The ROER4D Curation & Dissemination team provides an overview of the ROER4D open data initiative as well as some key insights and challenges experienced.

1. Challenges in Preparing and Sharing Open Data
OpenCon 2016 Cape Town
14 December 2016
Michelle Willmers and Thomas King
ROER4D Curation and Dissemination Manager
CC BY

2. Research
On Open Educational Resources (OER)
for
Development
• Imperative to establish empirical baseline research on OER in Global South
• 86 researchers in 26 countries across 3 continents
• Project ‘Open’ ethos manifests in Open Research strategy, bridging ‘Open’
silos
• Open content (typically used in a teaching and
learning content) that can be reused, revised, remixed,
redistributed and retained
• Made possible by open licensing, although increasing
focus on differentiating implicit vs. explicit open
content
• Focus on role OER can play in improving access to quality education
• Focus on role project can play in building Global South Open Education
research capacity
• Strong advocacy and activism component (NGO, CBO sectors – not only
career researchers)
Focus on empirical baseline manifests in focus on curatorial and publishing capacity
within the research project. The project acts as publisher, providing greater agency and
control (but presenting some challenges in terms of accreditation/reward).

3. ROER4D Curation & Dissemination Strategy
• Provide a content management and publishing service to SP researchers and the
Network Hub team in order to advance research capacity development efforts and
increase visibility of outputs.
• Support Principal Investigators and SP researchers in editorial development of
ROER4D outputs.
• Address infrastructure deficits and provide content management solutions
(including content hosting) in a research community with uneven institutional
support and capacity challenges.
• Ensure that the ROER4D legacy is freely accessible for reuse in line with international
curatorial and publishing standards.
• Complement Network Hub Communications efforts in an integrated
communications/dissemination approach.

4. • Data sharing as component of open content focus.
• Organising and profiling open content increases the potential for reuse and citation
(impact).
• Well-organised, strategic research management and content organisation promotes
rigour in the research process.
• Copyright vests with the author > data-sharing activity determined by their willingness
and capacity to engage.
• Format and platform/tool agnostic.
• Share openly by default on condition that it is valuable, legal and ethical
ROER4D data management principles

5. Project archive
(external)
Zenodo
Network Hub
(Google, Vula)
ROER4D project data flow
Internal
sharing and
collaboration
External
sharing and
collaboration

6. Five pillars of
ROER4D data
publication
approach

7. Step 1: Evaluate contractual framework, articulate strategy

8. Step 2: Get researchers on board

9. • Check ethics approval and consent
• Ensure first-tier de-identification takes place prior to Network Hub transfer in order to
ensure research subject confidentiality
• ROER4D agnostic in its approach (in terms of scale, format and technical
sophistication)
• Challenges of varying researcher sophistication in terms of data collection and
presentation
• Challenges of varying researcher sophistication in terms of technology employed to
capture, present, and analyse data
Step 3: Obtain source sub-project micro-data

10. • Archive in Vula and UCT e-Research Centre secure institutional archive
• Network Hub C&D team audits researchers’ submitted dataset
> What is the dataset comprised of?
> Are all the pieces there?
> What were the data collection processes, and do we have all the instruments to share?
> What languages are represented?
> Does something else like it exist?
> Who might it be of use to?
• Address file naming and format issues
• Articulate sub-project-specific data management plan
Step 4: Network Hub curation and quality assurance

11. • Scope and conceptualise the dataset
> Which components of the project-generated micro-data are you ethically and
legally allowed to share?
> Which components of the project-generated micro-data will you invest
resources in curating and sharing?
> Which instruments will you include?
• Identify focus of data and points of sensitivity
• Define appropriate second-tier de-identification approach
Step 5: Prepare data for publication

12. • Generate metadata and dataset description (accompanying narrative)
• Submit content to publisher (DataFirst)
• Link to published outputs
• Include description of process in research Methodology statements
• Profile in project communications activity
Step 6: Publish

13. Some
lessons
learned

14. 1. Openness increases rigour. Preparing data for publication promotes professional
approach to research process.
2. Preparing data for publication exposes weaknesses in instrument design and
research process.
3. Introducing C&D and data-sharing focus midway through a project poses many
challenges, particularly in terms of ethical and consent components.
4. Data sharing drives focus on reproducibility, transforming traditional approach to
crafting methodology statements.
5. The data preparation process takes time (approx. one week of researchers’ time in
ROER4D context).
6. Obtaining balance between utility and adequate protection in de-identification of
qualitative data is a challenge.
7. Openness is threatening to researchers in terms of exposing weakness in processes
and perceived threat of losing publication advantage.
8. C&D and data sharing activity require support, capacity development and
resourcing.

15. Qualitative de-identification
Thomas King

16. Terms and definitions
• De-identification – removing, eliding or replacing
pieces of information that reveal research
participants’ (possibly also referents’) identity.
• Anonymity – personal details are not gathered.
• Confidentiality – personal details are not shared.
• E.g. an anonymous survey contains no questions
about personal identifiers. A confidential survey
does contain these questions, but will not
share/publish them.

17. The two pillars of open data sharing
Consensual
ethical
legal
Comprehensible
coherent
valuable
Research Data Management &
Open Data sharing

18. The de-identification balancing act
First, do no harm
Remove as much as needed to ensure the
confidentiality or anonymity of the
research participants.
Ensure that all ethical and consent
processes have been adhered to.
Don’t go overboard
Remove as little as is ethical to ensure the
richness of the data.
Take the unit of analysis as the guide – de-
identify up to the Unit of Analysis.
E.g: If Study X compares two universities,
you can safely remove all identifiers lower
than the university affiliation.
HOWEVER
Your data may be useful to others. The
purpose of de-identification is to preserve
confidentiality – don’t de-identify for the
sake of it

19. Qualitative de-identification
• De-identification located in the same ecosystem
as data cleaning and data validation – no clear
line between data improvement and de-
identification
– Cleaning up typos
– Standardising presentation and layout
– Identifying unanswered questions (or additional
questions), mislabelled responses, etc.
• Much of these also apply to quantitative data
• Articulation of principles in RDM and description
of these processes included in metadata

20. READ
DATA
READ
DATA
Coherence
Format &
layout Editing
Fix typos &
identify
anomalous data
1.
2.
3.
4.
5.
De-identifying
Remove
identifiers
Validation
Identify and
account for missing
data
ROER4D data
interrogation
process

21. NETWORK HUB
Principal Investigator
Curation and Dissemination
team
Communication and Evaluation
consultants
NETWORK HUB
Principal Investigator
Curation and Dissemination
team
Communication and Evaluation
consultants
SUB PROJECTSSUB PROJECTS
ROER4D project structure
Using largely mixed-methods data (both
quantitative and qualitative)

22. ROER4D de-identification process
1. First-level de-identification by researcher
– Removal of direct identifiers (names of
people/institutions/companies, ID numbers, etc.)
– Important to ensure that raw data is not shared
1. Second-level de-identification by C&D team to
catch remaining direct identifiers
2. In-depth sweep of the text to identify indirect
identifiers
– Meticulous, thorough, repeated reading of the text
• (which ties back to general data enhancement)

23. Tricky situations
• Data collected in multiple languages
– De-identification (particularly in qualitative data) far
more difficult – greater reliance on the researcher
• Post-hoc consent process
– Departments merge or close, participants retire or
disappear
• Data collected by multiple researchers
– Different collection strategies, adherence to interview
schedules, use/non-use of clarifying questions, etc.

24. Open by design
• Help researchers write consent forms!
Particularly for open data sharing.
• ‘Red flag’ clauses abound in template consent
forms, including:
– “will be used for research purposes only”
– “data will be destroyed after use”
– “only researchers will have access to the data”
• More open consent forms allow for data
sharing but do not mandate it.