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1. BUILDING THE BLUE: CONFERENCE REPORT

   Moulton, Erica; Boehme, Linae (March 2025, NSF)

   Sager, Jessica (Ed.)

   The "Building the Blue" conference, presented on September 13th, 2024, and funded by the National Science Foundation, convened stakeholders from industry, educators, and future workforce members to uncover the skills, pathways and technologies needed to support the Blue Economy. Industry expert speakers covered many topics including the existing coursework and training for Blue Economy needs, the dynamic and exploration-based origins of the Blue Economy as well as the omnipresent need for more technology and data-based solutions. 

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2. Boosting the South: A Vision for an All-Atlantic Training Platform

   https://doi.org/10.5670/oceanog.2025.141  

   Ekau, Werner; Fock, Heino; Muelbert, José; Augustyn, Johann (January 2025, Oceanography)

   Multidisciplinary and multinational cooperation is a prerequisite for success in marine research. The only way to establish sustainable global and regional management of the ocean is to view it as a whole and study all its different facets holistically. In addition to large-scale data collection, such studies require an exchange of knowledge, harmonization of laboratory and evaluation methods, and the mutual use of infrastructure. To further these goals, international training and education opportunities as well as work experiences need to involve underdeveloped regions in a meaningful and equal manner in order to reduce differences among national stakeholders, help promote the blue economy, and lead to better life conditions in a sustainable environment. 

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3. Zooming in on Arctic urban nature: green and blue space in Nadym, Siberia

   https://doi.org/10.1088/1748-9326/ac0fa3  

   Fedorov, R; Kuklina, V; Sizov, O; Soromotin, A; Prihodko, N; Pechkin, A; Krasnenko, A; Lobanov, A; Esau, I (July 2021, Environmental Research Letters)

   Abstract Urban landscape combines built-up areas with strongly altered natural (green and blue) and other open spaces. Voluminous literature examines urban socio-environmental interactions in tropical and temperate cities, whereas high-latitude cities are rarely considered. Here, we create a historical perspective on urban green (vegetation) and blue (water) spaces in a sub-Arctic city of Nadym in Russia. Our study explores a novel way to combine quantitative information from satellite imagery and biometric studies with qualitative information from interviews with stakeholders and residents. Such a joint analysis helps to understand dynamics of the urban green and blue space as well as its value for society. Furthermore, we propose objective indicators reflecting societal values of spaces in connection with recreational and ecological services. By contrast to temperate city studies, we found that green space is less used in summer, but still highly valued, deep lakes are used and valued more than warmer shallow lakes, and winter white space do not shrink but enhance the urban public space. Satellite images reveal inevitable loss of green space to urban construction and its remediation by artificial plantings (almost by 30% at present), whereas less valued blue space decreased almost three-fold. Interviews reveal that shallow lakes have reduced recreational values due to ice bottom and algae bloom. High values are attributed to deep artificial lakes, which are more than ten times deeper than natural lakes and do not freeze throughout in winter. Our biometric studies show that trees in urban environment are significantly taller than in the corresponding undisturbed areas. Since majority of the Arctic cities are built using very similar planning ideas and technologies, our findings shall help objective appreciation of green and blue spaces in other settlements. 

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4. Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation

   https://doi.org/10.1145/3544548.3580886  

   Crosby, Alison; Orenstein, Eric Coughlin; Poulton, Susan E; Bell, Katherine L.C.; Woodward, Benjamin; Ruhl, Henry; Katija, Kakani; Forbes, Angus G. (April 2023, Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few resources for automated analysis. In this paper, we report on discussions with diverse stakeholders to identify community needs and develop a set of functional requirements for the ongoing development of ocean science-specific analysis tools. We conducted 36 in-depth interviews with individuals working in the Blue Economy space, revealing four central issues inhibiting the development of effective imaging analysis monitoring tools for marine science. We also identified twelve user archetypes that will engage with these services. Additionally, we held a workshop with 246 participants from 35 countries centered around FathomNet, a web-based open-source annotated image database for marine research. Findings from these discussions are being used to define the feature set and interface design of Ocean Vision AI, a suite of tools and services to advance observational capabilities of life in the ocean. 

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5. Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

   https://doi.org/10.1145/3411764.3445088  

   Suresh, Harini; Gomez, Steven R.; Nam, Kevin K.; Satyanarayan, Arvind (May 2021, 2021 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders’ knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research. 

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