DRYRivERS impact summary

This is an impact report of the citizen science project DRYRivERS. 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:: September 2021
Project Contacts:: Amélie Truchy - amelie.truchy@inrae.fr
Project URL:: https://www.dryver.eu/app
Impact Assesment progress:: 99% 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	22
Society	Involvement	1	17
Governance	Policy	25	13
Governance	Sustainable Development Goals	14	16
Economy	Economic productivity	0	13
Economy	Financial sustainability	27	19
Environment	Environmental awareness	26	21
Environment	Environmental footprint	21	13
Science	Scientific productivity	27	18
Science	Interdiscplinary science	34	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	Involvement	Participants can contribute to many more phases of a project than collecting or analysing data. Think about other phases of the project participants could be involved with in the future, such as sharing the outputs or assessing impact. Remember that different participants will have different interests, knowledge and availability, so try to offer them different levels of involvement and multiple project activities to take part in.
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.
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.
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 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.
Environment	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	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.