Ground Truth 2.0 impact summary

This is an impact report of the citizen science project Ground Truth 2.0 - Environmental knowledge discovery of human sensed data. 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:
September 2016
Project end date:
December 2019
Project Contacts:
Uta Wehn - u.wehn@un-ihe.org
Project URL:
https://gt20.eu
Impact Assesment progress:
61% 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 0 23
Involvement 0 18
Governance Policy 28 15
Sustainable Development Goals 26 17
Economy Economic productivity 42 14
Financial sustainability 25 20
Environment Environmental awareness 42 22
Environmental footprint 0 12
Science Scientific productivity 0 20
Interdiscplinary science 0 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. Efforts should be made to make participants aware they are contributing to a research project through clear communication channels, and to offer them the opportunity to be responsible for their activities. If the project has not measured participants' degree of satisfaction in the process, it might want to consider to consider investigating this further using this paper as a starting point.

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.

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

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.

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.

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.

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 25 Economy 25 Society 8 Society 8 Governance 26 Governance 26 Science and technology 23 Science and technology 23 Environment 27 Environment 27 max. 42
Total Score 22/42