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1,548 results for “Trajectory”
Trajectories of Stokes drifter deployed during the RV Pelagia Sargassum Cruise PE-455
<p>Trajectories of the 20 Stokes drifters deployed in the Tropical Atlantic Ocean during the RV Pelagia Sargassum Cruise PE-455. The code to analyse these drifters is available at https://github.com/OceanParcels/Sargassum_Drifters</p>
Collapse and Continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event (dataset and R scripts)
<p>The present digital archive is the outcome of the paper: <strong>Lawrence, D., Palmisano, A., and de Gruchy, M.W., 2021. <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244871">Collapse and Continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event</a></strong><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244871">.</a> <em><strong>PLoS ONE</strong></em><strong>,</strong> <strong><em>16</em></strong>(1).</p> <p>The dataset included here provides a collection of <strong>920 </strong>radiocarbon dates and <strong>1070</strong> sites from archaeological surveys. In addition, the digital archive related to this paper provides reproducible analyses in the form of three scripts written in R statistical computing language.</p>
MD Data for Patterns in protein flexibility: a comparison of NMR "ensembles", MD trajectories and crystallographic B-factors
<p>This data set comprises five zipped directories that contain the scripts and intermediate molecular dynamics (MD) results used in (initially as of April 24, 2017, updated with additional directories on December 15, 2020) a soon to be submitted paper, "Patterns in protein flexibility: a comparison of NMR 'ensembles', MD trajectories and crystallographic B-factors" written by the authors of this entry. An earlier version of this paper is available via BioRxiv, DOI: https://doi.org/10.1101/240655.</p> <p>This paper explores patterns in coordinate variance and coordinate uncertainty in MD trajectories and in protein structures derived from NMR and compares coordinate variances/uncertainties with those crystallographic B-factors. The files, MD_data.zip and MD_data2.zip, each unzip to contain input files and scripts for reproducing the MD trajectories used in this paper (using DESMOND): MD_data.zip contains input files/scripts for the MD trajectories used in the preprint; MD_data2.zip contains input files/scripts for trajectories ran following publication of the preprint. The file btab_analysis_scripts.zip contains key scripts for analyzing those trajectories (following file conversion with VMD and superimposition with THESEUS) in MATLAB (this analysis assumes the presence of the FindCore Toolbox, written by David Snyder and available via the MATLAB Central File Exchange, as well as the MATLAB Statistics and Machine Learning Toolbox). And the files, superimposed_MD_trajectories.zip and superimposed_MD_trajectories2.zip, each unzip to yield the trajectories (superimposed using THESEUS and in PDB multimodel file format) analyzed in the soon to be submitted paper: superimposed_MD_trajectories.zip contains trajectories reported in the preprint and superimposed_MD_trajectories2.zip contains the results of subsequent simulations. </p>
An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces: Video Results
<p>A video illustrating the results presented in the paper: <em>"Prédhumeau M., Mancheva L., Dugdale J., and Spalanzani A. 2021. An Agent-Based Model to Predict Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces. In the Proc. of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). IFAAMAS, Online."</em></p> <p> </p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 80 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
A dataset of 3D fly (Drosophila melanogaster) flight trajectories to study the role of neuropeptide degradation in visuo-motor behaviors.
<p>As part of a wide study on the role of neuropeptides in the visuo-motor behavior of Drosophila melanogaster, we exposed three fly strains with impaired neuropeptide degradation function, and corresponding controls, to different visual stimuli.</p> <p>Find further details in the provided README.</p>
Lipid center-of-mass trajectory: long-time dynamics
<p>This file contains the center-of-mass coordinates of the lipid molecules in a Molecular Dynamics simulation of a hydrated lipid bilayer. The simulated system consists of 2033 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) molecules and 57952 water beads (equivalent to 231808 water molecules), using the coarse-grained representation of the MARTINI force field. Note that the mass of a POPC molecule is significantly higher (936 g/mol) in this representation than the mass of a real POPC molecule (760 g/mol). The simulation was performed in an NVT ensemble at T = 320 K.</p> <p>This trajectory stores the long-time dynamics of the lipid centers of masses, sampled at a time step of 18 ps up to a total length of 600 ns. The short-time dynamics are available at http://dx.doi.org/10.5281/zenodo.61742.</p> <p>For a detailed description of the simulation, see the thesis of Sławomir Stachura, available at http://www.theses.fr/2014PA066239</p> <p>Two analyses of this simulation have already been published:</p> <ul> <li>S. Stachura and G.R. Kneller Anomalous lateral diffusion in lipid bilayers observed by molecular dynamics simulations with atomistic and coarse-grained force fields Mol. Sim. 40, 245-250 (2014) (http://dx.doi.org/10.1080/08927022.2013.840902)</li> <li>S. Stachura and G.R. Kneller Probing anomalous diffusion in frequency space J. Chem. Phys. 143, 191103 (2015) (http://dx.doi.org/10.1063/1.4936129)</li> </ul> <p>This work was funded by the French Agence Nationale de la Recherche (Contract No. ANR- 2010-COSI-01-001).</p> <p><strong>Trajectory data</strong></p> <p>The trajectory is stored in an HDF5 file that uses the ActivePapers conventions (http://www.activepapers.org/). Any HDF5-compatible software can be used to read the trajectory data. The ActivePapers software is only required to re-use the included conversion script.</p> <p>The trajectory is contained in the group<br /> /data/POPC_martini_nvt</p> <p>It is stored in H5MD/MOSAIC format. The positions and time labels are contained in the following datasets:<br /> /data/POPC_martini_nvt/particles/universe/position/value<br /> /data/POPC_martini_nvt/particles/universe/position/time</p> <p>For a complete specification of the H5MD/MOSAIC format, see:<br /> http://nongnu.org/h5md/index.html<br /> http://mosaic-data-model.github.io/mosaic-specification/h5md_mosaic_module.html</p> <p><strong>Plots</strong></p> <p>Two 3D plots are provided to give an overview of the lipid motions:<br /> /documentation/all_lipids.pdf<br /> shows all the lipid center-of-mass positions once every 0.3 ps.<br /> /documentation/one_lipids.pdf<br /> shows a single lipid center of mass position every 10 ps.</p>
Lipid center-of-mass trajectory: short-time dynamics
<p>This file contains the center-of-mass coordinates of the lipid molecules in a Molecular Dynamics simulation of a hydrated lipid bilayer. The simulated system consists of 2033 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) molecules and 57952 water beads (equivalent to 231808 water molecules), using the coarse-grained representation of the MARTINI force field. Note that the mass of a POPC molecule is significantly higher (936 g/mol) in this representation than the mass of a real POPC molecule (760 g/mol). The simulation was performed in an NVT ensemble at T = 320 K.</p> <p>This trajectory stores the short-time dynamics of the lipid centers of masses, sampled at a time step of 0.03 ps up to a total length of 300 ps. The long-time dynamics are available at http://dx.doi.org/10.5281/zenodo.61743.</p> <p>For a detailed description of the simulation, see the thesis of Sławomir Stachura, available at http://www.theses.fr/2014PA066239</p> <p>Two analyses of this simulation have already been published:</p> <ul> <li>S. Stachura and G.R. Kneller Anomalous lateral diffusion in lipid bilayers observed by molecular dynamics simulations with atomistic and coarse-grained force fields Mol. Sim. 40, 245-250 (2014) (http://dx.doi.org/10.1080/08927022.2013.840902)</li> <li>S. Stachura and G.R. Kneller Probing anomalous diffusion in frequency space J. Chem. Phys. 143, 191103 (2015) (http://dx.doi.org/10.1063/1.4936129)</li> </ul> <p>This work was funded by the French Agence Nationale de la Recherche (Contract No. ANR- 2010-COSI-01-001).</p> <p><strong>Trajectory data</strong></p> <p>The trajectory is stored in an HDF5 file that uses the ActivePapers conventions (http://www.activepapers.org/). Any HDF5-compatible software can be used to read the trajectory data. The ActivePapers software is only required to re-use the included conversion script.</p> <p>The trajectory is contained in the group<br /> /data/POPC_martini_nvt</p> <p>It is stored in H5MD/MOSAIC format. The positions and time labels are contained in the following datasets:<br /> /data/POPC_martini_nvt/particles/universe/position/value<br /> /data/POPC_martini_nvt/particles/universe/position/time</p> <p>For a complete specification of the H5MD/MOSAIC format, see:<br /> http://nongnu.org/h5md/index.html<br /> http://mosaic-data-model.github.io/mosaic-specification/h5md_mosaic_module.html</p> <p><strong>Plots</strong></p> <p>Two 3D plots are provided to give an overview of the lipid motions:<br /> /documentation/all_lipids.pdf<br /> shows all the lipid center-of-mass positions once every 3 ps.<br /> /documentation/one_lipids.pdf<br /> shows a single lipid center of mass position every 0.3 ps.</p>
Molecular dynamic trajectory of magnesium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_wt.dcd) and corresponding psf file (ATPsynth_mg_wt.psf)</p>
Molecular dynamic trajectory of calcium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_mut.dcd) and corresponding psf file (ATPsynth_ca_mut.psf)</p>
Molecular dynamic trajectory of calcium binding wild type for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for wild type of the beta subunit binding calcium:</p> <p>50ns trajectory (ATPsynth_woh2o_Ca_wt.dcd) and corresponding psf file (ATPsynth_ca_wt.psf)</p> <p> </p>
Molecular dynamic trajectory of magnesium binding T163S mutant for the article "Ca 2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition"
<p>ATP synthase molecular dynamics simulations files for T163S mutants of the beta subunit binding magnesium:</p> <p>50ns trajectory (ATPsynth_woh2o_Mg_mut.dcd) and corresponding psf file (ATPsynth_mg_mut.psf)</p> <p> </p>
Simulated Sea Ice and Snow Thickness along the MOSAIC drift trajectory, from AWI-CM-1 and AWI-CM-3 nudged simulations.
<p>Sea ice thickness and snow (on sea ice) thickness from nudged simulations performed using the coupled climate models AWI-CM-1 (zonal wavenumber truncated at 20) and AWI-CM-3 (T20 truncation; Pithan et al., 2023) with a 1h relaxation time. The model data is collocated to the drift trajectory of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAIC) across the Arctic Ocean, from 01 September 2019 until 31 August 2020. The collocation is done daily, by finding all model grid cells within the area covered by the distributed network of snow and sea ice measuring instruments deployed and maintained during MOSAIC. The sea ice and snow thickness is then spatially averaged for each day. </p><p>Data is provided in three .nc files for each model and variable (m_ice for sea ice thickness, m_snow for snow thickness) representing ensemble members 1 to 3.</p>
Sample MD trajectory
<p>MD trajectory used for training graph convolutional neural networks as coarse grained force fields as described in the following publications:</p> <ul> <li>E. Ricci, G. Giannakopoulos, V. Karkaletsis, D. N. Theodorou, N. Vergadou. 2022. "<em>Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls</em>". In Proceedings of 12th Conference on Artificial Intelligence (SETN). ACM, New York, NY, USA, 7 pages. <a href="https://doi.org/10.1145/3549737.3549793">https://doi.org/10.1145/3549737.3549793</a>. <strong>Open access</strong> <a href="../record/7078577">https://zenodo.org/record/7078577</a></li> <li>Gerakinis, D.-P., Ricci, E., Giannakopoulos, G., Karkaletsis, V., Theodorou, D. N., & Vergadou, N. (2024). <em>Machine Learning-Based Coarse Grained Interaction Potentials for Molecular Systems</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.10501037">https://doi.org/10.5281/zenodo.10501037</a></li> </ul> <p>The compressed archive contains input and output files for a NVT molecular dynamics simulation of a system containing 500 molecules of liquid benzene at 300 K, performed using LAMMPS.</p> <p>The reference code used to train the model, with detailed usage instructions, is available at: <a href="https://github.com/ml-multimem/schnetpack-for-bulk-systems">https://github.com/ml-multimem/schnetpack-for-bulk-systems</a></p>
Tracing evolutionary trajectories in the presence of gene flow in South American temperate lizards (Squamata: Liolaemus kingii group)
<p>Evolutionary processes behind lineage divergence often involve multidimensional differentiation. However, in the context of recent divergences, the signals exhibited by each dimension may not converge. In such scenarios, incomplete lineage sorting, gene flow, and scarce phenotypic differentiation are pervasive. Here, we integrated genomic (RAD loci of 90 individuals), phenotypic (linear and geometric traits of 823 and 411 individuals, respectively), spatial, and climatic data to reconstruct the evolutionary history of a speciation continuum of liolaemid lizards (<em>Liolaemus kingii</em> group). Specifically, we (i) inferred the population structure of the group and contrasted it with the phenotypic variability; (ii) assessed the role of post-divergence gene flow in shaping phylogeographic and phenotypic patterns; and (iii) explored eco-geographic drivers of diversification across time and space. We inferred eight genomic clusters exhibiting leaky genetic borders coincident with geographic transitions. We also found evidence of post-divergence gene flow resulting in transgressive phenotypic evolution in one species. Predicted ancestral niches unveiled suitable areas in southern and eastern Patagonia during glacial and interglacial periods. Our study underscores integrating different data and model-based approaches to determine the underlying causes of diversification, a challenge faced in the study of recently diverged groups. We also highlight <em>Liolaemus</em> as a model system for phylogeographic and broader evolutionary studies.</p>
24-hour HYSPLIT-STILT back-trajectories initialized at Storm Peak Laboratory, Colorado from April 1, 2022 to May 1, 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to AGU Geophysical Research Letters.</p> <p>24-hour HYSPLIT-STILT back trajectories were initialized at Storm Peak Laboratory, Colorado (40.455 degrees North, 106.744 degrees West) every three hours from April 1, 2022 to May 1, 2022 at initial altitudes of 5, 2000, 4000, 6000, 8000, and 10000 meters above ground level. In the model, Storm Peak Laboratory is 2890 meters above sea level (3209.848 meters above sea level in reality). One thousand back trajectories were initialized at each time-altitude pair, then averaged in three dimensions (latitude, longitude, altitude) to generate a single back trajectory for each time-altitude pair.</p> <p>Each file is named using the following convention: "STILT_StormPeakLaboratory_yyyymmdd_HHMMutc.txt", where yyyy is the four-digit year, mm is the two-digit month, dd is the two-digit day, HH is the two-digit hour, and MM is the two-digit minute. The date and time in the filename denote the back-trajectory initialization time (UTC).</p>
YJMob100K: City-Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories
<p>The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior. </p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz). The list of 85 POI categories can be found in POI_datacategories.csv. </p> <p>For details of the dataset, see Data Descriptor: </p> <ul> <li>Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. <em>Scientific Data</em>, <em>11</em>(1), 397. <a href="https://www.nature.com/articles/s41597-024-03237-9" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03237-9</a> </li> </ul> <p> </p> <p> </p> <p><strong>--- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) --- </strong></p> <p>The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space.</p> <p>The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior.</p> <p>There are 2 tasks in the Human Mobility Prediction Challenge.</p> <p>In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz).</p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz).</p> <p>For more details, see https://connection.mit.edu/humob-challenge-2023</p>
GPS trajectories of tourists in Öregrund 2023 from INCULTUM Sweden Pilot
<p>This dataset is part of ongoing work being written about changes in tourists' behaviour in Öregrund.</p>
GRN_MARVEL_ANOMALOUS_TRAJECTORIES
<p>The raw audio-video data was collected from Mgarr, a rural town on the western coast from IP cameras. Data has been manually annotated for anomalous trajectories. Annotation data in the form of a CSV file.</p>
North Sea drifter trajectories 2024
<p>Trajectories of twelve oceanic drifters deployed in the Wadden Sea just north of the town of Moddergat on 25 April 2024.</p> <p>The drifters are so-called <strong>Stokes drifters</strong>, built by <a href="https://metocean.com/products/stokes-drifter/">MetOcean</a> in Canada. They are small, white, floating devices that are designed to follow the water motion in the Wadden Sea. The drifters are equipped with a GPS and a satellite transmitter, so that we can track their position in real time.</p> <p>The data have not been cleaned for outliers. Note that transmission frequencies change during the trajectories (as indicated in the time variable)</p> <p>An interactive visualisation of the drifters can be seen <a href="https://oceanparcels.org/driftermap.html?fn=waddendrifters2024_detailed.json&anim_freq=0.2" target="_blank" rel="noopener">here</a>.</p>
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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.