Over the past 10 years, research systems have evolved from systems that focused on how to structure and record information on research, to systems capable of allowing significant insights to be derived based upon years of high quality information. In 2015, the maturity of the information now collected within many Current Research Information Systems, and the insights that this can provide is of equal or greater value than the insights that could be gleaned from established externally provided research metrics platforms alone. The ability to intersect these external and internal worlds provides new levels of strategic insight not previously available. With the addition of platforms that track altmetrics, and their ability to connect university publications data with a constant flow of real time attention level metrics, an image of a dynamic network of systems emerges, connected together by ever turning ‘cogs’ pushing and translating information. Add to this, the success of ORCID as pervasive researcher identifier infrastructure, and CASRAI as the emerging social contract for information exchange, and it becomes possible to extend this network back from the systems that track and record research information, through to the platforms through which research knowledge is created. The ‘Mechanics’ of this network of systems is more than just getting the ‘plumbing’ right. As research information moves through the network, its audience and purpose changes, the requirements for contextual metadata can also change. This presentation will explore the lived experience of Research Data Mechanics at Digital Science though illustrating how connections between Figshare, Altmetric, Symplectic Elements, and Dimensions can both enhance research system capability and reduce the burden on researchers, and research administration.

1. Work smart. Discover more.
The New Research Data
Mechanics…
Simon Porter
VP Research Engagement Knowledge Architecture
Digital Science
@sjcporter #CASRAI15
also presented at #VIVO15
http://dx.doi.org/10.6084/m9.figshare.1509911

2. Work smart. Discover more.
Before we begin…
This work extends on work and concepts that I began
whilst atThe University of Melbourne. I am grateful for the
permission to build upon it at Digital Science.

3. Work smart. Discover more.
Expectations around Research
Information Systems are undergoing a
period of rapid transformation
Images
Modified
from
Louis
K,
C-­‐0T
Autobot
Transforma;on
And
h=ps://www.flickr.com/photos/ppapadimitriou/
Blocks
source
Flikr
Paper based Administration
-mid late 90’s
Current Research Information Systems
-mid 2000’s Late 2000’s onwards:VIVO/ORCID’s/
Research Data Management /OA
compliance/ Altmetrics/Open Science/
Team Building/Interdisciplinary
Collaborations

4. Work smart. Discover more.
How do we
describe the
discipline that
provides the
foundations to
make these
aspirations
happen?
? ?
? ? ?

5. Work smart. Discover more.
Why is it safe to raise these expectations now?
We know that Universities can be good at
managing information about their research

6. • htcacheclean
-­‐d5
-­‐n
-­‐i
-­‐p/servers/
apache_mod_proxy
-­‐l150M
AOer
14
years
of
publica;ons
repor;ng,
there
are
over
150,000
data
points
on
this
visualiza;on
(presented
at
VIVO14)
Porter,
S
Examples
From
the
University
of
Melbourne

7. The
Funding
Pipeline
Funds
awarded
in:
q 2006
q 2007
q 2008
q 2009
q 2010
q 2011
q 2012
q 2013
q 2014
q 2015
In
2017,
almost
all
Research
will
be
funded
by
awards
yet
to
be
won
$
Total
Funding
by
Alloca;on
Year
for
Department
X
2014
9
years
of
sustained
quality
informa;on
on
agreements
went
into
construc;ng
this
pipeline
(presented
at
VIVO14,
Porter,
S)
Examples
From
the
University
of
Melbourne

8. Australian
Research
Ins;tu;ons
are
in
command
of
their
Research
Informa;on

9. Feeding
the
Machine….
h=ps://www.flickr.com/photos/hikosaemon/

10. Work smart. Discover more.
The Evolution from Data Entry to Data Glue
• Data Entry - 2009
• Harvesting a single source (like WOS or )-
2010
• Harvesting multiple sources (WOS, Scopus,
Repec,Arxiv, pubmed, …) 2012 (Symplectic)
• Over this time, researcher interaction has
moved from data entry (or email) to:
“we think this is yours, please confirm”
An
example
from
the
University
of
Melbourne

11. (Source:
TEST
DATA
from
UAT)

13. Now
an
ins;tu;ons
‘glued
data’
can
be
used
as
‘tentacles’
to
reach
out
and
gather
new
perspec;ves
h=ps://www.flickr.com/photos/tomsaint/

14. A
Tradi;onal
Use
Case:
Gluing
University
data
to
InCites
Bibliometric
data

15. Gluing
University
Publica;ons
data
with
WOS
Example
From
the
University
of
Melbourne:
(Porter
ARMS
2014)

16. Work smart. Discover more.
Altmetric For Institutions…

17. Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015

18. Melbourne
Ar;cles
with
the
highest
Altmetric
scores…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015

19. Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015

20. At
least
a
year
too
late…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015

21. Using
Altmetrics
to
their
fullest
poten;al
demands
a
different
way
of
engaging
with
informa;on…
Examples
From
the
University
of
Melbourne
Presented
at
Digital
Science
Showcase
2015

22. From
Data
Glue
to
Data
Mechanics…
h=ps://www.flickr.com/photos/ronwls/13987847602/
in/photolist-­‐nj4nLf-­‐F329z

23. The
Goal
of
Research
Data
Mechanics
1) In
all
cases,
we
seek
to
replace
manual
interven;on
with
cogs
turning
between
an
understood
system
of
research
2) To
build
and
increase
the
trust
network
of
researchers,
ins;tu;ons,
funding
bodies,
publishers,
and
internal
and
external
service
providers

24. Work smart. Discover more.
Another perspective on Research Data Mechanics:
In
the
case
of
QM
or
Classical
Mechanics
these
laws
of
mo;on
are
determined
by
the
forces
felt
by
the
par;cle
...in
the
case
of
Research
Data
Mechanics,
our
par;cles
are
items
of
data
and
the
underlying
laws
of
mo;on
are
university,
government,
publisher
and
funder
policies
and
prac;ces.

25. ORCID
as
the
‘people
cogs’

26. What
Becomes
Possible…

27. How
does
it
change
what
ins;tu;ons
can
measure/value?

28. Journal
ar;cles
+
Conversa;on
Ar;cles
If
we
can
measure
it,
we
can
reward
it
✔
✔
✔
✔
✔
✔
✔

32. And
from
publica;ons
to
Research
Data…

34. How doesThis.. Become… This?

35. A Possible Future…

36. Research
data
can
be
enhanced
as
it
travels
through
systems…
Enriched
data
publica;on
links
Research
grants…
Research
Data
as
it
is
shared
What
become
possible…..

37. And
another
thing…
Both
are
examples
of
reducing
barriers
between
the
act
of
research
collabora;on,
and
the
knowing
of
it

38. Formalizing
Research
Data
Mechanics…

39. A
Generic
System
Component
Component
Policy
(Informa;on
Transformed
by
People
processes)
Component
configura;on
and
behavior
is
Influenced
by
the
upstream
and
downstream
components

40. System
components
in
the
context
of
one
possible
VIVO
configura;on
HR
Policy
Finance
Policy
Policy
Grant
Management
Policy
{
{
J
J
J
J
F
F
F
F

41. Inves;ga;ve
Power
with
reference
to
the
system
• Examples
– University
Level
Benchmarking
– Compara;ve
Inter
-­‐
Department
Data
Analysis
JJJJ
JJJJ

42. Inves;ga;ve
Power
with
reference
to
the
system
– University
Level
Benchmarking
(Grants
Awarded)
– University
Level
Funding
Pipeline
Analysis
– University
Level
Funding
Pipeline
Analysis
(difficult)
FFFF
FFF
Grant
Management
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
F
F
F
Grant
Manag
ement
?

43. A
Deeper
view
of
Research
Data
Mechanics
STAR
METRICS
(2009)
FFF
Grant
Management
Finance
System
DUNS
database
Payroll
System
h=p://www.nsf.gov/sbe/sosp/workforce/lane.pdf
(an
extended
version
of
research
data
mechanics)

44. Work smart. Discover more.
Some challenges for Research Data Mechanics
• Extending
the
system
of
components
and
the
trust
network
• Crea;ng
common
‘core’
capacity
across
all
research
ins;tu;ons,
Funding
bodies,
Publishers
• Crea;ng
a
research
data
‘machine’
equally
capable
of
preserving
the
history
of
research,
as
well
facilita;ng
the
needs
of
the
‘now’

45. Work smart. Discover more.
Challenge 1) Identifying and removing system
boundaries
– System
boundaries
cause
• informa;on
that
is
already
know
to
be
recreated
• Informa;on
Loss
– Reasons
for
systems
boundaries
include
• Too
much
data
fric;on
created
from
a
lack
of
standards/apis
for
communica;ng
informa;on
• Insufficiently
structured
informa;on
at
the
source
of
crea;on
• Misconfigured
policy
• Insufficiently
developed
trust
networks
• A
lack
of
awareness
of
possibility

46. Work smart. Discover more.
Practical Ways thatVIVO is extending boundaries
HR
Policy
Finance
Policy
Policy
Grant
Management
Policy
Department
Websites
Department
Websites
Department
Websites
Department
Websites

47. Work smart. Discover more.
2) Creating common ‘core’ capacity across all research
institutions
• If
your
ins;tu;on
can
produce
‘sustainable’
VIVO
data
capable
of
represen;ng
your
en;re
research
ins;tu;on,
then,
as
of
now,
you
have
reached
core
capacity…
• What
is
the
core
capacity
for
a
funding
body?
• For
a
publisher?

48. Work smart. Discover more.
3) Creating a machine capable of writing history
C
RIS

49. Work smart. Discover more.
Or…
CHRIS

50. Work smart. Discover more.
h=ps://en.wikipedia.org/wiki/Aqueduct_(water_supply)#/media/File:Pont_du_Gard_Oct_2007.jpg
In Research Data Mechanics
we are not just building pipes…

51. Work smart. Discover more.

52. Work smart. Discover more.
Thanks.
Any
questions?