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Harnessing Data Science for Environmental Resilience: Bridging the Gap Between Big Data and Local Monitoring for Policy
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Edited by Hanna Koivula, Allan Souza
Data science focuses on extracting meaningful insights from complex datasets, making it increasingly vital in environmental sciences due to the interconnectedness and cross-disciplinary nature of this field. While there is an abundance of data available online, it must be complemented by systematically collected, quality-assured and FAIR monitoring data that provide a structured framework for data collection and representation, enhancing the quality, reliability, and comparability of environmental data that are essential for monitoring environmental changes, or evaluating policy effectiveness. Furthermore, advancements in data science, including knowledge graphs, (semi-)automated mappings and workflows, statistical techniques, machine learning, and citizen science, enhance the ability to process and interpret vast datasets from diverse sources. As the field evolves, the collaboration between data science and environmental research will be crucial in addressing challenges related to climate change and biodiversity loss. This session will review lessons learned and challenges identified in , but also look at the opportunities that can be exploited through collaboration between data science and environmental research (WAILS approach). Catalysing cross-disciplinary collaboration in producing FAIR born data and using advanced analysis and modelling methods such as digital twins, is crucial to addressing the urgent challenges of climate change and biodiversity loss.
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