Crowdsourcing Science

Crowdsourcing Science Organizing Virtual Participation in Knowledge Production Andrea Wiggins 5 May, 2010

Citizen Science • Projects involving the public with scientists in collaborative research – Diverse contributors, activities, and goals – Volunteers contribute data collection or reduction • Virtual citizen science – All public participation is ICT‐mediated

Motivation •What does it take to do science with strangers on the Internet? – Man is still the most extraordinary computer of all. ~John F. Kennedy – Many hands make light work. ~John Heywood

Problem Statement • Creating eﬀective technologies for virtual participation requires understanding how research design and implementation decisions inﬂuence processes and outcomes in citizen science. • Goal: support design and management of citizen science cyberinfrastructure

Research Questions • RQ1: – How do virtuality and technology alter organizing in citizen science? • RQ2: – How do virtuality and technology shape participation in citizen science? • RQ3: – How do organizing and participation inﬂuence scientiﬁc outcomes in citizen science?

Concepts Discontinuities, continuities, & physicality Virtuality Watson‐Manheim, Chudoba & Crowston; Harrison & Dourish Technology Tools & routines for participation Process of creating order; goal‐oriented activity Organizing Cyert & March; Stinchcombe; Weick; Galbraith Participation Taking part Scientiﬁc Formal knowledge production: papers, data sets Outcomes

Related Research • Public participation in science – Purposes and forms of engagement – Informal science education, policy, STS • Irwin, Bonney (et al, et al, et al) • Scientiﬁc collaboration – Broader context of practice • Sonnenwald, Finholt • Online communities – Participation in virtual environments • Crowston, Haythornthwaite, Preece & Shneiderman

Virtual Participation Process Model • Literature – Small group theory • Hackman, McGrath, Ilgen, Hollenbeck, Guzzo & Dickson – Volunteerism • Pearce, Wilson, Cnaan & Cascio, Sproull & Kiesler • Pilot study – Context – Processes

Pilot Study • Goals – Test theory & ﬁeld research methods – Familiarity with context of practice • Case study of a new citizen science project – Partnership for monitoring at National Parks – Phenology: the study of recurring plant and animal life cycle stages, or phenophases

Northeast Phenology Monitoring • Organized virtually by diverse partners – Federal agencies, nonproﬁts, networks, academics – Diﬀerent goals that rely on the same data – Coordinating via email & conference calls • Findings – Complex organizing in decentralized projects – Context shapes research design & implementation

Methodology • Comparative case study methodology – Complex sociotechnical phenomenon – Early stage of research – Explanatory process theory • Theoretical sampling – 2 ‐ 4 projects with diﬀerent characteristics • Multiple data sources and analysis techniques

Research Design • Four overlapping phases – Data collection from June 2010 ‐ December 2010 – Data analysis from July 2010 ‐ July 2011

Field Research Data Collection • Interviews – Ethnographic, semi‐structured & informal – Project leaders & volunteers • Participant observation – Contribute as a volunteer – Observe volunteers & project leaders • Secondary data, documents & artifacts

Qualitative Analysis • Analysis concurrent with data collection – Flexible set of techniques • Within‐case analysis – Case descriptions & process analysis • Cross‐case analysis – Comparisons building on within‐case analysis • Validation – Auditor, participant review, expert panel

Theoretical Sampling Criteria RQ Concept Model Dimensions Input: environment spatial, temporal, physical Virtuality Input: technology task support, social support Technology Processes: organizing, project development, research Organizing research design, coordination Participation Processes: participation idealized, task, social Scientiﬁc Outputs: knowledge, research products, discovery, Outcomes innovation new/revised approaches

Three Potential Cases Project Organization Tasks Spatial Temporal Physical Social IT Outcomes Multiple GalaxyZoo academic PIs Reduction ✓ ✓ ✓ ✓ Great Single Sunﬂower academic PI Collection ✓ ✓ ✓ ✓ Project 2 nonproﬁt Collection, eBird organizations analysis ✓ ✓ ✓ ✓

Great Sunﬂower Project • Collecting data on pollinator service (bees!) – Started in 2008 by a single academic researcher – Very successful in volunteer recruitment •2 weeks: 15K volunteers •1 year: 55K volunteers •2 years: 77K volunteers – Participation involves: •Planting sunﬂowers, 15‐minute observation samples •Online garden description & data entry on Drupal site

eBird • Collecting bird abundance and distribution data – Launched in 2002 by Cornell Lab of Ornithology (with National Audubon Society) – Considered a “gold standard” by other projects •Currently: 1.5M ‐ 2M observations/month •2002 ‐ 2009: 21M observations, 35K users, 180K locations – Participation involves: •Choosing count method & recording bird observations •Entering observations and metadata online

Galaxy Zoo • Classifying images of galaxies – Started in 2007 by a team of academic astronomers – Instant success and exciting new discoveries •Galaxy Zoo 1, Year 1: 50M classifications, 150K volunteers •Galaxy Zoo 2, Year 2: 60M classifications in 14 months •Galaxy Zoo : Hubble, Year 3: Launched April 2010 – Participation involves: •Online visual classification tasks using a custom platform

Expected Contributions • Practical and theoretical – Citizen science •Empirical study •Theorizing practice – Virtual organizations •Extending theory – Cyberinfrastructure •Conceptualizing large‐scale collaboration

Summary • What – Comparative case study of virtual citizen science • How – Field research methods with qualitative analysis • Why – Provide context for technology design and management in citizen science

Thanks! • www.ebird.org • www.galaxyzoo.org • www.greatsunﬂower.org • More: – www.scienceforcitizens.net – www.birds.cornell.edu/citscitoolkit/projects

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Crowdsourcing Science

by Andrea Wiggins

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