Walking the walk - the practical experience of Web2 in research

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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.

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Walking the walk - the practical experience of Web2 in research

  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
  20. • 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
  21. Deploying the LaBLog at RAL
  22. Screenshot - 26 March
  23. • “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”
  24. Where does this leave us?
  25. 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
  26. 1. The long tail
  27. 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 .
  28. 2. Data is the new Intel inside well duh!
  29. nt to a w ou 3. Users add value Y but why do they want t o?
  30. 4. Network effects by default
  31. 4. Network effects by default SP rU ou sy at’ th if ow ut pn b eu giv en th
  32. 5. Some rights reserved licensing matters, even i f everyone thinks it’s bori ng
  33. is not an excuse for giving people rubbish 6. Perpetual beta
  34. is not an excuse for giving people rubbish 6. Perpetual beta
  35. is not an excuse for giving people rubbish 6. Perpetual beta provement continual im
  36. ating tegr in in e lies valu l the Al g it ardin n ho not i data, 7. Cooperate don’t control
  37. 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
  38. 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
  39. 8. Software above the level of a single device
  40. collaboration 8. Software above the level of a single device
  41. collaboration 8. Software above the level of a single device vice ser
  42. What are the design patterns for successful research tools?
  43. 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
  44. Friendfeed as a research community
  45. http://friendfeed.com
  46. http://tinyurl.com/dku869
  47. Pulling items from external services via RSS
  48. • 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
  49. If you just build it they (probably) won’t come
  50. If you don’t build it they definitely won’t come
  51. The community is more important than the service
  52. Researchers are already the tail

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