This document discusses the implications of big data and data science for publishing. It defines big data and data science as using large, complex datasets to answer questions and make statistically significant conclusions. Data science creates new types of interdisciplinary research and content that crosses traditional academic silos. Examples provided include using text mining to study censorship, sensor data to study air pollution and forests, and text analysis to study normativity and ethics. The format of publications may also change as datasets and the process of exploring data become more important than standalone findings. Publishers can leverage the new research environments and types of content emerging from data science.

1. Implications of Big Data & Data
Science on Publishing
Philip E. Bourne PhD, FACMI
Stephenson Chair of Data Science
Director, Data Science Institute
Professor of Biomedical Engineering
peb6a@virginia.edu
https://www.slideshare.net/pebourne
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2. What Do I Mean by Big Data/Data
Science?
• Use of the ever increasing amount of open,
complex, diverse digital data
• Finding ways to ask and then answer relevant
questions by combing such diverse data sets
• Arriving at statistically significant conclusions
not otherwise obtainable
• Sharing such findings in a useful way
• Translating such findings into actions that
improve the human condition
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3. Data science by its transformative
nature creates a new type of
environment and sociology on
campuses…
Publishers can leverage this
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Working across the grounds
to break down traditional silos

5. Perspective (Bias)
• Leading a campus initiative in data science
where:
– Total commitment to Jeffersonian principles
• Academical village meets Google
– Student numbers rising rapidly
• On-line
• MS & Dual degree
• Certificate programs
– Research is way cool….
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6. Research is way cool…
Speaks to new types of content as well
as forms of content
Consider 3 quick examples...
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7. Censorship and Detecting Deception: A Data-
Driven Look at Obfuscation in Soviet Dissident
Writing Versus Misinformation in the USSR and
Post-Truth Journalism in America
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V’s
• Text mining
• Semantic reasoning Departments:
Slavic Languages
Literature

8. Air pollution-ecosystem feedbacks: unmanned
aerial vehicles and ecosystem models to
quantify ozone-forest interactions
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• Spatial heterogeneity
• Novel sampling
• Senor data
Departments:
Environmental Sciences
Electrical Engineering

9. Normativity is the phenomenon in human societies of
designating some actions or outcomes as good or
desirable or permissible and others as bad or
undesirable or impermissible. A norm in
this normative sense means a standard for evaluating
or making judgments about behavior or outcomes.
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What happens when machines define the norms?
• Text mining
• Feature extraction
• Ethics!
Departments:
Law School
Inst Practical Ethics (?)

10. Okay.. That was content what about
format …
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11. Discussion Points – Drivers of Change?
• Data and analytics are
pervading every field
• Academia is changing
• Students work this way then
retrofit to publications
loosing the context of the
work
• 88% of data in published
papers is dark data
• 80-90% of the work is
finding (when you can) and
engineering the data
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12. Acknowledgements
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The ~150 folks who have passed through my laboratory
https://docs.google.com/spreadsheets/d/1QZ48UaKcwDl_iFCvBmJsT03FK-bMchdfuIHe9Oxc-rw/edit#gid=0

13. Thank You
peb6a@virginia.edu
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