Javier Calzada Prado and Miguel Ángel Marzal
Incorporating Data Literacy into Information
Literacy Programs: Core Competencies and
Contents
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DOI 10.1515/libri-2013-0010 Libri 2013; 63(2): 123−134
Abstract: The growing importance of data in society in
general and scientic domains in particular, mirrored in
the Open Data initiative and in the advent of eScience,
requires public, school and academic libraries to contrib-
ute to both data and information literacy, as part of their
mission to further knowledge and innovation in their re-
spective elds of action. No specic library standards have
been proposed to date, however, and most research stud-
ies conducted adopt a partial view of data literacy, stress-
ing only the components needed in any given context. The
present paper aims to contribute to the advancement of
data literacy with the proposal of a set of core competen-
cies and contents that can serve as a framework of refer-
ence for its inclusion in libraries’ information literacy
programs. The various denitions of data literacy are dis-
cussed, the coverage of the competencies listed in infor-
mation literacy standards is described, and the compe-
tencies considered in the experiments conducted to date
in education and libraries are identied. The conclusion
drawn is that the model proposed can favour the develop-
ment of data literacy support resources and services. Top-
ics for further research are also specied.
Dr. Francisco Javier Calzada Prado: Assistant Professor, Library &
Information Science Department, Universidad Carlos III de Madrid,
Spain, Email: fcalzada@bib.uc3m.es
Dr. Miguel Ángel Marzal García-Quismondo: Professor, Library &
Information Science Department, Universidad Carlos III de Madrid,
Spain, Email: mmarzal@bib.uc3m.es
Introduction
The advent of the information society has brought a grad-
ual acknowledgement of individuals’ need to eectively
access, handle and use information to solve problems, en-
gage in life-long learning for the attainment of full social
integration and optimal personal and professional devel-
opment, and contribute actively to the societies in which
they live. The suite of competencies involved, which has
been dubbed “information literacy,” has been associated
from the outset with democratic participation (Owens 1976)
and has even come to be regarded as a basic civil right
(Sturges and Gastinger 2010). This state of aairs has ac-
centuated libraries’ educational task, for as key informa-
tion mediators, they occupy a strategic position for the
development of such competencies. This is particularly
obvious in the growing involvement of school, public and
academic libraries in the eld. Two types of activities to
further information competencies are generally conduct-
ed in these institutions: specialized reference services and
information literacy training programs.
To date, these services have focused on accessing and
handling sources of bibliographic information (books, ar-
ticles, reports, legal texts and so on). In recent years, how-
ever, the availability and volume of statistical, scientic or
technical source (or raw) data have grown exponentially.
In addition to traditional sources, some of which are pub-
lished and distributed under license or subscription (such
as IHS Global Insight or LexisNexis Statistical) while oth-
ers are available cost-free on the Web (such as UNdata,
World Bank or EUROSTAT), the recent presence of open
data sources is of enormous importance for researchers
and the general public alike. The Open Data movement,
heir in part to the Open Source and Open Access move-
ments, encourages the free publication of data from dier-
ent domains under licenses that favour their reuse.
Major initiatives in the scientic sphere are Open
Knowledge and Science Commons, with prominent state-
ments such as OECD’s communiqué “Science, Technology
and Innovation for the 21
st
Century” (OECD 2004). More
recently, the Obama Administration (U.S. Oce of Science
and Technology Policy 2012) has introduced Big Data,
and the European Commission has released its commu-
nication “Towards better access to scientic information:
boosting the benets of public investments in research”
(European Commission 2012). All these endeavours are in-
tended to favour open access to source data generated by
publicly funded research.
This premise of open access to source data has ac-
quired particular relevance in eScience. In this domain,
the challenges and opportunities oered by the ever-
growing data processing and analytical capacity of com-
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124 Javier Calzada Prado and Miguel Ángel Marzal, Incorporating Data Literacy
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puter technology combine with the new forms in which
researchers relate to the scientic community and society
with a view to facilitating methodological transparency,
data preservation, sharing and reuse, and accountability.
One example of the impact of this new scenario is the Na-
tional Science Foundation’s (NSF) 2011 decision to require
a data management plan in all requests for funding, which
must describe the resources to be devoted to managing,
preserving and publishing the data generated as a result
of the research conducted. Escience is usually character-
ized as ‘data-intensive’, which calls not only for the neces-
sary technological infrastructure and resources to handle
and mine data, but also the human resources able to make
sense of them and oversee the process. Both matters pose
substantial challenges at present for institutions and re-
searchers alike (Soehner, Steeves, and Ward 2010).
In the public domain, Open Data has favoured freer
access to the data generated by the public sector. This line
of work was formalized in the expert conference held at
Sebastopol in 2007 (Sunlight Foundation 2010), which
paved the way for both the Obama Administration’s Open
Government Initiative (The White House 2009) and the Eu-
ropean Union’s PSI (Public Sector Information) directives
of 2003 and 2009 (European Commission 2003, 2009).
Governments the world over have, then, been publish-
ing more and more public sector data on the Web in open
format (Open Government Partnership 2012). As Berners-
Lee (2010) noted, these data constitute an opportunity to
enhance the eectiveness of public institutions, reinforce
democratic values (insofar as they favour transparency
and the public’s control of government) and create busi-
ness opportunities based on data reuse and mining. In
practice, however, open data access appears to benet
government (as a political commitment) and the entrepre-
neurial class (as a business opportunity) more than the
public at large, which nds data dicult to interpret (as
statistics on numeracy show) and consequently depends
on apps developed by others to make sense out of, use and
derive value from them.
In another vein, data and their sources are increas-
ingly present in everyday life (primarily through the me-
dia), inuencing opinion and decision-making. A num-
ber of organizations, such as the International Statistical
Institute (ISI) (2013), the American Statistical Association
(ASA) (2011), the International Association for Social Sci-
ence Information Services and Technology (IASSIST), and
the United Nations (United Nations Economic Commission
for Europe 2012), have stressed the need for more statistical
competency instruction at all educational levels and for its
inclusion as a cross-curricular subject not linked solely to
mathematics, to enhance the public’s statistical know-how.
The above, obviously inter-related, spheres (educa-
tion systems contribute to training the public and profes-
sionals, researchers among them), pose one and the same
challenge: the need to prepare individuals to make signi-
cant use of data sources. A need exists, then, for greater
sensitization and training in “data literacy,” a suite of data
acquisition-, evaluation-, handling-, analysis- and inter-
pretation-related competencies that lie outside the scope
of statistical competencies. As in the case of information
literacy, libraries are well-positioned to play a strategic
role in the development of such competencies. Indeed,
several authors have called upon librarians, particularly
university librarians, to further data literacy in informa-
tion literacy training programs (Gray 2004; Schield 2004;
Stephenson and Caravello 2007; Kellam 2011; Haendel,
Vasilevsky, and Wirz 2012), going so far as to claim that
“it is time for academic libraries to invest in data literacy
programs” (Merrill 2011, 146). Nonetheless, we feel that
data literacy, like information literacy, should be acquired
gradually at all levels of schooling and even through-
out individuals’ lifetimes. We consequently believe that
school and public libraries should also include data lit-
eracy, adapted as necessary, in their information literacy
programs.
But, how can libraries include data literacy in their in-
structional programs? Exactly what competencies should
be covered? And how are they related to information lit-
eracy? The scant literature published to date on data lit-
eracy and the competencies it entails contains not one,
but many approaches, depending on the context and pur-
pose sought. If one lesson is to be learned from the vast
documentation on information literacy, however, it is that
it calls for a comprehensive model or framework as a basis
for ensuring that these competencies are consistent and
meaningful in all the contexts and situations where they
are to be applied, regardless of the sophistication or speci-
city involved.
The present paper aims to contribute to the advance-
ment of data literacy in libraries by identifying a set of
core competencies and contents that may constitute a
framework of reference for the inclusion of this topic in in-
formation literacy instructional programs. It begins with
a review of data literacy and its inter-relationship with
other types of literacy. Data literacy coverage in the in-
formation literacy models proposed is subsequently ana-
lyzed and the most signicant experiences conducted in
the eld to date in libraries are identied, along with the
competencies addressed. This paves the way for the pro-
posed reference framework that may serve as a guide for
libraries when including data literacy in their educational
programs.
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Javier Calzada Prado and Miguel Ángel Marzal, Incorporating Data Literacy 125
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Data Literacy
“Science literacy” is an educational priority world-wide.
In 1999 the “Declaration on Science and the Use of Scien-
tic Knowledge” identied its strategic role in sustainable
development and democracy (UNESCO and International
Council for Science [ICSU] 2002). The Rocard report (Eu-
ropean Commission 2007) highlighted the importance of
propagating scientic culture among young Europeans.
President Obama, in turn, recently launched his Educate
to Innovate campaign (The White House 2012), in an at-
tempt to improve young Americans’ performance in sci-
ence, technology, engineering and mathematics (STEM).
In Western higher education, particularly in North
America, Australia and Europe, quantitative literacy has
been encouraged since the late 1980s. It has recently been
given a fresh impetus, for instance, through the British
national strategy, headed by the Economic and Social Re-
search Council (ESRC), to further training for the use of
quantitative methods in social science. Quantitative litera-
cy is dened as “using simple mathematical concepts to
solve everyday problems” (Steele and Kiliç-Bahi 2008, 1),
a pragmatic approach to mathematics teaching. For Steen,
quantitative literacy is dened by its direct relationship to
the real world. He states that “the facility we want stu-
dents to acquire is not just about quantities… [R]easoning,
argument and insight are as essential to QL as are num-
bers; so too are notions of space, chance, and data” (Steen
2004, 25).
One of the essential components of quantitative lit-
eracy is statistical literacy. Wallman dened the latter as
“the ability to understand and critically evaluate statis-
tical results that permeate our daily lives - coupled with
the ability to appreciate the contribution that statistical
thinking can make in public and private, professional and
personal decisions” (Wallman 1993, 1). Watson, in turn,
identied three abilities that determine statistical com-
petence. In increasing order of complexity, they are: a) a
basic understanding of probabilistic and statistical termi-
nology and the ability to perform analytical and statistical
calculations; b) the ability to interpret probabilistic and
statistical language and concepts when they are embed-
ded in social media contexts; c) the ability to critically
evaluate statistical claims related to sampling, the distri-
bution of raw data, appropriate use of statistics, graphs,
causal claims made, and probabilistic statements (Watson
1997). In similar terms, Gal (2002, 2-4) claimed that sta-
tistical literacy comprises two inter-related competencies:
“a) people’s ability to interpret and critically evaluate
statistical information, data-related arguments, or sto-
chastic phenomena, which they may encounter in diverse
contexts, and when relevant; b) their ability to discuss or
communicate their reactions to such statistical informa-
tion, such as their understanding of the meaning of the
information, or their concerns regarding the acceptability
of given conclusions.”
A new term, furthered primarily by the social science
and open data communities, has arisen of late in connec-
tion with statistical literacy: data literacy. Some authors
have equated it to statistical literacy (Stephenson and Car-
avello 2007), while others, such as Schield (2004), observe
dierentiating features. For the latter author, data literacy
is the part of statistical literacy that involves training indi-
viduals to access, assess, manipulate, summarize and pre-
sent data, whereas statistical literacy aims to teach how
to “think critically about descriptive statistics.” The two
concepts share one essential component, critical think-
ing, which draws from mathematical and statistical apti-
tudes, general knowledge and the values of the persons
concerned. Moreover, this component positions both con-
ceits near the realm of information literacy, as noted by
Schield (2004), although data literacy is closer, inasmuch
as it shares other elements, including its name (“data”
and “information” both refer to the object of literacy) and
its focus on management: data literates must be able to
eectively access, handle and use data. However, as Hunt
(2004, 14) points out: “in the practical implementation (or
on the ground) data literacy is quite dierent than tradi-
tional information literacy.” The origin of this dierence
lies mainly in the greater complexity involved in handling
data than any other type of information (Thompson and
Edelstein 2005). Nonetheless, their similarities may lead
to regarding data literacy as a complement to or a form
of information literacy, as respectively suggested by Ste-
phenson and Caravello (2007) and Otto (2012), which
makes us think that data literacy would be the umbrella
concept covering statistical literacy, rather than the vice
versa.
While acknowledging the obvious interdependence
between data and statistical literacy (no data manipula-
tion, summary or presentation, to use Schield’s terms, is
possible unless the operations characteristic of statistical
analysis are performed, nor can statistics be critically ap-
praised unless the fact that they entail data manipulation
and interpretation is recognized), a broader perspective
for dealing with data is secured if the former approach is
taken as the construct. As a result, data literacy can be
viewed both as a whole and as an integrated assemblage
of other competencies, such as data collection, generation
and management in research projects or organizations.
Using this framework, statistical literacy is envisaged
as the component of data literacy involved in the criti-
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126 Javier Calzada Prado and Miguel Ángel Marzal, Incorporating Data Literacy
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cal appraisal, interpretation, processing, and statistical
analysis of data. Data literacy can be dened, then, as the
component of information literacy that enables individu-
als to access, interpret, critically assess, manage, handle
and ethically use data. From that perspective, informa-
tion literacy and data literacy form part of a continuum,
a gradual process of scientic-investigative education that
begins in school, is perfected and becomes specialized in
higher education and forms part of individuals’ skill set
throughout their lifetime. In the present paper the term
data literacy is based on this approach.
Data Literacy in Information
Literacy Standards
To date, no specic standards or guidelines have been
established for data literacy, although some of the com-
petencies entailed have recently been included as part of
information literacy standards. The latter provide a refer-
ence framework for the competencies needed to use infor-
mation eciently in problem solving and generating new
knowledge. These competencies generally include: the
ability to dene precisely the informational need; the abil-
ity to locate information sources suited to that need; the
ability to assess critically both the sources and the ideas
expressed therein; the ability to manage the information
selected; the ability to analyze and synthesize information
to support arguments or generate new ideas; the ability to
document the sources used; and the ability to record or
communicate the results in an ethical manner. In the con-
text of this overall scheme, some standards, such as the
Australian and New Zealand Information Literacy Frame-
work (ANZIL) (Bundy 2004), fail to deal with data speci-
cally, while others have attempted to assimilate or at least
partially develop the competencies associated with data
literacy.
In the domain of primary and secondary education,
the American Association of School Librarians’ (AASL)
“Standards for the 21st-Century Learner” contain no ex-
plicit reference to data, although the AASL proposes a
crosswalk to align them with the “Common Core State
Standards” (AASL 2011) in English language arts, history/
social studies, science and technical subjects, and math-
ematics.
In the university domain, both the “Seven Pillars of
Information Literacy: Core Model for Higher Education”
and its version for research, the “Seven Pillars of Informa-
tion Literacy: a Research Lens for Higher Education” (Soci-
ety of College, National and University Libraries [SCONUL]
2011a, 2011b) assume that the object of information liter-
acy is to work with data. In fact, data and information are
consistently mentioned jointly, although no specic crite-
ria or recommendations are provided for the former. Nei-
ther do the society’s working papers “The SCONUL Seven
Pillars of Information Literacy through a Digital Literacy
Lens” or “The SCONUL Seven Pillars of Information Liter-
acy through an Open Content Lens” (SCONUL 2012, 2011c)
accord data special treatment. And signicantly, despite
the focus on open content in the latter, it makes no refer-
ence to the Open Data Initiative.
The Association of College and Research Libraries
(ACRL) (2000) acknowledges the value of research data
and includes a number of explicit references to them in the
information competencies proposed in its “Information
Literacy Standards for Higher Education” (ILSHE). The
ACRL has likewise published several adaptations to these
standards for areas with specic needs, in which the com-
petencies associated with data literacy acquire particular
signicance. These include “Information Literacy Stand-
ards for Science and Engineering/Technology” (ILSSET),
“Information Literacy Standards for Anthropology and
Sociology Students” (ILSAS), “Political Science Research
Competency Guidelines” (PSRCG) and “Information
Literacy Competency Standards for Journalism Students
and Professionals” (ILCSJ) (ACRL 2006, 2008a, 2008b,
2011a).
Table 1 lists competencies explicitly associated with
data literacy (they either mention data, statistics and/or
quantitative methods) found in six of the standards men-
tioned above as particularly related to this construct: the
crosswalk between the AASL and the “Common Core State
Standards” (AASL/CCS) and the ve ACRL standards (IL-
SHE, ILSSET, ILSAS, PSRCG and ILCSJ). These competen-
cies have been grouped by type for easier identication of
the importance attached to each in the standards. Note,
rstly, that the competency consisting of the ability to de-
termine and use research methods suited to the problem
broached (both for collecting and using data) is present
in all the standards analyzed, and stressed in particular
in political science and journalism. The ability to handle
(transfer, transform) and analyze data, which includes
the ability to use data management tools and statistical
soware, is explicitly mentioned in all except the journal-
ism standards. The competencies relating to knowledge
of and access to sources and their critical assessment
are likewise particularly important for most of the stand-
ards.
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Javier Calzada Prado and Miguel Ángel Marzal, Incorporating Data Literacy 127
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Data Literacy in Libraries’
Instructional Programs and Services
While training to master some of the basic data literacy
competencies has formed or is gradually becoming part
of the curriculum in all levels of schooling, training de-
livered in libraries or with their support has focused pri-
marily on higher education. Indeed, data have tradition-
ally formed part of the resources in all manner of libraries,
although with greater weight in academic libraries in light
of their users’ special research-related needs. This has led
in the last few decades to the creation of data libraries or
data services as specialized research library facilities. Kel-
lam (2011) noted that in the nineteen sixties and seventies
some large American universities created data support
centres or archives in academic departments or computing
centres, while in the 1990s libraries began to create data
centres and services per se, such as the University of Min-
nesota libraries’ Machine Readable Data Centre (Treadwell
and Cogswell 1994). Bennett and Nicholson (2007), in an
analysis of a random sample of Association of Research
Libraries (ARL) members’ websites, observed that this
trend has continued over time, nding that libraries were
competing with other centres or services within their in-
stitutions to furnish data-related resources and services.
At present, the provision of data-related services is un-
evenly represented in two main spheres. In formal, natu-
ral and experimental science (such as statistics, physics,
mathematics, biology and astronomy), inter-departmental
data labs or even supercomputing centres or grid comput-
ing infrastructures are common and have the technology
and sta needed to provide highly specialized services for
analyzing and managing large volumes of data. Libraries
participate only sporadically in these cases, whereas in
humanities and social and applied sciences (sociology, ge-
ography, economics and business administration), where
such centres and services are much less common, they are
provided primarily by academic libraries. Nonetheless,
humanities and social and applied science are now also
facing the eScience challenge (Williford and Henry 2012),
which is particularly visible in areas such as geospatial
information (GIS) (Williford and Henry 2012) and its ap-
plications in the various disciplines.
Academic libraries are deploying a four-fold response
to eScience and the growing need to use research data: 1)
hiring specialized sta (data librarians or data specialists)
or furthering data management and analysis training for
(generally reference) librarians; 2) intensifying the collec-
tion or compilation of and providing access to data sourc-
es; 3) participating in the development of institutional
Table 1: Data literacy competences in information literacy standards
Data literacy competency AASL/CCS ILSHE ILSSET ILSAS PSRCG ILCSJ
Ability to identify the context in which data are
produced and reused (data lifecycle)
..f; ..e ..e ..a; ..d
Ability to recognize source data value, types and
formats
..; .. ..c ..e ..d
Ability to determine when data are needed ..f ..d ..b
Ability to access data sources appropriate to the
information needed
.. ..a ..e; ..g;
..c; ..f
..b; ..d;
..a; ..a
Ability to critically assess data and their sources ..; ..;
..; ..;
..
..e ..a; ..d ..e
Ability to determine and use suitable research
methods
.. ..d ..d ..a ..f; ..a;
..d
..c; ..b
Ability to handle and analyze data .. ..d ..c; ..b ..c ..a
Knowing how to select and synthesize data and
combine them with other information sources and
prior knowledge
.. ..b ..b ..e; ..c
Ability to present quantitative information
(specic data, tables, graphs, in reports and similar)
..; .. ..c;..b ..d
Using data ethically ..e ..d; ..e ..c
Ability to apply results to learning, decision-
making or problem-solving
..; ..
Ability to plan, organize and self-assess
throughout the process
..b ..c ..f
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