The document outlines the FAIR principles for data sharing, emphasizing that data should be Findable, Accessible, Interoperable, and Reusable. It discusses the importance of descriptive metadata, participant consent, and intellectual property rights while identifying key motivators such as research validation and funder requirements for data sharing. Furthermore, the document highlights the need for proper data documentation and standards, as well as the benefits of making data available for enhancing citation rates in research.

1. PREPARING DATA FOR
SHARING
The FAIR Principles
Gareth Knight
London School of Hygiene & Tropical Medicine
gareth.knight@lshtm.ac.uk
ADMIT Network Meeting
01 December 2015

2. FAIR Principles
Findable
•Descriptive
metadata
•Persistent
Identifiers
Accessible
•Determining
what to share
•Participant
consent and risk
management
•Access status
Interoperable
•XML standards
•Data
Documentation
Initiative
•CDISC
Reusable
•Rights and
licence models
•Permitted and
non-permitted
use
http://datafairport.org/
Make your data:
• Findable
• Accessible
• Interoperable
• Reusable

3. Data Sharing in the sciences
• Data sharing has always taken place in
some form
• Enlightenment during 17 – 18th
century built upon open debate and
sharing of knowledge
• Science depends on openness and
transparency to advance
– Replicate results
– Correct errors & address bias
• Negative as well as positive findings
need to be in the public domain
“Systematic Dictionary of the Sciences, Arts, and Crafts”
Diderot & d'Alembert (1751 onwards)

4. Data Sharing in the News
“To make progress in science, we need to be open and share.”
Neelie Kroes (2012)
vice president of the European Commission
http://europa.eu/rapid/press-release_SPEECH-12-258_en.htm

5. Key Motivators
Research / Policy development Ensure validity
Funder Requirement Publisher requirements

6. Data reuse improves citation rate
• Studies that made data available in a
public repository received 9% more
citations than similar studies where
data was not available
• Creators tend to cite own data up to 2
years
• Third party use grew over time: for 100
datasets deposited in year 0,
– 40 reuse papers in PubMed in year 2
– 100 by year 4
– 150+ by year 5.
Piwowar & Vision, T.J (2013). Data reuse and the open data citation advantage. https://peerj.com/articles/175/
Study of 10,557 articles published between 2001 and 2009 that
collected gene expression microarray data

7. DATA
DISCOVERY
Is your data findable?

8. Discovery Metadata
• Descriptive metadata created to
describe key attributes of data:
– Title
– Creator
– Content description
• Data repositories/journals capture
and publish discovery metadata in
several formats (DC, DataCite, DDI)
• Metadata ‘harvested’ by research
data catalogues & search engines
• Metadata available to all, even if data
is not
Registry of Research Data Repositories
http://service.re3data.org

9. Citing Data
• Research data are a citable resource, same as papers & books
• 44-75 days is the estimated average lifespan of web URLs
• A unique, long-term identifier is necessary to enable citation
• Many persistent ID systems developed to solve problem
– DOI, Handle, ARK, etc.
• Data citation in reports and publications
UK Data Service: Citing Data
https://www.ukdataservice.ac.uk/use-data/citing-data

10. DATA
ACCESS
Do you have permission to share? If so, what?

11. Data Selection
Meet funder /
journal obligations
Encourage research
use
Higher citation rate
Reproduce &
validate results
ConstraintsMotivation
Concern that will attract
lower rate of response or
people will be less honest
Intellectual Property
Rights issues
Participant consent
doesn’t address
sharing
Data Protection
legislation
Data sharing decisions built upon
recognition of all influencing factors
Information Commissioner Office. Data Sharing Code of Practice
http://www.ico.org.uk/for_organisations/data_protection/topic_guides/data_sharing/

12. Handling individual level data
• Collected and analysed for
specific purpose
• Stored no longer than is
necessary
• Kept securely and safely to
prevent unauthorised or
unlawful access, process, loss,
or destruction
EU Data Protection Directive 95/46/EC establishes limitations on
how information on living individuals is held and used
Reform of the data protection legal framework in the EU
http://ec.europa.eu/justice/data-protection/reform/index_en.htm

13. Data Sharing as a barrier
Investigation of influence of open data policies on consent rate:
• No participants declined to participate, regardless of condition
• Rates of drop-out vs completion did not vary between open/non-open policies
• No significant change in potential consent rates when participants openly asked
about the influence of open data policies on their likelihood of consent.
Some researchers consider sharing obligations to be a
barrier to research participation

14. Access Status
Control method
• Data Transfer Agreement
• Access controls
Application process:
• Request form
• Review process
Access criteria:
• Permitted users – how do you identify?
• Permitted use – topic, academic use,
• Other criteria: encryption, time period
Open Vs. controlled access
https://www.flickr.com/photos/toruokada/16958186672/

15. DATA
INTEROPERABILITY
Can data be analysed and harmonized?

16. Data Standards
Data exchange is dependent upon:
• Open formats
• Common standards
• Documented metadata specification
• Consistent vocabulary
• Documented workflows https://biosharing.org/

17. Clinical Data Interchange
Standards Consortium
Standards intended to improve consistency
across the clinical trial lifecycle
Protocol
Data
Collection
Data
Tabulation
Data
Analysis
Archiving
and
exchange
Protocol
Representation
Model
Clinical Data
Acquisition
Standards
Harmonization
(CDASH)
Operational Data
Model (ODM)
and
Define-XML
Study Data
Tabulation
Model
(SDTM)
Analysis
Data Model
(ADaM)

18. Data Documentation Initiative
• Maintained & developed by DDI Alliance
• Supported by data archives, producers,
research data centers, university data
libraries, statistics organizations, etc.
• Two versions:
– DDI2 / Codebook: An archived instance of
a study
– DDI3 / DDI Lifecycle: Suitable for
longitudinal and repeated surveys
An XML-based metadata standard developed for social science
and economic statistics
http://www.ddialliance.org/

19. Study
Concepts
measures
Survey
Instruments
using
Questions
made up of
Universes
about
Responses
collect
resulting in
with values of
Variables
Comprised of
Categories/
Codes,
Numbers
Data Files
Survey Data Model
Slide source:
https://www.unece.org/fileadmin/DAM/stat
s/documents/ece/ces/ge.33/2011/mtg2/W
P_1_Arofan.ppt

20. DDI Codebook
A codeBook consists of:
1. docDscr: describes the DDI document
2. stdyDscr: Title, abstract, methodologies, agencies, access policy
3. fileDscr: a description of files in the dataset
4. dataDscr: variables (name, code, etc.), variable groups, cubes
5. othMat: other related materials, e.g. document citation
3 levels - Study, dataset, variable
Preserves the collection of files associated with
an archival copy of a survey

21. DDI Lifecycle
http://www.ddialliance.org/what
Data collector
Data Analyst Data Curator
Secondary user
Each stage may be performed by different groups

22. DDI Metadata reuse
Basic metadata can be reused during study life:
• Concepts, questions, responses, variables, categories, codes, survey
instruments, etc. may be adopted from earlier waves
Referencing earlier iterations:
• Unique identifier
• Version number - control over time
Common metadata ‘groups’ maintained by specific agencies:
• Schemes: lists of items of a single type
• Modules: metadata for a specific purpose or lifecycle stage
• All maintainable metadata has a known owner or agency

23. Unique ID example
urn=“urn:ddi:3_0:VariableScheme.Variable=pop.umn.edu:
STUDY0145_VarSch01(1_0).V101(1_1)”
This is a URN From DDI Version 3.0 For a variable
The scheme agency is
pop.umn.edu
With identifier
STUDY012345_VarSch01
Version 1.0 Variable ID is
V101
Version 1.1
http://www.iza.org/conference_files/eddi09/ppt/thomas_wendy_course.pdf

24. DDI Cross-study comparison
Variables are comparable if they possess same properties:
• Age is comparable if has:
– Same concept (e.g., age at last birthday)
– Same top-level universe (people)
– Same representation (i.e., an integer from 0-99)
DDI Comparison module:
• Place similar items in same group and perform tailored comparison
• Mappings are context-dependent, i.e. sufficient for purposes of particular
research

25. DDI Tools
DDI Codebook:
• Nesstar Publisher & Server
• IHSN Microdata Management
Toolkit
• Collectica
• NADA
• UKDA - DExT, ODaF DeXtris
DDI Lifecycle
• Collectica Designer, Collectica for
Excel, Portal
• Sledgehammer
DDI Tools
http://www.ddialliance.org/resources/tools

26. DATA
REUSE
Can data be used for further research?

27. Data Rights
• Many rights apply to data
– Copyright
– Moral
– Database
– Patents & trade secrets
• Rights issues vary between
countries
• Ensure your project has clarified
rights issues before sharing
https://www.flickr.com/photos/riekhavoc/4813140176/
Rights issues influence how data can be shared, used and cited

28. FAIR data
• Consider permitted
use
• Apply appropriate
licence
• Use open formats
• Consistent vocabulary
• Common metadata
standards
• Consider what will be
shared
• Obtain participant
consent & perform
risk management
• Describe your
data in a data
repository
• Apply a
persistent
identifiers
Findable
ReusableInteroperable
Accessible

29. Thank You for your attention!
Questions