New Metaphors: Data Papers and Data Citations
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New Metaphors: Data Papers and Data Citations

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  • 1. New  Metaphors:  Data  Papers  and   Data  Cita4ons   2 7   F e b r u a r y   2 0 1 2   U C   C u r a 4 o n   C e n t e r   C a l i f o r n i a   D i g i t a l   L i b r a r y  
  • 2. Metaphors  we  live  by  “...  metaphor  is  pervasive  in  everyday  life,  not  just  in  language  but  in  thought  and  ac4on.      Our  ordinary  conceptual  system,  in  terms  of  which  we  both  think  and  act,  is  fundamentally  metaphorical  in  nature.”   From  Lakoff  and  Johnson,  Metaphors  We  Live  By,  1980   (thanks  to  Parsons  &  Fox,  Is  Data  Publica8on  the  Right  Metaphor?,  2011)  
  • 3. Digital  =  Metaphorical  Everything  is  a  story  on  top  of  sequences  of  bits  •  Fonts,  files,  folders,  formaXng,  phone  calls  •  Programs,  protocols,  data,  tweets,  even  bits   Old  metaphors  can  impede  technical  change   Disrup4ve  technical  change  is  inevitable  
  • 4. Roadmap  for  today’s  talk   • Who  we  are   • What’s  changed   • Forced  incrementalism   • Data  cita4on   • Tradi4onal  ar4cles   • Data  papers   • Closing  metaphor  
  • 5. California  Digital  Library  (CDL)  
  • 6. California  Digital  Library  –  born  1997  University  of  California  stakeholders   CDL  supports  the  research  lifecycle    •  10  campuses   •  Collec4ons  •  226K  students,  134K  faculty  &  staff   •  Digital  Special  Collec4ons  •  100’s  of  museums,  art  galleries,   •  Discovery  &  Delivery   observatories,  marine  centers,   •  Publishing  Group   botanical  gardens   •  UC  Cura4on  Center  (UC3)  •  5  medical  centers  •  5  law  schools  •  3  Dept.  of  Energy  na4onal  labs  
  • 7. Our  environment  circa  2002-­‐2008  Focus  on  preserva4on  For  memory  organiza4ons  Infrastructure:  sta4c  Services:  hosted  Content:  museum  &  library  Sustainability:  ?  
  • 8. Our  environment  since  2008  Focus  on  preserva4on      cura8on  (lifecycle)  For  memory  organiza4ons        and  now  data  producers  Infrastructure:  sta4c       +  cloud,  vm,  bitbucket    Services:  hosted        +  partnered,  self-­‐serve  Content:  museum  &  library        data,  web  crawls  Sustainability:  ?       cost  recovery,  pay  once  
  • 9. The  Library  Reality  •  Journal   expenditures  rising   Journal  expenditures   are  outpacing  library  •  Increase  in   budgets   research   publica4on  •  Increase  in   researchers  •  Declining  budgets  
  • 10. The  Library  Reality  •  Journal   expenditures  rising  •  Increase  in   research   publica4on  •  Increase  in   researchers  •  Declining  budgets   The  growth  of  acEve,  peer  reviewed  learned  journals  since  1665   (Mabe,  2003)  
  • 11. The  Library  Reality  •  Journal   expenditures  rising  •  Increase  in   research   publica4on  •  Increase  in   researchers  •  Declining  budgets                            (Mabe  2004,  based  on  data  from  ISI  and  NSF)  
  • 12. The  Library  Reality  •  Journal   expenditures  rising  •  Increase  in   research   publica4on  •  Increase  in   researchers  •  Declining  budgets  
  • 13. Trends  create  a  structural  problem;  calls  on  libraries  to  do  more  with  less  
  • 14. Trends  create  a  structural  problem;  climb  the  mountain  step  by  step  ...  
  • 15. Or  look  for  a  radical  solu4on?  
  • 16. Prac8cal  incrementalism  for  the   complex  problem  of  data  cura8on  •  Baby  steps  –  data  paper/cita4on  metaphors  •  Chipping  away  –  making  the  problem  smaller   •  DataONE  global  data  network  [NSF]   •  Merrio  data  repository   •  EZID  for  crea4ng  DOIs,  ARKs,  and  URNs   •  Data  management  plans  (DMPTool)   •  Web  archiving  service  (WAS)  [Library  of  Congress]   •  Open-­‐source  Excel  add-­‐in  [MS  Research  &  GBMF]  
  • 17. Prac8cal  incrementalism  for  the   complex  problem  of  data  cura8on  •  Baby  steps  –  data  paper/cita4on  metaphors  •  Chipping  away  –  making  the  problem  smaller   •  DataONE  global  data  network  [NSF]   •  Merrio  data  repository   •  EZID  for  crea4ng  DOIs,  ARKs,  and  URNs   •  Data  management  plans  (DMPTool)   •  Web  archiving  service  (WAS)  [Library  of  Congress]   •  Open-­‐source  Excel  add-­‐in  [MS  Research  &  GBMF]  
  • 18. The  scien4fic  record  is  at  risk  Data  dissemina4on  is  rare,  risky,  expensive,   labor-­‐intensive,  domain-­‐specific,  and   receives  liole  credit  as  research  output   Global  Change   Galac4c  Change  
  • 19. What  data  cita4on  offers  •  Credit  •  Discovery  •  Impact  tracking   –  Helping  data  authors  verify  use  of  their  data  and   –  Helping  iden4fy  how  others  have  used  the  data  •  With  archiving:  re-­‐use  and  reproducibility  
  • 20. Tradi4onal  ar4cles  vs  data  papers  
  • 21. Need  to  save  data  +  processing  
  • 22. Parallel  pyramids  
  • 23. The  collec4ve  data  product  
  • 24. Need  to  save  data  +  processing   Algorithms  +  Data  Structures  =  Programs    
  • 25. Vision  for  a  “data  paper”    •  Wrap  the  unfamiliar  in  a  familiar  façade  •  A  “data  paper”  is  minimally  a  cover  sheet   and  a  set  of  links  to  archived  ar4facts    •  Cover  sheet  contains  familiar  elements:   4tle,  date,  authors,  abstract,  and   persistent  iden4fier  (DOI,  ARK,  etc.)  •  Just  enough  to  permit  basic  exposure  and   discovery  –  Building  a  basic  data  cita4on    –  Indexing  by  services  such  as  Web  of   Science,  Google  Scholar  –  Ins4lling    confidence  in  the  iden4fier’s     stability    
  • 26. Data  Papers  at  the  CDL  UC  CuraEon  Center   Publishing  Services  Program  •  Merrio  Cura4on  repository   •  Online  journals,  with  peer  review  •  EZID:  Persistent  id  management   •  Scholarly  communica4on:  grey   and  resolu4on  (ARKs,  DOIs,  et  al.)   literature  to  post-­‐prints   •  Search  and  display  tools  (XTF)  
  • 27. Provide  incremental  benefit  for   incremental  effort   ...  plus  nano-­‐publicaEons  and  executable  papers.  
  • 28. Data  paper:  envisioned  outcomes  •  Familiar  look  and  feel  eases  adop4on  and  indexing  •  Aoribu4on  mo4vates  deposit  •  Stable  storage  and  ids  leads  to  cita4on  and  impact  •  Data  products  enter  the  record  instead  of  being  lost  •  Data  journals  spring  up  around  disciplines  
  • 29. Metaphors  we  close  with  “Our  ordinary  conceptual  system,  in  terms  of  which  we  both  think  and  act,  is  fundamentally  metaphorical  in  nature.”  OTOH,  “the  more  things  change  the  more  they  remain  the  same”  
  • 30. Ques4ons?   John.Kunze@ucop.edu   California  Digital  Library   hop://www.cdlib.org/   “Data  Paper”  Paper:    hop://escholarship.org/uc/item/9jw4964t