cRHS impact summary

This is an impact report of the citizen science project Citizen River Habitat Survey 2022. 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

Project Information

Project start date:
February 2021
Project Contacts:
Marc Naura - marc@therrc.co.uk
Project URL:
https://www.therrc.co.uk/crhs
Impact Assesment progress:
100% 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 26 23
Involvement 17 18
Governance Policy 20 15
Sustainable Development Goals 20 17
Economy Economic productivity 0 14
Financial sustainability 16 20
Environment Environmental awareness 26 22
Environmental footprint 0 12
Science Scientific productivity 5 20
Interdiscplinary science 3 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.

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 Policy

The project might not look like it has the highest score for policy influence, but the answers given suggest it is actually among the more successful citizen-science projects in terms of policy. The most commonly considered impact on policy is citizen-science data as a source of information for decision makers. But citizen science can also directly impact policy as an object of research policy or as a policy instrument (read more in this paper). Policy influence can also include affecting organisational policy not just governmental policy. It might be helpful to consider how the project is influencing policy currently and whether any of the other forms of policy influence could also be achieved in the project. The project might find further inspiration from example projects in this report.

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.

Financial sustainability

You are on the right path! It is clear that the project has considered its financial sustainability into the future. However, there could be more to do. If one does not already exist, an exploitation plan could help sustain project outputs, whilst considering open-source software and tools could reduce costs.

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.

Environmental awareness

The project clearly promotes environmental awareness, by educating participants on environmental challenges, or by contributing to participants' awareness of the natural environment through dissemination activities. Want to be able to measure participants' higher awareness, or increased stewardship? You might want to consider this paper.

Science Scientific productivity

It is important to share the outputs of a citizen-science project - through events, media and publications - otherwise learnings will not extend beyond the sphere of the project. Not every citizen-science project has an academic focus on publications. Neverthesless, by publishing the results of the project in peer-reviewed journals, the project could improve its scientific impact. Try to publish in high impact-factor journals so that the publications will be cited more. Perhaps the project could even support students' disseratations or theses in the future.

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.

Economy 12 Economy 12 Society 17 Society 17 Governance 11 Governance 11 Science and technology 24 Science and technology 24 Environment 16 Environment 16 max. 42
Total Score 16/42