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Ci#zen	  Science	  101            What	  Every	  Researcher	  Should	  Know	              About	  Crowdsourcing	  Science ...
What	  is	  ci#zen	  science?       Members	  of	  the	  public	  engaging	  in	  real-­‐world	         scien#fic	  researc...
What	  is	  ci#zen	  science?                                          public                                       partic...
By	  any	  other	  name...                                       4Tuesday, September 18, 12
Varia#ons	  on	  a	  theme       Label                       Research	  Domain            Key	  Features       Civic	  sci...
Scien#fic	  tasks       PPSR$models:                 Contributory*   Collabora1ve* CoACreated*       Define*a*ques1on/issue*...
Why	  do	  research	  this	  way?       Big	  data            • Ul#mate	  mobile	  intelligent	  sensor	  network         ...
Why	  do	  research	  this	  way?       Big	  data            • Ul#mate	  mobile	  intelligent	  sensor	  network         ...
Why	  do	  research	  this	  way?       Big	  data            • Ul#mate	  mobile	  intelligent	  sensor	  network         ...
Why	  do	  research	  this	  way?       Big	  data            • Ul#mate	  mobile	  intelligent	  sensor	  network         ...
Who	  par#cipates?       The	  public	  is	  diverse	  demographically	  and	  intellectually            • Make	  no	  ass...
Who	  par#cipates?       The	  public	  is	  diverse	  demographically	  and	  intellectually            • Make	  no	  ass...
Who	  par#cipates?       The	  public	  is	  diverse	  demographically	  and	  intellectually            • Make	  no	  ass...
Just	  a	  few	  examples                                      14Tuesday, September 18, 12
The	  Great	  Sunflower	  Project       Collec#ng	  data	  on	  pollinator	  service	  (bees!)            • Par#cipa#on	  i...
eBird       Collec#ng	  bird	  abundance	  and	  distribu#on	  data            • Par#cipa#on	  involves:               • C...
Galaxy	  Zoo       Classifying	  images	  of	  galaxies            • Par#cipa#on	  involves               • Looking	  at	 ...
Are	  the	  data	  any	  good?       #1	  concern	  of	  the	  unini#ated            • If	  the	  data	  aren’t	  good,	  ...
Are	  the	  data	  any	  good?       #1	  concern	  of	  the	  unini#ated            • If	  the	  data	  aren’t	  good,	  ...
Are	  the	  data	  any	  good?       #1	  concern	  of	  the	  unini#ated            • If	  the	  data	  aren’t	  good,	  ...
What	  does	  it	  accomplish?                            engage%cri)cal%thinking%                            (Trumbull%et...
What	  does	  it	  accomplish?       documen(ng*range*shi0s*       (Bonter*et*al.*unpublished*data)*       iden(fying*pote...
What	  does	  it	  accomplish?                            BIG	  DATA!                                           23Tuesday,...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable                                                        ...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor                              ...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor            • Managing	  volun...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor            • Managing	  volun...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor            • Managing	  volun...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor            • Managing	  volun...
Common	  myths       Non-­‐professionals’	  data	  is	  unreliable       It’s	  free	  labor            • Managing	  volun...
Ci#zen	  science	  in	  the	  21st	  century       Expansion	  into	  new	  areas            • Protein	  folding	  (Foldit...
Ci#zen	  science	  in	  the	  21st	  century       Expansion	  into	  new	  areas            • Protein	  folding	  (Foldit...
Ci#zen	  science	  in	  the	  21st	  century       Expansion	  into	  new	  areas            • Protein	  folding	  (Foldit...
DataONE	  PPSR	  Working	  Group       Purpose:            • Improve	  quality,	  quan#ty,	  and	  accessibility	  of	  PP...
Thanks!       andrea.wiggins@cornell.edu       @AndreaWiggins       dataone.org       birds.cornell.edu       ci#zenscienc...
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Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Science

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

Published in: Technology, News & Politics

Transcript of "Citizen Science 101: What Every Researcher Should Know About Crowdsourcing Science"

  1. 1. Ci#zen  Science  101 What  Every  Researcher  Should  Know   About  Crowdsourcing  Science Andrea  Wiggins Postdoctoral  Fellow DataONE  &  Cornell  Lab  of  Ornithology 17  September,  2012Tuesday, September 18, 12
  2. 2. What  is  ci#zen  science? Members  of  the  public  engaging  in  real-­‐world   scien#fic  research •Crowdsourcing •Collabora#on •Community 2Tuesday, September 18, 12
  3. 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 3Tuesday, September 18, 12
  4. 4. By  any  other  name... 4Tuesday, September 18, 12
  5. 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 5Tuesday, September 18, 12
  6. 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* 6Tuesday, September 18, 12
  7. 7. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range 7Tuesday, September 18, 12
  8. 8. Why  do  research  this  way? Big  data • Ul#mate  mobile  intelligent  sensor  network • Spa#otemporal  range Human  computa#on • Image  processing  &  puzzle  solving 8Tuesday, September 18, 12
  9. 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 9Tuesday, September 18, 12
  10. 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 10Tuesday, September 18, 12
  11. 11. Who  par#cipates? The  public  is  diverse  demographically  and  intellectually • Make  no  assump#ons! • But... 11Tuesday, September 18, 12
  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 12Tuesday, September 18, 12
  13. 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 13Tuesday, September 18, 12
  14. 14. Just  a  few  examples 14Tuesday, September 18, 12
  15. 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 15Tuesday, September 18, 12
  16. 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) 16Tuesday, September 18, 12
  17. 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 17Tuesday, September 18, 12
  18. 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 18Tuesday, September 18, 12
  19. 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% 19Tuesday, September 18, 12
  20. 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% 20Tuesday, September 18, 12
  21. 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)% 21Tuesday, September 18, 12
  22. 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) 22Tuesday, September 18, 12
  23. 23. What  does  it  accomplish? BIG  DATA! 23Tuesday, September 18, 12
  24. 24. Common  myths Non-­‐professionals’  data  is  unreliable 24Tuesday, September 18, 12
  25. 25. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor 25Tuesday, September 18, 12
  26. 26. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free 26Tuesday, September 18, 12
  27. 27. Common  myths Non-­‐professionals’  data  is  unreliable It’s  free  labor • Managing  volunteers  is  never  free It’s  just  outreach 27Tuesday, September 18, 12
  28. 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 28Tuesday, September 18, 12
  29. 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 29Tuesday, September 18, 12
  30. 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 30Tuesday, September 18, 12
  31. 31. Ci#zen  science  in  the  21st  century Expansion  into  new  areas • Protein  folding  (Foldit) • Synthe#c  RNA  design  (EteRNA) 31Tuesday, September 18, 12
  32. 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 32Tuesday, September 18, 12
  33. 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 33Tuesday, September 18, 12
  34. 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 34Tuesday, September 18, 12
  35. 35. Thanks! andrea.wiggins@cornell.edu @AndreaWiggins dataone.org birds.cornell.edu ci#zenscience.org andreawiggins.com 35Tuesday, September 18, 12
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