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1,548 results for “Trajectory”
Wadden Sea drifter trajectories 2023
<p>Trajectories of twentyfour oceanic drifters deployed in the Wadden Sea just east of the island of Texel on 14 November 2023.</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=waddendrifters2023_detailed.json&anim_freq=0.2" target="_blank" rel="noopener">here</a>.</p>
REMD trajectory of VPAA assemblies at 300K 1microsconds
<p>V-shaped polyaromatic amphiphiles (VPAA) are aromatic micelles, amphiphilic molecules with anthracene panels aligned in a V-shape.</p> <p>Microseconds molecular dynamics simulations show that VPAAs spontaneously self-assemble and disassemble in water, and at equilibrium, with size distributions of 5–7 and 4–6 for VPAA1 and VPAA2 at equilibrium, respectively.</p> <p>This dataset is a trajectory for investigating the nature of the equilibrium distribution of VPAA assemblies. All-atom simulations (~60,000 atoms) of aqueous systems containing 27 molecules of two types of VPAA were performed. The two VPAAs are designated VPAA1 and VPAA2 and differ only in their hydrophilic groups (See attached figure(a)).</p> <p>To obtain equilibrium samplings, the trajectory was subjected to equilibrium sampling at 1 atm for 1 microsecond by replica-exchange MD. Snapshots were recorded every 100 ps and non-aromatic carbon atoms were striped. The molecules were divided into assemblies, where molecules are defined as belonging to the same assembly if the nearest-neighbor distance of their aromatic carbon atoms is less than 5 Å. Note that due to the nature of the replica exchange method, the time series of the data has no physical meaning. Data only includes the last 900 ns processed.</p> <p>The detailed simulation settings are as follows: The force fields used were gaff and TIP3P, and some corrections were made using quantum mechanical calculations. First, energy minimizations of the systems were performed in 1,000 step. Then, the systems were heated up from 0 K to 300 K in 2 ns, and equilibrated for 2 ns at 300 K in NVT ensemble. NPT simulations for 10 ns at 300 K then followed to determine the simulation box sizes. After equilibrations, the box sizes were nearly constants and cubic, and they were determined to be 82.01 Å per side for VPAA1 and 82.07 Å per side for VPAA2, respctively. Finally, we heated up the systems to 1,000 K and then cooled down them to 300 K in NVT ensemble (equilibration at 300 K for 2 ns, heating up from 0 K to 300 K in 2 ns, equilibration at 1000 K for 10 ns, cooling down from 1000 K to 300 K in 2 ns, and equilibration at 300K for 2 ns) to randomize the solute positions. Calculations were performed with AMBER14/16.</p> <p>We set the maximum temperature of REMD to 380 K at which sufficient exchange of molecules between assemblies was observed. The initial structures of each replicas were prepared in the same manner as for the conventional MD simulations. The number of the replicas was 40 each for VPAA1 and VPAA2, and the target temperatures were set to realize enough exchange between each replica as 300.00, 301.70, 303.41, 305.12, 306.85, 308.60, 310.37, 312.16, 313.97, 315.80, 317.65, 319.52, 321.41, 323.32, 325.25, 327.20, 329.17, 331.16, 333.17, 335.20, 337.25, 339.32, 341.41, 343.52, 345.65, 347.80, 349.97, 352.16, 354.37, 356.60, 358.85, 361.12, 363.41, 365.72, 368.05, 370.40, 372.77, 375.16, 377.57, and 380.00 K. The exchange attempts were performed at every 2 ps. Equilibrium simulations were performed for 1 µs in NPT ensemble. The average exchange probability was 20 %.</p>
Global Mean Sea Level, Trajectory and Extrapolation
<p>Global Mean Sea Level, Trajectory and Extrapolation</p> <p>This file contains Global Mean Sea Level (GMSL) variations along, data for the quadratic fit (trajectory) to the GMSL variations, and an extrapolation of this trajectory to 2050.</p> <p>Column 1 provides the calendar year plus the decimal fraction of the current year. The GMSL variations(column 2) are computed at the NASA Goddard Space Flight Center under the auspices of the NASA Sea Level Change program. All units for sea level are in centimeters The GMSL was generated using the NASA-SSH Simple Gridded Sea Surface Height from Standardized Reference Missions Version 1: https://podaac.jpl.nasa.gov/dataset/NASA_SSH_REF_SIMPLE_GRID_V1. It combines Sea Surface Heights from the TOPEX/Poseidon, Jason-1, OSTM/Jason-2, HDR Jason-3, and Sentinel-6 Michael Freilich missions.</p> <p>In addition, the rate and acceleration are estimated from full record of GMSL relative to the midpoint of the record and then used to generate a quadratic fit to the data. This quadratic fit is provided in column 3. The rate associated with this quadratic fit at any time in the record is also provided (column 4). </p> <p>The parameters estimated from the quadratic fit are also used to generated an extrapolated time series out to 2050 (column 5). These are provided at yearly intervals. This is not a projection and is only considered an extrapolation of the current trajectory of GMSL variations. This also differs from Nerem et al. (2022) and Sweet et al. (2022) as additional signals are not removed from GMSL prior to estimating the rate and acceleration parameters. The yearly rate associated with this extrapolation is also provided (column 6).<br><br>If you use these data please cite:<br>Willis, J.K., Hamlington, B.D., and Fournier, S., Global Mean Sea Level Time Series, Trajectory and Extrapolation. Dataset access [YYYY-MM-DD] at 10.5281/zenodo.7702314.</p> <p>References:</p> <p>Nerem, R. S., Frederikse, T., & Hamlington, B. D. (2022). Extrapolating Empirical Models of Satellite‐Observed Global Mean Sea Level to Estimate Future Sea Level Change. <em>Earth's Future</em>, <em>10</em>(4), e2021EF002290.</p> <p>Sweet, W. V., Hamlington, B. D., Kopp, R. E., Weaver, C. P., Barnard, P. L., Bekaert, D., ... & Zuzak, C. (2022). <em>Global and regional sea level rise scenarios for the United States: updated mean projections and extreme water level probabilities along US coastlines</em>. Interagency Technical Report.</p>
Lagrangian Sequestration Efficiency Trajectories and Extracted Particle Metrics – 2000m Y1 & Y2
<p>A dataset of Lagrangian trajectories used to estimate North Atlantic sequestration efficiency and extracted metrics for the re-entrained and sequestered particles. All variables have long names and units. These files have been used for the analysis in Baker et al. ‘Biological carbon pump sequestration efficiency in the North Atlantic: a leaky or a long-term sink?’ with further information about the methodology available in the paper. Due to the size of the datasets, each DOI only contains two files. This dataset contains the 2000m particles releases for the years 1996 (Y1) and 1997 (Y2).</p>
Particle trajectories - Freilich et al. "Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean"
<p>The files provided here are the offline particle trajectories analyzed in Freilich, Flierl, and Mahadevan “Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean”</p> <p>Both files are sqlite databases containing information about the same particle trajectories which are identified by the variable “iD”</p> <p> </p> <p>ini_day135_z115_forward_biology.db contains nutrient concentration on particle trajectories. A different biological rate lambda is used for each variable denoted NX where X is 0-13. The rates are: 0.015,0.075,0.15,0.3,0.75,1.5,3,10,15,20,50,75,100,120</p> <p>The variable DOY is the model day. </p> <p> </p> <p>ini_day135_z115_physical_forward.db contains the physical variables on particle trajectories. The variables are:</p> <p>x - east-west position</p> <p>y - north-south position</p> <p>z - vertical position</p> <p>u - east-west velocity</p> <p>v - north-south velocity</p> <p>w - vertical velocity</p> <p>vorticity - vertical component of relative vorticity</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis</p> <p>2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis</p>
The Lagrangian particle trajectory output and the metadata of the Southern Ocean Biogeochemical Divide location for 'Localizing the Southern Ocean Biogeochemical Divide'
<p>1. Trajectory files<br> <br> The Lagrangian particle trajectory output files from virtual particle release experiments at the surface and 500m depth using Connectivity Modeling System (Paris et al. 2013, https://github.com/beatrixparis/connectivity-modeling-system) run offline in the ACCESS-OM2-01 model (Kiss et al., 2020), a global 0.1° ocean sea-ice model, with a JRA55-do repeat year neutral state atmospheric forcing (Stewart et al., 2020).<br> <br> These trajectory datasets are compressed to two .rar format files for 2 depths, which were outputted from the Connectivity Modeling System v2.0 (CMS) in NetCDF format. Trajectory files include latitude, longitude, depth, interpolated along-track salinity and interpolated along-track temperature for each particle which are outputted every five days in the virtual particle tracking experiment. In addition, these datasets also contain the "exitcode" and release date information of each particle. More information please see in the CMS user guide. Other experiment setup files included in each release directory are "nest_1.nml", "runconf.list" and "ibm.list".<br> <br> These datasets are the original data output by the CMS. Limited by multiple nodes and maximum running time on the supercomputer, the surface release experiment is composed of 5 consecutive sub-experiments, and the 500m release experiment is composed of 3 consecutive sub-experiments. Each sub-experiment contains 48 independent output NetCDF files. <br> <br> 2. SOBD files<br> <br> These two .rar SOBD files are original arrays of the percentage of the upper cell minus the lower cell (i.e., the SOBD percentage) at surface and 500m depth (as presented in Fig.3 in Localizing the Southern Ocean Biogeochemical Divide).<br> <br> We provide original arrays in both .csv and .npz formats. More information can be found in the "Readme.txt" file in each .rar file.<br> <br> Citation of associated paper: Y. Xie, V. Tamsitt, L. T. Bach Localizing the Southern Ocean Biogeochemical Divide. <strong><em>to be submitted to Geophysical Research Letters</em></strong></p> <p><br> References:</p> <p>Kiss, A. E., Hogg, A. M., Hannah, N., Dias, F. B., Brassington, G. B., Chamberlain, A., . . . Stewart, K. D. (2020). ACCESS-OM2 v1 . 0 : a global ocean – sea ice model at three resolutions. <strong><em>Geoscientific Model Development</em></strong>,13, 401–442. doi: https://doi.org/10.5194/gmd-13-401-2020</p> <p>Paris, A. C. B., Vaz, A. C., Helgers, J., & Wood, S.(2017).Connectivity Modeling System User 's Guide CMS v 2 . 0. Retrieved from https://github.com/beatrixparis/connectivity-modeling-system</p> <p>Stewart, K. D., Hogg, A. M. C., England, M. H., & Waugh, D. W.(2020).Response of the Southern Ocean Overturning Circulation to Extreme Southern408Annular Mode Conditions. <strong><em>Geophysical Research Letters</em></strong>,47(22), 1–10. doi:10.1029/2020GL091103</p>
Trajectories of RNA adsorption on a curved surface S1 S2
<p>Coarse-Grained simulations of two RNA fragments:</p> <p>- 22 nts. (Hairpin)</p> <p>- 40 nts. (Ext. Hairpin w/ Bulge)</p>
Synthetic populations and trajectories for sdB stars ejected from the single degenerate helium donor channel for thermonuclear supernovae
<p>This repository is a supplement to a journal paper (Neunteufel+ 2022) and contains the synthetic populations of sdB stars and sdB remnants ejected from the single degenerate helium donor channel for thermonuclear supernovae. See Neunteufel+ 2021 and Neunteufel+ 2022 for simulation parameters. </p> <p>Synthetic populations, including initial and final positions, are contained in the MXX-out.tr (XX=10..15) files where XX is the mass of the WD companion divided by 0.1 solar masses. Each population is a snapshot of stars ejected at the end of a 300 Myr period. (Note that stellar lifetimes are not taken into account here. See Neunteufel+ 2022 on how stellar lifetimes should be truncated in order to produce realistic populations.)</p> <p>Columns:</p> <p>Zeroth column (ID) is the ID of the trajectory. These are assigned consecutively.</p> <p>First column (unnamed) indicates initial (0) and final (1) positions.</p> <p>Third column (time) is the time since ejection in Myrs. Note that stars further down in the list were ejected earlier.</p> <p>Fourth to Ninth columns (vx, y, vy etc..) are velocity (km/s) and position (kpc) in Gal. Carthesian coordinates (velocity first, position second)</p> <p>Tenth to Twelfth column are accelerations in Gal. carthesian coordinates </p> <p>Thirteenth column (v_space) is the total galactocentric space velocity (km/s) </p> <p>Fourteenth column (Phi) is the local Gal. potential according to Model 1 presented by Irrgang+2013</p> <p>Fifteenth column (E_kin/E_pot) is the local kinetic energy of the object divided by its potential energy with respect to the Gal. potential.</p> <p>Sixteenth column (rho) is the local Gal. baryon density according to Model 1 presented by Irrgang+2013</p> <p>Seventeenth column (label_c) is the mass of the ejected sdB star or sdB remnant in solar masses.</p>
Preliminary In Vivo Pulse-Acquire MRI with Concentric Ring Trajectories at 7 Tesla
<p>Pulse-Acquire MRI with Concentric Ring Trajectories, 305 Hz Readout Bandwidth, 350x350x99 Matrix,<br> voxel size 0.63x0.63x1.34 mm3, TR 60 ms, 5° FA, Acquisition Delay 5 ms, TA 13 min</p>
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
Trajectories for EmrE (faRM)
<p>These are the molecular dynamics trajectories for a refined EmrE model, as described by Vermaas et al. 2016. There are 25 trajectories total, each with 1001 frames. 5 trajectories are for the doubly deprotonated (apo) state, 5 have a proton on the "A" monomer (PA), 5 on the "B" monomer (PB), 5 are protonated on both glutamate residues (Protonated), and 5 are doubly deprotonated states with TPP+ bound. To load a trajectory in VMD, one might do the following:</p> <pre><code>mol new Apo-01.psf mol addfile Apo-01.dcd waitfor all</code></pre> <p>For PyMol, the following is recommended:</p> <pre><code>load Apo-01.pdb load_traj Apo-01.dcd</code></pre> <p> </p>
Sex-specific body mass aging trajectories in adult Asian elephants
<p><span>In species with marked sexual dimorphism, the classic prediction is that the sex which undergoes stronger intrasexual competition ages earlier or quicker. However, more recently, alternative hypotheses have been put forward, showing that this association can be disrupted. Here, we utilise a unique, longitudinal dataset of a semi-captive population of Asian elephants (<em>Elephas maximus</em>), a species with marked male-biased intrasexual competition, with males being larger and having shorter lifespans, and investigate whether males show earlier and/or faster body mass ageing than females. We found evidence of sex-specific body mass ageing trajectories: adult males gained weight up to the age of 48 years old, followed by a decrease in body mass until natural death. In contrast, adult females gained body mass with age until a body mass decline in the last year of life. Our study shows sex-specific ageing patterns, with an earlier onset of body mass declines in males than females, which is consistent with the predictions of the classical theory of ageing.</span></p>
CH3CH2OCH3 conformer molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Molecular dynamics trajectories of C3 H8 O molecule and its structural isomers
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p>------------------------------------------------</p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Trajectories and driving profiles for hybrid h2 multiple units.
<p>Identifier: DOI</p> <p>Creator: German Aerospace Center, Institute of Vehicle Concepts</p> <p>nameType: Organizantional</p> <p>Title: Trajectories and driving profiles for hybrid h2 multiple units.</p> <p>Publisher: Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Fahrzeugkonzepte.</p> <p>Publication Year: 2022</p> <p>ResourceType: Simulated Trajectories</p> <p>Subject: This data set comprises driving profiles and simulated trajectories for bi-mode fuel cell hydrogen multiple units.</p> <p>Date: 2022-02-10</p> <p>Description:<br> This data set comprises driving profiles and simulated trajectories for bi-mode fuel cell hydrogen multiple units.<br> Methodology is described in relatedItem.</p> <p>FundingReference: FCH2Rail; Fuel Cell Hybrid Power Pack for Rail Applications; Grant Agreement Number: 101006633</p> <p>RelatedItem: "D1.1 - Report on line and use case based requirements" of the FCH2Rail project.</p> <p><br> This dataset comprises following files:</p> <p>characteristic_traction_curve.csv<br> Resembles the maximum force applicable by the electric engine at a given velocity to accelerate the train. Velocity<br> is given in kilometer per hour [km/h], traction_force is given in newton [N].</p> <p>characteristic_maximal_electrical_breaking_curve.csv<br> Resembles the maximum force applicable by the electric engine at a given velocity to decelerate the train. Velocity<br> is given in kilometer per hour [km/h], max_elec_braking_force is given in newton [N].</p> <p>characteristic_maximal_mechanical_breaking_curve.csv<br> Resembles the maximum force applicable by the mechanical brake at a given velocity to decelerate the train. Velocity<br> is given in kilometer per hour [km/h], max_mech_braking_force is given in newton [N].</p> <p>electrifications.csv<br> Electrifications are derived from open street map (OSM). pkm resembles distances from start in kilometers.<br> Electrification: 1 = electrified, 0 = not electrified.</p> <p>maxspeeds.csv<br> pkm resembles distances from start in kilometers. Maxspeeds are derived from Open Street Map data. Data gaps are<br> manually corrected with infrastructure maps from Adif. Maxspeeds in km/h.</p> <p>slopes.csv<br> pkm resembles distances from start in kilometers. Slopes in permille are derived from JAXA ALOS 0.1 X 0.1 DEM.<br> Underlying method is described in the public deliverable "D1.1 - Report on line and use case based requirements"<br> of the FCH2Rail project.</p> <p>timetable.csv<br> pkm resembles distances from start in kilometers. station_name represents common name of railway stations<br> where the train stops. standing_time [s] resembles the standing time within the station in seconds. driving_time [s]<br> resembles driving time to the next station in seconds.</p> <p>vehicle.csv<br> Contains the descriptive values for the vehicle. Vehicle resembles the name of the train to which the corresponding<br> values are assigned. Static_mass is specified in kilogram [kg], rotating_mass is specified in kilogram [kg]. Davis coefficient davis_a<br> is specified in newton [N], davis_b is specified in newton per kilometers per hour [N/(km/h)] & davis_c is specified in newton<br> per squared kilometers per hour [N/(km/h)^2]. </p> <p>simulated_trajectory.csv<br> Underlying simulation method, with which simulated trajectory is obtained is described in the public deliverable<br> "D1.1 - Report on line and use case based requirements" of the FCH2Rail project. Traveltime is specified in<br> seconds, starts with zero. Traveled distance is specified in meters [m]. Velocity is specified in meters per second [m/s].<br> Power at the wheel is specified in watt [W]. </p> <p> </p>
Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory
<p><span>The spatial organization of genes within plant genomes can drive evolution of specialized metabolic pathways. In this study we investigated the origin and subsequent evolution of a diterpenoid biosynthetic gene cluster (BGC) present throughout the Lamiaceae (mint) family. Terpenoids are important specialized metabolites in plants with </span><span>diverse</span><span> adaptive functions that enable environmental interactions, such as chemical defense. Based on core genes found in the BGCs of all species examined across the Lamiaceae, we predict a simplified version of this cluster evolved in an early Lamiaceae ancestor. The current composition of the extant BGCs highlights the dynamic nature of its evolution. We elucidate the terpene backbones made by the </span><span>Callicarpa americana</span><span> BGC enzymes, including miltiradiene and the novel terpene (+)-kaurene, and show oxidization activities of BGC cytochrome P450s. Our work reveals the fluid nature of BGC assembly and the importance of genome structure in contributing to the origin of novel metabolites.</span></p>
Argo Trajectories under ice (Southern Hemisphere, version 2022.05)
<p>Argo floats sometimes sample under ice, and do not return a measured position. Here, we provide estimates of positions using the multiple-constraint method described by Oke et al. (2022). </p> <p>Each file includes longitude and latitude from GPS measurements when floats are not under ice. When floats are under ice, positions are estimated by linearly interpolating between known locations (the traditional approach used by the Argo community), and using constraints: potential vorticity, f/H; mean sea-level, and density at 1000 m. A merged trajectory is also included, but users might select their preferred estimate based on their understanding of the ocean circulation at the time of measurement.</p> <p>Oke, P. R., T. Rykova, G. S. Pilo, J. L. Lovell, 2022: Estimating Argo float trajectories under ice, Journal of Geophysical Research - Earth and Space Science, under review.</p>
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </p>
Data from: Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago
<p>This archive contains data produced in a study of the vegetation trajectories of Ogasawara Islands in 77 years related to following article:</p> <p>Ohashi, H., Kato, H., Murao, M., Kato, H., Kawakami, K., Kurokawa, H., Oguro, M., Kimura, F., Niiyama, K., Matsui, T., and Shibata, M. (2024) Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago. <em>Applied Vegetation Science</em>, 27 (1), e12767. <a href="https://doi.org/10.1111/avsc.12767">https://doi.org/10.1111/avsc.12767</a></p> <p> </p> <p><strong>Archive contents</strong><br>The archive contents are organized into five parts, each stored as a .zip compressed file.</p> <p><strong>X1_tif_original_vegmap_scan_georeference</strong></p> <p>Scanned and georeferenced original vegetation maps in GeoTiff format, which was drawn in 1935, scanned at 300 dpi. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>kitanoshima_isl_WGS84.tif<br>mukojima_isl_WGS84.tif<br>yomejima_isl_WGS84.tif<br>ototojima_isl_WGS84.tif<br>anijima_isl_WGS84.tif<br>nishijima_isl_WGS84.tif<br>chichijima_isl_WGS84.tif<br>hahajima_isl_WGS84.tif<br>mukohjima_isl_WGS84.tif<br>kitaiwoto_isl_WGS84.tif<br>iwoto_isl_WGS84.tif</em></p> <p> </p> <p><strong>X2_shp_vegmap</strong></p> <p>Shapefile of the geospatial polygon data of vegetation map of Ogasawara Islands surveyed in 1935, and stored as a .zip compressed file. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.dbf<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.prj<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shp<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shx<br>attribute_ForSect_code_en.csv<br>attribute_Veg_name_en.csv<br>metadata_vegmap_shp_ogasawara1935_en.csv</em></p> <p>Following files includes Japanese character (which may corrupt in non-Japanese environment):</p> <p><em>attribute_ForSect_jp.csv<br>attribute_Veg_name_jp.csv<br>metadata_vegmap_shp_ogasawara1935_jp.csv</em></p> <p> </p> <p><strong>X3_tif_vegmap_converted_from_shp</strong></p> <p>Rasterized data of polygon data of vegetation map for analysis. Coordinate reference system was set at JGD2000 / Japan Plane Rectangular CS XIV (EPSG: 2456)</p> <p>This directory includes:</p> <p><em>vegmap_1935.zip (compressed “vegmap_1935.tif (0.7GB)”)<br>vegnap_1979.zip (compressed “vegmap_1979.tif (1.5GB)”)<br>vegmap_2011.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>islcode_raster.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>attribute_integratedveg_ecoltype.csv<br>attribute_vegid_1935.csv<br>attribute_vegid_1979.csv<br>attribute_vegid_2011.csv</em></p> <p> </p> <p><strong>X4_scanned_image_vegdata</strong></p> <p>Scanned images of original vegetation data in 1935.</p> <p>The directory includes:<br><em>vegetation_survey_sheet_1.pdf<br>vegetation_survey_sheet_2.pdf</em><br><em>vegetation_survey_sheet_3.pdf</em></p> <p> </p> <p><strong>X5_digitized_vegdata</strong></p> <p>Digitized vegetation data.</p> <p>The directory includes:<br><em>plot_species_abundance_matrix_v0.csv<br>plotinfo_v0.csv<br>attribute_Species_en_v0.csv</em></p> <p>Following file includes Japanese character (which may corrupt in non-Japanese environment)<br><em>attribute_Species_jp_v0.csv</em><br> </p> <p><strong>X6_code_for_analysis</strong></p> <p>Tentative.</p> <p> </p> <p>このアーカイブには、小笠原諸島の77年間の植生の変遷(1935年、1979年、2012年)に関するデータが含まれています。</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.