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1,548 results for “Trajectories”
Molecular dynamics trajectories of pYEEI:SH2 recognition, unbiased, at all-atom resolution.
<div> </div> <p>Set of 772 all-atom trajectories simulated from an unbound (apo) configuration of the human p56 -lck tyrosine kinase SH2 domain with its high-specificity phosphopeptide recognition substrate pYEEI (initial structure based on PDB:<a href="https://www.rcsb.org/structure/1LKK">1LKK</a> ). Approximately 24 trajectories spontaneously reach a bound state with ligand RMSD < 2 Â from the crystal. System building and run details are described in [1].</p> <p>A preliminary version of this dataset have been analyzed and discussed in [1] (approx 200 ns per trajectory were available and used in [1]). </p> <p>The trajectories provided here are extended to ~800 ns each, for a total of ~640 μs sampled time. The full dataset is analyzed in [2] with a SOM-based technique.</p> <div> <h2>Notes</h2> </div> <div> <ul> <li>These are all-atom simulations (with TIP3P water). Water molecules have been stripped off from these files (filtered).</li> <li>Not all trajectories have the same length. Some are cut short due to the distributed computing setup.</li> <li>Frame-to-frame interval is 1 ns.</li> </ul> </div> <h2>Acknowledgments</h2> <p>We thank the volunteers of the GPUGRID.net project for donating computing time.</p> <p> </p> <h2>References</h2> <p>[1] T. Giorgino, I. Buch, and G. De Fabritiis. <a href="https://pubs.acs.org/doi/10.1021/ct300003f">Visualizing the Induced Binding of SH2-Phosphopeptide</a>, J. Chem. Theory Comput. 2012, 8, 4, 1171-1175. doi:10.1021/ct300003f</p> <p>[2] Lara Callea, Camilla Caprai, Laura Bonati, Toni Giorgino, Stefano Motta. Self-Organizing Maps of Unbiased Ligand-Target Binding Pathways and Kinetics. J. Chem. Phys, 2024. https://doi.org/10.1063/5.0225183 </p> <p> </p> <div> </div> <div> <p> </p> </div>
Dataset of MoS2 monolayer from AIMD trajectory
<p><strong>MoS2 MD dataset</strong></p> <p>This directory contains the monolayer MoS2 dataset obtained from an AIMD trajectory run at 750K. This dataset was initially used for the development of a Stillinger-Weber potential. Detailed description of the data can be found in the paper below. </p> <p><br><strong>Units</strong></p> <p>Coordinates: Angstrom<br>Energy: eV<br>Forces: eV/Angstrom</p> <p><br><strong>Reference </strong></p> <p>A force-matching Stillinger-Weber potential for MoS2: Parameterization and Fisher information theory based sensitivity analysis. Wen, M., Shirodkar, S. N., Plecháč, P., Kaxiras, E., Elliott, R. S., & Tadmor, E. B. Journal of Applied Physics, 122(24), 2017.</p>
Population dynamics and socio-ecological trajectories explain the emergence of farming in Late Neolithic South-eastern Norway
<p>The repository contains radiocarbon data and code for the paper "Population dynamics and socio-ecological trajectories explain the emergence of farming in Late Neolithic South-eastern Norway", published in the Journal of Neolithic Archaeology.</p> <p><a href="https://www.jna.uni-kiel.de/index.php/jna/article/view/1562">Population Dynamics and Socio-ecological Trajectories Explain the Emergence of Farming in Late Neolithic Southeast Norway | Journal of Neolithic Archaeology</a>.</p> <p><strong>Abstract</strong></p> <p>This paper examines the emergence of farming in Late Neolithic Southeast Norway. A summed probability distribution of radiocarbon dates is used to infer population dynamics and together with a chronological model using dated samples from post-built houses, cultivation layers and cereal grains, it is argued that the introduction of farming was swift and caused by incoming farmers. It is further explored how a low population during the Middle Neolithic period, cause by shifting environmental conditions, was central to the rapid population shift and economic change seen in the Late Neolithic, from ca. 2200 cal BC. </p>
Fig. 8B in Influence of Dardanelles outflow induced thermal fronts and winds on drifter trajectories in the Aegean Sea
Fig. 8B: Same as Figure 8A but for the period 5-7 September 2008.
Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology
<p>Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>.</p> <p>cnv_segmentation.tsv: CNV segmentation file from Sequenza.</p> <p>cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file.</p> <p>cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file. </p> <p>RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts.</p> <p>snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.</p> <p> </p>
Vehicle trajectories collected from a swarm of drones in a Swiss city
<p>This is an open dataset of naturalistic vehicle trajectories that have been collected by two drones flying over the city of Pully, Switzerland. This detailed dataset includes 30 datapoints per second for each vehicle, covering three types: Car, Bus, and Truck.</p> <p><strong>How are the files organized?</strong></p> <p>The name of the file follows the format: DX_TIMEDAYZ_LOCATION</p> <ul> <li>DX - indicates the drone number, either Drone 1 or Drone 2</li> <li>TIMEDAYZ - indicates whether the dataset is collected during morning (AM), noon (NOON) or afternoon (PM) and then Z for the number of session.</li> <li>LOCATION - indicates the location that each drone was flying. As Drone 1 was only in 1 location, this value changes only for Drone 2.</li> </ul> <p><strong>How are the .csv files organized?</strong></p> <div>For each .csv file the following apply:</div> <div> <ul> <li>each row represents one datapoint</li> <li>the first column includes the unique track_id (per file)</li> <li>the second column includes the type of the specific vehicle</li> <li>the third and fourth column includes the longitude and latitude</li> <li>the fifth column includes the local time in ISO 8601 format</li> </ul> </div>
Initial model and Molecular Dynamics trajectory of WGR domain of PARP2 with ZN ions
<div>Files:</div> <div>1. Initial model of PARP2 WGR domain (based on PDB entry: 6F5B) with two sites potentially capable of binding zinc ions: one formed by residues E97, C98, H160 and another - by residues H106, C109, E138</div> <div>2. PARP2 WGR domain with Zn ions bound after energy minimization.</div> <div>3. 400 ns MD trajectory of PARP2 WGR domain with Zn ions.</div>
Improving Volcanic SO2 Cloud Modeling Through Data Fusion and Trajectory Analysis: A Case Study of 2022 Hunga Tonga Eruption
<p><strong>Dataset Overview</strong>: This dataset comprises approximately 500 clusters of aggregated observational data collected from January 16 to 20 during the ascending (ASC) and descending (DES) periods. We grouped a large number of observation points into these clusters and calculated trajectories from the center of each cluster. The choice of 500 clusters was driven by pragmatic considerations, aiming for a balance between computational feasibility and the level of detail needed for our analysis.</p> <p><strong>Data Unit Description</strong>: The "mass" values in this dataset for each cluster are calculated by multiplying the mass per unit area (<span><span>g/m2</span></span>) of individual data points by the area covered by each point, thus providing the total mass in grams (g). The "heights" are presented in units of kilometers (km), representing the observed top heights of each cluster.</p>
Educational and Career Trajectories in Russia: Open Datasets
<p>The datasets contain data retrieved from the “Jobs in Russia portal”. The datasets contain information about the education, work experience, desired salary and some demographic characteristics of candidates who posted their CVs on the portal between 2015 and 2023. The datasets cover 83 regions of Russia. Dataset 1 contains information on the education and career trajectories (N=6221439), while Dataset 2 contains the information about the general activity of unemployed and job seeking candidates (N=7662089). Each candidate has a unique ID and there are characteristics such as age, education type, region, working experience. The datasets are available as .csv files. </p> <p>Dataset 1 is designed to study educational and career trajectories. It was obtained by aggregating all CVs for each candidate into a single record with a unique id_candidate, then normalizing text information about the candidates' work experience and education, and finally excluding records that do not contain required information. </p> <p>Dataset 2 includes all records for the available period, but only covers general information and the activity metric. It might be useful to study the general portal dynamics and, therefore, the activity on the Russian labor market.</p> <p>For citation, please use the data paper: Valko, D., Vasilevskaia, M., Bunina, M., Kozlova, M., Filippova, A. M., & Rud, D. (2024). Educational and Career Trajectories in Russia: Introducing a New Source and Datasets with a High Granularity. Research Data Journal for the Humanities and Social Sciences. <a href="https://doi.org/10.1163/24523666-bja10046" rel="nofollow">https://doi.org/10.1163/24523666-bja10046</a></p>
FAIRmat Tutorial 7: Molecular Dynamics Trajectories and Workflows in NOMAD
<p>The FAIRmat consortium is committed to extending the NOMAD infrastructure to a wide variety of materials science data. To support soft matter simulations (e.g., atomistic molecular dynamics simulations), a number of challenges arise, primarily due to the volume and variety of data. The FAIRmat team is working to overcome these challenges, and the NOMAD infrastructure is now equipped with new metadata, features, and tools specifically designed to ease the FAIR treatment of trajectory data and workflows. Parsers have been implemented for two of the most popular molecular dynamics codes (Gromacs and Lammps), with plans for quick expansion to additional codes within the next year. The NOMAD Metainfo now describes the system’s hierarchical structure (in terms of bond topology) through the concept of fixed chemical bonds defined within classical force fields. The NOMAD GUI provides a bespoke overview page for molecular dynamics data, which includes tools that ease visualization of the system topology and automatically displays structural, dynamic, and thermodynamic observables that can assist in a fast assessment of system equilibration. Additionally, a native workflow visualizer allows the user to connect individual simulation entries into complex workflows. Finally, the NOMAD Python module facilitates custom trajectory analysis, for instance in a Jupyter notebook, with functions that convert a NOMAD archive entry to an instance of the MDAnalysis data class.</p> <p>This tutorial invites both experienced and completely novice NOMAD users to learn about these new features for molecular dynamics trajectories. A brief introduction to the FAIRmat consortium and the NOMAD infrastructure will be given, followed by guided and interactive tutorials highlighting the various features described above</p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
HYCOM-OceanTrack: Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset
<p>A combined dataset on the Registry of Open Data on AWS of simulated ocean sea surface height, near-surface velocities, and particle trajectories from a global 1/25th degree HYbrid Coordinate Ocean Model (HYCOM) 1-year run.</p>
Simulation CER160_50_POPC_512_NaCl_150mM_310K (rep1) - 10ps trajectory (Part1)
<p><strong>Title publication: </strong><strong><em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em></strong></p> <p><strong><strong>Year publication:</strong></strong><strong> 2018</strong></p> <p><strong>DOI publication: XXXXX</strong></p> <p><strong>Description:</strong> Simulation POPC membrane containing ~8% ceramide (CER160) using charmm36 at 310K.</p> <p>Contains Part 1 (0-500 ns) of the trajectory in <a href="https://zenodo.org/deposit/1219494">https://zenodo.org/deposit/1219494 </a>with saving a frequency 10 ps.</p> <p><strong>MD engine:</strong> Gromacs 2016.2-dev-20170105-4feb0be</p> <p><strong>Force field</strong>: charmm36</p> <p><strong>Temperature</strong>: 310 K</p> <p><strong>Pressure:</strong> 1 bar</p> <p><strong>Simulation time</strong>: 1069 ns</p> <p><strong>Saving frequency</strong>: 10 ps</p> <p><strong>Molecular content</strong>:<br> POPC 512<br> CER160 50<br> SOL 28100<br> NA 75<br> CL 75</p>
Simulation CER160_50_POPC_512_NaCl_150mM_310K (rep1) - 10ps trajectory (Part2)
<p><strong>Title publication: </strong><em>A Ceramide-Regulated Element in the Late Endosomal Protein LAPTM4B Controls Amino Acid Transporter Interaction</em></p> <p><strong><strong>Year publication:</strong></strong><strong> </strong>2018</p> <p><strong>DOI publication: </strong>10.1021/acscentsci.7b00556</p> <p><strong>Description:</strong> Simulation POPC membrane containing ~8% ceramide (CER160) using charmm36 at 310K.</p> <p>Contains Part 2&3 (500-1069 ns) of the trajectory in <a href="https://zenodo.org/deposit/1219494">https://zenodo.org/deposit/1219494 </a>with saving a frequency 10 ps.</p> <p><strong>MD engine:</strong> Gromacs 2016.2-dev-20170105-4feb0be</p> <p><strong>Force field</strong>: charmm36</p> <p><strong>Temperature</strong>: 310 K</p> <p><strong>Pressure:</strong> 1 bar</p> <p><strong>Simulation time</strong>: 1069 ns</p> <p><strong>Saving frequency</strong>: 10 ps</p> <p><strong>Molecular content</strong>:<br> POPC 512<br> CER160 50<br> SOL 28100<br> NA 75<br> CL 75</p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
Lever Up Trajectories for Various Shapes
<p>This dataset contains shape information for objects with different geometries and their trajectories for lever up motion. </p> <p>Shape Related:</p> <ol> <li>STL of objects</li> <li>Top Down image of object</li> </ol> <p>Position related:</p> <ol> <li>Trajectory of objects center of mass</li> <li>Trajectory of lever-up position( Where end effector contacts the object0)</li> </ol>
Final dataset of Trajectory Synopses over AIS kinematic messages in Brest area (ver. 0.8)
<p>Data provided by NARI (Institut de Recherche de l'�cole Navale) contains AIS kinematic messages from vessels sailing in the Atlantic Ocean around the port of Brest, Brittany, France and span a period from 1 October 2015 to 31 March 2016.</p> <p>Raw AIS messages - file: nari_dynamic.csv, 19,035,630 records.</p> <p>After deduplication of original AIS messages, this dataset yielded 18,495,677 point locations (kinematic AIS messages only), which was used as input for creating trajectory synopses.</p> <p>Attribute "MMSI" in the original data is used in processing as the identifier of each vessel (e.g., "244670495").</p>
RDF triples of raw and trajectory synopses over AIS kinematic messages in Brest
<p>The following list of data sets is derived from the final data set of trajectory synopses available at https://zenodo.org/record/2563256 . We have converted the original data into a) ESRI shapefiles and b) using RDF-Gen (https://zenodo.org/record/2556747) into RDF triples w.r.t. the datAcron ontology.</p>
Vehicle trajectory data in simulation network
<p>The project will use vehicle trajectory data generated from simulation platform. The simulation network was built in VISSIM containing a four-leg intersection with left-turn, through, and right-turn movements. The trajectory data were generated based on various traffic demand levels. The data set contains second-by-second vehicle speed and location. Details of the data set are explained as:</p> <p>Column 1 (NO): Number (Number/Index of the vehicle)<br> Column 2 (SimSec): Simulation second (Simulation time [s]) [s]<br> Column 3 (Lane\Link\No): Lane\Link\Number (Unique number of the link or connector)<br> Column 4 (Lane\Index): Lane\Index (Unique number of the lane)<br> Column 5 (Speed): Speed (Speed at the end of the time step) [km/h]<br> Column 6 (Pos): Position (Distance on the link from the beginning of the link or connector) [m]</p>
MD simulation trajectory of cannabidiol in the cannabinoid type 1 receptor
<p>MD simulation trajectory of cannabidiol in the presence of an orthosteric THC and Rimonabant in the CB1R active and inactive conformations respectively. The protein–ligand complexes were inserted in a POPC lipid bilayer and solvated in a TIP3 water box with Na+ and Cl- ions. Simulations were run with NAMD 2.9 software using periodic boundary conditions and Particle Mesh Ewald (PME) for long-range electrostatics. Total production time for the inactive and active receptor conformations were 25 ns and 50 ns respectively.<br> </p>
Supplementary Data for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting
<p>This data repository is provided as a supplement to the paper *Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* written by Håvard Heitlo Holm, Martin Lilleeng Sætra and Peter Jan van Leeuwen. It contains the complete datasets (initial conditions and results of the ensemble simulations) obtained from the experiments presented therein.</p> <p>This data set is generated by, and can be further post-processed and visualized by, the code published as *metno/gpu-ocean: Supplementary Software for Massively Parallel Implicit Equal-Weights Particle Filter for Ocean Drift Trajectory Forecasting* by Håvard Heitlo Holm, Martin Lilleeng Sætra and André Rigland Brodtkorb (DOI 10.5281/zenodo.3458291). </p> <p> </p>
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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.