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1,548 results for “Trajectories”
Dietary plasticity linked to divergent growth trajectories in a critically endangered sea turtle
<p>Foraging habitat selection and diet quality are key factors that influence individual fitness and metapopulation dynamics through effects on demographic rates. There is growing evidence that sea turtles exhibit regional differences in somatic growth linked to alternative dispersal patterns during the oceanic life stage. Yet, the role of habitat quality and diet in shaping somatic growth rates is poorly understood. Here, we evaluate whether diet variation is linked to regional growth variation in hawksbill sea turtles (Eretmochelys imbricata), which grow significantly slower in Texas versus Florida (USA), through novel integrations of skeletal growth, gastrointestinal content (GI), and bulk tissue and amino acid (AA)-specific stable nitrogen (δ15N) and carbon (δ13C) isotope analyses. We also used AA δ15N ΣV values (heterotrophic bacterial re-synthesis index) and δ13C essential AA (δ13CEAA) fingerprinting to test assumptions about the energy sources fueling hawksbill food webs regionally. GI content analyses, framed within a global synthesis of hawksbill dietary plasticity, revealed that relatively fast-growing hawksbills stranded in Florida conformed with assumptions of extensive spongivory for this species. In contrast, relatively slow-growing hawksbills stranded in Texas consumed considerable amounts of non-sponge invertebrate prey and appear to forage higher in the food web as indicated by isotopic niche metrics and higher AA δ15N-based trophic position estimates internally indexed to baseline N variation. However, regional differences in estimated trophic position may also be driven by unique isotope dynamics of sponge food webs. AA δ15N ΣV values and δ13CEAA fingerprinting indicated minimal bacterial resynthesis of organic matter (ΣV < 2) and that eukaryotic microalgae were the primary energy source supporting hawksbill food webs. These findings run contrary to assumptions that hawksbill diets predominantly comprise high microbial abundance sponges expected to primarily derive energy from bacterial symbionts. Our findings suggest alternative foraging patterns could underlie regional variation in hawksbill growth rates, as divergence from conventional sponge prey might correspond with increased energy expenditure and reduced foraging success or diet quality. As a result, differential dispersal patterns may infer substantial individual and population fitness costs and represent a previously unrecognized challenge to the persistence and recovery of this critically endangered species.</p>
Vehicle trajectory and pavement behavior data
<p>The dataset includes three documents.</p> <p><strong>HDV_data_NGSIM_I_80.xlsx</strong></p> <p>The vehicle trajectory data from Next Generation SIMulation (NGSIM) dataset was collected on eastbound I-80 in the San Francisco Bay area, in Emeryville, CA, on April 13, 2005, from 4:03:56 pm to 4:08:56 pm. Including vehicle id, frame id, the total count of frames of each vehicle, global time, local position, global position, vehicle length, vehicle width, vehicle class, speed, acceleration, lane id, preceding vehicle id, following vehicle id, space headway, time headway, and time.</p> <p><strong>CAV_data_CARLA_SUMO.xml</strong></p> <p>The simulated CAV trajectory data with CARLA and SUMO, including vehicle id, position, angle, type, speed, lane id, and slope of each frame.</p> <p><strong>LTPP_data.csv</strong></p> <p>The table including 21 columns is calculated from the Long-Term Pavement Performance (LTPP) database.</p> <ul> <li>IRI The IRI value measured when age was 0. (m/km)</li> <li>Cr_Gator Area of alligator cracking in square meters. (m^2)</li> <li>Cr_Lwp Length of longitudinal cracks within the defined wheel paths in meters. (m)</li> <li>Cr_Lnwp Length of longitudinal cracks not in the defined wheel paths in meters. (m)</li> <li>Pt_A Area of patches in square meters. (m^2)</li> <li>Pt_N Number of patches in square meters. (m^2)</li> <li>Cr_Wp Length of wheelpath cracks in meters. (m)</li> <li>Cr_Gt183 Total length of transverse cracks greater than 1.83. (m)</li> <li>Rt The depth of rutting in millimeters. (mm)</li> <li>Fr Friction number between the vehicle wheel tire and the pavement</li> <li>IRI_0 The IRI value measured when age was 0. (m/km)</li> <li>Tk_Sb Layer thickness measurement for surface coarse and binder course. (in)</li> <li>Md_s Average backcalculated elastic modulus of the surface layer.(psi)</li> <li>Hydr Average measured hydraulic conductivity of the specimen. (cm/sec)</li> <li>Prcp Average monthly precipitation in millimeters. (mm)</li> <li>Fz Average freeze index. (℃/day)</li> <li>Esal Annual average ESAL (kESAL)</li> <li>Esal_q quadratic form of Kesal (kESAL^2)</li> <li>Age Time duration between new construction to roughness survey date. (year)</li> <li>Gr Mean specific gravity of asphalt cement</li> <li>Pt_Ca Coarse aggregate amount percent by total weight of aggregate in percentage. (%) </li> </ul>
trajectory 61 ice nucleus in supercooled water tip4p/ice
<p>Gromacs MD trajectories of the evolution of ice nuclei in supercooled water with the tip4p/ice model. Also included center of mass of the nucleus (length units in nm) in long range corrections to the pressure (in bar).</p>
trajectory 53 ice nucleus in supercooled water tip4p/ice
<p>Gromacs MD trajectories of the evolution of ice nuclei in supercooled water with the tip4p/ice model. Also included center of mass of the nucleus (length units in nm) in long range corrections to the pressure (in bar). </p>
trajectory 65 ice nucleus in supercooled water tip4p/ice
<p>Gromacs MD trajectories of the evolution of ice nuclei in supercooled water with the tip4p/ice model. Also included center of mass of the nucleus (length units in nm) in long range corrections to the pressure (in bar). <br> </p>
trajectory 67 ice nucleus in supercooled water tip4p/ice
<p>Gromacs MD trajectories of the evolution of ice nuclei in supercooled water with the tip4p/ice model. Also included center of mass of the nucleus (length units in nm) in long range corrections to the pressure (in bar).</p>
trajectory 72 ice nucleus in supercooled water tip4p/ice
<p>Gromacs MD trajectories of the evolution of ice nuclei in supercooled water with the tip4p/ice model. Also included center of mass of the nucleus (length units in nm) in long range corrections to the pressure (in bar). <br> </p>
Molecular dynamics trajectories of MTDH-SND1 protein-protein interaction inhibitors
<p>This includes the prmtop and MD trajectories (production stage) and Amber outfiles of L1-L12, C26-A6/2, and MTDH peptide, GaMD trajectories of the protein, and the umbrella sampling trajectories of L5 and C26-A6/2. To reduce the file size, the trajectory files had been compressed using “xz” algorithm, and frames were saved by one frame per 1ns. However, for reference, one additional larger traj file with higher saving frequency for L5 was also uploaded. The input files and input coordinate files were uploaded as a separate attachment at http://pubs.acs.org.</p>
Rümlang3 Trajectory
<p>Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation"</p> <p> </p> <p>Abstract:</p> <p>We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.</p>
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Trajectories of simulated lipid membranes interacting with antimicrobial peptidomimetic AMC-109 and simulation setup files
<p>Trajectories of simulated lipid membranes with antimicrobial peptidomimetic AMC-109.<br> Trajectories are in GROMACS format ".xtc", the frame rate is 10 ns, and water and NaCl ions were omitted from the trajectory deposit to reduce the size to acceptable limits.</p> <p>The molecules are described using MARTINI 3 coarse grained force field.<br> This repository contains a set of simulations with varying ratio of POPC:POPG lipids from<br> 100% POPC, 0% POPG (denoted as "pg000p..") to<br> 0% POPC, 100% POPG (denoted as "pg100p..").<br> Ratios between these two extremes go in steps of 10%,<br> e.g. "pg060p.." denotes 40% POPC, 60% POPG.</p> <p> </p> <p>The files with ".out" or ".xvg" suffix are properties analyzed from the simulation. Namely, they are:<br> - "thickness" : thickness of the membrane<br> - "apl" : area per lipid of the lipid membrane<br> - "numcont" : number of contacts between the peptidomimetic AMC-109 and the lipids<br> - "ordPars" : order parameters of the end tail segments of the sn-1 tail of POPC lipids.</p> <p>More details about how these properties were generated can be found in the attached scripts (".sh" files) in this repository.</p> <p>Sample topology files in GROMACS format (binary ".tpr" and ASCII ".itp") are provided to complete the description of the molecular topologies used to generate the presented simulations.</p> <p> </p> <p>This repository was created as a Supporting Information to a scientific paper at Nature Communications,</p> <p>Lateral membrane organization as target of an antimicrobial peptidomimetic compound, 2023.</p>
Example data and scripts for: Processing IMU signals to recreate sacral trajectory during treadmill walking
<p>Example IMU data and scripts for reconstructing the trajectory of a sacral IMU and validation with motion capture data. Also contains example code of gait event detection and synchronization.</p>
Molecular Dynamics Trajectory of CsPbI3 produced using ACE MLFF
<p>Molecular dynamics trajectories of CsPbI3, with 2 fs timestep between computed frames, frames are recorded every 200 fs. Under each folder, there are three npz files (each contains 100 ps of trajectory) and one initial configuration LAMMPS data file. The npz files can be read with 'numpy.load'. There are three entries for each npz object, 'positions' for the atomic positions, 'cells' for the lattice dimensions, and 'numbers' for the atomic number of the species. </p>
Molecular Dynamics Trajectory of CH3NH3PbBr3 produced using VASP MLFF
<p>Molecular dynamics trajectories of CH3NH3PbBr3, with 1 fs timestep between computed frames, frames are recorded every 50 fs. </p>
Simulated trajectories of furylfulgide ring opening
<p><strong>Description</strong></p> <p>This dataset contains the results of nonadiabatic molecular dynamics (NAMD) simulations of the photoinduced ring-opening reaction of a prototypical furylfulgide. The nuclear wavepacket is represented by an ensemble of 60 trajectories, which are numbered 0000, 0001, ..., 0059. Each trajectory is formatted as a standard XYZ file, and it can be viewed with molecular editing software such as Jmol, GDIS, or VMD. The atomic coordinates are given in units of angstrom. The frames are written every time step (at intervals of 0.5 fs). Each trajectory lasts 1 ps. </p> <p>Each trajectory is accompanied by a CSV file which contains information on the state energies during given trajectory. The first line is a header: "t(fs),E0(Eh),E1(Eh),Ecur(Eh),Etot(Eh)". The subsequent lines give the time <em>t</em> (in units of fs), the energy of the S<sub>0</sub> state at time <em>t</em> (in units of hartree), the energy of the S<sub>1</sub> state at time <em>t</em>, the energy of the current state at time <em>t</em>, and the total energy at time <em>t</em>. (Total energy is not perfectly conserved.)</p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the NCN grant Miniatura no. 2022/06/X/ST4/00523, and by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847413.</p>
Trajectory Based RFI Subtraction and Calibration for Radio Interferometry
<p>Data and python notebooks for creating all the figures and tables in the paper.</p>
Data from: Critical role of insertion preference for invasion trajectory of transposons
<p>It is unclear how mobile DNA sequences (transposable elements, hereafter TEs) invade eukaryotic genomes and reach stable copy numbers, as transposition can decrease host fitness. This challenge is particularly stark early in the invasion of a TE family at which point hosts may lack the specialized machinery to repress the spread of these TEs. One possibility (in addition to the evolution of host regulation of TEs), is that TE families may evolve to preferentially insert into chromosomal regions that are less likely to impact host fitness. This may allow the mean TE copy number to grow while minimizing the risk for host population extinction. To test this, we constructed simulations to explore how the transposition probability and insertion preference of a TE family influence the evolution of mean TE copy number and host population size, allowing for extinction. We find that the effect of a TE family's insertion preference depends on a host's ability to regulate this TE family. Without host repression, a neutral insertion preference increases the frequency of and decreases the time to population extinction. With host repression, a preference for neutral insertions minimizes the cumulative deleterious load, increases population fitness, and, ultimately, avoids triggering an extinction vortex.</p>
Memory kernel extraction and mean first-passage time for fast-folding proteins (Q - trajectories)
<p>Fraction of native contacts reaction coordinate (Q) trajectories for the 8 proteins that appear in the Dalton et. al. PNAS 2023. For the original all-atom data from which the Q(t) were calculated, contact the group of David E. Shaw at Shaw Research (see the paper - K. Lindorff-Larsen, S. Piana, R. O. Dror, D. E. Shaw, How fast-folding proteins fold. Science 334, 517–520 (2011))</p> <p>Data contains:<br> - Q(t) trajectories for 8 proteins <br> - Corresponding free energy profiles<br> <br> Example analysis codes (written in C++) are included for:<br> - Free energy calculation<br> - Mean first-passage times<br> - Velocity-velocity correlation function and position-force correlation function calculations, needed for memory kernel extraction<br> - Memory kernel extraction (see Ayaz et. al. PNAS 2021)</p>
Lagrangian trajectories tracking the origin and fate of a hydrothermal plume observed in the southeastern Pacific sector of the Southern Ocean
<p>A dataset of Lagrangian trajectories which were used to estimate the origin and fate of a hydrothermal plume in the southeastern Pacific sector of the Southern Ocean observed during the DY111 CUSTARD cruise. All variables have long names and units. These files have been used for the analysis in Birchill and Baker et al. ‘Pathways and timescales of Southern Ocean hydrothermal iron and manganese transport’ with further information about the methodology available in the paper. There are five forward-tracking experiments from suspected vent sites (CUSTARD_NEPR.nc, CUSTARD_SEPR.nc, CUSTARD_PAR.nc, CUSTARD_WCR.nc and CUSTARD_ECR.nc) included in the paper and one forward-tracking experiment that was not included in the paper (CUSTARD_EM.nc) as no trajectories crossed our sampling site. There was one backward-tracking experiment undertaken from the Ocean Observatories Initiative (OOI) station at which the hydrothermal signal was observed (CUSTARD_OOI_backward.nc).</p>
MD trajectories for CyCsoSCA RuBP binding
<p>This data contains aligned MD trajectories used for the final analysis of conformational sampling differences between Cyanobium CsoSCA in the presence and absence of the ligand RuBP.</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.