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Humans have kept honeybees as livestock to harvest honey, wax and other products for thousands of years and still continue doing so. Today however, beekeepers in many parts of the world report unprecedented high numbers of colony losses. Sensor data from honey bee colonies can contribute to new insights about development and health factors for honey bee colonies. The data can be incorporated in smart decision support systems and warning tools for beekeepers. In this paper, we present sensor data from 78 honey bee colonies in Germany collected as part of a citizen science project. Each honey bee hive was equipped with five temperature sensors within the hive, one temperature sensor for outside measurements, a combined sensor for temperature, ambient air pressure and humidity, and a scale to measure the weight. During the data acquisition period, beekeepers used a web app to report their observations and beekeeping activities. We provide the raw data with a measurement interval of up to 5 s as well as aggregated data, with per minute, hourly or daily average values. Furthermore, we performed several preprocessing steps, removing outliers with a threshold based approach, excluding changes in weight that were induced by beekeeping activities and combining the sensor data with the most important meta-data from the beekeepers' observations. The data is organised in directories based on the year of recording. Alternatively, we provide subsets of the data structured based on the occurrence or non-occurrence of a swarming event or the death of a colony. The data can be analysed using methods from time series analysis, time series classification or other data science approaches to form a better understanding of specifics in the development of honey bee colonies., (© 2023 The Author(s).)

The investigation of human perception and movement kinematics during manipulation tasks provides insights that can be applied in the design of robotic systems in order to perform human-like manipulations in different contexts and with different performance requirements. In this paper we investigate control in a motor task, in which a tool is moved vertically until it touches a support surface. We evaluate how acoustic and haptic sensory information generated at the moment of contact modulates the kinematic parameters of the movement. Experimental results show differences in the achieved motor control precision and adaptation rate across conditions. We describe how the experimental results can be used in robotics applications in the fields of unsupervised learning, supervised learning from human demonstrators and teleoperations., (Copyright © 2019 Maldonado Cañón, Kluss and Zetzsche.)

It is often assumed that humans represent large-scale spatial environments as cognitive maps, but the exact features of these representations are still unclear. We investigate the structure of this representation with the impossible worlds paradigm by testing whether the information provided by virtual environments (VEs) with arbitrary violations of geometrical rules is rectified ("distorted") to become compatible with a map-like structure. The experiments were conducted in virtual reality using a natural locomotion interface. The subjects' task was to explore possible and impossible VEs carefully to achieve a "full understanding". After each trial, they had to "blindly" reproduce the path through the environment from memory in a VE with impoverished visual cues. We have found no evidence for angular or configurational distortions or alterations in the blind reproductions of impossible VEs. Blind reproduction indicates that impossible VEs do not require a transformation into a "possible" format to make them fit into the cognitive map. This suggests that the representation may not be similar to a map in a bounded sense of interpretation but requires more generalized concepts for its understanding.

We present a system for sensorimotor audio-visual source localization on a mobile robot. We utilize a particle filter for the combination of audio-visual information and for the temporal integration of consecutive measurements. Although the system only measures the current direction of the source, the position of the source can be estimated because the robot is able to move and can therefore obtain measurements from different directions. These actions by the robot successively reduce uncertainty about the source's position. An information gain mechanism is used for selecting the most informative actions in order to minimize the number of actions required to achieve accurate and precise position estimates in azimuth and distance. We show that this mechanism is an efficient solution to the action selection problem for source localization, and that it is able to produce precise position estimates despite simplified unisensory preprocessing. Because of the robot's mobility, this approach is suitable for use in complex and cluttered environments. We present qualitative and quantitative results of the system's performance and discuss possible areas of application.

We investigated audiovisual interactions in motion perception by behavioral experiments testing both, the influence of visual stimuli on auditory apparent motion and the influence of auditory stimuli on visual apparent motion perception. A set of loudspeakers with an LED mounted in the middle of each speaker cone was arranged in a semicircle. Apparent motion streams were presented for each modality alone in the unimodal conditions. In the bimodal conditions, stimuli of the second modality were added to fill the temporal and spatial gaps of the sampled trajectory of the reference stream. The participants' task was to observe the quasi-naturalistic stimulus sequences and to perform a standard classification. The addition of stimuli of the second modality indeed facilitated apparent motion perception. Bimodal presentation increased the upper temporal interval up to which the stimuli could be separated in time while still being perceived as continuous motion. We interpret these results as evidence for an ecologically advantageous audiovisual motion integration mechanism which operates beyond the constraints of strict spatiotemporal coincidence. Functional considerations suggest that this mechanism may represent an amodal stage suited for the processing of both unimodal and bimodal signals.