D-NOSES impact summary

This is an impact report of the citizen science project Distributed Network for Odour Sensing, Empowerment and Sustainability. 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:
April 2018
Project end date:
September 2021
Project Contacts:
Rosa Arias - rosa.arias@scienceforchange.eu
Project URL:
https://odourobservatory.org/es/
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 42 22
Involvement 38 17
Governance Policy 40 13
Sustainable Development Goals 42 16
Economy Economic productivity 0 13
Financial sustainability 15 19
Environment Environmental awareness 28 21
Environmental footprint 19 13
Science Scientific productivity 35 18
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 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!

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

The project is among the most successful on this platform at influencing policy. Congratulations! This suggests the project is quite exceptional in its interactions with policy makers. Maybe consider how you could share your experience with other projects so they can get inspiration and have similar success with policy influence?

Sustainable Development Goals

The project must be very closely aligned to the SDGs, either contributing data to the official reporting of the SDGs or with targets related to the majority of the goals. This makes this project one of the most impactful citizen-science projects with regards to the SDGs.

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

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. 

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

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.

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.

Economy 23 Economy 23 Society 37 Society 37 Governance 31 Governance 31 Science and technology 22 Science and technology 22 Environment 39 Environment 39 max. 42
Total Score 30/42