Digital History workshop: Crowdsourcing in the Humanities and cultural heritage sector. Victoria University of Wellington 23 April 2013 Session: Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects Presenter: Lynne Siemens http://wtap.vuw.ac.nz/wordpress/digital-history/events/crowdsourcing-workshop/presenters/

1. Crowd
in
the
Cloud:
Collaborative
Frameworks
for
Virtual
DH
Projects
Lynne
Siemens
siemensl@uvic.ca
Wellington,
April
2013

2. A
glorious
endeavour….
h"p://collec*on.cooperhewi".org/objects/18422089/,
Drawing,
"Group
of
Angels
on
a
Cloud
Bank
(study
for
a
ceiling
decora*on)",
1630–50,
Smithsonian
Cooper-­‐Hewi",
Na*onal
Design
Museum

3. Perhaps,
more
the
reality…
Devils,
from
the
Last
Judgement,
Luca
Signorelli,
hBp://
www.1st-­‐art-­‐gallery.com/Luca-­‐Signorelli/Devils,-­‐From-­‐
The-­‐Last-­‐Judgement.html

4. Appropriate
middle
ground?
• Crowdsourcing
offers
potenKal
to
academic
projects,
especially
for
those
with
large
amounts
of
data
to
process
and
relaKvely
small
budgets
• But
how
best
to
organize
the
work
to
ensure
that
this
crowd’s
contribuKon
is
delivered
within
an
academic
project’s
schedule,
budget
and
other
resources
and
to
the
required
quality
standard?
• ConsideraKon
of
more
than
the
moKvaKon
of
volunteers
•
Where
are
the
points
of
collaboraKon?

5. Collaboration
Points

6. Decision
points
for
collaboration
in
the
cloud
• These
include:
• the
type
of
experKse,
qualificaKon
and/or
knowledge
required
• the
presence
of
contributors
• the
mechanisms
by
which
they
will
parKcipate
and
contribute
• project
remuneraKon
• moKvators
to
keep
parKcipants
engaged
• quality
control
mechanisms

7. Frameworks:
Where/how/what/
who/when
to
collaborate?
Simple
tasks
Moderate
tasks
Complex
tasks
• Task:
Low
complexity
• Outcomes
and
quality:
Easy
to
evaluate
• “Any
Individual”
could
undertake
with
minimal
training,
skill,
and
special
experKse
• PotenKal
for
gamificaKon
• Example:
OCR
correcKon
and
tagging
• Task:
Medium
complexity
• Outcomes
and
quality:
More
difficult
to
evaluate
• “Most
people”
could
undertake
with
training,
skill,
and
special
experKse
• Example:
TranscripKon
• Task:
High
complexity
• Outcomes
and
quality:
Difficult
to
evaluate
• “Expert”
needed
with
special
knowledge
and
skills
• Example:
AnnotaKon
and
problem
solving
Australian
Historic
Newspapers
Transcribe
Bentham
Pynchonwiki

8. Questions/discussion