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One of the biggest barriers to conducting ocean science around the globe is limited access to computational tools and resources, including software, computing infrastructure, and data. Open tools, such as open-source software, open data, and online computing resources, offer promising solutions toward more equitable access to scientific resources. Here, we discuss the enabling power of these tools in under-resourced and non-English speaking regions, based on experience gained in the organization of three independent programs in West African, Latin American, and Indian Ocean nations. These programs have embraced the “hackweek” learning model that bridges the gap between data science and domain applications. Hackweeks function as knowledge exchange forums and foster meaningful international and regional connections among scientists. Lessons learned across the three case studies include the importance of using open computational and data resources, tailoring programs to regional and cultural differences, and the benefits and challenges of using cloud-based infrastructure. Sharing capacity in marine open data science through the regional hackweek approach can expand the participation of more diverse scientific communities and help incorporate different perspectives and broader solutions to threats to marine ecosystems and communities. 

The Coastal Ocean Environment Summer School In Nigeria and Ghana (COESSING; https://coessing.org) has been run for one week every year since 2015. The school, an endorsed project of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030), has provided a platform for approximately 1,000 scientists from Africa, the United States, and Europe to exchange scientific knowledge, to network, to learn, and to collaborate. Our interdisciplinary, multicultural, and multi-institutional approach offers a model for knowledge exchange across the globe and across different educational levels. 

Abstract Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.