We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed image recognition algorithm based on machine learning through artificial intelligence; this system is capable of rapidly detecting matches between individuals with an estimated 97–99% accuracy. For the 2001–2021 study period, a total of 27,956 unique individuals were documented in 157,350 encounters. Each individual was encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), with an annual average of 87% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

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We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for the majority of living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs supplemented with community science data. All available images were compared using a recently developed machine learning artificial intelligence image recognition algorithm able to rapidly and accurately detect matches between individuals. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,379 encounters, with each individual encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), and with an annual average of 87.1% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for most living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs, supplemented with community science data. Data from throughout the North Pacific were aggregated into 13 regions, including six breeding regions, six feeding regions, and one migratory corridor. All images were compared with minimal pre-processing using a recently developed machine learning artificial intelligence image recognition algorithm capable of rapidly detecting matches between individuals to an estimated 97–99% accuracy. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,350 encounters. Each individual was encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), with an annual average of 87% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.