This paper presents two metazoan zooplankton datasets obtained by imaging samples collected on the Bay of Biscay continental shelf in spring during the PELGAS integrated surveys, over the 2004–2019 period. The samples were collected at night, with a WP2 200 µm mesh size fitted with a Hydrobios (back-run stop) mechanical flowmeter, hauled vertically from the sea floor to the surface with a maximum depth set at 100 m when the bathymetry is deeper. The first dataset originates from samples collected from 2004 to 2016, imaged on land with the ZooScan and is composed of 1,153,507 imaged and measured objects. The second dataset originates from samples collected from 2016 to 2019, imaged on board the R/V Thalassa with the ZooCAM and is composed of 702,111 imaged and measured objects. The imaged objects is composed of zooplankton individuals, zooplankton pieces, non-living particles and imaging artefacts, ranging from 300 µm to 3.39 mm Equivalent Spherical Diameter, individually imaged, measured and identified. Each imaged object is geolocated, associated to a station, a survey, a year and other metadata. Each object is described by a set of morphological and grey level based features (8 bits encoding, 0 = black, 255 = white), including size, automatically extracted on each individual image. Each object was taxonomically identified using the web based application Ecotaxa with built-in, random forest and CNN based, semi-automatic sorting tools followed by expert validation or correction. The objects were sorted in 172 taxonomic and morphological groups. Each dataset features a table combining metadata and data, at the individual object granularity, from which one can easily derive quantitative population and communities descriptors such as abundances, mean sizes, biovolumes, biomasses, and size structure. Each object’s individual image is provided along with the data. These two datasets can be used combined together for ecological studies as the two instruments are interoperable, or as training sets for ZooScan and ZooCAM users.< Leer menos