews

Develop Educational Programs

Implement Programs Develop DIL Toolkit

Interview Results

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.”

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.

Background / Audience

Natural resources: long term studies

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.

http://ww

w.papabearoutdoors.com

/about/trout-fishing/

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.”

Educational Priorities / Needs

• Data management • Data organization • Data quality and

documentation • Data analysis and

visualization • Metadata

Response

NTRES 6940 Special Topics Course: Managing data to facilitate your research

Six session mini-course: • Intro to Data Management • Data Organization • Data Analysis &

Visualization • Data Sharing • Data Quality &

Documentation • Wrap-up

Background / Audience

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

UNIVERSITY OF MINNESOTA – TWIN CITIES

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 well-documented way

Access/Ownership IP and Rights Issues List stakeholders

Sharing Why share data? Recognize the reuse value of data

Preservation Maintaining Access Consider preservation-friendly file formats

Response (Open) Data Management Course: http://z.umn.edu/datamgmt

Seven Web-Based Modules 1. Introduction to Data

Management 2. Data to be Managed 3. Organization and

Documentation 4. Data Access and

Ownership 5. Data Sharing and Re-use 6. Preservation Techniques 7. Complete Your DMP

DMP can be shared with next student!

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)

Educational Priorities / Needs

Best practices promoted by professional societies Data management and organization Documentation and metadata Data sharing/publishing Data citation

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.

Background / Audience Team #1

• Discipline – Electrical & Computer Engineering

• Data types – Software Code

• Context – Engineering Projects in Community Service (EPICS)

Educational Priorities / Needs Team #1

• Documenting Code & Project

• Organizing Code & Project

• Transfer of Responsibility

• Archiving

Response Team #1

Embedded Librarianship: • Evaluation Rubric • Skills Session • Design Reviews • Lab Observations &

Consulting

Background / Audience Team #2

• Discipline – Ag & Biological Engineering • Data types – field data, modeling data,

and remote sensing data Context – a joint hydrology research group

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

Response Team #2

3 Workshops

• Checklists • Data Discovery • Metadata training

• Data deposit in IR

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

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

• 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

Next Steps: DIL Toolkit

• Static: As a book to be published by the Purdue University Press

Next Steps: Publishing the Toolkit

• Dynamically: As a wiki or other editable website

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

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

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?

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

Questions? Jake Carlson

Associate Professor of Library Science Purdue University

http://datainfolit.org