Barriers to stakeholder participation in citizen science

The UKEOF-funded report Understanding Motivations for Citizen Science included a useful summary of the barriers and challenges to stakeholder participation in citizen science projects. Here we have adapted the original table in order to reveal the key barriers faced by science, policy and practice stakeholders. Whilst these are not exhaustive summaries, this information will be useful for anyone looking to collaborate with other stakeholder groups on a citizen science or crowdsourcing initiative, as well as identify potential challenges in advance.

Key:

  Science-related stakeholder

 Policy & evidence stakeholder

 Practice-based stakeholder

• Inadequate equipment (e.g. low quality sensors) 
• Mistrust data from non-professionals  
• Biases influencing decision to participate 
• Scalability of data 
  Example: Quality of data is not at a level for use on a wide scale
• Partnerships with local authorities 
  Example: Local authorities can lack manpower to assist
• Patchiness of data 
  Example: Statistical techniques available to even out patchiness
• Specific evidence need beyond the scope of citizen science 
  Example: “it wasn’t our direct need, and with low resources that wasn’t our priority” (Scientist, monitoring, policy)

• Peer reviewer reservations during publication process 
• Citizen science frowned upon by colleagues 
  Example: Scientist (university) told: “should be doing proper science”. Another explained: “We as scientists are often a bit snobbish about our ability in comparison to other people’s ability, and you see that in the response to citizen science from a lot of the policymaking community. Citizen science equals poor data, that’s their starting point” (Scientist, monitoring, policy).
• Institutional reservations about citizen science 
  Example: Need to get board members on side

• Training required  
  Example: Scientist indicates specialist training is required due to challenges of identification: “Gone are the days where you used to have a huge visible injury on vegetation has gone, because there’s been acute exposure to everything.” (Scientist, policy, monitoring)
• Difficulties of recruitment and commitment of volunteers 
  Example: “And then continued engagement, the enthusiasm barrier, because you get a drop off, an exponential drop off of participation as time goes on. So how do you keep the exponential drop off … as low as possible?” (Practitioner, science, engagement)
• Inaccessible sites 
  Example: Linked to patchiness of data, “It’s difficult to tell people to go to a site that they think will be rubbish as well, I think if you want to see lots of dragonflies you go to a good dragonfly site rather than just anywhere” (Scientist, policy, monitoring)
• Unable to keep up with technological developments 
  Example: Once technology is in place, it must be maintained and updated
• Getting people to use technology  
  Example: “a survey that started off on paper, it’s actually very hard to move people over” (Practitioner, science, engagement)
• Crowded marketplace for citizen science projects in certain areas  
  Example: Technology and online data options are flooding the market with similar citizen science projects

• Promoting citizen science  
  Example: Launching apps requires time and resources for promotion and maintenance
• Communication 
  Example: No time to explain key scientific ideas (e.g. recording absence is as important as presence)
• Slow process 
  Example: Policy want answers yesterday
• Individual interactions 
  Example: Individual requests for support are time-consuming
• Mobilising and maintaining citizen science project   
  Example: Time spent validating, verifying, selecting appropriate technology, calibrating sensors; lack of funding, short-termism of funding, unable to prove concept, no funding for essential technical development
• Volunteers threaten job opportunities/security of professionals 
• Lack of interpretation may lead to poorly-informed public demands 

• Liability of organisers if don't act on citizen data 
• Unaware of using citizen science data 
  Example: Scientists often use published data sets unaware that they are citizen science data
• Nobody championing citizen science on high level 
  Example: Projects should identify someone high level to champion their project institutionally
• Differing science and engagement objectives 
  Example: “you have to decide where you sit along the spectrum for the mass engagement versus data quality question” (Practitioner, science, engagement)
• Activities require legislatory approval 
  Example: Challenges when species become scientific instruments. Legislated by Home Office
• Lack of interest in engaging the public through citizen science 

• Lack of attention to needs and expectations of the citizen science audience 
  Example: Must understand demands on participant’s time and project’s requirements may differ
• Little acknowledgement of different types of volunteer 
  Example: Differing volunteer types, e.g. paper-based volunteer, don’t want to lose them due to quality
• Need more volunteers 
  Example: There aren’t enough volunteers participating
• Survey design by committee, by professionals only 
  Example: Too much discussion of small issues, but decision has to eventually be taken

• Lack of clear research question 
  Example: Avoid “reverse engineering to a question”, instead “[be] led by a question” (Practitioner, science, engagement)
• Survey is inaccessible 
  Example: Participants must be able to understand questions
• Language barrier (scientific and linguistic) 
  Example: Avoid over-complication
• Assumption that people have access to the internet and to a mobile phone  
  Example: Not everyone has a mobile phone on a data plan, nor access to the internet
• Assumption that people are comfortable with technology  
  Example: Technological literacy should never be assumed
• Over-reliance on web-based solutions  
  Example: Web-based solutions can be exclusionary
• Designed a ‘boring’, yet scientifically important, survey 
  Example: “Well, it could be that it’s a really … important square, and when you go there and count your … your butterflies, you might only, you might see none, you might see one. And that’s a really boring day out” (Scientist, policy, monitoring)