ScienceUsamu impact summary

This is an impact report of the citizen science project ScienceUsamu. 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

Impact Assesment progress:: 98% 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	32	22
Society	Involvement	21	17
Governance	Policy	13	13
Governance	Sustainable Development Goals	32	16
Economy	Economic productivity	0	13
Economy	Financial sustainability	21	19
Environment	Environmental awareness	40	21
Environment	Environmental footprint	31	13
Science	Scientific productivity	0	18
Science	Interdiscplinary science	32	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	Policy	The project's score for this indicator suggests it has started to have some impact on policy already. Well done! A common barrier to policy influence for citizen science is concerns about the data. The project could therefore consider addressing concerns surrounding the quality of citizen-science data and aligning the project with the data standards of the policy makers.
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	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 awareness	Congratulations! This project goes to great lengths not only to promote environmental awareness and educate participants on environmental challenges, but also to measure improvements in participants' environmental attitudes, behaviour and knowledge.
Environment	Environmental footprint	This indicator considers the project's material footprint, polluting emissions, procurement policy, and pro-environmental actions for participants (such as litter picking). The project's score for this indicator shows that the project has considered some of these elements but to get a higher score the project needs to take measures to improve its environmental footprint in all these areas.
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.
Science	Interdiscplinary science	By working across multiple disciplines , this project is making efforts to promote interdisciplinary ways of working. There is evidence that interdisciplinarity is statistically significantly and positively associated with research impact (Okamura, 2019), largely through the engagement of a wider audience. Keep up the good work!

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.