Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “path integration”
Data from Churan et al. 2018 Eye movements during path integration
<p>Subjects</p> <p>Six human subjects (two male and four female, mean age 23 years) took part in the experiment. The subjects had normal or corrected‐to‐normal vision and normal hearing.</p> <p>Apparatus</p> <p>Experiments were conducted in a darkened (but not completely dark) sound attenuated room. Subjects were seated at a distance of 114 cm from a tangential screen (70° x 55° visual angle) and their head‐position was stabilized by a chin‐rest. Visual stimuli were generated on a windows PC using an in‐house built stimulus package and were back‐projected onto the screen by a CRT‐Projector (Electrohome Marquee 8000) at a resolution of 1152 x 864 pixels and a frame rate of 100 Hz. The auditory stimuli were also generated using MATLAB and presented to the subjects by head‐phones (Philips SHS390). The eye position was recorded by a video‐based eye‐tracker (EyeLink II, SR Research) at a sampling rate of 500 Hz and an average accuracy of ~0.5°. During the distance reproduction, the subjects controlled the speed of simulated self‐motion using an analog joystick (Logitech ATK3) that was placed on a desk in front of them. The speed of the simulated self‐motion was proportional to the inclination angle of the joystick. The data from the joystick were acquired at a rate of 100 Hz and minimal change in speed of simulated self‐motion that could be triggered by the joystick was 1/1000 of the maximum range of speeds used in the experiments.</p> <p>Stimuli</p> <p>The visual stimulus consisted of a horizontal plane of white (luminance: 90 cd/m<sup>2</sup>) randomly placed small squares on a dark (<0.1 cd/m<sup>2</sup>) background that filled the lower half of the screen. The size of the squares was scaled between 0.2° and 1.9° in order to simulate depth. The direction of the simulated self‐motion was always straight‐ahead.The distances are always quantified in arbitrary units (AU) and the speed of simulated self‐motion in AU/s.The auditory stimuli were sinusoidal tones (SPL approximately 80 dB) with a frequency proportional to the simulated speed. The frequencies were in a range between 220 and 440 Hz and changed linearly as a function of the speed of the simulated self‐motion, which was in the range of 0–20 AU/s.</p> <p>Procedure</p> <p>Each trial consisted of two phases. During the “Encoding phase” the subjects were presented with a simulated self‐motion at one of the three speeds (8, 12 or 16 AU/s). The presentation lasted 4 seconds each which resulted in three different traveled distances (32, 48, 64 AU). The presentation was always bimodal, i.e., visual motion was accompanied by a sound representing the respective speed. The sound frequencies corresponding to the three speeds used during the Encoding phase were 308, 354, and 396 Hz, respectively. The task of the subjects in this phase was to monitor the distance covered for later reproduction. After the Encoding phase, a dark screen was presented for 500 msec and then the subjects had to reproduce the previously observed distance using a joystick. In different conditions of this “Reproduction phase,” either only the visual display was presented (visual condition) or only the auditory stimulus was presented while the screen was dark (auditory condition) or both sources of information were available at the same time (bimodal condition). During reproduction, the subjects were able to change the simulated speed by changing the inclination of the joystick. After the subjects had reached the distance they perceived to be identical to that during the Encoding phase, they had to press a joystick button to complete the trial. The subjects were allowed to move their eyes freely during the Encoding and the Reproduction phases. There were thus nine different experimental conditions: three different speeds in three different modalities. In each experimental condition, 80 trials were recorded. All conditions were presented in a pseudo‐randomized order and the subjects were not informed in advance about the sensory modality of the Reproduction phase.</p> <p>Data</p> <p>Eye position as well as the speed of the simulated self‐motion were recorded at a sampling rate of 500 Hz.<br> The file 'all_data.mat' is a MATLAB data file that consists of the cell structure 'all_data' has the elements 'pas' that contains data from the Encoding phase and 'akt' that contains data from the Reproduction phase.<br> The sub-structure 'pas' consists of 6x9x80 elements. The first dimension represents single subjects (6)<br> The second dimension represents the nine different conditions: 1. 8AU/s auditory 2. 8AU/s visual 3. 8AU/s bimodal 4. 12AU/s auditory 5. 12AU/s visual 6. 12AU/s bimodal 7. 16AU/s auditory 8. 16AU/s visual 9. 16AU/s bimodal<br> The third dimension represents the number of (80) trials recorded for each subject and condition.<br> Each element of 'pas' is a matrix consisting of two rows, the first giving the horizontal eye position and the second row giving the vertical eye position. The sampling rate (columns) was 500 Hz. The simulated self-motion started at first sample and ended 4 sec (=2000 samples) later.</p> <p>The sub-structure 'akt' has the same general shape. The only difference is that each element consists of three rows; horizontal eye-position, vertical eye-position, and (the actively chosen) speed of simulated self-motion.</p>
Data from: Displacement experiments provide evidence for path integration in Drosophila
<p>Like many other animals, insects are capable of returning to previously visited locations using path integration, a memory of travelled direction and distance. Recent studies suggest that <em>Drosophila</em> can also use path integration to return to a food reward. However, the existing experimental evidence for path integration in <em>Drosophila</em> has a potential confound: pheromones deposited at the site of reward might enable flies to find previously rewarding locations even without memory. Thus, we designed an experiment to determine if flies can use path integration memory despite potential pheromonal cues by displacing the flies shortly after an optogenetic reward. We found that rewarded flies returned to the location predicted by a memory-based model. Several analyses are consistent with path integration as the mechanism by which flies returned to the reward. We conclude that while pheromones may often be important in fly navigation and must be carefully controlled in future experiments, <em>Drosophila</em> may indeed be capable of performing path integration.</p>
Hippocampal firing fields anchored to a moving object predict homing direction during path-integration-based behavior
Open the record for dataset details and reuse information.
Data from: Displacement experiments provide evidence for path integration in Drosophila
Open the record for dataset details and reuse information.
Data for: Thermodynamics of a dilute Bose gas: a path-integral Monte-Carlo study
<p>Path-integral Monte-Carlo results of the thermodynamics of a homogeneous dilute Bose gas. Data used to plot the figures for internal energy, pressure, isothermal compressibility and contact parameter.</p>
Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"
<p>Supporting data for "<a href="https://www.nature.com/articles/s41467-023-36666-y">Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics</a>" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008.</p> <p>This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A<B<5. More details on the folder architecture can be found in the README.md file.</p>
Grid cells accurately track movement during path integration-based navigation despite switching reference frames
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.