This document provides an overview of data science from the perspective of Philip Bourne. Some key points: - Data science is disruptive to higher education and all disciplines are being impacted by large amounts of digital data. - Data science can be defined using a 4+1 model focusing on value, design, systems, analytics, and practice. - Principles of excellence, inclusivity, openness, and fairness should guide data science work. - Lessons from advances in computational biology and AlphaFold2 show the importance of open data, collaboration, and engineering challenges. - A data science school should focus on responsible data practices while balancing open research that benefits patients.

1. What Data Science Will Mean to You -
One Person’s View
Philip E. Bourne PhD
peb6a@virginia.edu
https://www.slideshare.net/pebourne
September 28, 2022 UNC

2. Punchline – in 45+ Years in Academia I Have
Never Seen Anything Like It
• It is a response to the digital transformation of
society
• It is touching every discipline (aka vertical)
• We can’t keep the students out of our classes
• Cause – large amounts of digital data
• Effect – interdisciplinarity, openness, translation,
search for responsibility and more
In summary, it is disruptive and higher ed. better pay attention

3. My Perspective aka Biases
• Practical Science Long standing computational biomedical researcher
• Open Access Co-Founder and Founding Editor in Chief PLOS
Computational Biology
• Open Knowledge First President of FORCE11
• Data are Value Involved in FAIR
• Translation First Associate Vice Chancellor for Innovation and
Industrial Alliances
• Funders as Lever First Associate Director for Data Science NIH – preprints,
data sharing, BD2K, etc.
• Change Higher Ed Founding Dean School of Data Science

4. In My World There was a Precedent 20-30
Years Ago Which Points to What is Coming
http://www.ornl.gov/hgmis
• High throughput DNA digital data changed how
we think about biomedicine
• Spawned a new field – bioinformatics /
computational biology/ systems biology /
biomedical data science
• Spawned a multi-billion dollar industry
Is Bioinformatics Dead? PLOS Biology 2021

5. Big data and data science are like the Internet…
If I asked you to define them you would all say
something different, yet you use them every day…
http://vadlo.com/cartoons.php?id=357

6. Given these precedents about data science we
should start with a definition/framework
In the context of a new school this gets everyone on
the same page and helps in starting to build a culture

7. One Definition of Data Science –
The 4+1 Model (aka domains)
• Value – assuring societal
benefit
• Design - Communication
of the value of data
• Systems – the means to
communicate and
convey benefit
• Analytics – models and
methods
• Practice – where
everything happens
[From Raf Alvarado]

8. The Data Science Interplay
• Value + Design = Openness,
responsibility
• Value + Analytics = Human
centered AI, algorithmic bias
• Value + Systems =
sustainability, access,
environmental impact
• Design + Analytics = literate
programming, visualization
• Design + Systems =
dashboards, engineering
design
• Analytics + Systems = ML
engineering
[From Raf Alvarado]
Thinking of data as a science unto itself is novel and controversial

9. Okay, so we have a definition to ground what we
do now we need a set of principles to act as the
guard rails:
• Excellence
• Inclusivity
• Openness/
Transparency
• Be FAIR

10. Inclusivity

11. Openness/FAIR
Data Science would not exist if it were not for open
data and methods. It would be wrong for us to take
and not give back
https://sparcopen.org/
https://datascience.virginia.edu/policies

12. Openness/FAIR
https://doi.org/10.1038/sdata.2016.18
https://www.heliosopen.org/

13. So we have a definition of data science and we
have a set of guiding principles, where does this
take us?
Stated another way, what do we want to be
recognized for in 10 years?
https://pebourne.wordpress.com/

14. But wait there are more lessons to be learned….
https://medium.com/proteinqure/welcome-into-the-fold-bbd3f3b19fdd

16. Google’s DeepMind’s AlphaFold2 makes gigantic leap in solving
protein structures

17. AlphaFold2
Numerical optimization – differential programming
Overall gradient descent trained to win CASP
Jumper et al.., 2021. Nature, 596 (7873),
pp.583-589
Transformer models using attention
Geometry invariant to
translation/rotation

18. Logistics Behind the Win
● Nothing fundamentally new from an AI perspective
● Data Integration
● Collaboration not competition
● Engineering challenge beyond most labs
● Compute power beyond most labs
● Team size beyond most labs
● Worked with protein structure specialists

19. Downstream Implications
• Cooperation rather than competition
• Public-private partnership
• Translational possibilities are endless
• Made possible by curated open data
• Appreciate engineering

20. What do these lessons tell us about how we think
about our data science school?

21. Databases
organize data
around a project.
Data warehouses
organize the data
for an organization
Data commons
organize the data
for a scientific
discipline or field
Data
Warehouse
Data Ecosystems
How we think about our
infrastructure is important

22. Challenges
Fixed level of funding
Opportunities
data commons
Data commons co-locate data
with cloud computing
infrastructure and commonly
used software services, tools &
apps for managing, analyzing and
sharing data to create an
interoperable resource for the
research community.*
*Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson and Walt Wells, A Case for Data Commons Towards Data Science as a Service, IEEE
Computing in Science and Engineer, 2016. Source of image: The CDIS, GDC, & OCC data commons infrastructure at a University of Chicago data center.
Bonazzi VR, Bourne PE (2017) Should biomedical research be like Airbnb? PLoS Biol 15(4): e2001818.
Systems
[Adapted from Bob Grossman]

23. But wait the picture is more complicated….

24. Data Science versus Data Engineering – How
Much Emphasis Where?

25. A Data Integration Poster Child
Researcher and Assistant Professor of
Medicine Dr. Thomas Hartka, also a
current online Masters in Data Science
student, is combining two disparate
data sets—electronic health records
and DMV crash data—to save lives
after motor vehicle crashes.
“I enrolled in the MSDS program to
expand my research on automotive
safety. I have already used
techniques from classes in my work.
I hope to expand my research to
real-time analytics to improve
emergency room care.”
— Dr. Thomas Hartka, UVA School
of Medicine

27. Coming back to the question…
So we have a definition of data science and we
have a set of guiding principles, where does this
take us?
Stated another way, what do we want to be
recognized for in 10 years?
https://pebourne.wordpress.com/

28. Another way of thinking is alignment with the
university’s strengths….

29. Research ethics
committees (RECs) review
the ethical acceptability
of research involving
human participants.
Historically, the principal
emphases of RECs have
been to protect
participants from physical
harms and to provide
assurance as to
participants’ interests and
welfare.*
[The Framework] is
guided by, Article 27
of the 1948 Universal
Declaration of Human
Rights. Article 27
guarantees the rights
of every individual in
the world "to share in
scientific
advancement and its
benefits" (including to
freely engage in
responsible scientific
inquiry)…*
Protect human
subject data
The right of human
subjects to benefit
from research.
*GA4GH Framework for Responsible Sharing of Genomic and Health-Related Data, see goo.gl/CTavQR
Data sharing with protections provides the evidence
so patients can benefit from advances in research.
Balance protecting human subject data
with open research that benefits
patients
[Adapted from Bob Grossman]
Value

30. Why Responsible Data Science?
• A defining feature
• A partnership between STEM, social
sciences and the humanities
• Where UVA has strength

31. https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)
https://www.microsoft.com/en-us/research/wp-
content/uploads/2009/10/Fourth_Paradigm.pdf
https://twitter.com/aip_publishing/status/856825353645559808
Yet another way of thinking
about it – the fifth paradigm..

32. Model
Transportability
Horizontal
Integration
Multi-scale
Integration
human
mouse
zebrafish
DNA
Gene/Protein
Network
Cell
Tissue
Organ
Body
Population
CNV SNP methylation
3D structure Gene
expression Proteomics
Metabolomics
Metabolic
Signaling
transduction
Gene
regulation
Hepatic Myoepithelial Erythrocyte
Epithelial Muscle Nervous
Liver Kidney Pancreas Heart
Physiologically based
pharmacokinetics
GWAS
Population
dynamics
Microbiota
From Harnessing Big Data for Systems Pharmacology 2017
https://doi.org/10.1146/annurev-pharmtox-010716-104659
Current roadblocks are more cultural than technical
The Fifth Paradigm: Integration Across Scales?

33. Gohlke et al. 2022
https://onlinelibrary.wiley.com/doi/10.1002/ctm2.726
Real World Evidence for Preventive Effects of Statins on
Cancer Incidence: A Transatlantic Analysis
EHR
Animal Models
Pathways

34. Daily Challenges
• Deciding what not to do
• Competition for the best team members (faculty and staff)
• Establishing a diverse team
• Lack of a comprehensive enterprise-wide data infrastructure
• Its easier to conform

35. During my 5-year interview as dean I was asked,
“Will we need a school of data science in 10 years
wont it be ubiquitous throughout the university?”
My response,
“Will we need a university in ten years? Wont it be
one big school of data science?”
https://pebourne.wordpress.com/2022/06/29/deans-blog-
data-science-ten-years-from-now/

36. Questions I Leave You With ….
• Have I overstated the case for data science?
• Are we currently doing the best by our students?
• Are the models we propose the right ones?
• Where do we go from here?

37. Questions?

38. Growing the School
M.S. IN DATA SCIENCE
Residential & Online
202
0
2020-
2023
UNDERGRADUATE
MINOR
2022
PH.D. PROGRAM
2023
UNDERGRADUATE
MAJOR
Building occupied
Team Size (FTEs)
5
40
60
80
120
Research
$5M
$10M
$20M
$30M

39. SDS Current Research Portfolio
12
7
4
3
2
3
3
Research Areas
Healthcare/Life Sciences
Technology/Software
Defense/Cybersecurity
Finance/Fintech
Energy/Environment
Education & Digital
Humanities
SDS strives to be a connector – a place where interdisciplinary
research driven by common data, methods and expertise
comes together