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Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Science

Presentation for the Center for Nonlinear Studies at Los Alamos National Labs.

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Ci#zen	
  Science	
  101
            What	
  Every	
  Researcher	
  Should	
  Know	
  
            About	
  Crowdsourcing	
  Science



            Andrea	
  Wiggins
            Postdoctoral	
  Fellow
            DataONE	
  &	
  Cornell	
  Lab	
  of	
  Ornithology

            17	
  September,	
  2012




Tuesday, September 18, 12
What	
  is	
  ci#zen	
  science?
       Members	
  of	
  the	
  public	
  engaging	
  in	
  real-­‐world	
  
       scien#fic	
  research
         •Crowdsourcing
         •Collabora#on
         •Community




                                                                              2
Tuesday, September 18, 12
What	
  is	
  ci#zen	
  science?

                                          public
                                       participation
                                        in science
                                      cro                                 r
                                    so w d -                           ee g
                                                                     nt in
                                      urc
                                          ing                     olu itor
                                                                 v n
                                                                  mo

                            online
                            communities
                                                   *
                                                infrastructure
                                                    cyber-
                                                                         scientific
                                                                    collaboration




                                      = citizen science                              3
Tuesday, September 18, 12
By	
  any	
  other	
  name...




                                       4
Tuesday, September 18, 12
Varia#ons	
  on	
  a	
  theme
       Label                       Research	
  Domain            Key	
  Features

       Civic	
  science            Science	
  communica#on Public	
  par#cipa#on	
  in	
  decisions	
  about	
  science

       People’s	
  science         Poli#cal	
  science           Social	
  movements	
  for	
  people-­‐centered	
  science

       Ci#zen	
  science           Ecology                       Public	
  par#cipa#on	
  in	
  scien#fic	
  research

       Volunteer/community-­‐      Natural	
  resource	
  
                                                                 Long-­‐term	
  monitoring	
  and	
  interven#on
       based	
  monitoring         management
       Par#cipatory	
  ac#on	
  
                                   Behavioral	
  science         Researcher	
  &	
  community	
  par#cipa#on	
  &	
  ac#on
       research

       Ac#on	
  science            Behavioral	
  science         Par#cipatory,	
  emphasizes	
  tacit	
  theories-­‐in-­‐use

       Community	
  science        Psychology                    Par#cipatory	
  community-­‐centered	
  social	
  science

       Living	
  Labs              Management                    Public-­‐private	
  partnership	
  for	
  innova#on

                                                                                                                               5
Tuesday, September 18, 12
Scien#fic	
  tasks
       PPSR$models:                 Contributory*   Collabora1ve* CoACreated*


       Define*a*ques1on/issue*
       Gather*informa1on*
       Develop*explana1ons*
       Design*data*collec1on*methods*
       Collect*samples*
       Analyze*samples*
       Analyze*data*
       Interpret*data/conclude*
       Disseminate*conclusions*
       Discuss*results/inquire*further*
                                                                                6
Tuesday, September 18, 12
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Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Science

  • 1. Ci#zen  Science  101 What  Every  Researcher  Should  Know   About  Crowdsourcing  Science Andrea  Wiggins Postdoctoral  Fellow DataONE  &  Cornell  Lab  of  Ornithology 17  September,  2012 Tuesday, September 18, 12
  • 2. What  is  ci#zen  science? Members  of  the  public  engaging  in  real-­‐world   scien#fic  research •Crowdsourcing •Collabora#on •Community 2 Tuesday, September 18, 12
  • 3. What  is  ci#zen  science? public participation in science cro r so w d - ee g nt in urc ing olu itor v n mo online communities * infrastructure cyber- scientific collaboration = citizen science 3 Tuesday, September 18, 12
  • 4. By  any  other  name... 4 Tuesday, September 18, 12
  • 5. Varia#ons  on  a  theme Label Research  Domain Key  Features Civic  science Science  communica#on Public  par#cipa#on  in  decisions  about  science People’s  science Poli#cal  science Social  movements  for  people-­‐centered  science Ci#zen  science Ecology Public  par#cipa#on  in  scien#fic  research Volunteer/community-­‐ Natural  resource   Long-­‐term  monitoring  and  interven#on based  monitoring management Par#cipatory  ac#on   Behavioral  science Researcher  &  community  par#cipa#on  &  ac#on research Ac#on  science Behavioral  science Par#cipatory,  emphasizes  tacit  theories-­‐in-­‐use Community  science Psychology Par#cipatory  community-­‐centered  social  science Living  Labs Management Public-­‐private  partnership  for  innova#on 5 Tuesday, September 18, 12
  • 6. Scien#fic  tasks PPSR$models: Contributory* Collabora1ve* CoACreated* Define*a*ques1on/issue* Gather*informa1on* Develop*explana1ons* Design*data*collec1on*methods* Collect*samples* Analyze*samples* Analyze*data* Interpret*data/conclude* Disseminate*conclusions* Discuss*results/inquire*further* 6 Tuesday, September 18, 12
  • 7. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range 7 Tuesday, September 18, 12
  • 8. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range Human  computa#on • Image  processing  &  puzzle  solving 8 Tuesday, September 18, 12
  • 9. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range Human  computa#on • Image  processing  &  puzzle  solving Addressing  local  concerns • Water  quality,  noise  pollu#on  data 9 Tuesday, September 18, 12
  • 10. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range Human  computa#on • Image  processing  &  puzzle  solving Addressing  local  concerns • Water  quality,  noise  pollu#on  data Simple  economics • There  are  more  non-­‐scien#sts  than  scien#sts 10 Tuesday, September 18, 12
  • 11. Who  par#cipates? The  public  is  diverse  demographically  and  intellectually • Make  no  assump#ons! • But... 11 Tuesday, September 18, 12
  • 12. Who  par#cipates? The  public  is  diverse  demographically  and  intellectually • Make  no  assump#ons! • But... Many  non-­‐professional  communi#es  have  specialized  skills • Rock  climbers:  lichen • Gamers:  protein  folding • Weather  buffs:  precipita#on 12 Tuesday, September 18, 12
  • 13. Who  par#cipates? The  public  is  diverse  demographically  and  intellectually • Make  no  assump#ons! • But... Many  non-­‐professional  communi#es  have  specialized  skills • Rock  climbers:  lichen • Gamers:  protein  folding • Weather  buffs:  precipita#on Educa#on  ≠  exper#se,  exper#se  ≠  educa#on • Ornithologists  vs.  birders:  no  contest 13 Tuesday, September 18, 12
  • 14. Just  a  few  examples 14 Tuesday, September 18, 12
  • 15. The  Great  Sunflower  Project Collec#ng  data  on  pollinator  service  (bees!) • Par#cipa#on  involves: • Plan#ng  sunflowers • Crea#ng  garden  descrip#on  on  Drupal  website • Recording  15-­‐minute  observa#on  samples   on  data  sheet • Online  data  entry • Started  in  2008  by  a  single  academic  researcher • Collects  data  across  North  America • Very  successful  in  akrac#ng  volunteer  interest 15 Tuesday, September 18, 12
  • 16. eBird Collec#ng  bird  abundance  and  distribu#on  data • Par#cipa#on  involves: • Choosing  observa#on  methods • Recording  bird  observa#ons  (analog  or  digital) • Entering  observa#ons  and  metadata  online • Launched  in  2002  by  Cornell  Lab  of  Ornithology   (with  Na#onal  Audubon  Society) • World’s  largest  biodiversity  data  set:  100M  records • Currently  receives  about  3M  observa#ons/month • Data  used  in  research  and  decision-­‐making  for  land  management,  policy   (and  recrea#on) 16 Tuesday, September 18, 12
  • 17. Galaxy  Zoo Classifying  images  of  galaxies • Par#cipa#on  involves • Looking  at  pictures  of  galaxies  online • Answering  a  few  ques#ons  about  them • Started  in  2007  by  a  team  of  academic  astronomers • Instant  success  and  exci#ng  new  discoveries • Galaxy  Zoo  1,  Year  1:  50M  classifica#ons,  150K  volunteers • Galaxy  Zoo  2,  Year  2:  60M  classifica#ons  in  14  months • Hanny’s  Voorwerp • Green  Pea  galaxies 17 Tuesday, September 18, 12
  • 18. Are  the  data  any  good? #1  concern  of  the  unini#ated • If  the  data  aren’t  good,  it’s  because  the  design  is  wrong • Numerous  QA/QC  mechanisms;  75%  use  more  than  one 18 Tuesday, September 18, 12
  • 19. Are  the  data  any  good? #1  concern  of  the  unini#ated • If  the  data  aren’t  good,  it’s  because  the  design  is  wrong • Numerous  QA/QC  mechanisms;  75%  use  more  than  one Expert  review:  77% Photos:  40% Online  +  paper:  33% Replica#on:  23% QA/QC  training:  22% Automa#c  filtering:  18% Uniform  equipment:  15% 19 Tuesday, September 18, 12
  • 20. Are  the  data  any  good? #1  concern  of  the  unini#ated • If  the  data  aren’t  good,  it’s  because  the  design  is  wrong • Numerous  QA/QC  mechanisms;  75%  use  more  than  one Expert  review:  77% Expert  review  +... Photos:  40% Online  +  paper:  33% Photos:  23% Replica#on:  23% Automa#c  filtering:  18% QA/QC  training:  22% Paper  data  sheets:  17% Automa#c  filtering:  18% Replica#on:  17% Uniform  equipment:  15% Photos  +  paper:  10% 20 Tuesday, September 18, 12
  • 21. What  does  it  accomplish? engage%cri)cal%thinking% (Trumbull%et%al%2000)% science%learning,%bonding% (Kountoupes%and%Oberhauser%2008)% environmental%ac)on;%social%networks% (Overdevest%et%al.%2004)% social%capital% (Ballard%2008)% improved%policy% (Wing%et%al.%2008)% 21 Tuesday, September 18, 12
  • 22. What  does  it  accomplish? documen(ng*range*shi0s* (Bonter*et*al.*unpublished*data)* iden(fying*poten(al*mismatches* (Batalden*et*al.*2007)* iden(fying*vulnerable*species* (Crimmins*et*al*2008,*2009)* health*planning* (Leve(n*and*Van*de*Water*2008)* an(cipa(ng*effects*on*water*sources* (e.g.,*CoCoRaHS)* processing  large  image  data  sets (e.g.,  Zooniverse  projects) applying  human  computa#on  skills (e.g.,  Foldit) 22 Tuesday, September 18, 12
  • 23. What  does  it  accomplish? BIG  DATA! 23 Tuesday, September 18, 12
  • 24. Common  myths Non-­‐professionals’  data  is  unreliable 24 Tuesday, September 18, 12
  • 25. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor 25 Tuesday, September 18, 12
  • 26. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free 26 Tuesday, September 18, 12
  • 27. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free It’s  just  outreach 27 Tuesday, September 18, 12
  • 28. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free It’s  just  outreach • Some#mes,  but  not  that  oten 28 Tuesday, September 18, 12
  • 29. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free It’s  just  outreach • Some#mes,  but  not  that  oten Ci#zen  science  threatens  conven#onal  science 29 Tuesday, September 18, 12
  • 30. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free It’s  just  outreach • Some#mes,  but  not  that  oten Ci#zen  science  threatens  conven#onal  science • Not  a  replacement,  but  a  complement • Achieves  things  professional  science  can’t/wouldn’t 30 Tuesday, September 18, 12
  • 31. Ci#zen  science  in  the  21st  century Expansion  into  new  areas • Protein  folding  (Foldit) • Synthe#c  RNA  design  (EteRNA) 31 Tuesday, September 18, 12
  • 32. Ci#zen  science  in  the  21st  century Expansion  into  new  areas • Protein  folding  (Foldit) • Synthe#c  RNA  design  (EteRNA) Increasingly  ICT-­‐mediated • Mobile  technologies  in  the  field • Image  processing  and  problem  solving 32 Tuesday, September 18, 12
  • 33. Ci#zen  science  in  the  21st  century Expansion  into  new  areas • Protein  folding  (Foldit) • Synthe#c  RNA  design  (EteRNA) Increasingly  ICT-­‐mediated • Mobile  technologies  in  the  field • Image  processing  and  problem  solving Bigger  and  beker  data • Quality  is  an  issue,  but  not  a  showstopper • Global  workforce  of  cogni#ve  surplus • Public  has  more  exper#se  than  you  expect 33 Tuesday, September 18, 12
  • 34. DataONE  PPSR  Working  Group Purpose: • Improve  quality,  quan#ty,  and  accessibility  of  PPSR  data • Advance  integra#on  of  PPSR  data  in  conven#onal  science Products: • Data  Management  Guide  for  PPSR  -­‐  coming  soon! • Ar#cles  in  August  FREE  special  issue • Data  quality  &  valida#on  paper • Involved  in  several  ini#a#ves for  developing  a  community   of  prac#ce 34 Tuesday, September 18, 12
  • 35. Thanks! andrea.wiggins@cornell.edu @AndreaWiggins dataone.org birds.cornell.edu ci#zenscience.org andreawiggins.com 35 Tuesday, September 18, 12