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1,548 results for “Trajectories”
TIFF file for the frame-wise tracking trajectories in Figure 1 of the manuscript "Assessing the speed of individual bacteria dispersing on mycelial networks"
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Memory trajectories in lonely individuals: An analysis of the Survey of Health, Aging, and Retirement in Europe (SHARE)
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Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model - Data
<p>This repository contains the data and additional information for the paper 'Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model'. </p> <p>The following files are included:</p> <ul> <li>accuracy.zip: raw performance files for all models;</li> <li>plots.zip: additional plots;</li> <li>tuning.zip: tuning log files.</li> </ul>
Trajectories of Microsecond Simulations of the Orexin Receptor 1 System
<p>This data set contains psf files and trajectories (.dcd files) of the performed simulations (8 systems in total) for the orexin receptor 1 system:</p> <p>1) the orexin type 1 receptor (OX1R) in complex with lemborexant (nrk), one replica per ligand<br>2) the orexin type 1 receptor (OX1R) in complex with orexin A (OxA), four replicas</p>
Molecular dynamics trajectories for neuroBR
<p>The trajectories of all-atom MD simulations of neuroBR_A,B,C.</p> <p>Simulations have been performed using the CHARMM36m force field, running with the GROMACS 2022 package.</p>
Monosaccharide Trajectories ChemComm 10.1039/D4CC04415K
<p>monosaccharides in water boxes simulated with Tinker9 and Poltype2 parameters for 10.1039/D4CC04415K</p>
Trajectories of all MD simulations performed of Spike RBD/hACE2 complexes (WT, Delta, BA.1 to BA.4)
<p>Trajectories (.dcd) of the molecular dynamics simulations performed on Spike RBD / hACE2 complexes</p> <p>Protein structure files (.psf) prepared with Charmm36m topology / parameters</p> <p>For the WT (SARS-CoV-2 original strain), 6 additional replicas were uploaded, 3 including all glycans present on both spike and hACE2 et 3 replicas without glycan as control simulations.</p> <p> </p>
Trajectories of the plants
<p>Trajectories of the tendrils and support for each plant</p>
Model Comparison Benchmark Datasets, Results, and Peptide MD Trajectory
<p>Contents of this database include:</p> <ul> <li>aib9.out.zip: zipped file containing raw output of the Aib9 OpenMM MD simulation</li> <li>aib9_openmm.zip: zipped folder containing all input files for the Aib9 OpenMM MD simulation, as well as the generated trajectory</li> <li>data_input.zip: zipped folder containing input datasets for the GMM and Aib9 numerical experiments</li> <li>data_output.zip: zipped folder containing output data and experimental results for three generative models (NS, CFM, DDPM) on the input datasets, included for posterity (file names do not synchronize with most recent repository naming conventions)</li> </ul> <p>The files are automatically retrieved and unzipped via the data_accessor notebook in the GitHub repository.</p> <p> </p>
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
VRU Trajectory Dataset
<p><strong>VRUT_Dataset_complete.tar.gz</strong> :</p> <p>The VRU Trajectory Dataset consists of 1068 pedestrian and 464 cyclist trajectories recorded at an urban intersection using cameras and LiDARs. A detailed description of the intersection can be found in [1]. The pedestrian trajectories were recorded by using a wide angle stereo camera system to track the pedestrians' head position and generating the 3D position by triangulation. The cyclists trajectories were recorded by using LiDARs to track the center of gravity of the cyclists. The cameras operate at 50 Hz, the LiDARs at 12.5 Hz. The dataset partly results from the projects DeCoInt² [2] funded by the "German Reasearch Foundation" (DFG) and AFUSS funded by the "Bundesministerium für Bildung und Forschung" (BMBF). Additionally, our work is supported by "Zentrum Digitalisierung Bayern".</p> <p>Dataset Format:</p> <p>The complete dataset consists of 1532 files in csv format, where every file contains one VRU trajectory. A csv file consists of 4 columns:</p> <ul> <li>ID: Measurement IDs</li> <li>timestamp: Timestamp in seconds</li> <li>x: position in x-direction in meter</li> <li>y: position in y-direction in meter<br> </li> </ul> <p><strong>extended_cyclist_dataset.7z</strong> :</p> <p>To create the Extended Cyclist Trajectory Dataset, we changed the camera lenses to capture a wider filed of view enclosing the bike lane. The dataset currently consists of 1 746 cyclist trajectories including motion primitive labels. The motion primitve labels include the classes <em>wait</em>, which starts at the last visbible movement of the bicycle wheel and ends the first visible bicycle wheel, <em>starting movement, </em>which is the frame of the first visible movement of the cyclist before the end of <em>wait, </em><em>tr/tl </em>(turn left, turn right), which start at the first and end at the last visible turning movement of the cyclist, <em>and hand signal left/right, </em>which start at the first and end at the last visible frame of the hand signal.</p> <p>Dataset Format</p> <p>The dataset consists of 1746 files in json format, where every file contains one cyclist trajectory.</p> <p>A json file is structured as follows:</p> <p>{"vru_type": "bike",<br> "trajectory2: [{"Timestamp": [LIST OF UTC TIMESTAMPS],<br> "x": [LIST OF X POSISTIONS],<br> "y": [LIST OF Y POSISTIONS],<br> "z": [LIST OF Z POSISTIONS],<br> "x_smoothed": [LIST OF SMOOTHED X POSISTIONS],<br> "y_smoothed": [LIST OF SMOOTHED Y POSISTIONS],<br> "z_smoothed": [LIST OF SMOOTHED Z POSISTIONS]}],<br> "motion_primitives": {"mp_labels":<br> [{"mp_label": LABEL NAME,<br> "start_time": START UTC TIMESTAMP,<br> "end_time": END UTC TIMESTAMP, ...}, ...]}}</p> <p><strong>cyclist_starting_dataset.zip</strong>:</p> <p>The complete dataset is stored in csv format. It contains the sensor data and the corresponding label. The sensor data csv file consists of 19 columns:</p> <p>The data is split into meta data and payload data. The meta data contains the fields identifying<br> an experiment, i.e., ExperimentID, SceneID, VRUID. Additionally, the Timestamp field is used to identify the corresponding sensor readings.</p> <ul> <li>ExperimentID: meta data, identifies each different experiment</li> <li>SceneID: meta data</li> <li>VRUID: meta data, identifier for test subject</li> <li>Timestamp: meta data, Timestamp, starting at 0 for each separate experiment</li> </ul> <p>An experiment always contains one VRU but it may consist of several scenes, i.e., starting movements. In the following the fields containing the sensor values are described.</p> <ul> <li>Accelerometer_x: Acceleration force along the x axis (including gravity), in m/s²</li> <li>Accelerometer_y: Acceleration force along the y axis (including gravity), in m/s²</li> <li>Accelerometer_z: Acceleration force along the z axis (including gravity), in m/s²</li> <li>Linear_Accelerometer_x: Acceleration force along the x axis (excluding gravity), in m/s²</li> <li>Linear_Accelerometer_y: Acceleration force along the y axis (excluding gravity), in m/s²</li> <li>Linear_Accelerometer_z: Acceleration force along the z axis (excluding gravity), in m/s²</li> <li>Gyroscope_x: Rate of rotation around the x axis, in rad/s</li> <li>Gyroscope_y: Rate of rotation around the y axis, in rad/s</li> <li>Gyroscope_z: Rate of rotation around the z axis, in rad/s</li> <li>Rotation_w: Scalar component of the rotation vector</li> <li>Rotation_x: Rotation vector component along the x axis, unitless</li> <li>Rotation_y: Rotation vector component along the y axis, unitless</li> <li>Rotation_z: Rotation vector component along the z axis, unitless</li> <li>target: indicates the label. Nothing = no label, 0 = waiting, 1 = starting_movement, 2 = starting</li> </ul> <p> </p> <p>This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern".</p> <p><strong>References</strong></p> <table> <tbody> <tr> <td>[1] M. Goldhammer, E. Strigel, D. Meissner, U. Brunsmann, K. Doll and K. Dietmayer, "Cooperative multi sensor network for traffic safety applications at intersections," 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, 2012, pp. 1178-1183. doi: 10.1109/ITSC.2012.6338672</td> </tr> <tr> <td>[2] M. Bieshaar, G. Reitberger, S. Zernetsch, B. Sick, E. Fuchs and K. Doll, "Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence", AAET – Automatisiertes und vernetztes Fahren, Braunschweig, pp. 67-87, Available: https://www.its-mobility.de/download/AAET/Dokumentation/AAET_2017_Tagungsband_Download.pdf</td> </tr> <tr> <td>[3] M. Bieshaar, S. Zernetsch, A. Hubert, B. Sick, and K. Doll. Cooperative starting movement detection of cyclists using convolutional neural networks and a boosted stacking ensemble. CoRR, abs/1803.03487, 2018.</td> </tr> </tbody> </table>
Dataset of the study "Psychological factors and trajectories of post-surgical pain: A longitudinal prospective study"
<p>Dataset of the study "Psychological factors and trajectories of post-surgical pain: A longitudinal prospective study" published in Pain Practice, 2022 Feb; 22(2): 159-170. doi: 10.1111/papr.13074</p>
Molecular Dynamics trajectories of the human PDZ2 domain
<p>Molecular Dynamics trajectories of the human PDZ2 domain. This dataset contains simulations of two systems (PDZ2 apo and PDZ2 bound to the RA-GEF-2 peptide) started from either a crystal or NMR structure.</p>
Pedestrian movement trajectories and collision avoidance strategies in interweaving pedestrian flow experiment
<p>The mechanisms of Collision Avoidance(CA) behaviors in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. A series of controlled interweaving pedestrian flow experiments with different pedestrian flow densities are carried out to investigate the CA behaviors, especially CA strategy choices. This dataset consists the movement trajectory and the CA strategy choices information of the participants in the experiment and was provided as a supplementary material of a journal paper submitted to "Royal Society Open Science".</p> <p>All these experiments were conducted in an outdoor public square in the campus of Wuhan University of Technology. A total of 40 students aged 18-23 years participated these experiments. The experiment includes three "groups", representing low, medium and high density levels of interweaving pedestrian flow. Each group of experiment was repeated three times to increase the reliability of the observations. Therefore, there are totally 9 data files in this dataset, each file contains the data of one experiment.</p> <p>After the experiment, the PeTrack software was used to extract pedestrians' trajectories by identifying and tracking the coordinates of participants' heads in the video records. This dataset provides the extracted trajectories of all the pedestrians in each experiment. Trajectory of each RP is marked as Collision Avoidance Segment (CAS) and Normal Walking Segment (NWS). During the CAS, pedestrians have shown collision avoidance strategies. Four types of CA strategies, including "deceleration", "acceleration", "detour", and "stop" are manually identified in these experiments and marked in the dataset. More details of the data and the results of the study could be found in the associated journal paper. This dataset could be useful for researchers in this field.</p>
Master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation
<p>These videos are the result videos of the pedestrian model with trained neural networks of different Machine Learning methods in the master thesis: Comparison of Machine Learning Methods for Pedestrian Trajectory Generation.</p> <p>Turing_Learning_1: the first experiment of the Turing Learning method.</p> <p>Turing_Learning_1: the second experiment of the Turing Learning method.</p> <p>Inverse_Reinforcement_Learning_1: the first experiment of the Inverse Reinforcement Learning method.</p> <p>Inverse_Reinforcement_Learning_2: the second experiment of the Inverse Reinforcement Learning method.</p> <p>Deep_Q_Network_1: the first experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_2: the second experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_3: the third experiment of the Deep Q-Learning method.</p> <p>Deep_Q_Network_4: the fourth experiment of the Deep Q-Learning method.</p> <p>Policy_Distillation_1: the first experiment of the Policy Distillation method.</p> <p>Policy_Distillation_2: the second experiment of the Policy Distillation method.</p> <p>Policy_Distillation_3: the third experiment of the Policy Distillation method.</p>
Experimental evolution reveals differential evolutionary trajectories in male and female activity levels in response to sexual selection and metapopulation structure
<p>Behavior is central to interactions with the environment and thus has significant consequences for individual fitness. Sexual selection and demographic processes have been shown to independently shape behavioral evolution. However, while some studies have tested the simultaneous effects of these forces, no studies have investigated their interplay in behavioral evolution. We applied experimental evolution in the seed beetle <i>Callosobruchus maculatus</i> to investigate, for the first time, the interactive effects of sexual selection intensity (high (polygamy) vs. minimal (enforced monogamy)) and metapopulation structure (yes/no) on the evolution of movement activity, a crucial behavior involved in multiples functions (e.g., dispersal, predator avoidance or resource acquisition) and thus, closely related to <a>fitne</a>ss. We found that the interactive effects of the selection regimes did not affect individual activity, which was assayed under two different environments (absence vs. presence of conspecific cues from both sexes). However, contrasting selection regimes led to sex- and context-dependent divergence in activity. The relaxation of sexual selection favored an increase in female, but not male, movement activity that was consistent between environmental contexts. In contrast, selection associated with the presence/absence of metapopulation structure led to context-dependent responses only in male activity. In environments containing cues from conspecifics, males from selection lines under population subdivision showed increased levels of activity compared to those assayed in an environment devoid of conspecifics cues, while the opposite was true for males from panmictic lines. These results underscore that both the effects of sexual selection and population spatial structure may be crucial in shaping sex-specific behavioral evolution.</p>
Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data
<p>This repository contains the code and data for reproducing the results in the paper 'Per-Run Algorithm Selection with<br> Warm-starting using Trajectory-based Features'</p> <p>The file 'figure_generation.ipnb' contains the code used to create the heatmaps shown in the paper.</p> <p>The file 'Raw_data' contains the raw performance data, for both running A1 and recording all relevant features and for running the switching algorithms for each of the 6 algorithms in the portfolio while recording only performance. The processed version of the A2 data into relative performance is available in 'perf_relative'.</p> <p>The file 'data-collection' contains the code used to run the switching algorithms with the used warmstarting procedures.</p> <p>The file 'compute_features.ipnyb' contains the code used to extract the timeseries features, and those features themselves are included in 'TS_Features'.</p> <p>The file 'AlgorithmSelector' contains the scripts and data used to create the algorithm selector.</p> <p>The files 'selector_5_as_f' and 'selector_ng_f' contain the results of running the final models on the test-instances and nevergrad functions respectively.</p>
Simulated Trajectories of a Furylfulgide
<p>This dataset contains the results of nonadiabatic molecular dynamics simulations of the photorelaxation processes of two isomers, Eα and Eβ, of a prototypical furylfulgide. There are 75 simulated trajectories for the Eα isomer, and 50 for the Eβ isomer. The trajectories can be viewed with standard molecular editing software such as Jmol, GDIS, or VMD. Each trajectory is formatted as a standard .xyz file. The atomic coordinates are given in units of angstrom. The frames are written at intervals of 0.5 fs. Each trajectory lasts 500 fs.</p>
Monitoring resistance and resilience using carbon trajectories: analysis of forest management-disturbance interactions
<p>A changing climate is altering ecosystem carbon dynamics with consequences for natural systems and human economies, but there are few tools available for land managers to meaningfully incorporate carbon trajectories into planning efforts. To address uncertainties wrought by rapidly changing conditions, many practitioners adopt resistance and resilience as ecosystem management goals, but these concepts have proven difficult to monitor across landscapes. Here, we address the growing need to understand and plan for ecosystem carbon with concepts of resistance and resilience. Using time series of carbon fixation (n=103), we evaluate forest management treatments and their relative impacts on resistance and resilience in the context of an expansive and severe natural disturbance. Using subalpine spruce-fir forest with a known management history as a study system, we match metrics of ecosystem productivity (net primary production, g·C·m<sup>2</sup>·y<sup>-1</sup>) with site-level forest structural measurements to evaluate (1) whether past management efforts impacted forest resistance and resilience during a spruce beetle (<em>Dendroctonus rufipennis</em>) outbreak, and (2) how forest structure and physiography contribute to anomalies in carbon trajectories. Our analyses have several important implications. First, we show that the framework we applied was robust for detecting forest treatment impacts on carbon trajectories, closely tracked changes in site-level biomass, and was supported by multiple evaluation methods converging on similar management effects on resistance and resilience. Second, we found that stand species composition, site productivity, and elevation predicted resistance, but resilience was only related to elevation and aspect. Our analyses demonstrate application of a practical approach for comparing forest treatments and isolating specific site and physiographic factors associated with resistance and resilience to biotic disturbance in a forest system, which can be used by managers to monitor and plan for both outcomes. More broadly, the approach we take here can be applied to many scenarios, which can facilitate integrated management and monitoring efforts.</p>
Distribution. SE Madagascar, the range of this species currently appears to extend in a NE to SW trajectory from Ranomafana National Park to Andringitra National Park. Nonetheless, the relationships between the various taxa of sportive lemurs in SE Madagascar need further study, and the distributional limits of the Small-toothed Sportive Lemur are still poorly known. in Lepilemuridae
Distribution. SE Madagascar, the range of this species currently appears to extend in a NE to SW trajectory from Ranomafana National Park to Andringitra National Park. Nonetheless, the relationships between the various taxa of sportive lemurs in SE Madagascar need further study, and the distributional limits of the Small-toothed Sportive Lemur are still poorly known.
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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.