Slides presented by Helena Deus at BioIT 2019

1. FAIRness and
Accountability
Apr 2019
Helena F. Deus. PhD
Expectations vs. Reality in
health care data

3. Why I care about FAIR
Glioblast
oma
ALK
mutation Treatment
FDAClinVar
Cell/Nature
Disease ontology
NCCN
Lancet
EMA

4. Why I care about FAIR
Data Gathering
Data Cleaning
Data Science!
FAIR data
FAIR data

5. Accountability

6. Someone who is accountable is responsible
for what they do and must be able to give a
satisfactory reason for it.

7. Accountability requires agreement
Data Scientist
Make your data
FAIR!
Experimental scientist
But I never
agreed to that!

8. • Bibliometric analysis
• Acknowledgement sections from 1.51 million
research and review articles from 2014
• 1850 articles in “data” journals (2/3 from Data in
Brief)
• Large scale global survey: Jun/July 2016
• Scientists indexed in Scopus; 1162 researchers
responded (2.3% response)
• How and why are researchers sharing data?
• Why are researchers hesitant to share data
openly?
• What is the role of research data management
in research data sharing?
• How is reusability perceived?
Open Data Report – A researcher’s
perspective (2016)
https://www.elsevier.com/about/open-science/research-data/open-data-report

10. 1. Are scientists equipped to FAIRify their data?
2. Do they have the instruments (legal, technological,
incentive) to FAIRify the data?
3. Does FAIR mean the same? Have we agreed on
the expectations?

11. Different ownership expectations N=1126
“The general
public”
somewhere
here
“Not responsible for data”
“Unable to address ethical concerns”
“Unable to provide consent”
“Unable to prevent abuse/ misinterpretation of
data”
Institute

12. Data == Value

13. The funding / value cycle in research

14. The funding / value cycle in research

15. The perceived commercial value of Data
$1.15 billion $1.9 billion
$19 billion

16. The art of scientifically collecting data
X

17. The art of scientifically collecting data
X

18. • All animals were maintained in a pathogen-free barrier facility in
accordance with National Institutes of Health and American
Association for Assessment and Accreditation of Laboratory Animal
Care International guidelines.
• Mice were housed in standard temperature and humidity conditions,
and experimentation was conducted with accepted standards of
humane care in our animal facility (IBV-CSIC).
• For PCI treatment, the selected mice were photosensitized by
intratumoral administration of 50 μg of AlPcS2a and kept in the dark
for 48 h according to Selbo et al.
What we found instead
INTERNAL GUIDELINES
PUBLISHED ELSEWHERE
NO REFERENCE/YEAR

19. Contrast with TCGA

20. Contrast with TCGA

22. Videos and
Documents
Patient
Education
Gold
Standard
Drug
Database
Care
Plans
Order Sets
Clinical
Trial Data
MACRORadiology
Images
StatDX
Pathology
Images
ExpertPath
Books and
Journals
ScienceDire
ct
ICD-10
RxNorm LOINC
SNOMED
MeSH
External Vocabularies
H-Graph
Core
Clinical
Guidelines
Machine Learning,
NLP
1
2
3
LD API
LD API
LD API
LD API
LD API
LD API LD API
LD API
LD API
LD API
Childhood acute
lymphoblastic leukemia
hasTreatment
Stem cell transplant
Rank: 6

23. Analytics

24. From feature extraction to Deep Learning

25. Is FAIR enough? Ethics and correct data usage
Lie et al, Detecting Cancer
Metastases on Gigapixel Pathology
Images, 2017 – Google Brain team

26. Reproducibility of the analysis
“Statisticians and computer scientists - if there is no code, there is no paper”
– Jeff Leek
https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

27. Action

28. Set the right expectations
- H2020 – Guidelines on FAIR data management for 2020 - ACTIVE
- Asks for a rationale about keeping data closed; no data access policies
- NIH - Data Common Pilot – AWARENESS/PILOT
- Must be compliant with the NIH Data Sharing policy
- NSF – 2019 NSF FAIR hackathon – VERY EARLY DAYS
FAIR does NOT mean open

29. Smart data repos encourage FAIRification

30. Credit
Social
engagement
Reproducibility
Provenance
Reproducible analytics encourages FAIRification

31. FAIR is not enough for Health
Data – Data Access and Usage
Policies must be part of the
equation

32. Thank you