Gap Analysis
Introduction to Research Data Management Services, January 2013
Gap Analysis
•
Original undertaken in 2008 and
updated in 2011
• Research Data Strategy Working Group
(now called “Research Data Canada”)
• Examined the gaps between ideal state
and current state across several
indicators.
Introduction to Research Data Management Services, January 2013
Basic Research Data Lifecycle
Introduction to Research Data Management Services, January 2013
Policies
Ideal State: Canadian organizations have coherent and cohesive
policies that govern the management of data across disciplines.
Data
Production
Data
Dissemination
Long-term Data
Management
Discovery and
Re-purposing
In limited
cases
Yes, but
sometimes
policies
contradict each
other
In limited cases
In limited cases
e.g. IPY, SSHRC
(but not enforced),
CIHR (for selected
types of data),
Some provincial
and federal
agencies
*NOT THE
UNIVERSITIES!
e.g. IPY, funding
agencies require
that researchers
retain and share
data if requested
by others.
e.g
International
Polar Year
(IPY)
e.g. ethics and
privacy policies
vs. data sharing
policies
Introduction to Research Data Management Services, January 2013
Funding
Ideal State: Together, the range of funding mechanism cover
costs throughout the data lifecycle, including the preservation of
research data.
Data
Production
Data
Dissemination
Long-term
Data
Management
Discovery and
Re-purposing
Yes
In limited cases
In limited cases
In limited cases
CIHR, NSERC,
SSHRC, CFI
and some other
research
funding sources
all support data
production
CIHR, NSERC,
SSHRC, CFI
provide funding
for
disseminating
data
Federal
government and
universities
provide funding
for disciplinary
data
repositories in
some fields
Access services
part of
disciplinary
repositories, but
often fee-based
services
Introduction to Research Data Management Services, January 2013
Roles and Responsibilities
Ideal state: Each stakeholder in the data lifecycle has a distinct set of
responsibilities, and works together to pursue higher-level
stewardship goals; and Canada has a national entity serving as a
focal point to coordinate data management and stewardship activities
across the country.
Data
Production
Data
Dissemination
Long-term Data
Management
Discovery and
Re-purposing
Research
teams
(principle
investigators)
Research teams
(data must be
retained and
shared for 5 years
after completion of
project)
In some
disciplines
-government
agencies,
universities and
research
communities
Very limited
-Some
government
agencies,
universities and
research
communities
Introduction to Research Data Management Services, January 2013
Data Repositories
Ideal State: Canada has a comprehensive network of trusted
and interoperable data repositories.
Data
Production
Data
Disseminati
on
Long-term Data
Management
Discovery
and Repurposing
Short term
repositories
or
management
solutions to
assist with
data analysis
Limited
Limited
Very limited
e.g. Data
NRC (CISTI), LAC,
Liberation
Scholars Portal, U
Initiative (DLI) Alberta working on
TDRs
Discipline-based
repositories have some
preservation aspects
Discipline
based
repositories
Introduction to Research Data Management Services, January 2013
Standards
Ideal state: There is widespread adherence to standards
throughout the data lifecycle, including metadata standards.
Data
Production
Data
Dissemination
Long-term Data Discovery and
Management
Re-purposing
Varying levels of Variable- highly Variable- regular
adherence to
discipline
use in large data
standards
dependent
centres and ad
hoc
Discipline-based e.g. DDI in
implementation
standards
social science, in other cases
Introduction to Research Data Management Services, January 2013
XML is
commonly used
in social
sciences
Skills and Training
Ideal state: There is widespread adherence to standards
throughout the data lifecycle, including metadata standards.
Data
Production
Data
Dissemination
Long-term Data
Management
Yes
Limited
Very limited- a few Very limited
data sciences
employed to
e.E DLI Training
support
Program
researchers
e.g.CNC
CODATA,
workshops for
researchers
Introduction to Research Data Management Services, January 2013
Discovery and
Re-purposing
Rewards and Recognition
Ideal state: Reward systems for researchers widely recognize
contributions to research data management.
Data
Production
Data
Dissemination
Long-term Data
Management
Discovery
and Repurposing
Built in
incentives linked
to the
successful
undertaking of
the research
project
Some
disincentives for
data sharing at
universities and
funding
agencies
(data about
human
subjects)
Very limited
Very limited
Some research
project (IPY),
funding agencies
(CIHR) and
journals require
deposit of data
into data
repositories.
Some informal
recognition
within discpline
Introduction to Research Data Management Services, January 2013
Research and Development
Ideal state: Canada has a coordinated approach to R&D activities
in support of data stewardship needs.
Data
Production
Data
Dissemination
Long-term Data Discovery and
Management
Re-purposing
Significant
research and
development
support in order
to facilitate data
use and
analysis.
Unknown
Very limited
Some research
and
development at
Canadian
institutions
Introduction to Research Data Management Services, January 2013
Unknown
Research and Development
Ideal state: Canada has a coordinated approach to R&D activities
in support of data stewardship needs.
Data
Production
Data
Dissemination
Long-term Data Discovery and
Management
Re-purposing
Significant
research and
development
support in order
to facilitate data
use and
analysis.
Unknown
Very limited
Some research
and
development at
Canadian
institutions
Introduction to Research Data Management Services, January 2013
Unknown
Gap Analysis
Gap level
Preservation
Access
Research and development
Rewards and recognintion systems
Skills and training
Standards
[Trusted digital] data repositories
Roles and responsibilities
Funding
Policies
Introduction to Research Data Management Services, January 2013
3
Current Levels of Support Across
the Research Data Lifecycle
Project-based
Production
Dissemination
Institution-based
Long-Term
Management
Reuse and
Repurposing
Introduction to Research Data Management Services, January 2013