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This paper aims at highlighting the lessons learned from recent initiatives between public universities of Niger and the national statistics institute. Our investigation of the existing national statistical system revealed the need to increase the number of qualified human resources with advanced skills in open data, big data, data visualisation, machine learning, mathematical modeling and data-driven innovations. Moreover, the existing statistical literacy and data crowdsourcing activities need to be validated and upscaled; and we have found a lack of experience in managing big data and in the development of mathematical methods and fast computational algorithms to analyze them. Finally, the aforementioned collaboration can be improved by working closely with private sector, civil society and the data science community to generate new approaches to emerging issues including climate change and sustainable development.

This paper aims at highlighting initiatives in developing future statisticians directed at high-school and university levels in Niger. More specifically, it focuses on collaborations, partnerships, outreach initiatives and supporting mechanisms, which may contribute to increase engagement and interest in and attraction to the field of statistics in the era of data science and data-driven innovations. Providing sufficient exposure to modern statistical analysis, computational and graphical tools, written and oral communication skills, and the ever-growing interdisciplinary use of statistics are key activities for building future generations of statisticians. Furthermore, current curricula as well as pedagogical approaches, teaching materials, and assessment methods need to be re-thought in order tomeet the requirements of the skills needed in the 21st century ensuring effective interaction with scientists, public institutions, industry, civil society, and policy makers. First published February 2020 at Statistics Education Research Journal Archives

How can citizen science projects advance the achievement of transformative air quality-related Sustainable Development Goals (SDGs) in Germany and Niger? We investigate the promise of using citizen-generated data (CGD) as an input for official SDG monitoring and implementation in a multidisciplinary project, based on activities undertaken in Niger and Germany ranging from surveys, action research, policy and legislative analysis and environmental monitoring in Niamey and Leipzig, respectively. We critically describe and evaluate the great potential, but very limited actual use of CGD sources for these global goals in both contexts from technical and policy perspectives. Agenda 2030 provides an opportunity to tackle indoor and outdoor air quality in a more integrated and transformative perspective. However, we find this agenda to be remarkably absent in air quality policy and monitoring plans. Likewise, we find no meaningful links of existing citizen science initiatives to official air quality policy. We propose how SDGs-aligned citizen science initiatives could make major contributions to environmental and health monitoring and public debate, especially in the wake of the COVID-19 pandemic. This however requires researchers to more strategically link these initiatives to policymakers and policy frameworks, such as SDG indicators and the governance structures in which they are embedded.

International audience; The freshness and quality indices of whiting (Merlangius merlangus) influenced by a large number of chemical volatile compounds, are here analyzed in order to select the most relevant compounds as predictors for these indices. The selection process was performed by means of recent statistical variable selection methods, namely robust model-free feature screening, based on quantile correlation and composite quantile correlation. On the one hand, compounds 2-Methyl-1-butanol, 3-Methyl-1-butanol, Ethanol, Trimethylamine, 3-Methyl butanal, 2-Methyl-1-propanol, Ethylacetate, 1-Butanol and 2,3-Butanedione were identified as major predictors for the freshness index and on the other hand, compounds 3-Methyl-1-butanol, 2-Methyl-1- butanol, Ethanol, 3-Methyl butanal, 3-Hydroxy-2-butanone, 1-Butanol, 2,3-Butane- dione, 3-Pentanol, 3-Pentanone and 2-Methyl-1-propanol were identified as major predictors for the quality index.