Table 1.

Objects in BWS experiment.

Category†	Object	Detailed description	Evidence base‡
Science	Poor data quality	The quality of data collected and analyzed by projects might be poor. For example, data might be inaccurate because they were collected using improper techniques or were falsified or fabricated	a,b 8,9,11,12,14
	Conflicts of interest	Citizen scientists might have undisclosed conflicts of interest that bias their contributions. For example, citizen scientists might have political or financial relationships with organizations that could affect their participation	a,b 8,11,14
Risks	Physical harm	Citizen scientists might be physically harmed as a result of their participation in projects. For example, citizen scientists might be injured while collecting data or performing experiments	a 11
	Loss of privacy	Citizen scientists might experience a loss of privacy as a result of their participation in projects. For example, their address, relationships, or habits might be intentionally or unintentionally disclosed on the internet	a,b 10,36
	Exploitation	Projects might take advantage of their citizen scientists. For example, projects might overburden citizen scientists with work or require unreasonable amounts of time or money to participate	a,b 8,11,14
	No intellectual property	Projects might not respect the intellectual property interests of citizen scientists or their communities. For example, projects might require citizen scientists to give up their intellectual property rights as a condition of participating	a,b 11,16
	Conflicting expectations	Projects might use data or findings in ways that conflict with the expectations of citizen scientists or their communities. For example, projects might share data with individuals whom citizen scientists did not expect would have access to data	a,b 13,14
Returns	No return of results	Projects might not give citizen scientists or their communities access to study data, findings, or conclusions. For example, projects might not inform citizen scientists of findings that could be relevant to their communities	a,b 14
	No credit	Projects might not give credit to citizen scientists or their communities. For example, projects might not acknowledge the contributions of participants or communities on project websites or in publications	a,b,c 8,9,12,14
Inclusion	Lack of diversity	Projects might not recruit citizen scientists from diverse populations or it might be difficult for citizen scientists from diverse populations to participate. For example, online projects might not be accessible to individuals without access to the internet	a 37
	Power imbalance	Projects might not provide citizen scientists or their communities meaningful opportunities to be involved in important decisions. For example, projects might exclude citizen scientists from participating in decisions regarding project design, governance, or use of results	a,b,c 14

^†Objects and detailed descriptions were presented to survey respondents. Objects were conceptualized as falling into four categories, which were not presented to respondents: scientific integrity of citizen science projects; potential risks to citizen scientists from project participation; potential returns to citizen scientists from project participation; and structural features of projects relevant to inclusion.

^‡a = Identified during NSF-funded workshop. b = Endorsed by at least one pretester (excluding comments indicating general endorsement of all objects) during cognitive interviews; endorsements volunteered and specifically solicited. c = Endorsed by at least one pilot survey respondent (excluding comments indicating general endorsement of all objects) in free-text responses or during post-pilot interviews; endorsements volunteered but not specifically solicited. Relevant references published before and after survey development provided in brackets.