Source of information for policy-making

• This is about the mobilisation of information for policy-making. It describes the use of outputs from CS, such as data and knowledge, as inputs for the implementation and monitoring of regulation. The most simplified instantiation of this mode posits citizens as data collectors whose contributions to monitoring are shepherded by public authorities. 

Object of research policy

• CS can act as a legitimate approach to conducting research. In this sense, actors build links between the publics that they aim to represent and the stakeholders that they seek to influence. Most of the time, CS actors deal with traditional engagement with policy makers. These include members of public administrations who are responsible for creating and implementing laws and other rules that regulate research, science communication, and science education. 

Policy instrument

• CS as a policy instrument which can be used by policymakers to advance specific policy goals on relationships between science and society. CS can help in using cost-effective ways to address environmental regulations to the characterisation of the entrepreneurial citizen, who provides ideas and capital of increasing democracy, improving the societal mission of science. CS takes part of the landscape of disruptive innovation practices with its respective impacts and adaptation to the political context.   

Socio-technical governance

• CS projects tackle societal problems directly via material prototypes, protocols, and procedures without passing through discursive and deliberative policy channels. The impact of this approach is twofold. It enacts a materialisation of governance into devices, while also decentralising governance towards a local level where it involves affected members of the public, who can directly implement changes. Yet, this should not suggest that this approach involves only action and no research. On the contrary, the participants in this mode of CS are often more deeply involved in the scientific, technical, and policy literatures than in other forms of CS. 

Varies a lot, some projects have lasted 2 months. 

Before starting 

• Identify research question. Can be driven by scientists, community or policy needs.  
  
• Clear goal and expectations, and agree on the overall aims of project. 
  
• Consider training needs for participants and their existing skills.
  
• Start to build a partnership with those involved.  
  
• Important to consider whether the project is suitable for citizen science, eg. safety of participants when collecting data.  

First steps

• Establish the project team and aims.
  
• Identify the resources needed and target participants.  
  
• Co-created citizen science: partnership between scientists and the community. 

Development phase 

• Design the protocol, consider the data requirements, and analysis. Test the protocols and possibly modify them. 
  
• Consider the needed equipment if any for the protocol.  
  
• It's important to note that environmental recording shouldn't be done too frequently because it may be overwhelming for the participants and can reduce their motavtion to collect data. 
  
• It's always important to keep in mind the motivation and the time-frame for a citizen science project so that volunteers are motivated and can commit. This also entails that training and resources are sufficient for the period of the project. 

Live phase 

• Promote the project.
• Complete data analysis, report results and share data and take action. 

Flexibility: 

- Time-frame: Short or long-term, depends on the availability and willingness of volunteers.

- Amount of participants: There can also be mass participation projects that can help get an overview of the state of something. Such mass projects can help gain traction on the news and social media.   

- Site location: Better to be around where people are or are often passing through.  

- Materials: May need sensors, or small amount of material/none at all. 

- Types of data: The data that citizens record or observe can vary. Data can be images, sound recordings, video, measurements of something, or physical samples of water etc.  

Things to remember:

- That the project partnerns with experienced practitioners to avoid common pitfalls.  

- Important to note that the protocols should be easily learned and not too complex to understand. 

- The project will most likely have to invest in training of volunteers.