AAMC Great/Grand meeting plenary lecture, September 21, 2012, Nashville, TN

1. Publishing
3.0,
or:
Why
we
will
all
be
disintermediated,
(and
that
is
a
good
thing!)
Anita
de
Waard
Disrup@ve
Technologies
Director,
Elsevier
Labs,
Burlington,
VT
(=
not
what
the
program
says
J!)
AAMC
GREAT/GRAND
Mee@ng
September
21,
2012

2. What’s
the
big
deal
with
big
data?
Decoding
the
human
genome
involves
analysing
3
billion
base
pairs—it
took
ten
years
the
first
@me
it
was
done,
in
2003,
but
can
now
be
achieved
in
one
week.
Data,
Data
Everywhere,
The
Economist,
February
25,
Mobile
Internet
devices
will
outnumber
humans
this
year,
2010
Cisco
predicts…Global
mobile
data
traffic
is
expected
to
increase
18-­‐fold
over
the
next
five
years
to
10.8
exabytes
Facebook
stores
100
petabytes
in
Hadoop.
per
month.
Cloud
traffic
is
expected
to
account
for
71%,
or
7.6
exabytes
per
month,
of
total
mobile
data
traffic
by
2016.
‘Big
data’
offers
huge
challenges
for
biomedicine
in
an
era
of
massive
data
sets…
Francis
Collins,
Director
of
NIH,
Yesterday

3. Your
funders
are
telling
you
to
share
your
data:
• NSF
Data
Sharing
Policy:
Inves8gators
are
expected
to
share
with
other
researchers,
at
no
more
than
incremental
cost
and
within
a
reasonable
@me,
the
primary
data,
samples,
physical
collec8ons
and
other
suppor8ng
materials
created
or
gathered
in
the
course
of
the
work
under
NSF
grants.
• NIH
Data
Sharing
Policy:
Final
Research
Data
should
be
made
as
widely
and
freely
available
as
possible
while
safeguarding
the
privacy
of
par@cipants,
and
protec@ng
confiden@al
and
proprietary
data.
Final
Research
Data
means
recorded
factual
material
commonly
accepted
in
the
scien8fic
community
as
necessary
to
document
and
support
research
findings.
This
does
not
mean
summary
sta@s@cs
or
tables;
rather,
it
means
the
data
on
which
summary
sta@s@cs
and
tables
are
based.

4. So
are
you
sharing
your
data?
Really?

5. Crea@ng
more
data
by
the
minute.
Time:13.7min Search
(53%)
Search
(48%) Age
:
35.4
Bounce
:
2%
Pols.
and
docs.(15%)
Search
(35%)
N=
3,561 Time:2min Pols.
A nd
docs.
(53%)
Time:87.5min Age
:
20
Age
:
35.6 Pols.
and
docs.
(11%) Bounce
:
1%
Bounce
:
2.2%
Time:1.9min N=
523 Search
(15%)
N=
7980 Age
:
32.2 Search
(37%)
Search
(25%)
Search Bounce
:
0%
Policies
&
Docs.(16%) Pols.
and
docs.
(25%) Time:1.6
m in
N=
620
(36%) Age
:
22.2
Pols.
and
doc.
(44%)
Time:3.9
m in Bounce
:
0.8%
Search
(26%)
Age
:
27.7 Time:1.4min N=
761
Search
(28%)
Bounce
:
0.7%
Age
:
11.2
Time:8.8min Pols.
and
docs.
(49%)
N=
2681 Emp.
law
ref.
man.
(43%) Bounce
:
1.6%
Age
:
33.6 Emp.
law
ref.
man.
(40%)
Bounce
:
1%
N=
497
Emp.
law
Ref.
Man.
(11%)
N=
25,423 Employment
law.
(8%)
Time:31.9min Time:2.36
m in
Search
(25%) Age
:
33.5
Age
:
11.6 Pols.
and
docs.
(13%)
Bounce
:
1.2%
Bounce
:
0.7%
N=
1815 N=
427 Search
(35%)
Emp.
law
ref.
man.
(19%)
Home
(38%)
Time:2.5min
Employment
law
(86%) Age
:
4.8
Bounce
:
28.4%
Employment
law
(65%)
People
manager N=
5,780
Search
(19%)
Home (23%)
(64%) Emp.
law
ref.
man.
(24%)
Time:1.14min Policies
(13%) Statutory
rates
(4%)
Age
:
1 Statutory
rates
(37%)
Bounce
:
0%
Time:1.6
m in
N=
16 Age
:
4 Employment
law
(31%)
Bounce
:
1.4%
Home
(8%)
Emp.
L aw
(82%) Time:0.4min N=
141 Time:1.63min
Age
:
8.6 Policies
(8%) Age
:
32.5
Bounce
:
3.6%
Bounce
:
2.6%
Emp.
law
ref.
man.
(11%)
N=
8,563 N=
268
Employment
law Employment
law
(9%)
(15%) Search
(35%)
Time:2.4min Employment
law
(14%) Search
(48%)
Emp.
law
ref.
man.
(17%) Age
:
7.3
Time:0.4min Search
(9%) Emp.
law
ref.
man.
(63%)
Time:2.2
m in Bounce
:
2.1%
Age
:
8.5 N=
96
Age
:
7.9 Legal
guidance
(8%) Employment
law
(11%) Time:1.8min Legal
guidance
(28%)
Bounce
:
6.3%
Time:1.7min
Bounce
:
1.8%
Age
:
5.4
N=
10,562 Age
:
29.3 Search
(26%)
N=
115,498 Search
(28%) Bounce
:
0%
Bounce
:
1%
Pols.
and
doc.(9%) Time:2.8min N=
58 Employment
law
(14%)
N=
826 Age
:
40 Pols.
and
docs.
(32%)
Bounce
:
0%
N=
57 Employment
law
(16%)
Time:2.1
m in
What’s
new
(36%)
Age
:
10.2
What’s
new
(28%)
Bounce
:
1.3
%
Legal
r eports
(11%)
Time:1.1
m in What’s
new
(20%) N=
230
Age
:
8.9
What’s
new
(16%) Time:1.8
m in Legal
r eports
(33%)
Legal
guidance
(13%) Bounce
:
1
%
Age
:
9.02 N=
98 Time:0.7min
Search
(16%) Employment
law
(58%)
Bounce
:
5.2%
Age
:
9.2
What’s
new N=
910
Time:0.8min Legal
guidance
(24%)
Bounce
:
4.7
%
What’s
new
(17%)
1
(9%) Employment
law
(10%) N=
85
Age
:
8.8
Search
(16%)
Bounce
:
3.4
%
Search
(31%)
Legal
guidance
(17%) Legal
guidance
(24%) What’s
new
(13%)
Time:2.5min N=
174 Time:1.7min Pols.
and
doc.(17%)
Age
:
8.7 Time:1.1
m in Age
:
31.7
Time:2min Legal
r eports
(16%)
Bounce
:
0.9%
Age
:
9.3 Search
(16%) Age
:
8.8
Bounce
:
1.5
%
N=
6,219 Bounce
:
0.8
%
What’s
new
(14%) N=
136 Emp.
law
ref.
man.
(13%)
What’s
new
(13%) Bounce
:1%
N=
877 Legal
guidance
(11%)
N=
104
5

6. This
plant
tweets!
• Internet
of
things:
we
can
interact
with
‘objects
that
blog’
or
‘Blogjects’,
that
track
where
they
are
and
where
they’ve
been;
• have
histories
of
their
encounters
and
experiences
have
agency
• have
a
voice
on
the
social
web

7. Larry
Smarr
creates
lots
of
data:
• He
wears:
• A
Fitbit
to
count
his
every
step
• A
Zeo
to
track
his
sleep
pajerns
• A
Polar
WearLink
that
lets
him
regulate
his
maximum
heart
rate
during
exercise
• 23andMe
analyzed
his
DNA
for
disease
suscep@bility.
• Your
Future
Health
analyzed
blood
and
stool
samples
for
100
biomarkers:
• At
one
point,
C-­‐reac@ve
protein
stood
out
as
higher
than
normal.
• A
blood
test
showed
that
his
CRP
had
climbed
to
14.5
during
the
ajack.
• He
took
an@bio@cs,
the
symptoms
resolved,
and
his
CRP
dropped
to
4.9—
but
that
was
s@ll
unusually
high.
• Lactoferrin,
too,
rose
several
@mes
to
sky-­‐high
levels—200,
whereas
the
normal
count
is
less
than
7.3
–
and
in
tandem
with
CRP
• Smarr
now
thinks
his
diver@culi@s
ajack
was
actually
Crohn's
disease
–
and
his
gastroenterologist
(reluctantly)
agreed.

8. As
are
lots
of
other
‘Quan@fied
Selfers’:
Clearity
Founda@on:
A
transla@onal
medicine
and
public
service
founda@on
for:
•
Providing
doctors
access
to
molecular
profiling
for
their
ovarian
cancer
pa@ents
•
Providing
doctors
and
pa@ents
clinical
trial
op@ons
informed
by
individual
tumor
biology
•
Providing
financial
support
for
the
profiling
work
for
pa@ents
–
Oprah
approved!

9. But
who
uses
all
that
data?

10. does!
• It
knows
where
you
are
• And
who
you
talked
to
• And
what
you
bought
• And
how
much
you
paid..
• And
whether
you
need
another
pair
of
shoes
• And
when
and
where
you
can
get
them…

11. Brijany
Wenger
does!
Winner
of
the
Google
Science
Fair
2012
17-­‐year
old
Brijany
Wenger
developed
a
cloud-­‐based
neural
network
that
is
able
to
seamlessly
and
accurately
assess
8ssue
samples
for
signs/evidence
of
breast
cancer
to
give
more
credence
to
the
currently
used
(less
reliable)
minimally
invasive
procedure
called
Fine
Needle
Aspirates
(FNAs).
By
looking
at
nine
different
input
features
and
comparing
them
to
the
training
examples,
Brijany’s
cloud-­‐based
neural
network
can
detect
malignant
breast
tumors
with
an
accuracy
of
99.11%
Because
her
neural
network
is
deployed
in
the
cloud
using
Google’s
app
engine
it
means
it
can
be
accessed
from
exis8ng
medical
systems
as
well
as
through
a
web
browser
or
mobile
apps.

12. Mark
Wilkinson
does!
Given
a
protein
P
in
Species
X:
Find
proteins
similar
to
P
in
Species
Y
Retrieve
interactors
in
Species
Y
Sequence-­‐compare
Y-­‐interactors
with
Species
X
genome
(1)
à
Keep
only
those
with
homologue
in
Find
proteins
similar
to
P
in
Species
Z
Retrieve
interactors
in
Species
Z
Sequence-­‐compare
Z-­‐interactors
with
(1)
à
Puta8ve
interactors
in
Species
X
Using
what
is
known
about
interac@ons
in
fly
&
yeast,
predict
new
interac@ons
with
a
human
protein
–
Running
over
data
on
the
web
that
he
neither
created
nor
knew
about!

13. Running
the
web
like
an
experiment:
These
are
different
Web
services!
(and
neither
of
them
Mark’s)
...selected
at
run-­‐@me
based
on
the
same
model

14. Puyng
it
another
way:

15. Science
is
becoming
distributed:
Data
Tools
Thoughts

16. Science
is
becoming
distributed:
Data
Tools
Data
is
king!
• Data
needs
to
say
what
it’s
about
Thoughts
who
owns
it
• Data
needs
to
say
where
it
comes
from
• Data
needs
to
know
• Data
needs
to
be
sensi@ve
to
privacy
• Data
needs
to
know
how
it’s
used

17. Science
is
becoming
distributed:
Tools
Tools
rule!
Data
Tools
can
be
made
by
everyone:
Tools
are
open
and
free
Tools
will
know
where
data
lives
Thoughts
Tools
need
to
know
about
data:
• Privacy/ownership
• Trustworthiness
• Provenance

18. Science
is
becoming
distributed:
If
data
and
tools
are
ubiquitous,
what
majers
most
are
the
ques@ons
you
ask:
• What
is
interes@ng?
• What
is
important?
Tools
Data
• Who
cares?
Thoughts

19. Science
is
becoming
more
distributed:
So
where
does
that
leave
you?

20. How
can
you
prepare
(your
students)
for
this
future?
Well,
you
can’t
-­‐
not
really.
But
there
are
a
few
habits
you
can
ins@ll
(and
model):

21. Habit
#
1:
Be
a
good
data
producer
• Know
that
you
are
crea@ng
data
• Be
aware
of
privacy
and
IPR
issues
re.
your
data
• Assume
that
someone,
some
@me
will
be
using
this
data
for
some
purpose
you
cannot
imagine
• Learn
which
data
repositories
exist
in
your
field,
how
they
work,
what
they
need
from
you
• Set
up
your
work
habits
to
automa@cally
create
(or
force
you
to
add)
metadata
to
enable
discovery
and
use
of
your
data.
• Store
your
data
in
the
repositories.
Every
@me.

22. Habit
#2:
Be
a
good
data
consumer.
• Find
out
which
data
exists
that
might
be
relevant
to
your
work.
• Learn
how
to
query
available
data.
• Be
aware
of
privacy
and
IPR
licenses.
• Give
credit
where
it’s
due:
– Cite
any
data
sources
that
you
use
– Share
your
knowledge
on
querying
data
– Deposit
any
data
you’ve
derived
from
other
data!

23. Habit
#3:
Learn
to
code.
• Brijany
Wenger
was
born
in
1995!
• All
sorts
of
people
are
using
technology
that
was
invented
a{er
the
birth
of
your
oldest
grandchild.
• Use
anything
at
your
disposal
to
learn:
– Your
students
– Your
kids
– Online
forums
– Video
tutorials,
• Etc.
etc.
• E.g.
Coursera
course
on
Clinical
Research
InformaKcs
-­‐
see
Cynthia
Gadd
(Vanderbilt)

24. Habit
#
4:
Expect
to
keep
learning.
• This
will
only
get
worse!
(Or:
bejer?)
• Listen
to
Douglas
Engelbart:
(he
invented
the
mouse
and
the
cursor,
as
well
as
collabora@ve
work):
“[For]
improving
the
intellectual
effecKveness
of
the
individual
human
being…[o]ne
of
the
tools
that
shows
the
greatest
immediate
promise
is
the
computer…”
(1962)
“The
grand
challenge
is
to
boost
the
collecKve
IQ
of
organizaKons
and
of
society.”
(2000)
•
Expect
to
keep
learning
– from
anyone,
and
anywhere
– the
only
thing
that
can
limit
your
success
is
the
idea
that
you
can’t/don’t
have
to
learn/change/adapt/evolve

25. Habit
#
5:
Don’t
find
what
you
already
know.
Richard
Feynman
on
Scien@fic
Integrity:
if
you're
doing
an
experiment,
you
should
report
everything
that
you
think
might
make
it
invalid
-­‐
not
only
what
you
think
is
right
about
it
If
you
make
a
theory,
for
example,
and
adver@se
it,
or
put
it
out,
then
you
must
also
put
down
all
the
facts
that
disagree
with
it,
as
well
as
those
that
agree
with
it.
When
you
have
put
a
lot
of
ideas
together
to
make
an
elaborate
theory,
you
want
to
make
sure,
when
explaining
what
it
fits,
that
those
things
it
fits
are
not
just
the
things
that
gave
you
the
idea
for
the
theory;
but
that
the
finished
theory
makes
something
else
come
out
right,
in
addi@on.

26. Habit
#
6:
Anyone
can
come
up
with
a
great
idea.
• To
paraphrase
Remi
the
Rat
(Ratatouille):
‘Not
everyone
can
be
a
great
scienKst,
but
a
great
scienKst
can
come
from
anywhere’
• Grand
challenges,
hackathons,
open
invita@ons
etc
etc
can
offer
great
solu@ons
to
difficult
problems
(See
Cameron
for
the
story
of
Tim
Gowers,
who
crowdsourced
math)
• See
also
Collins’
talk
yesterday:
issues
with
race/ethnicity
need
to
be
overcome;
involve
students
from
around
the
world
• Involve
K-­‐12
students:
get
more
kids
excited
about
science!

27. Six
habits
that
might
help:
1.
Be
a
good
data
producer
3.
Learn
to
code
2.
Be
a
good
data
consumer
4.
Expect
to
keep
learning
Data
Tools
Thoughts
5.
Don’t
find
what
you
already
know
6.
Anyone
can
come
up
with
a
great
idea!

28. Anyway
-­‐
how
are
we
going
to
publish
all
of
this?

29. Not
like
this!

30. How
are
we
going
to
publish
all
of
this?
We’re
not.
YOU
are.
(With
support
from
‘us’
=
publishers,
libraries,
ins@tu@ons,
crowd…)

31. Maybe
as
Executable
Papers….

32. Or
by
linking
data
to
hospital
info
systems..
Step 1: Patient data +
diagnosis link to Guideline
recommendation
Clinical
Guideline
Electronic Patient Records
Step 2: Guideline recommendation
links to evidence in report or data
Data

33. Or
by
crea@ng
Linked
Data
stores...
Step
1:
Manually
iden@fy
DDIs
and
drug
names
in
wide
collec@on
of
content
sources
Step
2:
Develop
a
model
of
Drug-­‐Drug
Interac@on
and
define
candidates
Step
3:
Automate
this
process
and
store
as
Linked
Data
Images from: Discovering drug–drug interactions: a text-mining and reasoning
approach based on properties of drug metabolism, Luis Tari∗, Saadat Anwar, Shanshan
Liang, James Cai and Chitta Baral Vol. 26 ECCB 2010, pages i547–i553 doi:10.1093/
bioinformatics/btq382
33

34. Or
by
gra{ing
stories
onto
your
data…
metadata
1.
Add
metadata
to
everything
metadata
metadata
2.
Use
a
workflow
tool
3.
Write
in
a
shared
space
metadata
4.
Invite
reviews
metadata
5.
The
reviewer
approves
(or
comments,
author
revises,
etc)
Rats
were
subjected
to
two
6.
Run
ni{y
apps
over
all
of
this.
grueling
tests
(click
on
fig
2
to
see
underlying
data).
These
results
suggest
that
the
neurological
pain
pro-­‐
Calculate,
coordinate…
Review
Revise
Compile,
comment,
Edit
compare…

35. Or
by
other
ways…
• Force11.org:
‘Future
of
Research
Communica@ons
and
e-­‐Science’:
– ‘Society’
for
thinking
about
new
ways
of
communica@ng
science
and
the
humani@es
– Invi@ng
general
par@cipa@on
– Please
join!

36. In
summary:
• Big
data
and
linked
tools
are
completely
changing
the
face
of
science
by
distribu@ng
the
crea@on
of
data,
the
building
of
tools,
and
the
intelligent
use
of
both
• Social
media
and
open
educa@on
are
changing
who
can
do
science,
and
how
it
is
done
• Publishing
all
of
this
will
not
be
a
simple
act,
and
not
something
publishers
can
do
alone.
• All
of
this
offers
tremendous
opportuni@es
to
expand
the
prac@ce
and
promise
of
science
• The
best
thing
you
can
do
is
prepare
to
be
amazed…

37. P.S.:
Do
we
have
any
jobs
for
your
graduates?
Maybe!
Some
intriguing
ideas:
• Internships/traineeships?
• Use
cases
for
classes
on
informa@cs,
e.g.:
– Elsevier
provides
content/ontologies
– Students
develop
ways
to
integrate
data
and
publica@ons
– Students
help
user
tes@ng/UI,
model
development
• Host
joint
grand
challenges?
• Certainly
there
will
be
lots
of
work
in
the
informa@cs
arena
–
with
publishers,
digital
repositories,
startups,
etc,
etc…

38. Ques@ons?
a.dewaard@elsevier.com