This paper presents the SignBeach dataset, including 1401 lexical signs from Dutch Sign Language (NGT). The items in this dataset represent everyday vocabulary appropriate for primary school children and are part of a larger research project, investigating sign learning in a digital environment. Each sign is presented by four deaf signers in a controlled studio environment. For each item, high quality video recordings are available from five synchronised cameras, providing rich multi-view visual input suitable for linguistic analysis and the development of computer vision pipelines. In addition, we provide three types of computational derivatives: keypoint estimates using MediaPipe, handshape estimates using HaMeR, and 3D body reconstructions using SAM 3D Body. Signs are aligned with lexical entries in the NGT Signbank to provide interoperability of the database with other NGT resources. We outline the construction of the dataset and provide information on opportunities for reuse, for example in the context of psycholinguistic studies or in the context of sign language technology. All materials are available for non-commercial reuse under a CC BY-NC 4.0 license.