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2-6-14 ESI Supplemental Webinar: The Data Information  Literacy Project
 

2-6-14 ESI Supplemental Webinar: The Data Information Literacy Project

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E-Science Institute

E-Science Institute
Supplemental Webinar:
The Data Information Literacy Project
Presented by: Jake Carlson, Purdue University

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    2-6-14 ESI Supplemental Webinar: The Data Information  Literacy Project 2-6-14 ESI Supplemental Webinar: The Data Information Literacy Project Presentation Transcript

    • 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 nonspecialists 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.
    • 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
    • Project Structure Research Faculty Data Librarian Subject Librarian or Information Literacy Librarian Graduate Students Post-doc; Research assistant
    • 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)
    • Project Phases Literature Review Interviews Develop Educational Programs Develop DIL Toolkit Implement Programs
    • 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 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.
    • 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
    • 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
    • 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
    • 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
    • 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!
    • 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/
    • 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
    • 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
    • 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