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11,506 results for “Dynamics”
Trends in the global trade of live CITES-listed raptors: Trade volumes, spatiotemporal dynamics and conservation implications
<p>The global legal wildlife trade is worth US$4-20 billion to the world's economy every year. Raptors frequently enter the wildlife trade for use as display animals, by falconers or hobbyists for sport and recreation. Using data from the Convention on International Trade in Endangered Species of Wild Fauna and Flora's (CITES) Trade Database, we examined trends in the global, legal commercial trade of CITES-listed raptors between 1975-2020. Overall 272 raptor species were traded, totalling 188,149 individuals, with the number of traded raptors increasing over time. Hybrid Falcons (N = 50,366) were most commonly traded, comprising more than a third of the global diurnal CITES-listed raptor trade, followed by Gyrfalcons (<em>Falco rusticolus</em>; N = 30,510), Saker Falcons (<em>F. cherrug</em>; N = 21,679), Peregrine Falcons (<em>F. peregrinus</em>; N = 13,390) and Northern White-faced Owls (<em>Ptilopsis leucotis</em>; N = 6,725). More than half of wild-caught diurnal raptors were classified as globally threatened. The United Kingdom was the largest exporter of live raptors and the United Arab Emirates was the largest importer. More affluent countries were likely to import more raptors than those less affluent. Larger-bodied diurnal species were traded more relative to their smaller-bodied conspecifics. Following the introduction of the European Union's Wild Bird Trade Ban in 2005, the number of traded wild-caught raptors declined. Despite its limitations, the CITES Trade Database provides an important baseline of the legal trade of live raptors for commercial purposes. However, better understanding of illegal wildlife trade networks and smuggling routes, both on-the-ground and online, are essential for future conservation efforts.</p>
Dataset: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning
<p>Dataset of simulated and experimental spectra for the manuscript: Uncovering Obscured Phonon Dynamics from Powder Inelastic Neutron Scattering using Machine Learning. </p> <p>The data.zip contains all the simulated spectra and labels.</p> <p>The dataset.zip contains the divided subsets for training, validation and testing purposes, as well as the experimental dataset.</p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Database of 'Electron Dynamics in Self-Consistent Wave-Particle Interactions with Oblique Chorus Waves'
<p>Particle data selected at T=2840 and at T=3050.</p>
Host-level biodiversity shapes the dynamics and networks within the coral reef microbiome
<p>This is the repository associated with the manuscript <strong>Host-level biodiversity shapes the dynamics and networks within the coral reef microbiome</strong>. It contains all data files used for figures presented in the manuscript. The code to reproduce the files can be found on <a href="https://github.com/SushiLab/coralmicrobiome-hostbiodiversity)">GitHub</a>.</p> <p> </p> <h2>Files</h2> <p><br><code>raw_host-associated_1.asvs.tsv --> ASV table, host-associated samples</code><br><code>raw_host-associated_2.asvs.tsv --> ASV table, host-associated samples</code><br><code>raw_free-living_1.asvs.tsv --> ASV table, Sterivex samples</code><br><code>raw_free-living_2.asvs.tsv --> ASV table, Sterivex samples</code><br><code>metadata_host-associated.csv --> metadata, host-associated samples</code><br><code>metadata_free-living.csv --> ASV table, Sterivex samples</code><br><code>asv_dat_taxinfo.tsv --> taxonomy annotated using SILVA v138.1</code><br><code>asv_bctab.tsv --> square-root transformed Bray-Curtis dissimilarities </code><br><code>asv_richtab.tsv --> Hill number diversity indices</code><br><code>asv_shared.tsv --> number of shared ASVs</code><br><code>pathway_list_cat.csv --> KEGG pathway list with hand-annotated categories</code><br><code>KEGG_path_to_ko.tsv --> KEGG file mapping pathways to KOs</code></p> <p> </p>
Vitellogenins level as a biomarker of the honey bee colony seasonal dynamics
Open the record for dataset details and reuse information.
Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763
<p><span>The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J.<em> Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763</em>’ are provided here.</span></p> <p><span>The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. <span>Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if </span></span><span>𝑡</span><span> corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1<sup>st</sup>, January 6<sup>th</sup>, May 1<sup>st</sup>, August 15<sup>th</sup>, October 12<sup>th</sup>, November 1<sup>st</sup>, December 6<sup>th</sup>, December 8<sup>th</sup>, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.</span></p>
Data and code for "Topography and climate drive recent shifts in vegetation productivity dynamics"
<p>This zip file includes data and code for manuscript "<span><span>Topography and climate drive recent shifts in vegetation productivity dynamics</span></span>".</p>
Repository for "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics"
<p>These files contain data and generation codes for figures, a numerical code for the analysis of structure factors, and a numerical code for the simulation, in an article "Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics".</p>
Kreysing_et_al_2024_molecular_dynamics_simulation_data
<p>The molecular dynamics simulation data set accompanying Kreysing et al, 2024, containing (i) representative coordinates and measurements described therein, (ii) selected simulation trajectories, and (iii) LAMMPS input files.</p>
Molecular dynamics trajectories for neuroBR
<p>The trajectories of all-atom MD simulations of neuroBR_A,B,C.</p> <p>Simulations have been performed using the CHARMM36m force field, running with the GROMACS 2022 package.</p>
Datasets associated with paper "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", by Enrico Mattea et al.
<p>This archive contains Digital Elevation Models and orthoimages produced within the study "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", published on The Cryosphere by Enrico Mattea et al.</p> <p>Assets include derivative products of SPOT satellite scenes, acquired by CNES’s Spot World Heritage Programme, and of Pléiades <span><span><strong>©</strong></span></span>CNES 2015, 2020, 2022, Distribution AIRBUS DS.</p>
Dataset for "History and dynamics of Fennoscandian Ice Sheet retreat, contemporary ice-dammed lake evolution, and faulting in the Torneträsk area, northwestern Sweden"
<p>This dataset contains the GIS shapefiles and the high resolution map for the manuscript "Ice-dammed lakes and contemporary faulting in the Torneträsk region of northern Sweden are key to understanding regional Fennoscandian Ice Sheet deglaciation patterns and dynamics" in preparation for <em>The Cryosphere</em>. </p>
Dynamic Visualization of ResNet Layer Activations for Brain Health Classification
<p>This GIF file provides a dynamic visualization of the internal representations (activations) from the ResNet 18 model layers (2 to 69) during a brain health classification task. The sequence begins by showing the original input image, followed by successive activation maps visualized using the "jet" colormap. Each frame corresponds to the activations extracted from a specific layer in the ResNet, resized to match the input image dimensions for better interpretability. </p> <p>The dataset used for this visualization is from S. Bhuvaji, "Brain Tumor Classification MRI," published on Kaggle in 2023. This dataset contains MRI images of brain tumors and has been utilized to train and evaluate the ResNet model for the classification of brain health states. The input sample displayed in the GIF is one such MRI image from the dataset, highlighting the model's ability to extract and analyze features relevant to brain tumor diagnosis.</p> <p>The activations reveal how the ResNet processes the input image hierarchically. In the <strong>early layers (e.g., Layers 2-10)</strong>, the network preserves much of the spatial structure of the original image, focusing on edges and low-level features. Moving to the <strong>intermediate layers (e.g., Layers 11-40)</strong>, the network begins to emphasize localized patterns while filtering out irrelevant structures such as the skull, concentrating instead on regions associated with tumors or health-related features. Finally, the <strong>deep layers (e.g., Layers 41-69)</strong> extract highly abstract and classification-relevant patterns, concentrating on tumor-related features while discarding most of the background.</p> <p>The progression of the activations in the GIF demonstrates how the network transitions from general image features to highly specialized, diagnostic features that are critical for the classification task. This visualization helps provide an intuitive understanding of the hierarchical processing capabilities of convolutional neural networks (CNNs) in medical image analysis.</p> <h3>Key Features:</h3> <p>The visualization includes an input brain image and its corresponding activation maps, extracted from each ResNet layer. The activation maps are resized to match the original image for consistency, and the "jet" colormap is applied to enhance visual interpretation of activation intensities. Each frame in the GIF dynamically updates to show the activations of the next layer, offering an engaging representation of the network's internal behavior.</p> <h3>Use Cases:</h3> <p>This GIF is a valuable resource for education, research, and presentations. It can be used to illustrate how deep learning models process medical images, providing insights into the hierarchical feature extraction process. Researchers and educators can leverage this visualization to explain the concept of feature abstraction in CNNs. It is also ideal for inclusion in talks, posters, and papers to showcase the dynamic analysis of neural network activations.</p>
Molecular dynamics simulations for "Evolutionary Dynamics of RuBisCO: Emergence of the Small Subunit and its Impact Through Time"
<p>This repository contains the molecular dynamics simulation files for the extant and ancestral RuBisCOs, presented in Amritkar2024 et. al.</p> <p>There are two separate folders, one for the regular MD simulations and the other for MD simulations with gas trajectories.</p> <p>Both folders have the simulation trajectory `.dcd` files and the `.pdb` for their corresponding protein structures.</p> <p>The trajectory files are dried, i.e. water molecules have been removed from them.</p> <p>The data encompasses over 7 extant (pdb ids: 1BWV, 3ZXW, 6FTL, 6URA, 7SNV, 8RUC, 9RUB) and 8 ancestral (Anc-I/I', Anc-I', Anc-I, Anc-IAB, Anc-ICD, Anc-IA, Anc-IB, Anc-I-without-RbcS) RuBisCO complexes.</p> <p>This study performed three types of simulations: with water molecules (labeled as exp01), with water + CO2 molecules (labeled as exp02), and with water + O2 molecules (labeled as exp04).</p> <p>There are two replicates for the exp01 simulations and the simulation length for each is 250 ns.</p> <p>The <em>exp02</em> and <em>exp04</em> files are not present for the RbcS-less (Anc-I/I', Anc-I', Anc-I-without_RbcS, 6URA, and 9RUB) RuBisCOs. The simulation length for the gas simulations is 75 ns and one replicate.</p> <p>Each file is named "<em>RuBisCO-system"</em>."<em>simulation-type</em>".md"replicate-number".dry."<em>pdb or dcd</em>".</p> <p>The files are named with respect to each RuBisCO-id in small (No caps). Anc-I/I' is represented as "anciip" and Anc-I' is represented as "ancip".</p>
Harnessing Pore Size in COF Membranes: A Concentration Gradient-Driven Molecular Dynamics (CGD-MD) Study on Enhanced H₂/CH₄ Separation
Open the record for dataset details and reuse information.
Raw Data and Analysis Codes for the Dynamic control of the NHSE manuscript
<p>Raw Data and Analysis Codes for the Dynamic control of the NHSE manuscript</p>
Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"
<p>Data and code for "Preconfigured cortico-thalamic neural dynamics constrain movement-associated thalamic activity"</p> <p>Nature Communications; 2024 Nov 24;15(1):10185. doi: 10.1038/s41467-024-54742-9.</p>
Sub-Mesoscale Ocean Dynamics Experiment: Surface currents and Sea Surface Temperature
<p>Airborne observations of surface currents from Doppler Scatterometer and sea surface temperature from infrared camera.</p> <p><span>This data set is derived from the S-MODE project <span> </span><span><a title="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$" href="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$">https://github.com/podaac/2022-SMODE-Open-Data-Workshop</a></span>.<br></span></p> <p> </p> <p>Description of the data:</p> <p>DopplerScatt surface current spatial resolution of 200 m</p> <p>MOSES SST nominal spatial resolution of 10 m, but interpolated to DopplerScatt grid at 200 m resolution.</p> <p>Description of the name: SMODE_DScatt_and_MOSES_20221023_120546_ver_L2E_Smoothing_1km_Submesoscale_Frontogenesis.nc</p> <p>Date = 20221023_120546 (YYYY/mm/dd HH:MM:SS)</p> <p>Level of smoothing = 1 kilometer</p> <p>Submesoscale_Frontogenesis = The variables included in the file are necessary to compute frontogenesis</p> <p> </p> <p>Variables:</p> <p>SST_moses = sea surface temperature</p> <p>U_hp = high-pass east-west velocity component</p> <p>V_hp = high-pass north-south velocity component</p> <p>Tx = SST gradients in east-west direction</p> <p>Ty = SST gradients in north-south direction</p> <p>U_hp_(rot,div) = high-pass east-west velocity component rotational and divergent component</p> <p>V_hp_(rot,div) = high-pass north-south velocity component rotational and divergent component</p> <p>Fs_rot = Frontogenetic tendency due to the rotational component of the velocity field</p> <p>Fs_div = Frontogenetic tendency due to the divergent component of the velocity field</p> <p>Fs_tot = Frontogenetic tendency</p> <p>Strain_rot = Strain deformation due to the rotational component of the velocity field smoothed at 1 km</p> <p>Strain_div = Strain deformation due to the divergent component of the velocity field smoothed at 1 km</p> <p>rv_lp = relative vorticity smoothed at 1 km</p> <p>dv_lp = divergence smoothed at 1 km</p> <p>Strain_lp = strain deformation smoothed at 1 km</p> <p>Strain_normal = normal strain deformation smoothed at 1 km</p> <p>Strain_shear = shear component of the strain deformation smoothed at 1 km</p> <p> </p> <p> </p>
ATELIER. Self-Labelling dynamic Loss function training data
<p>This dataset contains all the statistics collected during the training of multiple Neural Networks, which uses the following architecture technologies:</p> <p>Siamese Neural Networks, Dynamic loss function, modified during the training through Reinforcement learning and a 'Curriculum-Learning' cycle in between training cycles. The goal of the Models is to correctly identify anomalies in the QoE of real-time videos through the network KPIs.</p> <p>The dataset contains 3 type of experiment with different levels of model complexity.</p> <p>Please refer to the associated github repository and the published paper for a detailed description of both the dataset and the infrastructure that generated the dataset.<br>Journal paper title: "ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning"</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.