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

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

Andrea WigginsPostdoctoral FellowDataONE & 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

2Tuesday, September 18, 12

What is ci#zen science?

3= citizen science*

*volunteer

monitoring

cybe

r-in

frast

ruct

ure

crowd-sourcing

publicparticipationin science

scientificcollaboration

onlinecommunities


By any other name...


Varia#ons on a theme

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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-‐based monitoring

Natural resource management

Long-‐term monitoring and interven#on

Par#cipatory ac#on research

Behavioral science Researcher & community par#cipa#on & ac#on

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


Scien#fic tasks

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Contributory*

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*

Collabora1ve* CoACreated*PPSR$models:


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





Addressing local concerns•Water quality, noise pollu#on data

Simple economics• There are more non-‐scien#sts than scien#sts


Who par#cipates?

The public is diverse demographically and intellectually•Make no assump#ons!• But...


Who par#cipates?


Many non-‐professional communi#es have specialized skills• Rock climbers: lichen• Gamers: protein folding•Weather buffs: precipita#on


Who par#cipates?


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


Just a few examples


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


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)


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


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




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Expert review: 77%Photos: 40%Online + paper: 33%Replica#on: 23%QA/QC training: 22%Automa#c filtering: 18%Uniform equipment: 15%




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Expert review: 77%Photos: 40%Online + paper: 33%Replica#on: 23%QA/QC training: 22%Automa#c filtering: 18%Uniform equipment: 15%

Expert review +...

Photos: 23%Automa#c filtering: 18%Paper data sheets: 17%Replica#on: 17%Photos + paper: 10%


What does it accomplish?

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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)%



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processing large image data sets(e.g., Zooniverse projects)

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)*

applying human computa#on skills(e.g., Foldit)


BIG DATA!

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Common myths

Non-‐professionals’ data is unreliable


Common myths


It’s free labor


Common myths


It’s free labor•Managing volunteers is never free


Common myths



It’s just outreach


Common myths



It’s just outreach• Some#mes, but not that oten


Common myths




Ci#zen science threatens conven#onal science


Common myths




Ci#zen science threatens conven#onal science• Not a replacement, but a complement• Achieves things professional science can’t/wouldn’t


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




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


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#vesfor developing a community of prac#ce


Thanks!

[email protected]@AndreaWiggins

dataone.orgbirds.cornell.educi#zenscience.organdreawiggins.com


Technology

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