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11,506 results for “Dynamics”
F I G U R E 1 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 1 Species accumulation curve for invertebrate listings collected from the public classifieds website, representing the total cumulative number of species identified over time as listings were collected from the website.
F I G U R E 4 The top 10 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 4 The top 10 most popular invertebrate species in online pet stores, and those that also feature in the top 10 most popular species on the Australian classifieds website. Note that Urodacus elongatus (Flinders Ranges scorpion) has the same relative abundance in online pet stores as Exatosoma tiaratum (giant prickly stick insect), the overall most traded species online.
F I G U R E 7 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 7 (a) Histogram of mean species price-per-unit differences (AUD) between classifieds and online pet stores (classifieds price minus pet store price, i.e., negative price difference indicates that classifieds species are less expensive). Median value of 7.442 is represented by the red line. (b) Examples of species with large price differences on the two e-commerce platforms. Photo credits: (a) Rosie Steinberg, (b) Ajay Narendra, (c) Stephan Höhne.
F I G U R E 3 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 3 Bar graph of the top 10 most popular invertebrate species on the classifieds website by proportion of total listings, and those that also feature in the top 10 most popular species in online pet stores.
F I G U R E 2 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 2 (a) Total number of invertebrate orders traded on the classifieds website. (b) Total number of species within each invertebrate order traded on the classifieds website.
Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna
<p>The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.<br> </p> <p><strong>Version 2</strong> includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data.<br><br><strong>Version 3 </strong>includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data. <br><br><strong>Version 4 </strong>Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data. <br><br>fluxes: it includes data related to net ecosystem C and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details.</p> <p>soil_carbon: it includes carbon soil data.<br><br>biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details.</p> <p>aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details.</p> <p>belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details.</p> <p>species_composition: plant community composition. See the method section in the manuscript for full details.</p> <p><br><strong>Abstract</strong><br>Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO<sub>2</sub>)<sub> </sub>exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO<sub>2</sub> efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.</p>
Computer simulations of photorelaxation dynamics of fluorazene
<p><strong>Description</strong></p> <p>This dataset contains the results of nonadiabatic molecular dynamics (NAMD) simulations of the photorelaxation dynamics of fluorazene in acetonitrile solution. The dilute solution phase is modeled as a single fluorazene molecule at the center of a spherical 500-molecule acetonitrile nanodroplet. The nuclear wavepacket is represented by an ensemble of 60 trajectories, which are numbered trajectory_0001.xyz to trajectory_0060.xyz 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 Ångström. The frames are written at intervals of 10 fs. Each trajectory lasts 1.5 ps.</p> <p>Each trajectory is accompanied by a CSV file (trajectory_0001.csv etc.) which contains information on the state energies during the given trajectory. The first line is a header: "t(fs),E0(Eh),E1(Eh),E2(Eh),E3(Eh),E4(Eh),Eocc(Eh),Etot(Eh)". The subsequent lines give the time t (in units of fs), the energies of states S0 to S4 in units of Eh (hartree), the energy of the occupied state at time t, and the total energy at time t. (Total energy is not perfectly conserved.)</p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Alexander von Humboldt Foundation, and by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 847413.</p>
Dynamics of Alongshore Current in the Taiwan Strait: A Perspective on the Southward Kuroshio Branch in Winter
<p>Data for submitted paper "Dynamics of Alongshore Current in the Taiwan Strait: A Perspective on the Southward Kuroshio Branch in Winter ". Those files "Fig.S1-Fig.S4" contain the data of figures in the Supporting Information.</p>
Research data for "Predicting Dynamics from Structure in a Sodium Silicate Glass"
<p>This dataset supports the paper "Predicting Dynamics from Structure in a Sodium Silicate Glass".</p> <p>The following files are provided.</p> <p>File: dataset_800.zip</p> <p>- Pickle files for:</p> <ul> <li>400 Sodium silicate glass structures of 3000 atoms</li> <li>30 Trajectories sampled eight times at various timescales up to 1 ns for each if the 400 glass structures</li> </ul> <p>File: in.comb</p> <p>- Lammps inputfile used to generate simulation from with the data in dataset was sampled</p>
FastEddy-model Tutorials: Moist Dynamics, Example04_BOMEX
<p>This archive contains the data required for the FastEddy-model tutorial on moist dynamics validation. The dataset includes a FastEddy initial conditions file for the validation case, FE_BOMEX.0 along with simulation results from the 11 models that participated in the original Siebesma et al. 2003 model intercomparison.</p>
Forest gap dynamics with repeat lidar in Berchtesgaden National Park - Data and analysis scripts
<p>This repository holds data and code for the paper: Krüger, K., Senf, C., Jucker, T., Pflugmacher, D., Seidl, R. (2024). Gap expansion is the dominant driver of canopy openings in a temperate mountain forest landscape. Journal of Ecology. <span><a href="http://doi.org/10.1111/1365-2745.14320">http://doi.org/10.1111/1365-2745.14320</a> </span></p> <p><strong>NOTE:</strong> This is a static repository, but the project might evolve. See the connected GitHub repository for latest updates!</p> <p>All data to reproduce the results are available, all other layers can be generated with the code provided in this repository. The Canopy Height Models underlying the analysis and respective processing scripts for the lidar data, are available from the corresponding author upon reasonable request. Gap layers derived from the Canopy Height Models are available in this repository.</p> <p>Empty folders are set up to follow the directory structure of the scripts. </p>
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Tremor catalogs produced (output data) in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns"
<p>This repository contains tremor catalogs produced as output data in the paper "Dynamics of the 2021 Fagradalsfjall eruption (Iceland) revealed by volcanic tremor patterns" submitted to Journal of Geophysical Research - Solid Earth by Soubestre J., Caudron C., Melnik O., Lecocq T., Jaupart C., Shapiro N.M., Journeau C., Çubuk-Sabuncu Y., and Jónsdóttir K.</p> <p>In particular, it contains three catalogs :<br>+ time and 3-D location of <strong>tremor sources</strong> shown in Figure 5 ;<br>+ time and duration of <strong>tremor bursts</strong> associated with pulsating lava fountains shown in Figure 6 ;<br>+ time and duration of <strong>repose times</strong> associated with pulsating lava fountains shown in Figure 6 ;</p>
Figure 1 in Advances in understanding bat infection dynamics across biological scales
Figure 1. Map illustrating the geographical and taxonomic diversity of bat species highlighted in case studies throughout the main text, with approximate study location and photo. Species names are coloured according to bat family, with a simplified phylogeny showing relationships between families. See electronic supplementary material for photo permissions.
Figure 2 in Advances in understanding bat infection dynamics across biological scales
Figure 2. Overarching research priorities for future studies on bat infection dynamics, organized at the individual, population and community scales; S, susceptible; I, infected; R, recovered.
Research Data - Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures
<p>Source data from ferromagnetic resonance experiments and micromagnetic simulations in <em>tetmag</em> software (<a href="https://github.com/R-Hertel/tetmag">https://github.com/R-Hertel/tetmag</a>), used in the paper "Collective Spin-Wave Dynamics in Gyroid Ferromagnetic Nanostructures"<em> </em>in <em>ACS Applied Materials & Interfaces </em>(<a href="https://doi.org/10.1021/acsami.4c02366">https://doi.org/10.1021/acsami.4c02366</a>).</p>
Data for fitting a statistical global burned area model for seamless integration into Dynamic Global Vegetation Models
<p>The dataset is a large R data.table object saved in RDS format. It contains global, monthly data spanning the period from 2002 to 2018, with a 0.5 degrees spatial resolution. The dataset is utilized to develop and validate statistical models for predicting global burnt areas resulting from wildfires.</p>
Investigating the Unsteady Dynamics of a Multi-Jet Impingement Cooling Flow Using Large Eddy Simulation - Promotional Video and Image
<p>Video: Volume rendering of temperature field obtained from a large eddy simulation of 9 inline impinging jets in a narrow channel.</p> <p>Image: Turbulent structures as isosurface of Q-criterion with an illustration of laser sheet used for PIV measurement.</p> <p>The results were presented at the ASME Turbo Expo 2024 (paper number GT2024-122465) and published in the ASME Journal of Turbomachinery (<a href="https://doi.org/10.1115/1.4066508">https://doi.org/10.1115/1.4066508</a>). The accepted manuscript of the paper is available under <a href="https://elib.dlr.de/207257/">https://elib.dlr.de/207257/</a>.</p> <p>The simulations were performed on DLR's HPC system <a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/hpc-cluster/cara">CARA</a> within the DLR project InnoCool.</p>
Data and code for Decoding dynamic landslide hazard processes for a massive refugee camp (KTP) in Bangladesh
<p>The codes have been implemented using R 4.4.0. Landslide priority zonation using Monte Carlo simulation is implemented in Google Colab.</p> <p>A Dynamic Landslide Hazard Assessment has been conducted using a Generalized Additive Model (GAM). The results of the GAM are also compared with standard machine learning algorithms (MLs): NNET, RF, LDA, xgBoost, and SVM.</p> <p>The code is jointly developed by Dewan Haque and Ritu Roy, with collaboration from many others. The GAM code is an update from the study published by Zhice, F. (2023), <a href="https://doi.org/10.5281/zenodo.10395153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10395153</a>, adapted to apply it across settings. The ML code has been developed from scratch.</p> <p>The required data from intensive fieldwork and satellite image analysis is uploaded here to reproduce the results. Additionally, R Markdown files are provided.</p> <p>The ReadMe file here, as well as on GitHub, will be useful for further instructions.</p> <p>GitHub Link: https://github.com/Dewan-cpu/Decoding-Landslide-Hazard-Assessment</p>
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</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.