There is a big push at the moment, specifically for the monitoring of the SDGs, on how to integrate citizen science data in official statistics (e.g. see Fritz et al., 2019, Fraisl et al., 2020). However, this is taking a long time, and the value of citizen science to inform policy on certain issues may be lost. This blog post wants to highlight other added values that citizen science data can offer without it being integrated in official statistics to policy makers.

Insights relevant to policy makers from a citizen science dataset

This is about considering citizen science datasets for its ‘fit-for-purpose’. This means that citizen science datasets should not be ‘at any cost’ integrated in official statistics but that citizen science datasets should be taken into consideration for a context in which the data is ‘fit-for-purpose’. This also considers the bias scare that policy makers may have towards citizen science generated data (i.e. citizen science data can be representative of a small portion of the population). For example a citizen science dataset may represent an issue at a city scale and not a rural one, as the former has more inhabitants. If the data is not taken out of its context (i.e. that it is based on a city scale) the dataset should not be disregarded for the information it can give to a city about an issue.

In addition, citizen science generated data, may provide a window to another policy perspective. For example one of our interviewees from the RESTART project, a project that seeks to bring an alternative story to electronic products lifespan, highlights that their dataset offers an unrepresented perspective to the usual data that informs policy for electronic disposal. For example, their dataset showcases the lifespan of electronic products and the feasibility of repairing them. This information can stimulate new kinds of policies around electronic disposal – e.g. the possibility of repair, and the facilitation to repair any electronic sold.

Therefore, the information that is generated by citizen science datasets should not be taken out of its context and should not be disregarded as an alternative source of information. It may offer new perspectives on policy issues and new solutions to them.

Methods to make the practice of citizen science more reliable

Citizen science engages members of the public in scientific research. Official definitions ask for professionally trained scientists to be involved in a citizen science project to ensure the scientific process is respected. Because the practice of citizen science is a mix between science and public participation, it can embrace a variety of ways to ensure that the data it generates is reliable. This blog posts highlights three methods: 1) the coupling of artificial intelligence (AI) to citizen science, 2) ensuring data reliability by collecting data from organized community events, 3) meta-tagging knowledge.

The first method looks at how citizen science can be used alongside other technologies to ensure the robustness of the study. For example, to understand pollinator biodiversity it is essential to understand both the number of species and the total number of pollinators. Species recognition may be a harder task and more prone to errors due to limited participants’ knowledge. In these cases, artificial intelligence may be a more reliable tool to use for species identification alongside using citizen science for pollinator count. Still, citizen science projects exist that ask participants to recognize species and whose contributions are checked by specialists. This showcases the flexibility with which policy makers can engage citizen science for policy. Citizen science includes a variety of practices that engage participants in many different ways (e.g. through monitoring observations to sorting images) and to different extents (e.g. participants are involved in defining the policy issue and monitoring it, or simply are monitoring the issue). Different forms of citizen science therefore can be adapted for different policy needs, issues and capacity.

A second method highlights a method used by a citizen science project to increase the reliability of the incoming data. To increase the reliability of the data, RESTART (i.e. the citizen science project mentioned above) gathers data from individuals that participate in community repair events. This increases the reliability of where the data comes from. It decreases the chances that individuals are contributing without having repaired an electronic product. This showcases that innovative data collection methods, such as restricting the source of data, can contribute to making citizen science data reliable.

A third method, which contextualizes citizen science data within other forms of data, is the idea of meta-tagging data generated knowledge on its degree of certainty and comparing it to other forms of data generated knowledge. Meta-tagging data generated knowledge means that the data is contextualized with its accompanying meta-data. This would inform its level of accuracy, precision and relevance for a certain situation. This refers back to using a dataset ‘fit-for-purpose’. If the knowledge produced by a citizen science project is representative of an issue at a city scale, meta-tagging this data with this detail allows the generated knowledge to be used to inform an issue at the city scale. Data generated knowledge meta-tagging would allow different kinds of generated knowledge to be compared for their relevance, accuracy and precision for an issue.

Citizen science is a scientific participatory practice that has many forms which can be more or less suitable to different policy needs. In addition the forms of knowledge that it produces can be relevant to policy makers when kept in context and as an alternative perspective to a policy issue that is not usually represented in data that informs policy. Therefore, citizen science that is not incorporated in official statistics can still be useful for policy makers to make better informed policy decisions.

The information within this blog post are part of a context scoping phase I was a part of during my internship at DRIFT for the development of policy mastercterclass for the EU-funded ACTION project.


Fraisl, D., Campbell, J., See, L., Wehn, U., Wardlaw, J., Gold, M., … & Fritz, S. (2020). Mapping citizen science contributions to the UN sustainable development goals. Sustainability Science, 15(6), 1735-1751. 

Fritz, S., See, L., Carlson, T., Haklay, M. M., Oliver, J. L., Fraisl, D., … & West, S. (2019). Citizen science and the United Nations sustainable development goals. Nature Sustainability, 2(10), 922-930.