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Big data, sound science, lasting impact: A framework for passive acoustic monitoring Wall, Carrie C.; McKenna, Megan F.; Hatch, Leila T.; Van Parijs, Sofie M.; Bochenek, Rob; Dugan, Peter; Parcerisas, Clea; Ryan, John; Anderson, Charles D.; Becker, Kyle; Berchok, Catherine; Biddle, Mathew; Boebel, Olaf; Canino, Adrienne; Canonico, Gabrielle; Cline, Danelle; Davis, Genevieve E.; Frasier, Kaitlin; Gedamke, Jason; Haver, Samara M.; Khazmutdinova, Karina; Kinneging, Niels; Kumar, Anurag; Marian, Alyssa; Miksis-Olds, Jennifer L.; Montie, Eric W.; Ponirakis, Dimitri; Rice, Aaron N.; Rowell, Timothy J.; Rueda, Carlos; Shumchenia, Emily; Shyka, Thomas; Staaterman, Erica; Thomisch, Karolin (2025). Big data, sound science, lasting impact: A framework for passive acoustic monitoring. Big Earth Data Early view: 1-35. https://dx.doi.org/10.1080/20964471.2025.2583505 In: Big Earth Data. Taylor & Francis: Abingdon. ISSN 2096-4471; e-ISSN 2574-5417

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Beschikbaar in	Auteur
VLIZ: Open access 419537 [ download pdf ]

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Author keywords

Passive acoustic monitoring; big data analytics; ocean soundscape; cyberinfrastructure; FAIR principles; open science; analysis-readydatasets; AI/ ML-ready datasets

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Project	Top | Auteur
Flemish contribution to LifeWatch.eu

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Auteur		Top
Parcerisas, C.

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Abstract

Marine passive acoustic monitoring (PAM) has produced petabytes of data that are used by researchers, resource managers, industry, and regulators to understand how marine animals use sound and the impacts of anthropogenic noise on species and ecosystems throughout the global ocean. These big data provide unprecedented opportunities to study underwater soundscapes but also enormous challenges to efficiently extract information. To address these challenges, the Sound Cooperative (SoundCoop) project built community-focused cyberinfrastructure to promote improved, scalable and sustainable processing and access of marine PAM data for management, science, industry and military applications. Driven by cross-institutional participation representing a diversity of data collection methods and conditions, SoundCoop established guidance for standardized processing of ocean sound level metrics using freeware software toolkits and developed core tools and processes that support open science. Four comparative analyses that connect PAM monitoring efforts and integrate non-acoustic data illustrate how interoperable sound level metrics support a more coherent and synoptic perspective on ocean soundscapes using methods that current and future PAM projects can leverage. Such a framework around PAM big data offers the opportunity to revolutionize large-scale marine ecology and oceanography in similar ways to other transformative approaches for understanding environmental patterns and processes at global scales

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