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1,548 results for “Trajectory”
1D cell trajectories as studied in "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"
<p>Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. Here, we present trajectories of MDA-MB-231 breast cancer cells and MCF-10A breast epithelial cells. Cells were confined to 1D using fibronectin lanes and exposed to three different treatments, namely the actin polymerisation inhibitor Latrunculin A (LatA), the ROCK inhibitor Y-27632 (Y27) and a control. Each csv file contains a number of 24h long trajectories of cells corresponding to the name of the file. The column names are:</p> <p>`traject_id`: The trajectories are numbered, starting from 0 in each file.</p> <p>`time (h)`: Time in h, starting at 0h for each trajctory and ending at 24h with a temporal resolution of 2min.</p> <p>`x_front`: Position of the cell's front.</p> <p>`x_nucleus`: Position of the cell's nucleus, where x_nucleus=0 for the first time point of the trajectory</p> <p>`x_rear`: Position of the cell's rear.</p> <p>The data was analysed in our study "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference". Further information can be found there. </p>
A Dataset with Synthetic Landing Trajectories for Zurich Airport
<p>The archive contains synthetic datasets in npy format generated using a TimeGAN-based model designed to capture a range of aircraft landing trajectories at Zurich airport across different operational scenarios and environmental conditions. Each dataset consists of multiple groups representing distinct patterns or behaviors in the trajectory data. The trajectories are segmented in clusters, and to some of them a smoothing filter was applied.</p> <p><span>The datasets incorporate a range of variables critical for modeling aircraft landing behaviors. Continuous variables such as longitude, latitude, and altitude exhibit multimodal distributions, capturing different operational phases and conditions within each cluster. The data is stored in an array format with dimensions (number of samples, sequence length, feature dimensions). Here, the number of samples corresponds to the total number of recorded flight trajectories included in the dataset, while the sequence length represents the duration or the number of time steps over which each trajectory is recorded. The feature dimensions denote the various variables (state vector) measured at each time step, consisting of longitude, latitude and altitude. Categorical variables, such as runway identifiers and cluster labels, follow distributions that reflect operational frequencies, with certain clusters or runways being more common under specific conditions. </span></p> <p>The archive contains the following files:</p> <p>- 5clust0.npy, 5clust1.npy, 5clust2.npy, 5clust3.np & 5clust4.npy (5 clusters of landing trajectories separated)<br>- ma_5clust0.npy, ma_5clust1.npy, ma_5clust2.npy, ma_5clust3.np & ma_5clust4.npy (5 clusters of landing trajectories separated, moving average filter applied)<br>- ma_3clust0.npy, ma_3clust1.npy & ma_3clust2.npy (3 clusters of landing trajectories separated, moving average filter applied)<br>- run28_syn.npy & run24_syn.npy (groups of landing trajectories per runway)<br>- ma_run28_syn.npy & ma_run24_syn.npy (groups of landing trajectories per runway, moving average filter applied)<br>- go_around_synthetic.npy (go-around landing trajectories on runway 14)</p>
Data and R-scripts for "Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot" (V2)
<p>Sustainable land system transformations are necessary to avert biodiversity and climate collapse. However, it remains unclear where entry points for transformations exist in complex land systems. Here, we conceptualize land systems along land-use trajectories, which allows us to identify and evaluate leverage points; i.e., entry points on the trajectory where targeted interventions have particular leverage to influence land-use decisions. We apply this framework in the biodiversity hotspot Madagascar. In the Northeast, smallholder agriculture results in a land-use trajectory originating in old-growth forests, spanning forest fragments, and reaching shifting hill rice cultivation and vanilla agroforests. Integrating interdisciplinary empirical data on seven taxa, five ecosystem services, and three measures of agricultural productivity, we assess trade-offs and co-benefits of land-use decisions at three leverage points along the trajectory. These trade-offs and co-benefits differ between leverage points: two leverage points are situated at the conversion of old-growth forests and forest fragments to shifting cultivation and agroforestry, resulting in considerable trade-offs, especially between endemic biodiversity and agricultural productivity. Here, interventions enabling smallholders to conserve forests are necessary. This is urgent since ongoing forest loss threatens to eliminate these leverage points due to path-dependency. The third leverage point allows for the restoration of land under shifting cultivation through vanilla agroforests and offers co-benefits between restoration goals and agricultural productivity. The co-occurring leverage points highlight that conservation and restoration are simultaneously necessary. Methodologically, the framework shows how leverage points can be identified, evaluated, and harnessed for land system transformations under the consideration of path-dependency along trajectories.</p>
Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code
<p>Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)</p>
Trajectory data with sensitivities to cloud microphysical parameters
<p>This netCDF-4 file contains twenty trajectories that are associated with the extratropical cyclone "Vladiana" which occurred between 22-25 September 2016 over the North Atlantic.</p> <p>The trajectories are selected such that ten start their fastest ascent in the south and in the north, respectively. From those ten trajectories, there are five that ascend slowly (slantwise), and five that ascend fast (convective).<br> The data contains sensitivities of rain mass density (QR) to different parameters of cloud microphysical processes (variables starting with 'd'). The sensitivities are computed with algorithmic differentiation via Hieronymus et al. (2022). The trajectories are taken from a simulation by Oertel et al. (2020) using the NWP model COSMO version 5.1, where the online trajectory scheme by Miltenberger et al. (2013) was applied.</p>
Example data set for NG-QTAIM eigenvector-following trajectories: rotary f-NAIBP motor
<p>Example dataset for NG-QTAIM eigenvector following trajectories: the F-NAIBP molecular rotary motor.</p>
The SOSAA Trajectories Dataset
<p>The SOSAA Trajectories Dataset includes the settings, inputs, and outputs of several trajectory runs of the SOSAA model.</p>
VEDSIB: VEhicular trajectory Dataset at Signalised1 Intersection in Bremen Dataset
<p> VEDSIB dataset contains 26,007 trajectories recorded over 32 hours in a signalised intersection in Bremen, Germany.</p> <p>VEDSIB contains:</p> <ul> <li>Date: the date of the collected information</li> <li>Time: Time when the information was collected</li> <li>Direction: at the intersection, which direction the vehicle came from</li> <li>Number: the number of vehicles par minutes</li> <li>Type: The type of vehicles; either a car, a bus or a tram.</li> <li>Temperature in °F: the temperature observed at the time of collecting the information</li> <li>Humidity: humidity level</li> <li>Wind information (speed, Gust): the wind information</li> <li>Pressure, Precipitation, Condition: the general condition of the weather</li> <li>Rainy information: if there is rain or not at the time of collecting the data.</li> </ul>
Air mass trajectory and connectivity data generated with tropolink (Richard et al., 2023)
<p>Archive containing trajectory and connectivity data generated with tropolink for the preparation of the manuscript Richard et al. (2023, <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>), as well as the corresponding specifications (node coordinates, dates and other tropolink options). The archive contains specifications, trajectories and connectivities for the three applications presented in the manuscript:</p><p>- the study of airborne connectivity between areas of production of sugar beet, with starting altitude equal to 250m, 500m and 750m above ground level;</p><p>- the study of airborne connectivity between potyvirus populations;</p><p>- the study of invasion risk of Spodoptera frugiperda in Europe, North Africa and western Asia;</p><p> </p><p>Web application tropolink: https://tropolink.fr/</p><p>Associated gitlab: https://forgemia.inra.fr/tropo-group</p><p>Accompanying wiki: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis</p><p>R code for analyzing tropolink output: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis/Examples</p><p>Richard H., Martinetti D., Lercier D., Fouillat Y., Hadi B., Elkahky M., Ding J., Michel L., Morris C.E., Berthier K., Maupas F., <br>Soubeyrand S. (2023). Computing geographical networks generated by air-mass movement. GeoHealth 7:e2023GH000885. <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>.</p>
Molecular dynamics trajectories for "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations
<p>The molecular dynamics-generated ensemble dataset for cyclo-(cGHHQKLV), used in the manuscript "Reservoir-REMD facilitates kinetic rescue from metastable peptide conformations". The dataset consists of 14 + 6 =20 .dcd files, and one .pdb file for rendering.</p>
ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory Balloon-borne ozonesonde record 2016-2019
<p>The ATLAS Backward Trajectory Dataset for the Palau Atmospheric Observatory (PAO) Balloon-borne ozonesonde record V1.0 provides backward trajectory data in NetCDF format calculated by the transport module of the Lagrangian Chemistry and Transport Model ATLAS (Wohltmann and Rex, 2009; Wohltmann et al., 2010) for coinciding Electrochemical Concentration Cell (ECC) ozonesonde measurements from the PAO located in Koror, Palau (7.3420° N, 134.4722° E), in the Tropical West Pacific (TWP) from 2016- October 2019 (Müller 2020, Müller et al. 2023). 30-days backward trajectories with a time step of 10 minutes were initialized at the time of an ozonesonde measurement at the PAO for every tenth ozonesonde reading within a profile and for a total number of 138 soundings (= days) and between 0 and 20 km altitude.<br> The model was driven by 3D wind fields, temperatures and diabatic heating rates from the ECMWF ERA5 reanalysis dataset (1.125° x 1.125°) with a 3 hour temporal resolution (compare Hoffmann et al., 2019). The model uses a hybrid vertical coordinate (zeta) which gradually transforms from "pressure" at the surface to "potential temperature" in the stratosphere (see Wohltmann and Rex, 2009). The corresponding vertical velocities change from vertical winds in pressure coordinates to diabatic heating rates.</p> <p> </p> <p>The Palau ECC record is currently being continued, will be available in the SHADOZ database in SHADOZ standard format in the near future, and can for now be found here: https://zenodo.org/record/6920648.</p> <p><strong>Please email katrin.mueller@awi.de and let us know what your intended purpose for the use of the data is. You will then receive updates if an improved version becomes available.</strong></p>
NAMD Trajectory of a Palmitoyl Sphingomyelin Bilayer
<p>All-atom PSM bilayer simulated in NPT ensemble with NAMD and the CHARMM36 force field from Doktorova et al. 2020 J. Phys. Chem. B. article (DOI 10.1021/acs.jpcb.0c03389). The trajectory represents the last 117 ns used for analysis where the area per lipid is equilibrated (the file has 5874 frames output every 20 ps). The bilayer has 200 lipids total (100 lipids per leaflet) and is hydrated with 45 waters/lipid. The simulation was done at 55C (328.15K) and the trajectory is centered on the bilayer midplane.</p>
NAMD Trajectory of a Tetraoleoyl Cardiolipin Bilayer
<p>All-atom TOCL (tetraoleoyl cardiolipin) bilayer simulated in NPT ensemble with NAMD and the CHARMM36 force field from Doktorova et al. 2017 Phys. Chem. Chem. Phys. article (DOI 10.1039/c7cp01921a). The trajectory represents the last 155 ns used for analysis where the area per lipid is equilibrated (the file has 7752 frames output every 20 ps). The bilayer has 100 lipids total (50 lipids per leaflet) and is hydrated with 60 waters/lipid and 140mM NaCl. The simulation was done at 30C (303.15K) and the trajectory is centered on the bilayer midplane.</p>
NAMD Trajectory of a DLiPC (di18:2PC) bilayer
<p>All-atom DLiPC (1,2-dilinoleoyl-sn-glycero-3-phosphocholine) bilayer simulated in NPT ensemble with NAMD and the CHARMM36 force field from Doktorova et al. 2017 Phys. Chem. Chem. Phys. article (DOI 10.1039/c7cp01921a). The trajectory represents the last 245 ns used for analysis where the area per lipid is equilibrated (the file has 12250 frames output every 20 ps). The bilayer has 200 lipids total (100 lipids per leaflet) and is hydrated with 45 waters/lipid. The simulation was done at 25C (298.15K) and the trajectory is centered on the bilayer midplane.</p> <p> </p> <p> </p>
Mesofauna community influences litter chemical trajectories during early-stage litter decay in compost
Decomposition of organic material is a fundamental ecosystem process, the rate of which is moderated by both litter chemistry and decomposer communities. Because litter chemistry changes throughout decomposition, we would expect the decomposer food web to interact with these changes in their basal resource to alter the trajectories of chemical changes during decay. To investigate how decomposer mesofauna influence patterns of litter chemical change throughout early stages of decay, we tracked mass loss, macro- and micronutrient elements, and fiber chemistry dynamics in Arizona sycamore (Platanus wrightii) leaves decomposed under optimal decay conditions in a biotically diverse compost pile. By using litterbags of two different mesh sizes to manipulate the mesofauna gaining access to the litter, we record how the complexity of the soil mesofauna community changes the trajectory of litter chemistry.
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
BIDS wildtype data selection from "Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories"
<p>Data package selecting wildtype animals from the “Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories” article, formatted corresponding to the Brain Imaging Data Structure. The relevant publication can be found via DOI <a href="https://doi.org/10.1093/cercor/bhy046">10.1093/cercor/bhy046</a> .</p>
Perception Sensor Dataset For Bioinspired Landing Trajectories Of An Ornithopter Robot
<p>The dataset contains the measurements captured by several onboard sensors during the landing maneuvers of an ornithopter robot. Each dataset contains a ROS bag file with the sensor measurements, a file with the bioinspired trajectory, a file with the events generated by the simulated event-based sensor, and a README file with the instructions to use the dataset.</p> <p>The bioinspired landing trajectories are computed using Tau Theory. Each landing trajectory test was performed in a simulated scenario. The object models of each scene can be found in the /model/meshes folder of each scene. There are two testing scenes: (i) a warehouse and (ii) a refinery. The file object_pose.csv includes the position and orientation of each object in the scene. The sensor measurements were saved in rosbag file that contains a topic for each sensor measurement. The dataset includes information from the following simulated sensors:</p> <ul> <li>Velodyne HDL-32E</li> <li>Sonar sensor with a range of 20 m</li> <li>IMU</li> <li>Frame based monocular camera</li> <li>Event camera</li> </ul>
Molecular dynamics trajectories for SARS-CoV-2 Mpro with 7 HIV inhibitors
<p>Raw trajectory data (GROMACS format) of all atom molecular dynamics simulation of COVID-19 related SARS-CoV-2 dimeric main protease (based on PDB 6LU7) with 7 kinds of HIV inhibitors (darunavir, indinavir, lopinavir, nelfinavir, ritonavir, saquinavir, and tipranavir) were calculated on massively parallel supercomputer HOKUSAI Big Waterfall at RIKEN ISC, and a special-purpose computer, MDGRAPE-4A, at RIKEN BDR, JAPAN. For each ligand, 200ns length 28 trajectories were calculated. Some of these trajectories were calculated further longer. We can observe formation of encounter complex and investigate potential binding sites on the surface of the dimeric protease. We hope that these raw data are valuable for further drug repurposing/development research targeting the SARS-CoV-2 main protease. We will submit analysis of these data to refereed journal.</p> <p>Molecular dynamics simulations were performed under NVT at 310K, with the time step 2.5fs. The starting structure was prepared based on PDB 6LU7, with amber14sb force field in about 10nm cubic box with periodic boundary conditions. The ligands were initially placed apart from the active sites of the dimeric main protease.</p> <p>We have also already deposited 10 microseconds trajectories of the dimeric protease without ligand (with amber99sb-ildn force field) in the repository https://data.mendeley.com/datasets/vpps4vhryg/1 (DOI:10.17632/vpps4vhryg.1).</p> <p>Files:</p> <ul> <li><strong><em>LIG</em></strong>_28traj200ns_every200ps.zip (28trajectories for each ligand) <ul> <li>traj200ns_every200ps/<strong><em>LIG</em></strong>/<strong><em>a</em></strong>/traj200ns_every200ps_<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>.xtc <ul> <li>(trajectory in GROMACS XTC)</li> </ul> </li> <li>traj200ns_every200ps/<strong><em>LIG</em></strong>/<strong><em>a</em></strong>/conf.gro <ul> <li>(initial condition in GROMACS GRO)</li> </ul> </li> <li>traj200ns_every200ps/<em><strong>LIG</strong></em>/topology/ <ul> <li>(contains topology files)</li> </ul> </li> <li>traj200ns_every200ps/<strong><em>LIG</em></strong>/mdp/ <ul> <li>(contains run paramter files)</li> </ul> </li> </ul> </li> <li>ZZZ_20traj1us_every200ps.zip (20trajectories extended to 1microsecond) <ul> <li>traj1us_every200ps/traj1us_every200ps_<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>.xtc <ul> <li>DAR-C-06, DAR-D-07</li> <li>IND-C-05, IND-C-06, IND-D-06</li> <li>LOP-A-02, LOP-D-03</li> <li>NEL-B-01, NEL-C-07, NEL-D-02</li> <li>RIT-B-07, RIT-C-07</li> <li>SAQ-B-01, SAQ-C-04, SAQ-D-03</li> <li>TPR-A-07, TPR-B-04, TPR-B-06, TPR-C-05, TPR-D-02</li> </ul> </li> </ul> </li> <li>ZZZ_3traj6us_every1ns.zip (3trajectories extended to 6microseconds or more) <ul> <li>traj6us_every1ns/traj6us_every1ns_<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>.xtc <ul> <li>IND-D-06, NEL-B-01, TPR-B-04</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>ZZZ_LigandBindingPosePDBs.zip (pickup 3 snapshots for each ligand) <ul> <li>LigandBindingPosePDBs/<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>_frame.pdb</li> </ul> </li> </ul> <p> </p> <ul> <li>movies_overlooking_28traj200ns.zip (7x2movies) <ul> <li>movies_28traj200ns/movie_overlooking_<strong><em>LIG</em></strong>_28traj200ns-viewA.mp4 <ul> <li>inspecting 28traj at once</li> </ul> </li> <li>movies_28traj200ns/movie_overlooking_<strong><em>LIG</em></strong>_28traj200ns-viewB.mp4 <ul> <li>from the opposite angle</li> </ul> </li> </ul> </li> <li>movies_1us.zip (17movies) <ul> <li>movies_1us/movie_<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>_1us.mp4</li> </ul> </li> <li>movies_6us.zip (3movies) <ul> <li>movies_6us/movie_<em><strong>LIG</strong>-<strong>a</strong>-<strong>n</strong></em>_6us.mp4</li> </ul> </li> </ul> <p> where</p> <p> <em><strong>LIG</strong></em>={DAR,IND,LOP,NEL,RIT,SAQ,TPR}<br> DAR:darunavir<br> IND:indinavir<br> NEL:nelfinavir<br> RIT:ritonavir<br> SAQ:saquinavir<br> TPR:tipranavir<br> <em><strong>a</strong></em>={A,B,C,D}<br> <em><strong>n</strong></em>={01,02,03,04,05,06,07}</p> <p> </p> <ul> <li>ZZZz_3traj1us_every200ps_unbinding.zip (3trajectories extended to 1microsecond exhibiting unbinding)</li> <li>ZZZz_56traj200ns_every200ps_negative_control.zip (56trajectories as a negative control)</li> <li>ZZZz_LigandBindingPosePDBsRevised.zip (pickuped 3 snapshots for each ligand)</li> </ul> <p> </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.