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72 results for “Trajectory Analysis”
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Micro-CT scans, whole-test meshes, and internal chamber segments of planktonic foraminifera for three-dimensional analysis of inter- and intra-specific variation in ontogenetic growth trajectories
<p> </p> <p>Here, we release tomographic reconstructions of 42 planktonic foraminifera from plankton tows and sediment traps, along with meshes and shrinkwrap meshes the whole tests and internal meshes of segmented chambers. Shrinkwrap meshes are test meshes that have been modified to close all pores and apertures in the test. Additionally, we have provided sample metadata for each specimen and volumetric measurements for the tests and chambers. This dataset was used in a study of ontogenetic growth in planktonic foraminifera and its variation within and among species.</p> <p> The CT-scans and reconstructions were obtained at Naturalis Biodiversity Center in Leiden, the Netherlands with a Zeiss Xradia 520 Versa micro-CT scanner. The meshes and segments were created at Yale University.</p> <ol> <li>Sample_Metadata.csv: Spreadsheet containing information on the sampling localities and dates for all specimens.</li> <li>Scan_data.csv: Spreadsheet containing metadata for all micro-CT scans including current strength, pixel size, voltage, image height, image width, and the number of images taken.</li> <li>Whole_Test_Measurements.csv: Spreadsheet containing measurements of linear dimensions (axis1, axis2, axis 3), total number of chambers, calcite test volume, calcite test surface area, shrinkwrap volumes, and and shrinkwrap surface areas for all specimens.</li> <li>Chamber_Measurements.csv: Spreadsheet containing measurements of individual internal chamber segments, including position from the final chamber (F-chamber), position from the first chamber (Chamber), volume, and surface area.</li> <li>CT_Scan_Stacks.zip: reconstructed micro-CT image stacks (.tif files) for each specimen.</li> <li>Meshes.zip: Meshes of the test calcite, the shrinkwrap, and the internal chamber segments for each specimen (.stl 3D mesh files). Regular test meshes are named with the format “SampleID.stl”, and shrinkwrap meshes are named “SampleID-WRAP.stl”. Chamber meshes are named “SampleID-CH#.stl” and “SampleID-CH#-Wrap.stl”. Chambers are numbered in relation to their position from the final chamber, with “CH1” being the final chamber and “CH2” being the penultimate chamber.</li> </ol> <p>This data is described and analyzed in the manuscript “Three-Dimensional Analysis of Inter- and Intraspecific Variation in Ontogenetic Growth Trajectories of Planktonic Foraminifera” submitted to the journal <em>Marine Micropaleontology.</em></p>
Long-term coastal macrobenthic Community Trajectory Analysis reveals habitat-dependent stability patterns
<p>Long-term monitoring programs are fundamental to detecting changes in ecosystem health and understanding ecological processes. In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential. We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North-East Atlantic, covering a total of 26 sites and 979 taxa. Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones. We used Community Trajectory Analysis, a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems. We compared observed community trajectories to trajectories simulated under a non-directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics. Despite strong differences in the community compositions between sites and habitats, the communities followed non-directional dynamics during the 14 years monitored, which suggested stability at the regional scale. However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics. Results showed a higher variability in community composition in the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability. They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses. Finally, our study confirmed the usefulness of Community Trajectory Analysis to link observed trajectory patterns to fundamental ecological processes.</p>
Long-term coastal macrobenthic Community Trajectory Analysis reveals habitat-dependent stability patterns
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Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Adolescents' mental health and maladaptive behaviors before the Covid-19 pandemic and one-year after: analysis of trajectories over time and associated factors
<p>The database reports data about psychopathological indexes in a sample of adolescent students (N=153) assessed before Covid-19 pandemic (T0, November 2019-January 2020) and one year after (T1, April-May 2021).</p>
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>
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>
ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation
<p><span>ReaxFF simulations, QM calculation input/output files, and Jupyter notebooks for data analysis and visualization.</span></p>
Raw data for: Stable Isotope Trajectory Analysis (SITA): A new approach to quantify and visualize dynamics in stable isotope studies. Sturbois et al., in revision in Ecological Monographs
<p>These data sets are used as ecological applications in Sturbois et al., in revision, Stable Isotope Trajectory Analysis (SITA): A new approach to quantify and visualize dynamics in stable isotope studies. submitted in Ecological Monographs.</p> <p>- DataS1_furseals.Rdata originates from: Kernaléguen, L., Cazelles, B., Arnould, J.P.Y., Richard, P., Guinet, C., Cherel, Y., 2012. Long-Term Species, Sexual and Individual Variations in Foraging Strategies of Fur Seals Revealed by Stable Isotopes in Whiskers. PLoS ONE 7, e32916. https://doi.org/10.1371/journal.pone.0032916</p> <p>- DataS2_Pike.Rdata originates from: Cucherousset, J., Paillisson, J.-M., Roussel, J.-M., 2013. Natal departure timing from spatially varying environments is dependent of individual ontogenetic status. Naturwissenschaften 100, 761–768. https://doi.org/10.1007/s00114-013-1073-y</p> <p>- DataS4_GT1.Rdata and DataS5_GT2.Rdata originate from: Quillien, N., Nordström, M.C., Schaal, G., Bonsdorff, E., Grall, J., 2016. Opportunistic basal resource simplifies food web structure and functioning of a highly dynamic marine environment. Journal of Experimental Marine Biology and Ecology 477, 92–102.</p> <p>- DataS6_Lakes.Rdata originates from: Zhao, T., Villéger, S., Cucherousset, J., 2019. Accounting for intraspecific diversity when examining relationships between non-native species and functional diversity. Oecologia 189, 171–183. https://doi.org/10.1007/s00442-018-4311-3</p> <p>Information about respective sampling strategies and sample preparation are available in these original articles. All use of this data sets must cite original article as well as the SITA article.</p>
Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression.
<p>This file contains vst-normalized gene expression data along with annotations which can be used to reproduce our findings.</p>
Associations Between COVID-19 ARDS Treatment, Clinical Trajectories and Liberation From Mechanical Ventilator - an Analysis of the NorthCARDS Dataset
ClinicalTrials.gov study NCT04729075. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Trans-omics analysis of post-injury thromboinflammation plasma identifies endotypes and trajectories in trauma patients
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Data, analysis scripts, and simulations files for "Direct formation of nitrogen-vacancy centers in nitrogen doped diamond along the trajectories of swift heavy ions"
<p>Measured data and analysis script, as well as, simulated data, and input scripts for our publication "Direct formation of nitrogen-vacancy centers in nitrogen doped diamond along the trajectories of swift heavy ions"</p>
Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis - Dataset
<p><span>Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.</span></p>
UAS Trajectory Model Dynamics at different flight heights: An In-depth Analysis of PPK Georeferencing Results for an Urban Area
<p>In-depth analysis of the PPK georeferencing results when using three different Continuously Operating Reference Station (CORS) stations and one local base station.</p>
Memory trajectories in lonely individuals: An analysis of the Survey of Health, Aging, and Retirement in Europe (SHARE)
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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>
Trajectory Analysis using Monocle3 - Galaxy Training Material
<p>Input datasets for the trajectory analysis tutorial from the case study series.<br> https://training.galaxyproject.org/training-material/topics/transcriptomics/ </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.