CoAct impact summary

This is an impact report of the citizen science project CoAct. The scores displayed summarise the results of the assessment process designed by the MICS project. For more information on how they were calculated, visit https://mics.tools

Organisers Participants Observations

Project Information

Project start date:: January 2020
Project cost:: 2047226
Funding amount:: 2047226
Project URL:: https://coactproject.eu/
Impact Assesment progress:: 32% complete

Rules-based scores

These scores are calculated based on a set of rules written to combine a specific set of impact metrics on the same theme into a single indicator. A higher score means the project is carrying out more activities related to the theme of the indicator and is, therefore, more likely to have a higher positive impact in this area. Rule-based scores are only calculated for specific themes. Overall assessments can be found below in the machine-learning--based scoring. Descriptions and explanations of impact indicators are provided at about.mics.tools/indicators (e.g., the score is low on economic productivity because the project did not include specific aspects related to improving efficiency). Different scores trigger different recommendations presented in the following section. Also, scores are not linked to project objectives; they try to capture a broad range of impacts even if the project does not consider or care about all of them. All scores are out of 42.

Impact Indicators		Impact score (max 42)	Average score (of projects on platform)
Society	Activeness	30	22
Society	Involvement	24	17
Governance	Policy	0	13
Governance	Sustainable Development Goals	0	16
Economy	Economic productivity	0	13
Economy	Financial sustainability	0	19
Environment	Environmental awareness	0	21
Environment	Environmental footprint	0	13
Science	Scientific productivity	10	18
Science	Interdiscplinary science	0	21

Recommendations

The following recommendations are determined by the scores the project received in the previous section. The recommendations are based on citizen-science best practice as defined in the current scientific literature and how other projects have taken action to improve their impact in specific areas. Of course, following these recommendations does not guarantee the project will suddenly have a higher impact; it all depends on the specific context of each project, but they might provide helpful inspiration.

Society	Activeness	The activeness of participants within a project is an important aspect of citizen science. Activeness depends on participants being aware that they are contributing to a project, having a lot of responsibility in the project, and being satisfied with the process of participation. This project should ensure that all aspects of activeness have been considered.
Society	Involvement	The degree of involvement of participants in a project is an important aspect of citizen science, and includes involving participants in multiple stages of the project, offering them multiple activities to take part in, and offering different levels of involvements depending on individual interests and availability. This project could consider whether there are more stages of the project that participants could be involved in for example by considering co-design or co-evaluation.
Governance	Sustainable Development Goals	The Sustainable Development Goals are a collection of 17 interlinked global goals designed to be a "blueprint to achieve a better and more sustainable future for all". However, citizen science has a much broader remit, so it is ok that your project does not directly consider them. You can find more information on SDGs here, to see if there are any possible links between their aims and your project.
Governance	Policy	It looks like policy influence might not be a priority for the project. Of course, not every project can affect policy and some projects have a large impact on governance without ever interacting with official policy. If you're interested in the idea of citizen science as a form of socio-technical governance you can read more in this paper. If the project is interested in influencing policy it could find inspiration from example projects in this report. It might not be a viable option if the project has already started, but citizen-science projects most often have success influencing policy when specific policies are considered in the design of the project and policy makers are engaged from the start of the project.
Economy	Economic productivity	We know that economic productivity isn't a priority for most citizen-science projects. If you are interested in improving the economic productivity of the project, it might help to fully appraise any potential developments and advances made through the creation of a dedicated IPR plan. This will help reveal any economic potential that might have been overlooked, and support its exploitation.
Economy	Financial sustainability	If the project wants to improve its financial sustainability, it could consider creating an exploitation plan. To reduce recurring investments in technology and the cost per observation, the project could consider using open-source software and tools.
Environment	Environmental awareness	Being environmentally aware means understanding how our behaviour impacts the environment and committing to making changes to our activities. The project could do more to educate participants on environmental challenges and contribute to participants' awareness of the natural environment, by explicitly disseminating information on sustainable lifestyles.
Environment	Environmental footprint	The project could do more to decrease its material footprint, take measures to reduce its polluting emissions, or use a sustainable procurement policy.
Science	Interdiscplinary science	Explicitly promoting interdisciplinary ways of working could increase the impact of the project. There is evidence that interdisciplinarity is statistically significantly and positively associated with research impact (Okamura, 2019), largely through the engagement of a wider audience

Machine Learning Scores

The following scores were calculated using a statistically-driven machine-learning approach, a type of AI that learns to perform a task by analysing patterns in data. This is an experimental approach to citizen-science impact assessment, and the exact reasoning behind the scores is not explainable. The scores represent a best guess of the impact the project is having in each domain. How can you use the score? Well, this platform gives a common framework for impact assessment so you can use the scores: to see how the project's impact evolves over time; to compare the project with others; to report to funders and participants; or for your organisation's internal reporting. All scores are out of 42.