1. Data Literacy Conceptions
and Pedagogies
Redefining Information Literacy Frameworks for
the 21st Century
Professor Sheila Corrall
Centre for Information Literacy Research

2. Context for data literacy development
• History of library involvement with print and electronic
statistical sources and data archives in social sciences
− social science librarians and specialist data librarians/archivists
• Growth of computer/network-enabled scientific research
− need to raise data literacy of science students and develop
workforce of data managers able to contribute to e-research
• Current interest among information literacy practitioners
in strengthening support for research students and staff
− revision of Seven Pillars Model to improve relevance to research
• Debate on roles and responsibilities in data management
− including questions about library capacity, institutional mandates
and the education, training and development of key players
20/04/11 © University of Sheffield / Information School / Sheila Corrall

3. Libraries, librarians and data
‘Providing data services is a natural fit for the
academic library's core mission of helping users
find information in a variety of formats’
(Read, 2007: 72)
‘Datasets are heavier, more feral, and require more
resources than, say, monograph shipments or e-journal
subscriptions, but managing and improving the organization
of and access to them is still the obligation of the library
and information scientist.’
(Miller, 2010)
‘…we also advocate the integration of pedagogies
for data literacy and information literacy’
(Stephenson & Caravello, 2007: 535)
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4. What is Data Literacy?
Who should be developing
knowledge and skills in
dealing with data?
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5. Conceptions of data literacy (1)
A social science perspective
Data literacy almost synonymous with statistical literacy,
quantitative literacy and numeracy – but involving more
than basic statistics and mathematical functions
• understanding data and its tabular and graphical
representations, including statistical concepts and terms
• finding, evaluating and using statistical information
effectively and ethically as evidence for social inquiries
• reading, interpreting and thinking critically about stats
Data literacy is an essential and critical component
of information competence in social sciences
(e.g. Read, 2007; Schield, 1999; Stephenson & Caravello, 2007)
20/04/11 © University of Sheffield / Information School / Sheila Corrall

6. Conceptions of data literacy (2)
Alternative (hierarchical) social science perspectives
CRITICAL THINKING SOCIAL SCIENCE DATA
Analysis, Interpretation, Evaluation Analysis, Interpretation, Evaluation
Information Literacy Data Literacy
Statistical Literacy Statistical Literacy
Data Literacy Information Literacy
Critical thinking perspective Discipline perspective
(Schield, 2004)
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7. Conceptions of data literacy (3)
A science (STEM/information science) perspective
Science data literacy shares aspects of social science
conceptions, but requires awareness of the data life cycle,
metadata issues, data tools and collaboration mechanisms
• managing the data generated from experiments, surveys
and observations by using sensors and other devices
• understanding the attributes, quality and history of data
to produce valid, reliable answers to scientific inquiries
• accessing, collecting, processing, manipulating,
converting, transforming, evaluating and using data
SDL goes beyond ‘pushing’ the data to students by
developing abilities and skills in ‘pulling’ data
(Qin & D’Ignazio, 2010)
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8. Strategic and
operational roles
Research data Influence
national data
for research
management policy libraries
pyramid for Lead on local
(Univ) data
libraries Develop policy Identify
local data required data
curation skills with LIS
capacity schools
Bring data into Teach data
UG research- literacy to post-
Develop based graduates Develop
library learning Provide researcher
workforce researcher data
data data advice awareness
confidence
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(Lewis, 2010: 154)

9. ‘Scientific datasets may be thought
of as the ‘special collections’ of the
digital age’ (Choudhury, 2008: 218)
Examples of tactical adaptation of existing
LIS practices to managing research data
• Conducting data interviews with researchers
• Adding data sets to institutional repositories
• Developing subject librarians into data liaisons
• Including data literacy in information instruction
(classroom sessions, teachable moments at the
reference desk, drop-in research consultations)
(e.g. Delserone, 2008; Gabridge, 2009; MacMillan, 2010;
Miller, 2010; Witt & Carlson, 2007)
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10. Pedagogies for data literacy (1)
McGill Libraries Electronic Data Resources Service
Supporting multidisciplinary research and instruction with
historical, socio-economic and GIS data
• preparing web pages tailored to particular courses,
highlighting appropriate data sources
− and offering class presentations based on the pages
• providing computer facilities for student use and
technical assistance for work involving digital data
• scheduling departmental orientations for grad students to
demonstrate the wide array of research resources
• delivering training sessions and workshops on software
(e.g. Excel, SPSS, Stata and SAS)
(Czarnocki & Khouri, 2004)
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12. Pedagogies for data literacy (2)
UCLA 105 Sociology Information Literacy Lab
Developing students’ skills in searching for, retrieving,
customising and critically evaluating statistical resources
• standalone unit taught by librarian and data archivist
− 10 weeks, 7 credit-bearing assignments + credit for attendance
• aim not to teach statistics, but to use statistical resources
• intended learning outcomes
− able to read and critically evaluate simple 2 x 2- or 3-way tables
− produce accurate bibliographic citations for data tables
− use American Factfinder to create a table, which they could
describe and cite correctly
− read an article containing a graphical representation of data and
discuss it in relation to the article content
(Stephenson & Caravello, 2007)
20/04/11 © University of Sheffield / Information School / Sheila Corrall

13. Pedagogies for data literacy (3)
Calgary 311 Biology Information Literacy Lab
Incorporating genetic data resources in IL instruction by
simulating pathways of experienced researchers
• integrated unit taught by librarian(s) and lab instructors
− 90 minutes (workshop, structured exercise and credit-bearing
poster assignment, supported by workbook and online resource)
• authentic workflow designed with academic collaborator
− step-by-step exercise based on tool-specific modules, providing
demonstration, practice and discussion of each resource
− progressing from online encyclopedias and journal dbases
through Google Patents to gene and protein databanks and tools
− highlighting synergies and relationships between key resources
• value added by infolit expertise and student perspective
− contextualising sources in disciplinary information environment
and identifying where extra scaffolding needed (Macmillan, 2010)
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15. Pedagogies for data literacy (4)
Purdue Libraries GIS Librarian
Raising awareness of the importance of data among
students and faculty
‘the technological barrier between libraries and
geospatial research is surprisingly low’
• inserting single-session drop-ins into existing courses
• exploiting reference and consultation sessions
‘the librarian lays a heavy rap about data access and reuse
on the unsuspecting student that has stopped by for some
help with this or that’
• delivering multidisciplinary credit-bearing courses
− applying geoinformatics technologies to diverse subject fields
− 3 weeks (credits for labs, project, participation and quizzes)
(Miller, 2010)
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17. Pedagogies for data literacy (5)
Syracuse Science Data Management Course
Learning how data management solutions support scientific
practice, balancing info, tech, social and policy issues
• elective unit, taught by iSchool academic and PhD
− 14 weeks (aimed at STEM UGs, taken by iSchool UGs and PGs)
• intended learning outcomes
− understand the fundamental concepts in scientific data
− use the data for scientific inquiry
• teaching strategies deployed
− clearly differentiated modules/sub-units, tiered skill development
− extensive treatment of metadata through wide set of readings
− real-world cases studies (e.g. geography as accessible example)
− authentic project involvement (pairing UG and PG students)
(Qin & D’Ignazio, 2010)
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19. Redefining frameworks for the 21C
• Work in progress on revising
the Seven Pillars Model to
meet researcher needs
• Can the ‘skills’ be expanded
sufficiently to provide the
necessary focus on:
− the attributes and life cycle
of data resources?
− the management and
processing of data?
(See Qin & D’Ignazio, 2010)
20/04/11 © University of Sheffield / Information School / Sheila Corrall

20. Redefining frameworks
Should we develop more
subject-specific models?
20/04/11 © University of Sheffield / Information School / Sheila Corrall

21. Redefining frameworks for the 21C
• Should we update our Plain English definition?
literacy definitions: ‘Data literacy is knowing
− add scope notes? when and why you need
data, where to find them,
− insert ‘data’ into the what their attributes are,
text as appropriate? and how to evaluate,
− produce statements to process, use, manage
supplement existing and communicate them in
definitions? an ethical manner’
(Adapted from CILIP, 2004
and Qin & D’Ignazio, 2010)
20/04/11 © University of Sheffield / Information School / Sheila Corrall

22. Points for reflection and discussion
• How should we incorporate data literacy into
information literacy frameworks?
− Amend current definitions, models and standards?
− Produce expanded versions of existing statements?
− Develop discipline-based frameworks for information
and data literacy?
• How should we provide data literacy education?
− Standalone or integrated?
− Part of research methods, theory course or integrated
across curricula?
• Who should teach and support learners?
− Librarians, academic domain experts, LIS academics?
20/04/11 © University of Sheffield / Information School / Sheila Corrall