This article describes a dataset providing sensory and nutritional information for 30 commercial cooked hams (without rind, not flavored) representative of the French commercial segment. The sensory data were collected in two phases. During the first phase (fall 2019, field experiment), 483 consumers, regular consumers of cooked hams, were recruited in seven cities and vicinities of France. They were instructed to choose and buy cooked hams at the supermarket and evaluate them at home over a period of three months. They were provided with a list of 30 eligible cooked hams selected by the experimenters. A total of 2758 evaluations were collected (an average of 5.7 evaluations per consumer). During the second phase (fall 2020, lab experiment), a selection of 16 cooked hams were evaluated at blind by 86 consumers in a sensory analysis laboratory using a complete balanced design. Sensory evaluation at home and in the laboratory included liking Just-About-Right (colour, fat, salt and texture) measurements. In the field experiment, consumers were additionally asked to describe with free comments the appearance, texture and flavour of tested hams and of a virtual “ideal ham”. They also had to report the price they paid for a pack of four slices of ham and their intentions to repurchase the tested hams. Other data on cooked hams included actual salt and fat contents (measured using physicochemical analyses) and information displayed on the packaging (type of brand, nutritional claims, labels). This dataset offers a broad overview of the perception and the appreciation of cooked hams representative of the French market, and it could allow the joint analysis of intrinsic and extrinsic food properties. Moreover, this data paper describes an innovative protocol of field experiment bridging the gap between the controlled lab environment (panelized consumers, selection of the list of hams by the experimenter) and the real-life settings (hams chosen by the consumers and tasted at home with access to information). Such a protocol could be reused to collect large sensory datasets and aggregate them into open databases interoperable with other food databases (nutritional, economic, sustainability, etc.).