Talk on changes in scholarly publishing, University of Lethbridge Dept of e-Humanities

1. How
to
Execute
A
Research
Paper
Anita
de
Waard
Disrup8ve
Technologies
Director
Elsevier
Labs
University
of
Lethbridge,
April
3,
2012

2. Outline
• Ten
people/ideas
who/that
are
changing
scholarly
publishing:
– New
forms
– Workflow/data
integra8on
– New
models
of
business/aHribu8on
• So
what
does
this
mean?
• Some
projects
to
help
us
move
towards
these
new
models
2

3. Theme
1:
New
forms
of
publica8on
• Main
issue:
the
format
of
the
scien8fic
paper
comes
from
a
8me
when
our
communica8on
was
paper-­‐
centric
• Solu8on:
Rethink
the
unit
and
form
of
the
scholarly
publica8on
from
the
ground
(i.e.,
the
experiment)
up
• Three
projects
doing
that:
3

4. Steve
PeTfer,
U
Manchester
• Utopia:
‘Everything
you
always
wanted
to
do
with
a
PDF….’:
interac8ve,
sharable
• Working
on
integra8on
with
DOMEO
to
add/
share
annota8ons
• Final
goal:
don’t
‘reconstruct
the
cow
from
a
hamburger’:
include
workflows
and
models
4

5. Gully
Burns,
USC
ISI
• KEfED:
model
of
research
as
an
ac8vity
• Map
out
dependent/
independent
variables
within
an
experiment
and
model
them
• Start:
appendix
to
paper;
later:
precede
paper,
gra`
paper
on
top
of
model.
5

6. Tim
Clark,
Harvard/MGH
• DOMEO:
automated
en8ty
markup
+
manual
mark
up
of
claim/evidence
networks
• Working
on
plagorm
for
workflow
integra8on
rdf:type
<hHp://8nyurl.com/4h2am3a>
swande:Claim
dct:title
Intramembranous
Aβ
behaves
as
chaperones
of
other
membrane
proteins
G1
swanrel:referencesAsSupportiveEvidence
<hHp://example.info/cita8on/1>
G5
pav:contributedBy
<hHp://example.info/person/1>
G6

7. Theme
2:
data
and
workflow
integra8on
• Issues:
– Format
of
the
research
paper
hard
to
integrate
within
a
scien8fic/clinical
workflow
– Hard
to
reproduce/deduce:
what
methods
were
used
and
what
data
was
created
for
a
piece
of
research,
making
reproduc8on
or
even
review
difficult
• Some
solu8ons
for
sharing
workflows
and
data:
7

8. Dave
DeRoure,
Oxford
e-­‐Research
Centre
• Research
objects:
consist
of
all
Workflow
16
academic
output,
including:
Results
produc Q
es
T
L
- Papers
Include
d
in
Published
- Workflows
Included
in
Feeds
in
into
-
Logs
Data
produc
es
Include
d
in
Included
in
- Talks,
lectures
Metadata
Slides
Paper
- Blogs
produce
s
Published
in
Common
pathways
Workflow
13
• Move
towards
executable
work:
Results
- Execute
periodically
to
validate
- Run
automa8cally
when
data
updates
–
by
self
or
others!
- No8fy
researchers
of
new
results
8

9. Phil
Bourne,
UCSD
• Big
need:
keep
track
of
the
data
in
my
lab!
• Other
need:
know
what
I
did/what
other
people
did
–
Yolanda
Gil
made
workflow
representa8on,
was
hard
to
remember
what
we
did…
• Need:
beHer
ways
to
record,
share,
archive
what
we
did.
• New
role
for
the
publisher
>
9

10. Deborah
McGuinness,
RPI
• Future
Web:
• ‘if
everything
is
everywhere,
how
do
we
find
it/know
what
we
want?’
• Internet,
Web,
Grid,
Cloud,
Seman8c
Grid
Middleware
• Xinforma8cs:
• Where
X
=
geo,
eco,
econo…
• Linked
Data
to
Seman8cs
• Seman8c
Founda8ons:
• Pushing
the
boundaries
of
Seman8c
Web
standards
• Ontology
evolu8on
10

11. Theme
3:
New
Models
for
Access/AHribu8on
• Issues:
– User-­‐created
content,
crowdsourcing
means
(scien8fic)
impact
is
measured
very
differently
from
the
past
– Need
new
models
for
copyright/IP
– Ci8zen
scien8sts
par8cipate
as
well
• Some
efforts
to
address
this:
11

12. Paul
Groth,
VU
Amsterdam
Altmetrics:
“the
crea8on
and
study
of
new
metrics
based
on
the
Social
Web
for
analyzing
and
informing
scholarship.”
Including:
- Downloads
- Where
readers
read
- Data
cita8on
- Social
network
diffusion
- Slide
reuse
- Peer
review
contribu8ons
- Youtube
views
12

13. Leslie
Chan,
U.
Toronto
Scarborough
• ElPub
conference
series
that
focus
on
globally
connec8ng
informa8on
scien8sts
• Bioline
Interna8onal
system
“a
not-­‐for-­‐profit
scholarly
publishing
coopera8ve
commiHed
to
providing
open
access
to
quality
research
journals
published
in
developing
countries”:
13

14. John
Wilbanks,
Kauffman/CC
• As
data
becomes
more
accessible,
need:
• raw
metadata
• standards
processes
• consensus
processes
• document
submission
standards
• data
archives
• Ways
of
governing
access:
• Privacy
vs.
IP
vs.
policies
• Technology
only
helps
so
much…
• This
is
mostly
a
social/policy
issue
14

15. Cameron
Neylon,
Cambridge
• Main
arguments
for
Open
Access:
• Ci8zen
science
is
becoming
more
important
• Science
changes
when
it
is
crowdsourced:
Tim
Gowers:
‘ This
is
to
normal
research
as
driving
is
to
pushing
a
car’
• Three
principles:
• Scale
and
connec8vity
• Reduced
fric8on
to
access
• Demand-­‐side
filters
15

16. In
summary,
scien8sts
are
working
on:
• Tools
for
knowledge…
– Visualisa8on
(Steve
PeTfer)
– Modeling
(Gully
Burns)
– Annota8on
(Tim
Clark)
• Ways
to
link
to
– Workflows
(Dave
De
Roure)
– Lab
data
(Phil
Bourne)
– Linked
research
data
(Deborah
McGuinness)
• And
models
for
– AHribu8on/credit
(Paul
Groth)
– Allowing
new
players
to
par8cipate
(Leslie
Chan)
– Copyright/IP
rights
(John
Wilbanks)
– Networked
science
(Cameron
Neylon).
16

17. So
do
we
s8ll
need
publishers?
Or libraries?
• Technically,
there
is
no
reason
to
publish
in
a
journal–
or
even,
for
that
maHer,
to
publish
a
paper
at
all!
• A
few
good
blog
posts
linked
to
workflows
and
data
with
some
valida8on
from
peers
and
good
download
sta8s8cs
might
serve
you
just
as
well
–
or,
in
fact,
much
beHer….
• Is
publishing
in
journals
mostly
a
habit?
17

18. “Publishers
have
been
thinking
we’re
going
out
of
business
for
20
years,
what
has
suddenly
changed?”
The
internet!
Not
the
technical
web,
but
the
social
web….
‘The
value
of
a
[…]
network
is
propor8onal
to
the
square
of
the
number
of
users
of
the
system
(n²)’
1990’s: 2000’s: 2015:
Big Player Medium Participant Irrelevant!18

19. What
do
we
need?
Internet of things: (Bleecker, [1])
Interact with ‘objects that blog’ or ‘Blogjects’, that:
track where they are and where they’ve been;have
histories of their encounters and experienceshave
agency - an assertive voice on the social web [2]
Research Objects: (Bechofer et al, [2])
Create semantically rich aggregations of resources,
that can possess some scientific intent or support
some research objective
Networked Knowledge: (Neylon, [3])
If we care about taking advantage of the web and
internet for research then we must tackle the building
of scholarly communication networks.
These networks will have two critical characteristics:
scale and a lack of friction. [3]
[[1] Bleecker, J. ‘A Manifesto for Networked Objects — Cohabiting with Pigeons, Arphids and Aibos in the Internet of Things
http://nearfuturelaboratory.com/2006/02/26/a-manifesto-for-networked-objects/
2] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and
Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
http://precedings.nature.com/documents/4626/version/1
[3] Neylon, C. ‘Network Enabled Research: Maximise scale and connectivity, minimise friction’, http://cameronneylon.net/blog/
19 19
network-enabled-research/ ‘

20. Some
examples
of
networked
science:
• Mathoverflow:
virtual
network
of
mathemagicians
working
collec8vely
to
answer
big,
small,
clear
and
fuzzy
ques8ons
• Galaxy
Zoo:
ci8zen
science:
classify
galaxies
in
the
comfort
of
your
own
home
–
like
Hanny!
• Tim
Gowers,
Polymath:
“…the
real
contributors
will
be
the
process
owners
and
project
leaders
that
are
able
to
provide
horizontal
leadership.
To
support
this
shi`,
organiza8ons
will
need
to
reward
and
recognize
horizontal
contribu8ons
as
much,
if
not
more,
than
hierarchical
posi8ons.”
20

21. Some
further
parts
of
a
solu8on:
• Iden8fying
the
key
claims
the
authors
make
and
linking
them
to
their
suppor8ng
evidence
both
within
and
across
papers
• Develop
‘executable
papers’
that
contain
computable
and
‘living’
components
• BeHer
integra8ng
papers
with
research
workflows
and
data
• New
models
for
business,
aHribu8on
and
copyright
in
scholarly
publishing
21

22. DOMEO:
Annota8ng
claims
22 22

23. Finding
‘Claimed
Knowledge
Updates’
23 23

24. Executable
Papers
• E.g.:
hHp://www.vistrails.org/index.php/User:Tohline/CPM/
Levels2and3
24

25. Wrapping
a
story
around
your
data:
metadata
1. Research: Each item in the system has metadata (including
metadata provenance) and relations to other data items added to it.
2. Workflow: All data items created in the lab are added to a
metadata
(lab-owned) workflow system.
3. Authoring: A paper is written in an authoring tool which can pull
data with provenance from the workflow tool in the appropriate
representation into the document.
metadata 4. Editing and review: Once the co-authors agree, the paper is
‘exposed’ to the editors, who in turn expose it to reviewers.
metadata
Reports are stored in the authoring/editing system, the paper gets
updated, until it is validated.
5. Publishing and distribution: When a paper is published, a
collection of validated information is exposed to the world. It
remains connected to its related data item, and its heritage can
Rats were subjected to two be traced.
grueling tests
(click on fig 2 to see underlying 6. User applications: distributed applications run on this
data). These results suggest that ‘exposed data’ universe.
the neurological pain pro-
Some other publisher
Review
Revise
Edit
Concept developed with Ed Hovy, Phil Bourne,
25
Gully Burns and Cartic Ramakrishnan

26. FORCE11
Community
of
Prac8ce
• Workshop
in
August
of
2011:
35
invited
aHendees
from
different
parts
of
science,
industry,
funding
agencies,
data
centers
• Goal:
map
main
obstacles
preven8ng
new
models
of
science
publishing
and
develop
ways
to
overcome
them
• Just
received
funding
from
Sloan
founda8on
to:
• Start
online
community
• Hold
next
workshop
• Look
at
new
efforts
26

27. Summary:
• Ten
people
who
are
changing
scholarly
publishing:
– New
forms
– Workflow/data
integra8on
– New
models
of
business/aHribu8on
– Networked
science!
• We
(publishers,
editors,
libraries,
etc)need
to
revisit
if
and
how
we
are
needed
• Some
projects
are
underway
to
help
us
move
towards
these
new
models…
27

28. ….
but
I
am
sure
you
can
come
up
with
beHer
ideas!
hHp://elsatglabs.com/labs/anita
a.dewaard@elsevier.com
28