The necessity for effective automatic fall detection mechanisms in older adults is driven by the growing demographic of elderly individuals who are at substantial health risk from falls, particularly when residing alone. Despite the existence of numerous fall detection systems (FDSs) that utilize machine learning and predictive modeling, accurately distinguishing between everyday activities and genuine falls continues to pose significant challenges, exacerbated by the varied nature of residential settings. Adaptable solutions are essential to cater to the diverse conditions under which falls occur. In this context, sensor fusion emerges as a promising solution, harnessing the unique physical properties of falls. The success of developing effective detection algorithms is dependent on the availability of comprehensive datasets that integrate data from multiple synchronized sensors. Our research introduces a novel multisensor dataset designed to support the creation and evaluation of advanced multisensor fall detection algorithms. This dataset was compiled from simulations of ten different fall types by ten participants, ensuring a wide array of scenarios. Data were collected using four types of sensors: a mobile phone equipped with a single-channel, three-dimensional accelerometer; a far infrared (FIR) thermal camera; an $8x8$ LIDAR; and a 60-64 GHz radar. These sensors were selected for their combined effectiveness in capturing detailed aspects of fall events while mitigating privacy issues linked to visual recordings. Characterization of the dataset was undertaken using two key metrics: the instantaneous norm of the signal and the temporal difference between consecutive frames. This analysis highlights the distinct variations between fall and non-fall events across different sensors and signal characteristics. Through the provision of this dataset, our objective is to facilitate the development of sensor fusion algorithms that surpass the accuracy and reliability of traditional single-sensor FDSs.