Mapping the Landscape of Citizen Science in Africa: Assessing its Potential Contributions to Sustainable Development Goals 6 and 11 on Access to Clean Water and Sanitation and Sustainable Cities

Data are vital for and creating knowledge-based solutions to development challenges facing Africa. As a result of gaps in government-funded data collection, and in the interest of promoting community engagement, there is a global movement towards consideration of nontraditional sources of data, including citizen science (CS) data. These data are particularly valuable when collected at a high resolution over large spatial extents and long time periods. CS projects and infrastructure are abundant and well documented in the Global North, while needs for participatory projects to fill environmental monitoring gaps may be greater in the Global South. The paper explores the contributions of citizen science projects originating in Africa for two Sustainable Development Goals (SDGs), namely SDG 6, and SDG 11 which are particularly important to the millions of low-income residents of cities across Africa. Using a mixed methods approach that involves online surveys, interviews, expert conference panels, and a desk review, we analyze a total of 53 CS projects focusing on water, sanitation, and urban planning. The paper addresses CS in Africa and CS for SDGs, and documents evidence for participatory and CS data collection in Africa. It also describes the survey methods, including approaches to training of volunteers, sources of funding, data collection methods, and objectives of the tools and projects. Finally, it provides reflections on the challenges of integrating CS into official statistics in Africa, and some lessons learnt from CS projects in Africa. This paper recommends the establishment of an open-source database, creation of a network of CS projects for communication and collaboration, the uptake of citizen-generated data, and continuous funding for such projects in Africa

Mapping the landscape of citizen science in Africa : assessing its potential contributions to sustainable development goals 6 and 11 on access to clean water and sanitation and sustainable cities

SUPPLEMENTARY FILES : The supplementary files for this article can be found as follows: • SUPPLEMENTARY FILE 1: Appendix A. Survey Instrument & Appendix B: List of Citizen Science Projects Surveyed. DOI: https://doi.org/10.5334/cstp.601.s1 • SUPPLEMENTARY FILE 2: Supplemental Figure 1 (Citizen Science Projects by African subregions). DOI: https://doi. org/10.5334/cstp.601.s2 • SUPPLEMENTARY FILE 3: Supplemental Figure 2 (Citizen Science projects by year established). DOI: https://doi. org/10.5334/cstp.601.s3 • SUPPLEMENTARY FILE 4: CODATA CITIZEN SCIENCE FOR THE SDGs AFRICA SURVEY DATABASE+March 2023. DOI: https://doi.org/10.5334/cstp.601.s4Data are vital for and creating knowledge-based solutions to development challenges facing Africa. As a result of gaps in government-funded data collection, and in the interest of promoting community engagement, there is a global movement towards consideration of nontraditional sources of data, including citizen science (CS) data. These data are particularly valuable when collected at a high resolution over large spatial extents and long time periods. CS projects and infrastructure are abundant and well documented in the Global North, while needs for participatory projects to fill environmental monitoring gaps may be greater in the Global South. The paper explores the contributions of citizen science projects originating in Africa for two Sustainable Development Goals (SDGs), namely SDG 6, and SDG 11 which are particularly important to the millions of lowincome residents of cities across Africa. Using a mixed methods approach that involves online surveys, interviews, expert conference panels, and a desk review, we analyze a total of 53 CS projects focusing on water, sanitation, and urban planning. The paper addresses CS in Africa and CS for SDGs, and documents evidence for participatory and CS data collection in Africa. It also describes the survey methods, including approaches to training of volunteers, sources of funding, data collection methods, and objectives of the tools and projects. Finally, it provides reflections on the challenges of integrating CS into official statistics in Africa, and some lessons learnt from CS projects in Africa. This paper recommends the establishment of an open-source database, creation of a network of CS projects for communication and collaboration, the uptake of citizen-generated data, and continuous funding for such projects in Africa.CODATA, the LIRA 2030 Africa Programme by the International Science Council and NASAC and NASA Contract 80GSFC18C0111 for the continued operation of the Socioeconomic Data and Applications Center (SEDAC).http://theoryandpractice.citizenscienceassociation.orgam2024Information ScienceSDG-06:Clean water and sanitationSDG-11:Sustainable cities and communitie

Comprehensive analysis of DNA repair gene variants and risk of meningioma.

BACKGROUND: Meningiomas account for up to 37% of all primary brain tumors. Genetic susceptibility to meningioma is well established, with the risk among relatives of meningioma patients being approximately threefold higher than that in the general population. A relationship between risk of meningioma and exposure to ionizing radiation is also well known and led us to examine whether variants in DNA repair genes contribute to disease susceptibility. METHODS: We analyzed 1127 tagging single-nucleotide polymorphisms (SNPs) that were selected to capture most of the common variation in 136 DNA repair genes in five case-control series (631 case patients and 637 control subjects) from four countries in Europe. We also analyzed 388 putative functional SNPs in these genes for their association with meningioma. All statistical tests were two-sided. RESULTS: The SNP rs4968451, which maps to intron 4 of the gene that encodes breast cancer susceptibility gene 1-interacting protein 1, was consistently associated with an increased risk of developing meningioma. Across the five studies, the association was highly statistically significant (trend odds ratio = 1.57, 95% confidence interval = 1.28 to 1.93; P(trend) = 8.95 x 10(-6); P = .009 after adjusting for multiple testing). CONCLUSIONS: We have identified a novel association between rs4968451 and meningioma risk. Because approximately 28% of the European population are carriers of at-risk genotypes for rs4968451, the variant is likely to make a substantial contribution to the development of meningioma