NCBO webinar pertaining to Force11 http://www.bioontology.org/Force11

1. Force11:
the
Future
of
Research
Communica4ons
and
eScholarship
Anita
de
Waard
Disrup4ve
Technologies
Director,
Elsevier
Labs,
Burlington,
VT
Maryann
E.
Martone
University
of
California,
San
Diego

2. Outline:
• Background:
distribu4on
of
data,
tools
and
ideas
=>
we
need
social
change!
• Past:
BtPDF,
Dagstuhl
• Present:
Sloan
grant,
force11.org
• Future:
Plans,
ideas
–
input?

3. Mo4va4on:
the
well-­‐known
issue
of
data
overload…

4. 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
4

5. Even
plants
make
data!
• 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

6. Larry
Smarr
makes
lots
of
data:
• He
wears:
• A
Fitbit
to
count
his
every
step
• A
Zeo
to
track
his
sleep
pa]erns
• A
Polar
WearLink
that
lets
him
regulate
his
maximum
heart
rate
during
exercise
• 23andMe
analyzed
his
DNA
for
disease
suscep4bility.
• Your
Future
Health
analyzed
blood
and
stool
samples
for
100
biomarkers:
• At
one
point,
C-­‐reac4ve
protein
stood
out
as
higher
than
normal.
• A
blood
test
showed
that
his
CRP
had
climbed
to
14.5
during
the
a]ack.
• He
took
an4bio4cs,
the
symptoms
resolved,
and
his
CRP
dropped
to
4.9—
but
that
was
s4ll
unusually
high.
• Lactoferrin,
too,
rose
several
4mes
to
sky-­‐high
levels—200,
whereas
the
normal
count
is
less
than
7.3
–
and
in
tandem
with
CRP
• Smarr
now
thinks
his
diver4culi4s
a]ack
was
actually
Crohn's
disease
–
and
his
gastroenterologist
(reluctantly)
agreed.

7. As
do
lots
of
other
‘Quan4fied
Selfers’:
Clearity
Founda4on:
A
transla4onal
medicine
and
public
service
founda4on
for:
•
Providing
doctors
access
to
molecular
profiling
for
their
ovarian
cancer
pa4ents
•
Providing
doctors
and
pa4ents
clinical
trial
op4ons
informed
by
individual
tumor
biology
•
Providing
financial
support
for
the
profiling
work
for
pa4ents
–
Oprah
approved!

8. uses
data
• 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…

9. Bri]any
Wenger
uses
this
data:
Winner
of
the
Google
Science
Fair
2012
17-­‐year
old
Bri]any
Wenger
developed
a
cloud-­‐based
neural
network
that
is
able
to
seamlessly
and
accurately
assess
3ssue
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,
Bri]any’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
exis3ng
medical
systems
as
well
as
through
a
web
browser
or
mobile
apps.

10. Mark
Wilkinson
uses
this
data:
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
Puta3ve
interactors
in
Species
X
Using
what
is
known
about
interac4ons
in
fly
&
yeast,
predict
new
interac4ons
with
a
human
protein
–
Running
over
data
on
the
web
that
he
neither
created
nor
knew
about!

11. In
summary:
science
is
becoming
distributed:
Data
Tools
Thoughts

12. 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
sensi4ve
to
privacy
• Data
needs
to
know
how
it’s
used

13. 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

14. Science
is
becoming
distributed:
If
data
and
tools
are
ubiquitous,
what
ma]ers
most
are
the
ques4ons
you
ask:
• What
is
interes4ng?
• What
is
important?
Tools
Data
• Who
cares?
Thoughts

15. Science
publishing
can
be
distributed?
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
niwy
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…

16. What
do
we
need
to
get
there?
• 1.
Metadata
standards:
Standards
that
allow
interoperable
exchange
of
informa4on
on
any
knowledge
item
created
in
a
lab,
including
provenance
and
privacy/IPR
rights
• 2.
Tools:
Workflow
tools
that
work
for
all
science,
are
scalable,
safe,
and
user-­‐friendly
• 3,
4,
5.
Seman4c/Linked
Data-­‐Centric
authoring,
annota3on
and
edi3ng
environments
that
enable
interlinked,
distributed
knowledge
crea4on.
• 6.
Publishing
systems
that
run
as
applica3on
servers.
=>
Social
change:
– Scien4sts
need
to
realize
they
should
annotate
their
work
– Libraries
change
their
visions
and
jobs
– Publishers
realize
they
need
to
take
on
new
roles

17. The
History
of
Force11:
• 2009/2010:
– Awer
Elsevier
Grand
challenge,
clear
there
was
a
community
interested
in
discussing
the
Future
of
Science
Publishing
Research
Communica4on
– Ini4al
plans:
mee4ng
in
Harvard,
didn’t
end
up
happening;
proposed
&
accepted
Dagstuhl
workshop
• 2011:
– Beyond
the
PDF
was
being
planned
by
Phil
Bourne
–
we
joined
Forces!
– Force11
at
Dagstuhl

18. Beyond
the
PDF
Jan
2011
San
Diego

19. Common
Goal
Applica:on
of
emergent
technologies
to
measurably
improve
the
way
that
scholarship
is
conveyed
and
comprehended
Beyond
the
PDF
Jan
2011
San
Diego

20. Ques4ons
• What
approaches
to
review
and
assessment
can
work?
What
evidence
do
we
have?
• What
tools,
systems,
and
framework
are
needed
to
support
pre-­‐pub
review
and
post-­‐
pub
review?
• How
do
we
persuade
the
research
community
to
change
aka
“It’s
a
cultural
issue…”
Beyond
the
PDF
Jan
2011
San
Diego

21. Outcome
of
Beyond
the
PDF:
• Community
interested
in
connec4ng
• Topics:
– New
formats
for
the
research
paper
– Tools
for
crea4ng,
(re)viewing,
assessing,
edi4ng
– Connec4ng
workflows
and
data
to
papers
– New
metrics
for
success
– New
business
models?
• Some
discussion;
many
ini4a4ves-­‐
no
real
coordina4on
• Forc:
how
do
we
take
this
a
step
further?

22. Future
of
Research
Communica4ons:
Many workshops, papers, conferences, meetings, reports, about
innovation in science publishing:
•
•
•
Many great ideas, but still a lack of large-scale change
Some arguments: ‘I can’t get funded for that’, or ‘the publishers will
never agree to that’ or ‘the reward system is just not set up that way’
or ‘my university/dean/provost doesn’t believe in it’
Here (hopefully) the people you are pointing at are in the room!
22

23. FoRCe11
at
Dagstuhl

24. The
Manifesto

25. Core
issues
of
Force11
Manifesto

26. Next
step:
Force11
• Phil
Bourne
requested
and
obtained
funding
for
2012
from
the
Sloan
Founda4on
to
take
this
to
the
next
step
• Goals:
– Establish
Web
pla{orm
as
site
for
discussions
– Codevelop
proposals
for
concrete
next
steps
– Plan
next
workshop

27. FORCE11
is
distributed!
-­‐Tools
and
Resource
catalog
via
the
Neuroscience
Informa4on
Framework
-­‐Ar4cle
database
in
Mendeley
-­‐Discussion
Forum
via
Google
-­‐Blogs
courtesy
of
blog
sites
and
RSS
feeds
-­‐Web
site
via
Drupal
-­‐Announcements
via
Twi]er
FORCE11
draws
on
a
wealth
of
tools
-­‐
gets
our
“brand”
out
there
for
others
to
find
h]p://force11.org

28. We
will
invent
the
future...
• Like
Larry’s
quan4fied
self,
scien4sts
have
ways
of
exposing
their
exper4se
and
products
on
the
web
unfiltered
– Blogs,
videos,
data
sets
• The
web
leads
to
new
metrics
of
impact
– Connec4vity,
social
presence
– Altmetrics
garfield.library.upenn.edu/essays/v4p394y1979-­‐80.pdf

29. Beyond
the
PDF2
• Planning
is
underway
for
the
next
Beyond
the
PDF
conference
(March
19-­‐20,
2013,
Amsterdam)
• The
FORCE11
challenge
project:
The
Future
is
Now:
– Move
the
FORCE11
Manifesto
beyond
the
PDF...
– Engage
users
beyond
the
evangelical
community
• Give
us
your
use
cases!!!
• Beyond
the
Horizon:
– Openness
is
more
than
open
access
• Open
courses,
Open
conferences,
Open
abstracts
– New
Business
models
for
openness
• Join
FORCE11
now
(members
get
first
chance
to
a]end
Beyond
the
PDF2)

30. Ques4ons!
• Are
we
represen4ng
the
issues
discussed
in
these
webinars?
• If
not
–
what
are
we
missing?
• How
to
work
with
other
groups
be]er
(W3C,
NCBO,
OBO,
…?)
• Aspects
that
could
be
emphasized/taken
up
by
Force11?
• Would
you
be
interested
in
joining??