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11,506 results for “Dynamics”
High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change (Dupont et al., GBC)
<p>Files used to make the analysis in the paper "High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change" (Dupont et al., accepted in GBC)</p> <p>- HTL_LTL_figures.ipynb is the python notebook in which are computed the different terms to make the figures of the paper </p> <p>- histrcp85.1-PISAPE-N-OW** and piCtrl2-PISAPE-N-OW** files contain the raw outputs of the one way (OW) simulation</p> <p>- histrcp85.1-PISAPE-N-TW* and piCtrl2-PISAPE-N-TW* files contain the raw outputs of the two way (TW) simulation</p> <p>- all files ending with *rmp_f.nc/ *regrid.nc/ *f20.nc are regridded files to make maps used in the paper. More details can be found in the python noteboook (briefly, dAT_* = change in active export, dDIC_*= change in dissolved inorganic carbon, dEPC200_* = change in carbon export at 200m depth, OW/TW_<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">dBMape</a>* = OW/TW change in small high trophic levels biomass, OW/<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">TW_dBM</a>meszo* = OW/TW change in mesozooplankton biomass)</p> <p>- <a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/egestt2_2.nc">egestt2_2.nc</a>, excrett2_2.nc and graztt2_2.nc are the outputs of egestion, excretion and grazing terms used to compute the active export (AT). </p>
Figure 2 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 2. Schematic diagram of the experiment's plantation and IPM methodology, C.n: Cydnoseius negevi, A.s: Amblyseius swirskii, and P.p Phytoseiulus persimilis. (Photo credits: Dr. Zidan has created this diagram on www.biorender.com).
Figure 1 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 1. Google Earth map photography of the experimental locations (pointed with pin) – i) Om Sabir, Kom Hamada, El Beheira Governorate (30° 29' 50.6" N, 30° 46' 18.8" E), and ii) Kom Oshim, Fayoum Governorate (29° 34' 40.9" N, 30° 55' 38.3" E).
Dynamic analysis of pathogenesis and a suppression function of TREM2-dependent macrophages in skin fibrosis
<p>Systemic sclerosis (SSc) is a chronic and incurable autoimmune disease with high mortality rates, and fibrosis is the distinguishing hallmark in the pathogenesis. Identification of the precise, time dependent composition of stromal and immune cells may provide clues for the establishment of new biomarkers and therapeutic approaches targeting SSc fibrosis. Here, the temporal dynamics of pathological process in a mouse model of skin fibrosis is investigated using single-cell RNA-sequencing (scRNA-seq). we collected skin single cell suspensions for transcriptome profiling by scRNA-seq from four time points after BLM-treatment (day 3, 7, 14, and 28) and untreated control skin (day 0) with on average three replicate mice per time point, and repeated once at each time point.{mouse_SSc_scRNAseq_day0_sample1_B1_1, mouse_SSc_scRNAseq_day3_sample1_B2_1, mouse_SSc_scRNAseq_day3_sample2_B2_2, mouse_SSc_scRNAseq_day7_sample1_B3_1, mouse_SSc_scRNAseq_day7_sample2_B3_2, mouse_SSc_scRNAseq_day14_sample1_B4_1, mouse_SSc_scRNAseq_day14_sample2_B4_2, mouse_SSc_scRNAseq_day28_sample1_B5_1, mouse_SSc_scRNAseq_day28_sample2_B5_2}</p>
Dataset of traffic dynamics during the 2020 Glass Wildfire Evacuation
<p>This dataset contains the </p> <p>This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: <em>Rohaert et al., ‘T</em>he analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire<em>’, [Submitted for peer-review to an international journal.], 2023.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work has been funded under award 60NANB21D118 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce.</p>
Dynamical simulation of EBSD master pattern of nickel
<p>Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of nickel (<em>Fm<span class="math-tex">\(\bar{3}\)</span>m</em>, <em>a</em> = 3.5236 Å). The master pattern was simulated with EMsoft v4.3. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 5 to 20 kV with an increment of 1 kV.</p> <p>The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python 3.7 or higher and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/ni_mc_mp_20kv.h5") s.plot()</code></pre> <p>The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: ni.xtal), EMMCOpenCL (input: ni.xtal, mcopencl.nml; output: ni_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, ni_mc_mp_20kv.h5; output: added to existing ni_mc_mp_20kv.h5).</p>
Dynamical simulation of EBSD master pattern of silicon
<p>Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of silicon (<em>Fd<span class="math-tex">\(\bar{3}\)</span>m</em>, <em>a</em> = 5.4307 Å). The master pattern was simulated with EMsoft v5.0. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 5 to 20 kV with an increment of 1 kV.</p> <p>The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python 3.7 or higher and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/si_mc_mp_20kv.h5") s.plot()</code></pre> <p>The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: si.xtal), EMMCOpenCL (input: si.xtal, mcopencl.nml; output: si_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, si_mc_mp_20kv.h5; output: added to existing si_mc_mp_20kv.h5).</p>
Dataset: Remotely sensed soil moisture can capture dynamics relevant to plant water uptake
<p><strong>Dataset Description</strong><br> Stable isotope water uptake profiles were consulted across 45 datasets to determine the primary zone of root water uptake ("Uptake Range Top" to "Uptake Range Bottom"), whether the uptake increases in proportion nearer to the surface ("Decay of Water Uptake With Depth"), and whether uptake temporarily switches to shallow soils ("Temporary Uptake of Upper Layers"). More details on the data collection are shared in our Water Resources Research publication (in revision).</p> <p>Correlation length scales, or the effective depth of representation of L-band satellite soil moisture, are estimates in Short Gianotti et al. 2019 using SMAP surface soil moisture and GPM precipitation retrievals.</p> <p><strong>Citations</strong><br> Those that use the stable isotope table are asked to cite our Water Resources Research publication (in revision) as well as the 45 references contributing to the table.<br> Those that use the correlation length scale dataset are asked to cite:<br> Short Gianotti, D.J., Salvucci, G.D., Akbar, R., McColl, K.A., Cuenca, R., Entekhabi, D., 2019. Landscape water storage and subsurface correlation from satellite surface soil moisture and precipitation observations. Water Resour. Res. 9111–9132. https://doi.org/10.1029/2019wr025332</p>
Data for: Emergent dynamics of adult stem cell lineages from single nucleus and single cell RNA-Seq of Drosophila testes
<p><span>Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in <em>Drosophila</em> starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study (Li et al., 2022). With over 44,000 nuclei and 6,000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, <em>in situ</em> hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function <em>in vivo</em>.</span></p>
Multi-proxy agreement on Atlantic circulation dynamics since the last ice age: Model output data
<p>This dataset contains model output for the simulations presented in: <em>"Multi-proxy agreement on Atlantic circulation dynamics since the last ice age"</em>.</p> <p> </p>
A population in perpetual motion: highly dynamic roosting behaviour of a tropical island endemic bat
<p>Dataset and R script for the manuscript "<strong>A population in perpetual motion: highly dynamic roosting behaviour of a tropical island endemic bat</strong>". During the embargo period, data is available upon request from the authors (muriel.dietrich@ird.fr).</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases
<p>The files included here are a set of Galaxy workflows, starting structure files (PDB, mol2, and frcmod), and specialized force field files (ZAFF) for the simulation of coronavirus helicases in the apo and drug-bound state. The inhibitor molecules include those from virtual screening (FCID1 and thioguanine), as well as experimentally validated candidates (Lumacaftor and SSYA10-001).</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Flavivirus Helicases
<p>The files included here are a set of Galaxy workflows and starting structure files (PDB, mol2, and frcmod) for the simulation of flavivirus helicases in the apo and drug-bound state. The inhibitors include the 4th highest ranking compound from a virtual screening of more than 12.7 million drug-like molecules.</p>
Data set for "On the interplay between lipids and asymmetric dynamics of an NBS degenerate ABC transporter"
<p>Supplementary data from molecular dynamics simulations performed on different states of bMRP1 (IF apo, IF ATP- and/or LTX-bound, as well as OF ATP-bound states) and embedded in different lipid bilayer membranes (namely POPC, POPE, POPC:POPE (3:1), POPC-Chol (3:1) and POPC:POPE:Chol (2:1:1).</p> <p>Are included:</p> <p>- Initial and postMD data</p> <p>- MD inputs used</p> <p>- Raw source data used for plot (ABC structural parameters, H-bond and non-covalent analyses, lipid order parameters, and efficiencies from Allopath tool)</p> <p>- PCA supplementary movies (PC1)</p>
Computational analyses of dynamic visual courtship display reveal diet-dependent and plastic male signaling in Rabidosa rabida wolf spiders
<p>It has long been a challenge to quantify the variation in dynamic motions to understand how those displays function in animal communication. The traditional approach is dependent on labor-intensive manual identification/annotation by experts. However, the recent progress in computational techniques provides researchers with toolsets for rapid, objective, and reproducible quantification of dynamic visual displays. In the present study, we investigated the effects of diet manipulation on dynamic visual components of male courtship displays of <em>Rabidosa</em> <em>rabida</em> wolf spiders using machine learning algorithms. Our results suggest that (i) the computational approach can provide an insight into the variation in the dynamic visual display between high- and low-diet males which is not clearly shown with the traditional approach and (ii) males may plastically alter their courtship display according to the body size of females they encounter. Through the present study, we add an example of the utilization of recent computational techniques for understanding the evolution of animal behaviors.</p>
A complete picture of cation dynamics in hybrid perovskite materials from solid-state NMR spectroscopy
<p> Raw, collated NMR and XRD data for the article "A Complete Picture of Cation Dynamics in Hybrid Perovskite Materials from Solid-State NMR Spectroscopy". For further details see the readme.txt file.</p>
Non-reproductive dispersal: An important driver of migratory range dynamics and connectivity
<p>Dispersal is the primary ecological process underpinning spatial dynamics in motile species by generating flux in reproductive locations over time. In migratory species, dispersal can also occur around non-breeding ranges, but this form currently lacks a unifying theoretical framework. We present a novel conceptual model for dispersal in migrants that builds upon existing literature, differentiating 'reproductive' dispersal (i.e. changes in breeding locations) from 'non-reproductive' dispersal, which we define as movements resulting in inter-annual or inter-generational changes in non-breeding locations. Crucially, unlike reproductive dispersal where movement outcomes are naturally propagated between generations, the outcomes of non-reproductive dispersal can be non-heritable even if dispersers survive to reproduce. We simulate a non-social migratory population with a genetically-determined migratory programme to model how heritability of this program influences both migratory connectivity and range shift propensity. When exposed to spatially-uncoupled shifts in habitable ranges (i.e. seasonal climate niches shifting at different rates), long-term persistence of simulated populations required changes in migratory programmes to arise through heritable forms of non-reproductive dispersal (e.g. mutations in migratory gene complexes). By contrast, non-heritable dispersal mechanisms (e.g. weather drift, navigation errors) did not drive long-term shifts in non-breeding ranges, despite being a major component of realised dispersal and migratory connectivity patterns. Migratory connectivity metrics conflate these heritable and non-heritable drivers of non-reproductive dispersal and therefore have limited power in predicting spatial population responses to environmental change. Our models provide a framework for improving our understanding of spatial dynamics in migratory populations and highlight the importance of teasing apart the genetic or cultural mechanisms that drive inter-generational migratory variability in order to evaluate and predict range plasticity in migrants.</p>
Literature review of occurrence and transmission dynamics of SARS-CoV2 in mammal animals
<p>A literature review has been conducted, focusing on susceptibility of wild and domestic animal species reported under field and laboratory conditions, considering different diagnostic tests (e.g., virus isolation, RNA and antibody detection), infection dynamic, pathogenesis, immunity and further transmission of the virus. Review protocol and data extraction tables are available.</p> <p>The following review questions were considered:</p> <ul> <li>Which mammal species have been reported positive to SARS CoV 2 in the field (either at RNA, isolated virus, antibodies), and their geographical distribution?</li> </ul> <ul> <li>Which wild and domestic mammal species are susceptible to SARS CoV 2 in laboratory conditions</li> <li><em>Dynamic of infection and pathogenesis</em>: <ul> <li>What are confirmed infection routes for these species?</li> <li>What is the length of the incubation period in these species</li> <li>What are the clinical signs, if any, their severity, duration, etc.</li> <li>Which species are able to shed the virus (even in absence of clinical signs)</li> <li>Do these species develop protective immunity</li> </ul> </li> <li>Which of these species can further transmit the disease to same/other species ?</li> <li>What are the genetic virus variants in each species where it is isolated?</li> <li>In which animals vaccines against SARS CoV 2 have been tested, and what are the results in terms of immunogenicity, protection to challenge, or safety?</li> </ul> <ul> <li>Which diagnostic tests are used in animals to detect SARS CoV <ul> <li>Type of test</li> <li>Test performance (Se, Sp)</li> <li>Sample matrix used</li> </ul> </li> </ul>
X-ray scattering datasets associated with the publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation"
<p>X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG).</p>
The evolution of dynamic and flexible courtship displays that reveal individual quality
Abstract Sexual selection is a major force shaping morphological and behavioral diversity. Existing theory focuses on courtship display traits such as morphological ornaments whose costs and benefits are assumed be to fixed across individuals' lifetimes. In contrast, empirically observed displays are often inherently dynamic, as vividly illustrated by the acrobatic dances, loud vocalizations, and vigorous motor displays involved in courtship behavior across a broad range of taxa. One empirically observed form of display flexibility occurs when signalers adjust their courtship investment based on the number of rival signalers. The predictions of established sexual selection theory cannot readily be extended to such displays because display expression varies between courtship events, such that any given display may not reliably reflect signaler quality. We thus lack an understanding of how dynamic displays coevolve with sexual preferences and how signalers should tactically adjust their display investment across multiple courtship opportunities. To address these questions, we extended an established model of the coevolution of a female sexual preference and a male display trait to allow for flexible, dynamic displays. We find that such a display can coevolve with a sexual preference away from their naturally selected optima, though display intensity is a weaker signal of male quality than for non-flexible displays. Furthermore, we find that males evolve to decrease their display investment when displaying alongside more rivals. This research represents a first step towards generalizing the findings of sexual selection theory to account for the ubiquitous dynamism of animal courtship. Significance statement Animal courtship displays are typically costly for survival: songs attract predators; dances are exhausting; extravagant plumage is cumbersome. Because of the trade-off between mating benefits and survival costs, displaying individuals often vary their displays across time, courting more intensely when the potential benefit is higher or the cost is lower. Despite the ubiquity of such adjustment in nature, existing theory cannot account for how this flexibility might affect the coevolution of displays with sexual preferences, nor for the patterns of tactical display adjustment that might result, because those models treat displays as static, with fixed costs and benefits. Generalizing a well-studied model of sexual selection, we find that a static display and a flexible display can evolve under similar conditions. Our model predicts that courtship should be less intense when more competitors are present.
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.