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Teaching Data
Information LiteracySarah J. Wright
Life Sciences Librarian for
Research, Cornell University
What I’ll talk
about
Data Information Literacy
IMLS-funded Data Information Literacy
research project
needs identified
approaches
lessons learned
DIL +
Related
Literacies
Data Literacy
Access, assess, manipulate, summarize, and
present data
Statistical Literacy
Think critically about basic stats in everyday
media
Information Literacy
Think critically about concepts; read, interpret,
evaluate information
Data Information Literacy
The ability to use, understand, and manage dataSchield, Milo. "Information literacy, statistical
literacy and data literacy." I ASSIST Quarterly
28.2/3 (2004): 6-11.
Discovery & Acquisition
Databases & Data formats
Data Conversion & Interoperability
Data Processing & Analysis
Data Visualization & Representation
Data Management &
Organization
Data Quality & Documentation
Metadata & Description
Cultures of Practice
Ethics & Attribution
Data Curation & Re-use
Data Preservation
Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data
information literacy needs: A study of students and research faculty.
Portal: Libraries & the Academy, 11(2), 629-657.
Cornell University
University of
Minnesota
University of
Oregon Purdue University 1 Purdue University 2
Natural Resources Civil Engineering Ecology
Electrical & Computer
Engineering
Agricultural &
Biological
Engineering
Longitudinal
data of fisheries
and water
quality
Real-time
sensor data on
bridge structures
Climate change
and plant growth
data
Software code in
community
service projects
Simulation data
of hydrological
processes
http://datainfolit.org
Cornell University
University of
Minnesota
University of
Oregon Purdue University 1 Purdue University 2
for credit course online modules seminar workshop series embedded librarian
Data sharing
Databases
Data ownership
Long-term
access
Cultures of
Practice
Metadata
Documenta-tion
& organization
Standard
Operating
Procedures
Metadata
http://datainfolit.org
Courses Developed
at Cornell:
NTRES 6600: Research Data
Management Seminar
Six session, 1-credit mini-course
for grad students in Natural
Resources
BIOG 3020: Seminar in
Research Skills for Biologists
1-credit semester long course for
undergraduates involved in
research; data management
portion of course
Lessons Learned
• The competencies were almost universally considered
important by students and faculty interviewed.
• Students were considered lacking in these competencies.
• Faculty assumed that students have or should have
acquired the competencies earlier.
• Lack of formal training for students working with data.
http://www.slideshare.net/asist_org/rdap-15-lessons-learned-from-the-data-information-literacy-project
PhD comics, http://www.phdcomics.com/comics.php?f=1323http://www.phdcomics.com/comics/archive.php/tellafriend.php?comicid=1323
Lessons Learned
• Needs may not
be as complex as
you might think.
Lessons Learned
• Learning is largely self-directed through “trial and error.”
• Training often at point of need, often in the context of the
immediate issue.
• Faculty were often unsure of best practices or how to
approach the competencies themselves.
http://www.slideshare.net/asist_org/rdap-15-lessons-learned-from-the-data-information-literacy-project
DIL Resources
Data Information Literacy Project
Website: http://www.datainfolit.org/
Book: http://www.thepress.purdue.edu/titles/format/9781612493527
Data Q (for your data questions):
http://researchdataq.org/
Contact Information
SARAH J. WRIGHT
Life Sciences Librarian for Research
Cornell University
Digital Social
Science Lab- connecting academia
with data literacy
Christian Lauersen
Copenhagen University Library
Email: [email protected]
Twitter: @clauersen
Library Connect Webinar Dec 8th 2016
Research Data Literacy and The Library
Why?
The master’s thesis case
Kub kort
Hvorfor?3 Data Labs
Humanities
Social Sciences
Natural and
Health Science
An open platform for education and events on digital methods
Hardware and software for harvesting, cleaning,
analyzing and visualizing data
A dynamic and aesthetically inspiring learning environment
What we do•Events and instruction
•Facilitating and curating
•Community building
The library as hub:Community and peer-to-peer
The Space:
•Flexibility
•Functionality
•Inspiration
An alternative to
the classic
learning setup
The Evolving DSSL Network
DSSL
Aalborg
University
DTU
Faculty
members
Students
Ethnographic
Exploratorium
ETHOS
Lab
Teaching
and
learning
unit
Faculty
BADASS
Higher education
Danish
Business
Authority
Open Data
Network
Libraries
and
archives
Society
Hvad er Digital Social Science Lab?
• Et fysisk rum til understøttelse af
forskning, uddannelse og læring
• Relevant software og hardware +
vejledning og support
• En platform for digitale metoder og
værktøjer indenfor samfundsvidenskaben
Key to impact?Stakeholders
Ownership
Collaboration
Challenges in the process
• ”Is this a library task?”
• ”On the expense of what?”
• How do we get the relevant skills?
• How do we talk about this project?
• How do we position ourselves toward the local
research and educational environment?
What we’ve learned
• It’s not enough to provide access to software and hardware
• Skill development is a long process and has to be in context of need and resources
• The facilitating role is a good way to createvalue
• Network is key
• The Library is a very strong platform for bringing people together within academia
• Library support of data literacy might not fit with all subjects
Digital Social Science Lab
http://kub.kb.dk/DSSL
Christian Lauersen
Mail: [email protected]
Twitter: @clauersen
The Library Lab
https://christianlauersen.net
Thanks for
listening
| 29
Elsevier‘s RDM Program:
Ten Habits of Highly
Effective Data
Anita de Waard
VP Research Data Collaborations
Elsevier RDM Services
December 8, 2016
| 30
https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data
10.
Inte
gra
te u
pstr
eam
and d
ow
nstr
eam
–m
ake m
eta
da
ta t
o s
erv
e u
se.
Save
Share
Use
9. Re-usable (allow tools to run on it)
8. Reproducible
7. Trusted (e.g. reviewed)
6. Comprehensible (description / method is available)
5. Citable
4. Discoverable (data is indexed or data is linked from article)
3. Accessible
1. Stored (existing in some form)
2. Preserved (long-term & format-independent)
A Maslow Hierarchy for Research Data:
| 31
Store, Preserve: Data Rescue Award
| 33
https://data.mendeley.com/
Linked to published
papers – or not
Linked to Github
– or not
Versioning and
provenance tracking
Store, Access: Mendeley Data
Different Licenses:
GNU-PL, CC-BY CC0,
etc
| 34
Access, Cite: Data Linking
• Integrated in paper
submission process
• Supplementary data is
never behind a firewall
• Closely integrated with >
150 databases
| 35
Access, Discover: Scholix/DLIs
• ICSU-WDS/RDA Publishing Data Service Working group,
merged with National Data Service pilot
• Cross-stakeholder – with input from CrossRef, DataCite, OpenAIRE, Europe PubMed Central, ANDS,
PANGAEA, Thomson Reuters, Elsevier, and others
• Proposed long-term architecture and interoperability framework: www.scholix.org
• Operational prototype at http://dliservice.research-infrastructures.eu/#/api (including 1.4 Million links
from various sources)
| 36
Cite: Force11
https://www.elsevier.com/connect/data-citation-is-becoming-real-with-force11-and-elsevier
| 38
Data
articles
Software
articles
Method
articles
Protocols
Video
articlesHardware
articles
Lab
resources
Full Research
paper
• Brief article types designed to
communicate a specific element of
the research cycle
• Complementary to full research
papers
• Easy to prepare and submit
• Peer-reviewed and indexed
• Receive a DOI and fully citable
• Allow citable post-publication updates
• Primarily Open Access (CC-BY)
• Published in Multidisciplinary and
domain-specific journals
https://www.elsevier.com/books-and-journals/research-elements
Review: Research Elements
| 39
Reuse: Cortex Registered Reports
39
• Two-step submission
process:
• Method and proposed
analysis are submitted
for pre-registration
• Paper is conditionally
accepted
• Research is executed
• Full paper submitted,
accepted provided that
protocol is followed
• All experimental data made
available Open Access
Featured in The Guardian:
| 40
Research article
published
Initial inquiry
Share, publish and
link data
Monitor progress and
provide guidance
Generate reports
111110 00011
1101110 0000
001
10011
1
011100
101
Metrics for Institutions: Data Lighthouse
What?
Service for Research Institutes (esp.
librarians) to engage with researchers
throughout the research data life cycle.
How?
Offer service for Librarians to interact with
researchers regarding the RDM Process to:
• Offer solutions to store, share, link and
publish data
• Monitor progress report on posting, citation,
downloads of dataset
• Provide monthly reporting
DATA LIGHTHOUSE
| 41
10.
Inte
gra
te u
pstr
eam
and d
ow
nstr
eam
–m
ake m
eta
da
ta t
o s
erv
e u
se.
Save
Share
Use
9. Re-usable
8. Reproducible
7. Trusted
6. Comprehensible
5. Citable
4. Discoverable
3. Accessible
1. Stored
2. Preserved
https://www.elsevier.com/connect/10-aspects-of-highly-effective-research-data
A Maslow Hierarchy for Research Data:
Data at Risk
Reproducibility PapersD
ata
Lig
hth
ouse
