Cos4Cloud impact summary

This is an impact report of the citizen science project Co-designed citizen observatories for the EOS-Cloud. 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:: November 2019
Project end date:: February 2023
Project Contacts:: Jaume Piera - coordination@cos4cloud-eosc.eu
Project URL:: https://cos4cloud-eosc.eu/
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	34	22
Society	Involvement	30	17
Governance	Policy	6	13
Governance	Sustainable Development Goals	16	16
Economy	Economic productivity	42	13
Economy	Financial sustainability	26	19
Environment	Environmental awareness	40	21
Environment	Environmental footprint	32	13
Science	Scientific productivity	37	18
Science	Interdiscplinary science	33	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, and this project has made great efforts to ensure participants are aware they are contributing to a research project, have responsibility in the project, and are satisfied with the process of participation. Great job!
Society	Involvement	The degree of involvement of participants in a project is an important aspect of citizen science, and this project goes to great lengths to ensure that participants are involved in multiple stages of the project. It is positive that participants are offered multiple project activities to take part in, and that they are offered different levels of involvement depending on their individual interests and availability. Good work!
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	It is great that the project has produced outputs that contribute to the economy through industry, commerce, innovation or technological development. If you haven't already, it might be worth considering any legal implications through a dedicated IPR plan.
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	Congratulations - in a world of "publish or perish", this project has high scientific productivity. With a large number of publications in high impact-factor journals, the project's research has been well cited, indicating outcomes have been widely shared.
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.