Increasing transparency in Medical Education through Open Data

Information Classification: General
Increasing transparency in Medical
Education through Open Data
Dr. Rebecca Grant, Head of Data & Software Publishing, F1000
Prof. Barbara Jennings, MedEdPublish Advisory Board, UEA
Niall Rundle, Portfolio Manager, Taylor & Francis

• An introduction to MedEdPublish - Why AMEE is moving to a new Platform, and
what opportunities does that allow?
• What is data in Medical Education research?
• Sharing Medical Education data transparently and safely
• Breakout discussion
• Closing statements and next steps
• Q&A session
Today’s session

Speaker introductions
• Dr. Rebecca Grant, Head of Data & Software
Publishing, F1000
• Prof. Barbara Jennings, MedEdPublish Advisory
Board, UEA
• Niall Rundle, Portfolio Manager, Taylor & Francis

An Introduction to MedEdPublish
• Articles are peer reviewed AFTER publication #PPPR
• Open approach to peer review that avoids editorial bias and
increases speed of publication
• Promotes scholarship & sharing expertise with colleagues at
all levels
• Online format facilitates comprehensive data presentation

1. Speed of Publication, world leading
technology and an improved user experience
2. A Platform fully compliant with major funder
mandates including Plan S and DORA
3. The opportunity to expand MedEdPublish
with new content, features and opportunities
4. A continued commitment to openness,
including a new focus on open data…
Why is MedEdPublish moving to a new Platform?

• MedEdPublish will advocate an Open Data policy. This means all articles should
include citations to repositories that host the data underlying their results, together with
details of any software used to process those results.
• This is so that other researchers can see the raw data, be able to replicate published
studies and analyze the data, as well as in some circumstances, reuse it.
• Others who then reuse this data for their own studies can cite this data (which can be
cited separately from the original article if appropriate).
• Failure to openly provide data for publication without good justification is likely to result
in your article being rejected.
• Exceptions: We recognise that there may be cases where openly sharing data may
not be feasible (due to ethical, data protection or confidentiality considerations), or
because the data have been obtained from a third party and access restrictions apply.
What is the new MedEdPublish data policy?

Do you know what “data” means in the context
of medical education research?

Research Data
Data produced for the
purpose of the
reported study
Data produced by
others
Materials produced for
the purposes of the
reported study
Code produced either
as the primary output
or for purposes of
replication
What do we mean by research data?

What does MedEdPublish require?

Data in Medical Education Research
“Participants completed application forms, pre-
course, post-course and daily evaluation
questionnaires..”
“The participants were asked to experience and
evaluate … and the results were discussed in a
Focus Group Discussion.”
“Anonymous online surveys were distributed..”
“Semi-structured interviews with final year
medical students…”
• Quant, qual or both
• Text, audio, video,
transcripts
• Sensitive topics
discussed

The challenges of sharing sensitive human data
My participants
didn’t consent to
sharing their data
I’m concerned
about the legal
implications
The topic of my
study is too
sensitive

The challenges of sharing qualitative data
Where can I
store my data?
How can I
anonymise my
data?
What about my
audio and video
files?

Have you shared your data before?

1. Plan for data sharing in advance
2. Seek consent for sharing from your
participants
3. Anonymise data or shared in a controlled
access repository
4. Describe any sharing limitations in your
data availability statement
How to ensure you can safely share your data

1. Plan for data sharing in advance
Create a Data Management Plan (DMP) before your research begins
Consider data collection, storage, team roles, discipline specific
guidance and how you will share your data when the project ends
Keep your plan up-to-date as your research progresses
Key benefits:
Comply with funder and institutional policies
Prepare for data sharing and publication from the beginning
Ensure you will have informed consent from participants

2. Seek consent from your participants
Ensure that your participants can provide informed consent for
future data sharing
Outline any anonymisation or de-identification techniques you will
use, where data will be stored and who will have access
Key benefits:
Assists with compliance with any legal frameworks like GDPR
Necessary in order to share data ethically

3. Anonymise or store in controlled access repository
Based on your DMP and participant consent you will likely:
• Anonymise the data
• Deposit the data into a controlled access repository
• OR both
Key benefits:
Protects the privacy of your participants and safeguards them from harm
Allows maximum possible reuse of your data by other researchers

A process which removes information from a dataset so that research
participants can no longer be identified
What does it mean to anonymise data?
Indirect identifiers:
Information which, in combination,
uniquely identifies a research
participant.
Ethnicity +
Sex +
Place of birth +
Job title +
Direct identifiers:
Information which uniquely
identifies a research participant.
Full names
Dates of birth
Address
Phone number
Biometric information

Key techniques in data anonymisation
Remove the variable (e.g. lat-long, date of birth)
Generalise (e.g. swap an address for a city)
Pseudonymise (e.g. swap names for falsified versions)
Create bands (e.g. age ranges, salary ranges)

Examples of data anonymisation
Quantitative data Qualitative data

Anonymising qualitative data
Data formats:
Transcripts, focus group
notes and narrative survey
responses are more
difficult to anonymise
Challenging to automate as
meaningful information
may be left in the text
Necessary to indicate
where changes have been
made, e.g. diacritics
Technical challenges:
Audio and video files are
extremely time-consuming
to anonymise
May be useful to include
budget for this in initial
Data Management Plan
Value of data may be lost:
Removal of information
damages the intrinsic value
of the data collected?

Anonymising qualitative data
Data formats:
Transcripts, focus group
notes and narrative survey
responses are more
difficult to anonymise
Challenging to automate as
meaningful information
may be left in the text
Necessary to indicate
where changes have been
made, e.g. diacritics
Technical challenges:
Audio and video files are
extremely time-consuming
to anonymise
May be useful to include
budget for this in initial
Data Management Plan
Value of data may be lost:
Removal of information
damages the intrinsic value
of the data collected?
Use of controlled
access repository
may be appropriate

Choosing a data repository
✓ A location on the web for your data to be stored and accessed
✓ Allows you to provide contextual information so that data can be reused
✓ Provides a persistent identifier (e.g. a DOI) so that your data can be cited
✓ Provides peace of mind – data is managed by the repository, not by you
Controlled access repositories:
✓ Do not share data openly on the web
✓ Require certain access conditions to be met by users
✓ Protect participant privacy
✓ May remove need for anonymisation

Choosing a data repository
Is it used by other researchers in your
discipline?
Does it guarantee storage for
a particular length of time?
What metadata
format does it use?
Is there a cost for
storage?
DOIs?
Is it
recommended?

The F1000 recommended repositories list
What type of data Which repository
Any Dryad
Any, but especially data in SAV and POR
formats
Dataverse
Any Figshare
Any, but especially deposits with mixed
data and code
Zenodo
Any, but reserved for ISCPR member
institutions
Open ICPSR
Social and economic data UK Data Service

4. Describe any limitations in your data availability
statement
Your data availability statement:
A required section of your manuscript
Should describe all data underpinning your research and where it can
be found.
If relevant include:
An explanation of why the data is not open;
Links to any intermediary data that can be de-identified without
compromising anonymity;
What, if anything, the relevant Institutional Review Board (IRB) or
equivalent said about data sharing;
Where applicable, all necessary information required for a reader or
reviewer to apply for access to the data and the conditions under which
access will be granted

Sharing medical education research data
✓ Plan for sharing
✓ Protect your participants and share ethically
✓ Share data appropriately
✓ Explain what you’ve shared (or not shared) and why
Need more help?
https://think.f1000research.com/open-data:
Repositories, licensing, data collections, spreadsheets, writing a data availability statement,
sharing code…

Standards for high quality, reusable data: FAIR

1. Findable: Include a persistent identifier in your data availability statement,
linking to a data repository.
2. Accessible: Use a recommended data repository that’s accessible on the
web.
3. Interoperable: Use any applicable reporting standards, vocabularies or
ontologies which are common in your discipline.
4. Reusable: Include a standard licence for your data.
Show your data’s “FAIR”-ness

The benefits of transparent, open data
✓ Boost your credibility – work is replicable and can be validated
✓ Enhance the visibility of your work – both your article and your dataset can be found
by others
✓ Develop a better understanding of your field – support a deeper, richer understanding
of your topic
✓ Progress your career – open data sharing is associated with an increase of
citations to your published paper of up to 25%
*(Colavizza et al., https://doi.org/10.1371/journal.pone.0230416)

In your breakout groups, please discuss the following and make some notes
to feedback. Take 10 minutes.
• What challenges have you faced in sharing data, or what concerns do you have
if you’ve not done so before?
• Have you identified solutions during today’s session?
• Are there other solutions you require?
Breakout session

In your breakout discussions explore ideas about sharing data in clinical & educational research.
Open the hyperlink to a padlet forum in a browser & capture your ideas and queries:
www.tinyurl.com/MedEdData

Breakout feedback
• What challenges have you faced in
sharing data, or what concerns do you
have if you’ve not done so before?
• Have you identified solutions during
today’s session?
• Are there other solutions you require?

Do you feel more confident about sharing your
research data?

• Submissions to the new Platform are
now open, with the first publications
due October 2021
• The new Platform has a full set of
article guidelines, instructions and
FAQs
• You can visit the URL here, or visit
the current MEP site
How to submit to the new MedEdPublish Platform,
and what’s next?
submission.mededpublish.org

Final Q&A session
• Do you have any questions about the workshop, publishing
your data or the new MedEdPublish Platform?
• Was this a useful workshop? Are there any areas where we
could have expanded?
• What further resources around data would be helpful for you,
and the wider medical education community?

Publish fast.
Openly.
Without restrictions.

Appendix Slides

The MedEdPublish post-publication
peer review publishing model
Designed to maximise discoverability, reach, use and potential impact
DOI issued
Indexed: Google Scholar
Scopus
PubMed
DOAJ
etc

• A fully transparent APC
policy, with a breakdown of
the costs for different article
types
• 100% and 50% APC
discounts in place for
authors from HINARI
Band-A and Band B
countries
• 5% discount for all AMEE
members
How much will it cost to publish in MedEdPublish?

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