Multibeam sonar water-column data show potential for the detection of aquatic vegetation that extends significantly from the seabed, such as kelp, and may therefore contribute to improving marine habitat mapping. In order to determine if such data can be used to detect kelp and estimate their biomass, we acquired a comprehensive water-column dataset over a controlled kelp site. The experimental site was a collection of fourteen Giant kelp (Macrocystis pyrifera) plants harvested from a nearby forest and arranged on an area of seafloor previously bare, after having been measured and weighed at port. The arrangement consisted in a 4 m by 4 m quadrat with individual 1 m wide cells, and one plant set at random per cell. The water-column dataset was acquired with a Kongsberg EM2040C MBES at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). For each combination of these settings, we acquired data by running the vessel three times on two parallel paths with opposite direction on either side of the surface canopy. These 3 replicates in 2 directions of data recorded with 9 different settings resulted in 54 data files. The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest, leading to another set of 54 data files. The Giant kelp plants produced evident echoes in the water-column data at all settings. This dataset may be used to develop water-column data processing methodologies (such as methods attempting at filtering the acoustic noise), or models to relate EM2040C water-column data to kelp biomass.