The Center for Data Innovation spoke with Dilek Fraisl, a researcher with the Novel Data Ecosystems for Sustainability (NODES) Research Group of the IIASA Advancing Systems Analysis Program, where she works on the integration of new data sources into official statistics to address the world’s greatest development challenges. Fraisl spoke about her research concerning the potential of citizen science to contribute data to the United Nations Sustainable Development Goals (SDG) indicator framework.
Gillian Diebold: What is citizen science data, and what are its benefits?
Dilek Fraisl: The term citizen science itself is not easy to define due to the diversity of applications and the variety of contexts and disciplines that citizen science activities originate from. Very broadly, we can define citizen science as “public participation in scientific research and knowledge production.” Classifying galaxies, sharing observations related to biodiversity, collecting plastics and relevant data from rivers, seas, and oceans, and measuring water or air quality are a few examples of citizen science activities. Based on this definition, citizen science data refers to the data that volunteers generate by participating in citizen science activities and contributing their time and skills to produce new knowledge.
Citizen science data has a wide range of benefits to science, society, and individuals. For example, this data can support science and scientific research on diverse topics and in varied locations over a short period of time, which professional scientists would not have been able to produce due to time and resource constraints. Citizen science data can also help bridge gaps between science and society by making science more relevant to the needs and priorities of society. This data can contribute to decision making, policymaking, and can help to make science and policies more “democratic” by promoting participation and transparency. It can help raise awareness of environmental and other societal issues and mobilize action among the public. Individuals can also gain a better understanding of these types of issues, as well as science and scientific processes while participating in citizen science activities.
Diebold: Why are national statistical offices hesitant to incorporate citizen science data into official statistics?
Fraisl: There are several reasons for that, but I think one of the most commonly discussed barriers to the use of citizen science data for official statistics is related to concerns about data quality. Because the data collected through citizen science initiatives are not gathered by experts but instead by volunteers who do not necessarily need to have expertise or a background in the field, this data is sometimes considered to be lower quality. However, scientific literature shows many examples demonstrating that citizens can make valuable and scientifically valid contributions that are comparable to professional scientists. Actually, there are various ways to address data quality in citizen science, from volunteer training and ongoing feedback to comparison with professionally collected data, validation by experts, peer review and statistical models, and others. Most citizen science initiatives plan and implement quality assurance and quality control processes and use multiple methods together to ensure high-quality data. Communicating these measures openly is really important to overcome this barrier related to data quality.
Another reason for hesitancy is the lack of awareness of citizen science and its potential within the official statistics community. This is also connected to the lack of human and financial resources that many countries are facing, which does not allow this community to invest time and effort into understanding citizen science methods and approaches, how they can support official statistics, or how they can be improved to address the data gaps in official monitoring. It is important to recognize that many of these challenges are related to communication and lack of capacities and resources.
Diebold: You work with indicators for the United Nations sustainable development goals (SDGs). What are the biggest gaps you’ve identified in SDG data?
Fraisl: There are large data gaps in the SDG framework covering both social and environmental indicators. Just to highlight a few examples, according to a UNEP study that I was involved in, 58 percent of the environmental SDG indicators lack data, and according to a 2020 OECD study, only 33 percent of the 104 gender-related SDG indicators data are available in OECD countries. Additionally, the 2022 SDG Report highlights that significant data gaps still remain in terms of geographic coverage, timeliness, and level of disaggregation of data, and, on a goal level, only around 20 percent of countries have data for Goal 13—climate action.
A study published by IIASA in 2020 shows that citizen science data can support the monitoring of one-third of SDG indicators, and the greatest contribution from citizen science data to SDG monitoring would be in environmental indicators, 58 percent of which lack data, as I mentioned before. That said, we should not give the impression that citizen science data comes without challenges and can be directly or easily integrated into official statistics. This still requires some work; most importantly, building trusted partnerships around citizen science data, which are already happening for various indicators.
Diebold: Citizen science projects are often smaller-scale, so how can communities or countries scale up their initiatives to increase the size of their contributions?
Fraisl: Standardization is a way to address this issue at a technical level, as it could help make different citizen science data sets comparable and thus improve data quality. Without common definitions and standards, it is difficult to compare data and understand progress both within countries and between countries, which is very important for the SDGs. There are initiatives within the citizen science community to address this standardization issue, such as working groups in regional citizen science associations that aim to improve collaboration within the citizen science community to develop and improve standards.
One example is a project that we implemented at IIASA, together with the Ghana Statistical Service and the Environmental Protection Agency, as well as UNEP, Ocean Conservancy, and local citizen science communities in Ghana. As part of the project, we integrated citizen science beach litter data collected by local-level citizen science groups, such as the Smart Nature Freak Youth Volunteers Foundation and Plastic Punch, into the official statistics of Ghana. This data was used to report on the SDG indicator related to marine plastic litter (14.1.1b) in the country and then fed into the global level SDG monitoring and reporting efforts. Thanks to this project, Ghana is now the first country to report on this SDG indicator and the first country to use citizen science data for that purpose. The success of the project, among other things, is due to the standard methodology the mentioned local-level citizen science initiatives use while collecting beach litter and data related to it, which is developed by the Ocean Conservancy’s International Coastal Cleanup (ICC) initiative. Additionally, the ICC has been working with UNEP to improve their methodology to align with the 14.1.1b global methodology so that the uptake of data collected through local citizen science initiatives using the ICC methodology can be much more easily scaled for SDG monitoring and reporting efforts globally.
Diebold: Beyond enriching environmental data, what other areas can citizen science-type projects support SDG data?
Fraisl: The study we published on the link between the SDGs and citizen science showed that potential contributions from citizen science data to SDG monitoring can also cover social indicators, such as sexual violence (5.2.2, 16.1.3, 16.2.3), access to basic services (1.4.1), child development (4.2.1) and child labor (8.7.1), in addition to the environmental indicators. For example, SDG indicator 16.6.2, “proportion of population satisfied with their last experience of public services,” shows a number of significant data gaps. This indicator can benefit from citizen science approaches by creating an ongoing feedback loop between citizens and public authorities that can support the improvement of public services. Citizen science data can help fill substantial data gaps in monitoring this indicator, providing useful complementary data to official surveys outside traditional settings and delivering a more comprehensive overview of citizen satisfaction with public services in a timelier and more cost-effective way. Through citizen science, the data can be collected more frequently depending on the needs and can improve governance significantly. Another example would be SDG indicator 16.1.3, “proportion of population subjected to (a) physical violence, (b) psychological violence and (c) sexual violence in the previous 12 months.” There are citizen science projects and platforms such as Safecity that highlight personal stories of sexual harassment and abuse in public spaces. This data is aggregated as hot spots on a map showing trends at a local level. The objective of the project is to make these data useful for individuals, local communities, and local administrations, to identify factors that cause behavior leading to violence, and to work on strategies for solutions.