Talk given at the e-science meeting on Web2 in research. Focuses on a couple of case studies trying to draw out what makes an effective and successful Web2 service for researchers.

1. Walking the Walk
The experience of using Web 2.0
tools in active research projects
Cameron Neylon

2. 1. The long tail
2. Data is the next Intel inside
3. Users add value
4. Network effects by default
5. Some rights reserved
6. Perpetual Beta
7. Cooperate don’t control
8. Software above the level of a single device
http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html?page=5

3. What does Web 2.0 offer a
researcher in practice?
http://flickr.com/photos/heymans/480396810/

4. http://tinyurl.com/friendfeed-mthk-request

5. • Open tender and response
• Not mass participation or opinion markets
• There just aren’t that many researchers
• A good community and a well built and
cared for network are critical

6. The Polymath Project

7. 27 January 2009
http://gowers.wordpress.com/

8. 27 January 2009
February 1 2009
http://gowers.wordpress.com/

9. 27 January 2009
February 1 2009
March 10 2009
http://gowers.wordpress.com/

10. • Successful project
• Small core group of participants
• Much larger group of watchers
• Concerns over embarrassment, keeping up,
mechanisms for giving credit
• Issues over management of large numbers
of very active threads

11. Open Notebook Challenge

12. http://onschallenge.wikispaces.com/Exp026

13. http://tinyurl.com/ons-challenge-spreadsheet

14. http://oru.edu/cccda/sl/descriptorspace/ds.php

15. http://slurl.com/secondlife/Drexel/165/178/24

16. 113 individual measurements
(plus 71 literature values)
14 researchers in four countries
One undergraduate chemistry class
$6000 funding (for prizes and chemicals)

17. 113 individual measurements
(plus 71 literature values)
14 researchers in four countries
One undergraduate chemistry class
$6000 funding (for prizes and chemicals)
Four months
One (invited) book chapter submitted
Second paper in preparation

18. • Collaboration enabled via open data licensing
• Still relatively small numbers of people
• Are massive collaborative projects even possible?
• 90% aren’t aware of it, 9% are passive watchers
• 0.9% make occasional contributions and 0.1% are
core players - does that add up to more than one?

19. Galaxy Zoo

22. • Compelling and comprehensible story
• Much work gone into building a system that
enables people to make a contribution
• Responsive and appealing user experience
• Still a self selecting community but drawn
from a much wider pool

23. Deploying the LaBLog at RAL

24. Screenshot - 26 March

25. • “It’s going to be great but we need to put a lot of
work into getting it going...”
• “I don’t have the time to put all the stuff in...”
• Get what the advantages are but haven’t
necessarily bought in to the process
• Concerns over data re-use and “scooping”

26. Where does this leave us?

27. 1. The long tail
2. Data is the next Intel inside
3. Users add value
4. Network effects by default
5. Some rights reserved
6. Perpetual Beta
7. Cooperate don’t control
8. Software above the level of a single device
http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html?page=5

28. 1. The long tail

29. 1. The long tail
ugh
eno
fat
sn’t
i
unless you w
ork real hard
at it
c.f. galaxy zo
o, FoldIt etc
.

30. 2. Data is the new Intel inside
well duh!

31. nt to
a
w
ou 3. Users add value
Y
but why do they want t
o?

32. 4. Network effects by default

33. 4. Network effects by default
SP
rU
ou
sy
at’
th
if ow
ut pn
b eu
giv
en
th

34. 5. Some rights reserved
licensing matters, even i
f
everyone thinks it’s bori
ng

35. is not an excuse for
giving people rubbish
6. Perpetual beta

36. is not an excuse for
giving people rubbish
6. Perpetual beta

37. is not an excuse for
giving people rubbish
6. Perpetual beta
provement
continual im

38. ating
tegr
in in
e lies
valu
l the
Al g it
ardin
n ho
not i
data,
7. Cooperate don’t control

39. ating
tegr
in in
e lies
valu
l the
Al g it
ardin
n ho
not i
data,
7. Cooperate don’t control
licensing matters, even i
f
everyone thinks it’s bori
ng

40. ating
tegr
in in
e lies
valu
l the
Al g it
ardin
n ho
not i
data,
7. Cooperate don’t control
licensing matters, even i
f
everyone thinks it’s bori
ng
ecause it
ike is bad b
share-al
perability
eaks intero
br

41. 8. Software above the level of a
single device

42. collaboration
8. Software above the level of a
single device

43. collaboration
8. Software above the level of a
single device
vice
ser

44. What are the design patterns
for successful research tools?

45. 1. Define and understand your target audience
2. Solve a pressing problem they have or tell them a story
they understand and want to contribute to
3. Build the service into an existing workflow or a established
framework that the target audience understands
4. Get the licensing right and give users a sense of control
over their own data and contribution
5. Build for network effects but don’t rely on them
6. Plan to build (and resource the building of) your community
7. Build for interoperability; technical and legal

46. Friendfeed as a research community

47. http://friendfeed.com

48. http://tinyurl.com/dku869

49. Pulling items from external services via RSS

50. • Aggregating content (solving a problem)
• Works without a network; network effects follow
• Collecting comments and ratings (data)
• Straightforward pumping of data in and pulling it
backout via API (licensing, interoperability)
• Community, community, community
• Building your own network

51. If you just build it they
(probably) won’t come

52. If you don’t build it they
definitely won’t come

53. The community is more
important than the service

54. Researchers are already the tail