Scientific Publication, Data
Sharing, Learning Health and
Incentives
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe #VIVO18

Take homes
• Pace of consumer computing
• Data generation is no longer the
bottleneck
• 4th Industrial Revolution is here
• Biomedical research and medicine is
a data enterprise
• Data sharing as an accelerant

2017200220072012
10/23/2001
(~5yrsold)
997
1/9/2007
(~10yrsold)
iPod(10GBmax)
P(mp3)
iPhone(EDGE,16GBmax)
9/16/1999
(~3yrsold)
802.11bWiFi
4/3/2010
(~13yrsold)
iPad(EDGE,64GBmax)
4/23/2005
(~8yrsold)
9/26/2006
(~9yrsold)
7/15/2006
2/7/2007
Google
Drive
4/24/2012
(~15yrsold)7/11/2008
(~11yrsold)
iPhone3G
(16GBmax)
9/12/2012
(~15yrsold)
iPhone5(LTE,128GBmax)
Google
Baseline
3/9/2015
(~18yrsold)
Apple
ResearchKit
HTCVRHeadset
4/5/2016
(~19yrsold)
7/14/2014
(~17yrsold)
NextGen
Courtesy of Jerry Lee, NCI

Access to data has changed-Epic

From Apple Health App

$640M
(FY74)
$5.39 B
(FY16)

Science has evolved
• The role of societies and journals
have slowly evolved during the past
150 years
• Our ability to generate data has
rapidly evolved in the past 40 years
• Sharing knowledge, primary data,
observations are throttled by our
current publication process

How did we get here
• A slight digression taking a narrow
slice of history, technology, and
science

Printing
First Knowledge
Revolution –
printing press and
access to mass
produced print.

First Industrial Revolution
Humans having access to cheap energy
to do work (steam)
William M. Connolley - Picture of the "Puffing Billy" steam
engine taken in the Science Museum. on 2004/03/13.
https://en.wikipedia.org/wiki/Steam_engine
taken May 2018

First Industrial Revolution
In addition to changing manufacturing
and transportation, steam changed
printing
Meggs, Philip B. A History of Graphic Design. John Wiley & Sons, Inc. 1998. (p 132)

Scientific Publication 1.0
Journals, primarily
the output of
scientific societies,
began in the 1860s.
Peer review became
the norm after WWII
First issue of Nature, 1869

Second Industrial Revolution
Mass production, better materials
(steel) and manufacturing, distribution
of energy using electricity & petroleum
Robert Friedrich Stieler (1847–1908) - alte Postkarte, https://www.basf.com/de/company/about-us/history/1865-1901.html

Third Industrial Revolution
The Digital Revolution
2008-03-19 21:41 Transisto from Wikipedia
10 August 2016 Thomas Nguyen - Wikipedia
Mike1024 from wikipedia - University of
Warwick 2006

Fourth Industrial Revolution
Industry 4.0
• Communications
• Connectivity
• Ubiquituous
• Pervasive
• Internet of Things
• Embedded Sensors
• Process Automation
• Cloud Computing
Mass access to data generation, processing, visualization

Impact on Biomedical Research
Challenges and Opportunities
– Workforce!
– Ethics!
– Data management!
– New instrumentation & tech
– Computing, Analytics, Visualization,
Usability – Data Science
Biomedical research is a data driven
enterprise

Biomedical Science 2.0
Workforce
– Need more Quantitative Scientists
– Biologically aware Data Scientists
– Data aware researchers
– Formal and informal training
– Incentives for team science
– Professional recognition
Everyone in biomedical research and in
medicine needs to be ‘Data Savvy’

Biomedical Science 2.0
Data Management
– Data provenance
– Validated devices
– Credit for individual &
team contributions
– Persistence & immutability
– DOIs
– Reproducible workflows
Cloud computing and data commons have
many of the features we need

Biomedical Science 2.0
Some features of 2.0
• EHRs are now deployed
• Smart devices nearly ubiquitous
• Broadband a ‘human right’
• Patient experience opportunities
• Ability to scale research
Data management is crucial

Biomedical Science 2.0
• Instrumentation
– Next Gen Sequencing
– Mass Spec (proteomics, metabolomics)
– Digital Imaging (Pathology, Radiology)
– High Throughput Screening
– Sensors
Massive reduction in the cost of
generating datasets

Biomedical Science 2.0
• Computing & Data Science
• Modeling at many size and time scales
• Causal inference
• Width vs Depth
• Complex vs simple relationships
• Usability
• Visualization and Human Cognition
Deep learning, networks, mechanism,
prediction, testing and validation

Where does all that leave us?

Data Science

Data Science Hype
Data is the new oil

History

Data science training

Data Science Ethics

Machine Learning and Data Analytics are
mainstream
0
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1993
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Publications in PubMed, 12/29/17
Machine Learning Deep Learning

Human Cognitive Capacity

Sebastian Thrun

4/3/2017

Back to Medicine

Understanding Cancer
• Precision medicine will lead to fundamental
understanding of the complex interplay between
genetics, epigenetics, nutrition, environment and
clinical presentation and direct effective,
evidence-based prevention and treatment.
Ramifications across many aspects of health care

This redefinition has been driven by improved biological understanding

This change has been driven by improved technology - sequencing, imaging,
nanotech, drug developing, computing and the availability of data about
patient response to therapy

Scientific publication 2.0
• Journals are fully digital
• Work flow automated
• Open dissemination after embargo
• Open Access still not the norm
• Role of datasets and data publication
still in flux
• Data sharing of primary data still not
the norm

Scientific publication 2.0
• Observations (data!) are
accumulating at a rapid pace
• Insights, information, analytics, and
knowledge follow and conform to
more classic versions of peer review
and publication
• IMO - We need to separate data
sharing from knowledge sharing

Validation
• Validation and Harmonization of
primary and secondary data is
crucial, but does not need to be done
through the current publication
process

Data Sharing Index
• We need metrics for data, software,
algorithm use, usability, conformance
• FAIR!

Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe

NIH
• NIH Strategic Plan for Data Science
https://datascience.nih.gov/sites/default/files
/NIH_Strategic_Plan_for_Data_Science_Final
_508.pdf
• Written by the NIH Scientific Data
Council
• About data management and analytics
• Misses the change from data generation
to data analytics

NIH Chief Data Strategist
LinkedIn, June 6, 2018