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From Open Access to Open Data


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Presentation given at the DPC Workshop, York, UK, 5 July 2013

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From Open Access to Open Data

  1. 1.                      /  @ubiquitypress   Brian  Hole   DPC  Workshop,  York,  5  July  2013   From  Open  Access  to  Open  Data  
  2. 2.                      /  @ubiquitypress   The  Social  Contract   of  Science   •  ValidaKon   •  DisseminaKon   •  Further  development   ScienKfic  MalpracKce   •  Publishers   •  Researchers   •  Libraries,  repositories…   •  All  outputs  
  3. 3.                      /  @ubiquitypress  
  4. 4.                      /  @ubiquitypress   Repositories   Modified  from:  XKCD  
  5. 5.                      /  @ubiquitypress   Metajournals  as  incen6ves  
  6. 6.                      /  @ubiquitypress  
  7. 7.                      /  @ubiquitypress   Why  data  journals?   Amsterdam  manifesto:   4.  A  data  citaKon  in  a  publicaKon  should   resemble  a  bibliographic  citaKon  and  be   located  in  the  publicaKon’s  reference  list.   •  Data  can  (and  should)  be  cited  using  DataCite  DOIs   in  arKcles,  but  this  is  not  enough.   •  Researchers  understand  the  value  of  papers   •  University  departments  and  the  REF  understand   papers   •  Researchers  know  where  to  put  paper  refs,  no   need  for  extra  guidelines   •  Publishers  rouKnely  strip  out  anything  else   •  Familiar  impact  metrics  can  be  collected  
  8. 8.                      /  @ubiquitypress   What  is  a  data  paper?   A  data  paper  is  not…   •  …  a  research  paper.  A  data  paper  only     describes  a  dataset.  But  it  will  reference   research  papers  that  are  based  on  the  data.   •  …  simply  replicaKon  of  the  informaKon  in  a     data  repository.   A  data  paper…   •  …  describes  the  methodology  with  which   a  dataset  was  created.   •  …  describes  the  dataset  itself.   •  …  details  the  reuse  potenKal  of  the  data.   •  …  is  oaen  authored  by  a  data  scienKst.   •  …  is  citable,  enabling  reuse  to  be  tracked.  
  9. 9.                      /  @ubiquitypress   General  structure   •  Title   •  Authors,  affiliaKons   •  Abstract   •  Keywords   •  Context   •  SpaKal  coverage,  temporal  coverage   •  Methods   •  Steps,  sampling  strategy,  quality  control,  constraints,   ethical  consideraKons       •  Dataset  descripKon   •  Object  names,  data  type,  format  names  &  versions,   creators,  creaKon  dates,  language,  license,  locaKon   (DOI),  publicaKon  date   •  Reuse  potenKal   •  Acknowledgements   •  References  
  10. 10.                      /  @ubiquitypress   1. The  paper  contents   a.  The  methods  secKon  of  the  paper  must  provide   sufficient  detail  that  a  reader  can  understand  how   the  resource  was  created.   b.  The  resource  must  be  correctly  described.   c.  The  reuse  secKon  must  provide  concrete  and  useful   suggesKons  for  reuse  of  the  reuse.   2.  The  deposited  resource   a.  The  repository  must  be  suitable  for  resource   and  have  a  sustainability  model.   b. Open  license  permits  unrestricted  access  (e.g.  CC0).   c.  A  version  in  an  open,  non-­‐proprietary  format.   d. Labeled  in  such  a  way  that  a  3rd  party  can  make   sense  of  it.   e.  Must  be  acKonable.   Peer  review  
  11. 11.                      /  @ubiquitypress   •  Data  journals  need  to  be  built  within  the  community,  and   to  adapt  to  its  requirements   Important  principles   •  Community  ownership  and  trust  is  important   •  Full  transparency  in  processes  and  finances   •  Sustainability   •  Low  barriers  essenKal   •  Zero  to  low  fees   •  Quick  online  authoring   •  Repository  integraKon  
  12. 12.                      /  @ubiquitypress   PRIME:  Use  Case  #1   •  A  UCL  Researcher  deposits  data  in  an  external  subject  repository.     •  The  subject  repository  sends  the  metadata  and  DOI  of  the  data  to  the   UCL  insKtuKonal  repository  so  that  it  has  a  record  of  the  output.    
  13. 13.                      /  @ubiquitypress   Text  and  data  mining   [the  benefits  of  text  mining  include]:  “increased  researcher  efficiency;   unlocking  hidden  informaKon  and  developing  new  knowledge;  exploring   new  horizons;  improved  research  and  evidence  base;  and  improving  the   search  process  and  quality.  Broader  economic  and  societal  benefits   include  cost  savings  and  producKvity  gains,  innovaKve  new  service   development,  new  business  models  and  new  medical  treatments.”   JISC   “The  downstream  value  of  high  quality,  high  throughput  chemical   informaKon  extracted  from  the  literature  can  be  measured  against   convenKonal  abstracKon  services…  with  a  combined  annual  turnover  of   perhaps  $500-­‐1,000  million  dollars.  We  believe  our  tools  are  capable  of   building  the  next  and  beoer  generaKon  of  services.”   Peter  Murray-­‐Rust  
  14. 14.                      /  @ubiquitypress   “Licences  for  Europe”   •  Focus  was  to  create  new  licenses  to  enable  TDM   •  I.e.  researcher  would  need  one  license  from  each   publisher.  Much  TDM  work  involves  hundreds  of   publishers,  can  take  weeks  just  for  one.   •  Focus  pre-­‐determined  from  start:  to  come  up  with   proposals  on  licenses  only.  Discussion  of  excepKons   allowed  but  not  to  be  part  of  recommendaKons.   •  Unbalanced  setup:  large  corporate  publishers,  technology   sector  poorly  represented.   Working  Group  4:  Text  and  Data  Mining   •  UP  walked  out  with  civil  society  groups.  Not  prepared  to   endorse  licenses  as  acceptable.     •  Tell  your  publisher  or  associaKon  that  this  is  important  to  you.   •  Workshop  at  the  BL  to  inform  policy  makers  in  late  Sept  2013.  
  15. 15.                      /  @ubiquitypress   Links   hop://   hop://     hop://   hop://