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R2DaLT: thoughts about data literacy - Koltay
R2DALT: THOUGHTS ABOUT
TEACHING DATA LITERACY
• Research 2.0
• The changing view on data,
• Data literacy,
• The sequence of supporting researchers,
• Data curation,
• Contents and courses,
• Data-intensive research
• Open Science
= Open Data
= Open Access to scholarly publications
Research 2.0 requires a high level of attention to
data management and data curation, data citation
and data quality (Vilar, & Zabukovec, 2019).
ABUNDANCE OF DATA
Source and consequence of
Information overload → data overload
NEW VIEWS ON DATA
With its perceived importance,
the views on data have changed.
• Is data something different from
Data can be
Texts can be
interpreted as data.
IS THE DIK(W) PYRAMID
• Any information in binary digital
form (Digital Curation Centre).
• Information literacy is related not only to
print, but data, images, etc. (CILIP, 2018).
← Documents, not born digital may
become digital at some point.
PRIMARY SOURCE DATA
• Humanities – unstructured data (from
archives and manuscripts),
• Social sciences – tabulated data (from
surveys, polls, census),
• Natural sciences – tabulated data (from
controlled studies) (Fontichiaro et al,
• More specific than ‘data’
= ‘data collected as part of a research
• data collected for curation and
preservation, can become research data,
• research data is archived for curation and
= competences needed for any work with
research data (Schneider, 2013).
= the ability to process, sort and filter vast
quantities of information, for which
competences for searching, filtering,
processing, creation and synthesizing
information are needed (Koltay, 2015).
• Is cognate to information literacy.
• Is compatible with the information literacy
focus of academic librarianship.
• Focuses on data quality.
It involves elements of
• Statistical literacy,
• Data governance principles,
• Data science,
• and Open Data.
• in the context of education and training (of
researchers, students, etc.)
• in the context of providing data services of
informational and/or technical nature (for
librarians and other providers, technical
• Data literacy education’s main targets are
• Librarians and teaching staff members
also should be data literate, but educating
the latter is a delicate issue.
• The concept of data,
• Critical thinking,
• Ethical issues,
• Research Data Management (RDM),
• Data quality
• Data citation
• Data visualization
• Metadata (Ridsdale et al., 2015).
OTHER IMPORTANT TOPICS
• Big data, little data, no data (Borgman,
• The existence of grey data (useful data,
produced by universities outside their
research realm, but not subjected to peer
review, similarly to grey literature
INCLUDE FAIR PRINCIPLES
• and Re-usable.
Mention the existence of Research Data
• Provides answers about the availability
and access possibilities, provenance,
trustworthiness and meaning.
Provenance as indicator of quality:
• Who generated it?
• Who funded the study where the data
• What was the research agenda?
(Koltay, 2016; Fontichiaro et al., 2017)
HOW TO TEACH DATA
• Include mechanics related to research
• Focus on practice;
• Use real world data, when appropriate
(Ridsdale et al. 2015).
THE SEQUENCE OF
1. Data literacy instruction,
2. Research Data Management,
3. Data curation,
4. Data preservation.
• RDM is an integral element of everyday
research work for many researchers
→ supporting it should become routine
activity in academic libraries.
• Academic libraries show varied levels of
but they provide a wide array of
informational RDM services in many
• Informational services (with high
• Technological/technical services (with
Consulting with staff and students
• On Data Management Plans,
• On metadata standards,
• reference support (for finding and citing)
• Creating or transforming metadata for
• identifying datasets that could be
candidates for repositories,
• selecting and preparing datasets for
• deaccessioning, or deselecting datasets.
Data curation is close to technological RDM
The encompassing work
taken by curators of a data repository
in order to provide meaningful and enduring
access to data (Johnston et al., 2018).
• Anyone wanting to support researchers in
storing, managing, archiving and sharing
= a data supporter
= (data) librarians, IT staff and researchers
Acquiring the basic knowledge and skills
that enable data supporters
to take the first steps towards supporting
researchers in storing, managing, archiving
and sharing their research data.
A DATA SUPPORTER
• Shows proactive attitude to improve data
• Sees data and information services as part
of larger whole in which decisions are
• Can handle questions efficiently and
knows when to address a dedicated
• Is cooperative.
UNDERSTANDS / KNOWS
• The structure of a data management
• The various ways to store, backup,
organize and document research data;
• Types of archives, data publication and
• How to advise researchers in balancing
legislation and practice;
(Verbakel, & Grootveld, 2016).
Do-It-Yourself Research Data Management
Training Kit for Librarians
• Data management planning
• Organising & documenting data
• Data storage & security
• Ethics & copyright
• Data sharing
DATA CURATION PROFILES
• Can be used to provide a foundational
base of information about a particular set
• that may be curated by an academic
library or other institution (Carlson, 2010).
AN EXCERPT FROM A
Linguistics / Etymology
• Significant amount of data;
• Composed of videos, spreadsheets and a
finalized report captured in MS Word files;
• Lack the means of managing, curating and
sharing this data effectively.
Ensure that researchers understand
• Libraries will be responsible stewards of
• They will not be taking ownership of the
• Original owners will be able to access their
• or take it out of library systems
• at any point and in-perpetuity
• Relieving researchers’ technical and
administrative burdens is a respectable
• Transcending this status by achieving true
collaboration with researchers, requires
focused and intensive work of library
managers, who must understand the
advantages of RDM
(Burton & Lyon, 2017, Koltay, 2019).
TELL TO ACADEMIC LIBRARIES
THAT THEY SHOULD…
• Fill gaps at all levels of professional skills
• Change librarians’ self-identification
• Gain an appreciation of technical and
socio-ethical issues related to research
data (Robinson & Bawden, 2017).
NOT ONLY FOR DATA
• There is a clear need for teaching data
literacy to academic librarians (Koltay,
• At smaller institutions, there is a greater
need for diverse and generalized skills,
• Being data literate is a need for all future
academic librarians (Morrison & Weech,
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Borgman, C. L. (2018). Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier. Berkeley
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