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Save the Cows! Cyberinfrastructure for the Rest of Us

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Expanded version of the Save the Cows presentation, for a mixed librarian/IT-professional audience.

Save the Cows! Cyberinfrastructure for the Rest of Us

  1. Save the Cows! Cyberinfrastructure for the rest of us Dorothea Salo Digital Repository Librarian University of Wisconsin 11 March 2009
  2. Cyberinfrastructure Petabytes Y TE S Data mining X A B Grid Computing E tio n Terabytes Id aE-Research entit or E-Science ab ta oll da y C a G H !Stan et Data Curation s dard M A A R IT? A A Faculty? Libraries?
  3. It’s simpler than that. (thank goodness!)
  4. Scholars use in their research
  5. This produces DATA.
  6. In addition to DATA.
  7. So now we have to support that. Data generation Data management Data storage Data certification Data discovery and reuse
  8. That’s all this is about. Really.
  9. What I will not talk about today • Collaboration technology • Identity-management, authentication, authorization, etc. • Grid computing • Instrument science • Open Notebook Science Of course these are important. I’m just not competent to opine. Fortunately, you have Melissa!
  10. What I’m on about DATA.
  11. Data?
  12. Charts and graphs are DEAD data Killed! Cut in pieces! Ground up! Unrecognizable! Not revivable! Not reusable!
  13. Okay, what’s data, then? We have to save the cows!
  14. In case you’re wondering... ike it l ” ab ML nto co is ws. F to X ers i ay/ PD rg l K 607 > ng ambu aev/200html erti g h nv in ichl-de 0509. —Mes/xm sg0 “Co vert arc hiv m co n l .org / ist s.xm /l ht tp:/ <
  15. Do we have to keep data? SOMETIMES. (but it’s often a good idea even if you don’t have to)
  16. Funders may require it.
  17. Journals may require it.
  18. Here’s the catch Some of these places have built barns Many haven’t. for the cows.
  19. Guess who’s on if they don’t?
  20. What can be done with data? • Experimental validation • Meta-analysis, data-mining, mashups • Interdisciplinary investigation • Historical investigation • Modeling and model validation • ... the possibilities are endless—IF we have the cows the data.
  21. Is all data from “BIG SCIENCE”?
  22. Absolutely not. (they don’t even need our help)
  23. “Small Science” Less money Less know-how In aggregate? MORE COWS.
  24. Arts & Humanities
  25. Here’s the catch.
  26. Nobody knows how to do all this. (yet)
  27. But we do know a few things...
  28. Cows are dumb. They will not save themselves.
  29. It takes a village to save the cows.
  30. Researchers Can you tell a Holstein from an Angus? Me neither. But researchers know their cows.
  31. Information Technologists
  32. Librarians i ful f e aut re o oes s b uctu at g le thi str ... he e th peop g is ng t in di e ocod the talk t’s pen tan g th le t ed to tha ap ers h d n din sab us now see f un rsta e it u at we — at I n o h io u nde mak k th rian 5” n 1 ..” w B ut inat n, and w to I thin libra b “Li Suc ia rar cess. b tio o . d c om ma nd h ess it hybri rs o f or t, a acc the inf ind i t to g Fac to o r fyin b eh wan ded, I den ti n l., “ ho t ble n. w u e re ta ia alm abo librar P the
  33. Grant administrators Cows don’t corral themselves. Neither do researchers.
  34. The big gray area Informaticists? Researchers who code? IT pros who grok metadata? Librarians who model data?
  35. Great. So now what?
  36. Find use cases
  37. Plan for infrastructure
  38. Build alliances
  39. Start conversations
  40. Ten Questions 1. What is the story of your data? 2. What form and format are the data in? 3. What is the expected lifecycle of your data? 4. How could your data be used, reused, and repurposed? 5. How large is your dataset, and what is its rate of growth? 6. Who are the potential audiences for your data? 7. Who owns the data? 8. Does the dataset include any sensitive information? 9. What publications or discoveries have resulted from the data? 10. How should the data be made accessible? —Michael Witt and Jake Carlson, Purdue University
  41. Keep an eye out
  42. If this seems like common sense... ... good! It mostly is!
  43. Thank you! (and save a cow today!)
  44. Credits • Title slide: http://www.flickr.com/photos/flikr/131673772/ • Server rack: http://www.flickr.com/photos/dumbledad/3276756770/ • Command centre: http://www.flickr.com/photos/soundman1024/2054512893/ • Laptop: http://www.flickr.com/photos/arbron/56216464/ • Dual-monitor setup: http://www.flickr.com/photos/blakespot/2372432028/ • Photo-data: http://www.flickr.com/photos/51114580@N00/1597765466/ • Word cloud: http://www.flickr.com/photos/55772089@N00/3291287830/ • Internet map: http://www.flickr.com/photos/jurvetson/63009926/ • Dhaka image: http://www.flickr.com/photos/ahaqueusa/1268467179/ • Plant cross-section: http://www.flickr.com/photos/tonios-pics/387510805/ • Journals: http://www.flickr.com/photos/emdot/56157732/ • Books: http://www.flickr.com/photos/guwashi999/2635608241/ • Manuscript: http://www.flickr.com/photos/86624586@N00/10187684/ • Hamburger: http://www.flickr.com/photos/nadya/1019816514/ • Row of cows: http://www.flickr.com/photos/flikr/230379411/ • Beware of cow: http://www.flickr.com/photos/tm-tm/2339539399/ • Cowboys: http://www.flickr.com/photos/bistrosavage/30710414/ • Hands: http://www.flickr.com/photos/iandesign/1204632335/ • Money: http://www.flickr.com/photos/emraya/2867188734/
  45. Thank you! (and save a cow today!)

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