Meaningful engagement of all the actors to build trust in citizen science data and results

Fraisl, D. ORCID: https://orcid.org/0000-0001-7523-7967 (2023). Meaningful engagement of all the actors to build trust in citizen science data and results. In: SciDataCon 2023, 23 - 26 October, 2023, Salzburg, Austria.

Slideshow CODATA Fraisl v0.pptx - Presentation Available under License Creative Commons Attribution Non-commercial. Download (7MB)

Abstract

Presentation for session "Modes of engagement across scales: Conceptualising equity in citizen-science data governance".

Citizen-science data - understood as data generated voluntarily and consciously by individuals or community groups with the aim of contributing to knowledge production regarding a specific topic - increasingly complements conventional statistical and administrative datasets at the various government levels. At the neighbourhood level citizen-science data generation ranges from residents taking environmental measurements such as air quality (West et al., 2020), contributing with data on pluvial flooding (See, 2019) for early warning and validation of flood forecasting models in marginalised neighbourhoods, to participatory mapping of access to healthcare (Porto de Albuquerque et al., 2019). Data from such local-level initiatives has enabled analytical granularity, contributed to the identification of intersectional impacts (e.g., socio-spatial inequalities in air quality), and made visible differential demand for public services in informal neighbourhoods of marginalised communities. National Statistical Offices (NSOs) have also started to collaborate with civil society organisations (CSOs), academia, citizens and communities and other stakeholders to integrate citizen-generated data into official statistics at a national level, such as data regarding the socio-spatial determinants of education outcomes (KNBS/P4R/PARIS21, 2023) or citizen science beach cleanup data into official SDG reporting (Olen, 2022). At the global level, Fraisl et al. (2020) argue that existing citizen science projects could help close the data gaps for the monitoring of the UN Sustainable Development Goals (SDGs) by providing data for 33% of the SDG indicators while addressing countries’ SDG reporting challenges related to statistical capacity, frequency and spatial disaggregation. The technologically enabled progress over the last decade which has strengthened the voice of historically underrepresented actors is clearly a positive development. Yet, we still have a limited systematic understanding of the inherent roles and power relationships of data and the data actors across the governance scale.

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