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NCBO webinar pertaining to Force11 http://www.bioontology.org/Force11

NCBO webinar pertaining to Force11 http://www.bioontology.org/Force11

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Ncbo webinar force11 Ncbo webinar force11 Presentation Transcript

  • Force11:  the  Future  of  Research  Communica4ons  and  eScholarship   Anita  de  Waard     Disrup4ve  Technologies  Director,     Elsevier  Labs,  Burlington,  VT   Maryann  E.    Martone   University  of  California,  San  Diego  
  • Outline:  •  Background:  distribu4on  of  data,  tools  and   ideas  =>  we  need  social  change!  •  Past:  BtPDF,  Dagstuhl  •  Present:  Sloan  grant,  force11.org  •  Future:  Plans,  ideas  –  input?    
  • Mo4va4on:  the  well-­‐known  issue     of  data  overload…  
  • More  data  by  the  minute.   Time:13.7min Search  (53%) Search  (48%) Age  :  35.4 Bounce  :  2%   Pols.  and  docs.(15%) Search  (35%) N=  3,561 Time:2min Pols.  A nd  docs.  (53%) Time:87.5min Age  :  20 Age  :  35.6 Pols.  and  docs.  (11%) Bounce  :  1%   Bounce  :  2.2%   Time:1.9min N=  523 Search  (15%) N=  7980 Age  :  32.2 Search  (37%) Search  (25%) Search Bounce  :  0%   Policies  &  Docs.(16%) Pols.  and  docs.  (25%) Time:1.6  m in N=  620 (36%) Age  :  22.2 Pols.  and  doc.  (44%) Time:3.9  m in Bounce  :  0.8%   Search  (26%) Age  :  27.7 Time:1.4min N=  761 Search  (28%) Bounce  :  0.7%   Age  :  11.2 Time:8.8min Pols.  and  docs.  (49%) N=  2681 Emp.  law  ref.  man.  (43%) Bounce  :  1.6%   Age  :  33.6 Emp.  law  ref.  man.  (40%) Bounce  :  1%   N=  497 Emp.  law  Ref.  Man.  (11%) N=  25,423 Employment  law.  (8%) Time:31.9min Time:2.36  m in Search  (25%) Age  :  33.5 Age  :  11.6 Pols.  and  docs.  (13%) Bounce  :  1.2%   Bounce  :   0.7%   N=  1815 N=  427 Search  (35%) Emp.  law  ref.  man.   (19%) Home  (38%) Time:2.5min Employment  law  (86%) Age  :  4.8 Bounce  :  28.4%   Employment  law  (65%) People  manager N=  5,780 Search  (19%) Home (23%) (64%) Emp.  law  ref.  man.  (24%) Time:1.14min Policies  (13%) Statutory  rates  (4%) Age  :  1 Statutory  rates  (37%) Bounce  :  0%   Time:1.6  m in N=  16 Age  :  4 Employment  law  (31%) Bounce  :  1.4%   Home  (8%) Emp.  L aw  (82%) Time:0.4min N=  141 Time:1.63min Age  :  8.6 Policies  (8%) Age  :  32.5 Bounce  :  3.6%   Bounce  :  2.6%   Emp.  law  ref.  man.  (11%) N=  8,563 N=  268 Employment  law Employment  law  (9%) (15%) Search  (35%) Time:2.4min Employment  law  (14%) Search  (48%) Emp.  law  ref.  man.  (17%) Age  :  7.3 Time:0.4min Search  (9%) Emp.  law  ref.  man.  (63%)Time:2.2  m in Bounce  :  2.1%   Age  :  8.5 N=  96Age  :  7.9 Legal  guidance  (8%) Employment  law  (11%) Time:1.8min Legal  guidance  (28%) Bounce  :  6.3%   Time:1.7minBounce  :  1.8%   Age  :  5.4 N=  10,562 Age  :  29.3 Search  (26%)N=  115,498 Search  (28%) Bounce  :   0%   Bounce  :  1%   Pols.  and  doc.(9%) Time:2.8min N=  58 Employment  law  (14%) N=  826 Age  :  40 Pols.  and  docs.  (32%) Bounce  :  0%   N=  57 Employment  law  (16%) Time:2.1  m in What’s  new  (36%) Age  :  10.2 What’s  new  (28%) Bounce  :  1.3  %   Legal  r eports  (11%) Time:1.1  m in What’s  new  (20%) N=  230 Age  :  8.9 What’s  new  (16%) Time:1.8  m in Legal  r eports  (33%) Legal  guidance  (13%) Bounce  :  1  %   Age  :  9.02 N=  98 Time:0.7min Search  (16%) Employment  law  (58%) Bounce  :  5.2%   Age  :  9.2 What’s  new N=  910 Time:0.8min Legal  guidance  (24%) Bounce  :  4.7  %   What’s  new  (17%) 1 (9%) Employment  law  (10%) N=  85 Age  :  8.8 Search  (16%) Bounce  :  3.4  %   Search  (31%) Legal  guidance  (17%) Legal  guidance  (24%) What’s  new  (13%) Time:2.5min N=  174 Time:1.7min Pols.  and  doc.(17%) Age  :  8.7 Time:1.1  m in Age  :  31.7 Time:2min Legal  r eports  (16%) Bounce  :  0.9%   Age  :  9.3 Search  (16%) Age  :  8.8 Bounce  :  1.5  %   N=  6,219 Bounce  :  0.8  %   What’s  new  (14%) N=  136 Emp.  law  ref.  man.  (13%) What’s  new  (13%) Bounce  :1%   N=  877 Legal  guidance  (11%) N=  104 4  
  • Even  plants  make  data!  •  Internet  of  things:  we  can  interact  with  ‘objects   that  blog’  or  ‘Blogjects’,  that  track  where  they  are   and  where  they’ve  been;    •  have  histories  of  their  encounters  and  experiences   have  agency    •  have  a  voice  on  the  social  web  
  • Larry  Smarr  makes  lots  of  data:  •  He  wears:     •  A  Fitbit  to  count  his  every  step   •  A  Zeo  to  track  his  sleep  pa]erns   •  A  Polar  WearLink  that  lets  him  regulate  his     maximum  heart  rate  during  exercise   •  23andMe  analyzed  his  DNA  for  disease  suscep4bility.  •  Your  Future  Health  analyzed  blood  and  stool  samples  for  100   biomarkers:   •  At  one  point,  C-­‐reac4ve  protein  stood  out  as  higher  than  normal.   •  A  blood  test  showed  that  his  CRP  had  climbed  to  14.5  during  the  a]ack.     •  He  took  an4bio4cs,  the  symptoms  resolved,  and  his  CRP  dropped  to  4.9— but  that  was  s4ll  unusually  high.   •  Lactoferrin,  too,  rose  several  4mes  to  sky-­‐high  levels—200,  whereas  the   normal  count  is  less  than  7.3  –  and  in  tandem  with  CRP   •  Smarr  now  thinks  his  diver4culi4s  a]ack  was  actually  Crohns  disease  –  and   his  gastroenterologist  (reluctantly)  agreed.  
  • As  do  lots  of  other  ‘Quan4fied  Selfers’:    Clearity  Founda4on:  A  transla4onal  medicine  and  public  service  founda4on  for:  •  Providing  doctors  access  to  molecular  profiling    for  their  ovarian  cancer  pa4ents  •  Providing  doctors  and  pa4ents  clinical  trial    op4ons  informed  by  individual  tumor  biology  •  Providing  financial  support  for  the  profiling  work    for  pa4ents  –  Oprah  approved!  
  • uses  data  •  It  knows  where  you  are  •  And  who  you  talked  to  •  And  what  you  bought    •  And  how  much  you  paid..  •  And  whether  you  need  another  pair  of  shoes  •  And  when  and  where  you  can  get  them…  
  • Bri]any  Wenger  uses  this  data:   Winner  of  the  Google  Science  Fair  2012  17-­‐year  old  Bri]any  Wenger  developed  a  cloud-­‐based  neural  network  that  is  able  to  seamlessly  and  accurately  assess  3ssue  samples  for  signs/evidence  of  breast  cancer  to  give  more  credence  to  the  currently  used  (less  reliable)  minimally  invasive  procedure  called  Fine  Needle  Aspirates  (FNAs).  By  looking  at  nine  different  input  features  and  comparing  them  to  the  training  examples,  Bri]any’s  cloud-­‐based  neural  network  can  detect  malignant  breast  tumors  with  an  accuracy  of  99.11%    Because  her  neural  network  is  deployed  in  the  cloud  using  Google’s  app  engine  it  means  it  can  be  accessed  from  exis3ng  medical  systems  as  well  as  through  a  web  browser  or  mobile  apps.  
  • Mark  Wilkinson  uses  this  data:   Given  a  protein  P  in  Species  X:    Find  proteins  similar  to  P  in  Species  Y      Retrieve  interactors  in  Species  Y      Sequence-­‐compare  Y-­‐interactors  with  Species  X   genome                        (1)    à  Keep  only  those  with  homologue  in        Find  proteins  similar  to  P  in  Species  Z      Retrieve  interactors  in  Species  Z      Sequence-­‐compare  Z-­‐interactors  with  (1)                            à  Puta3ve  interactors  in  Species  X     Using  what  is  known  about  interac4ons  in  fly  &  yeast,   predict  new  interac4ons  with  a  human  protein  –  Running  over  data  on  the  web  that  he  neither  created  nor  knew  about!  
  • In  summary:    science  is  becoming  distributed:   Data   Tools   Thoughts  
  • Science  is  becoming  distributed:   Data   Tools   Data  is  king!   •  Data  needs  to  say  what  it’s  about   Thoughts   who  owns  it   •  Data  needs  to  say  where  it  comes  from   •  Data  needs  to  know   •  Data  needs  to  be  sensi4ve  to  privacy   •  Data  needs  to  know  how  it’s  used  
  • Science  is  becoming  distributed:   Tools  Tools  rule!     Data  Tools  can  be  made  by  everyone:  Tools  are  open  and  free  Tools  will  know  where  data  lives   Thoughts  Tools  need  to  know  about  data:  •  Privacy/ownership    •  Trustworthiness  •  Provenance  
  • Science  is  becoming  distributed:  If  data  and  tools  are  ubiquitous,  what  ma]ers  most  are  the  ques4ons  you  ask:  •  What  is  interes4ng?    •  What  is  important?    Tools   Data  •  Who  cares?     Thoughts  
  • Science  publishing  can  be  distributed?   metadata   1.  Add  metadata  to  everything   metadata   metadata   2.  Use  a  workflow  tool   3.  Write  in  a  shared  space   metadata   4.  Invite  reviews   metadata   5.  The  reviewer  approves     (or  comments,  author  revises,  etc)   Rats  were  subjected  to  two   6.  Run  niwy  apps  over  all  of  this.   grueling  tests   (click  on  fig  2  to  see  underlying   data).  These  results  suggest     that  the  neurological  pain  pro-­‐   Calculate,  coordinate…     Review   Revise   Compile,  comment,   Edit   compare…  
  • What  do  we  need  to  get  there?    •  1.  Metadata  standards:  Standards  that  allow  interoperable   exchange  of  informa4on  on  any  knowledge  item  created  in  a  lab,   including  provenance  and  privacy/IPR  rights  •  2.  Tools:  Workflow  tools  that  work  for  all  science,  are  scalable,  safe,   and  user-­‐friendly  •  3,  4,  5.  Seman4c/Linked  Data-­‐Centric  authoring,  annota3on  and   edi3ng  environments  that  enable  interlinked,  distributed  knowledge   crea4on.    •  6.  Publishing  systems  that  run  as  applica3on  servers.    =>  Social  change:     –  Scien4sts  need  to  realize  they  should  annotate  their  work   –  Libraries  change  their  visions  and  jobs   –  Publishers  realize  they  need  to  take  on  new  roles  
  • The  History  of  Force11:  •  2009/2010:     –  Awer  Elsevier  Grand  challenge,  clear  there  was  a   community  interested  in  discussing     the  Future  of  Science  Publishing     Research  Communica4on   –  Ini4al  plans:  mee4ng  in  Harvard,  didn’t  end  up   happening;  proposed  &  accepted  Dagstuhl  workshop  •  2011:   –  Beyond  the  PDF  was  being  planned  by  Phil  Bourne     –  we  joined  Forces!   –  Force11  at  Dagstuhl  
  • Beyond  the  PDF      Jan  2011  San  Diego  
  • Common  Goal   Applica:on  of  emergent  technologies  to  measurably   improve  the  way  that  scholarship  is  conveyed  and   comprehended   Beyond  the  PDF    Jan  2011  San  Diego  
  • Ques4ons  •  What  approaches  to  review  and  assessment   can  work?  What  evidence  do  we  have?  •  What  tools,  systems,  and  framework  are   needed  to  support  pre-­‐pub  review  and  post-­‐ pub  review?  •  How  do  we  persuade  the  research  community   to  change  aka  “It’s  a  cultural  issue…”   Beyond  the  PDF    Jan  2011  San  Diego  
  • Outcome  of  Beyond  the  PDF:  •  Community  interested  in  connec4ng  •  Topics:   –  New  formats  for  the  research  paper   –  Tools  for  crea4ng,  (re)viewing,  assessing,  edi4ng   –  Connec4ng  workflows  and  data  to  papers   –  New  metrics  for  success   –  New  business  models?    •  Some  discussion;  many  ini4a4ves-­‐  no  real   coordina4on  •  Forc:  how  do  we  take  this  a  step  further?    
  • Future  of  Research  Communica4ons:   Many workshops, papers, conferences, meetings, reports, about innovation in science publishing: • • • Many great ideas, but still a lack of large-scale change Some arguments: ‘I can’t get funded for that’, or ‘the publishers will never agree to that’ or ‘the reward system is just not set up that way’ or ‘my university/dean/provost doesn’t believe in it’ Here (hopefully) the people you are pointing at are in the room!22  
  • FoRCe11  at  Dagstuhl  
  • The  Manifesto  
  • Core  issues  of  Force11  Manifesto  
  • Next  step:  Force11  •  Phil  Bourne  requested  and  obtained  funding   for  2012  from  the  Sloan  Founda4on  to  take   this  to  the  next  step  •  Goals:     –  Establish  Web  pla{orm     as  site  for  discussions   –  Codevelop  proposals     for  concrete  next  steps   –  Plan  next  workshop  
  • FORCE11  is  distributed!   -­‐Tools  and  Resource  catalog   via  the  Neuroscience   Informa4on  Framework   -­‐Ar4cle  database  in  Mendeley   -­‐Discussion  Forum  via  Google   -­‐Blogs  courtesy  of  blog  sites   and  RSS  feeds   -­‐Web  site  via  Drupal   -­‐Announcements  via  Twi]er   FORCE11  draws  on  a  wealth  of  tools   -­‐  gets  our  “brand”  out    there  for  others  to  find  h]p://force11.org  
  • We  will  invent  the  future...  •  Like  Larry’s  quan4fied  self,  scien4sts   have  ways  of  exposing  their  exper4se   and  products  on  the  web  unfiltered   –  Blogs,  videos,  data  sets  •  The  web  leads  to  new  metrics  of   impact   –  Connec4vity,  social  presence   –  Altmetrics  garfield.library.upenn.edu/essays/v4p394y1979-­‐80.pdf  
  • Beyond  the  PDF2  •  Planning  is  underway  for  the  next  Beyond  the  PDF   conference  (March  19-­‐20,  2013,  Amsterdam)  •  The  FORCE11  challenge  project:  The  Future  is  Now:   –  Move  the  FORCE11  Manifesto  beyond  the  PDF...   –  Engage  users  beyond  the  evangelical  community   •  Give  us  your  use  cases!!!  •  Beyond  the  Horizon:   –  Openness  is  more  than  open  access   •  Open  courses,  Open  conferences,  Open  abstracts   –  New  Business  models  for  openness  •  Join  FORCE11  now  (members  get  first  chance  to  a]end   Beyond  the  PDF2)  
  • Ques4ons!  •  Are  we  represen4ng  the  issues  discussed  in   these  webinars?  •  If  not  –  what  are  we  missing?    •  How  to  work  with  other  groups  be]er     (W3C,  NCBO,  OBO,  …?)  •  Aspects  that  could  be  emphasized/taken  up   by  Force11?  •  Would  you  be  interested  in  joining??