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11,506 results for “Dynamics”
The determinants of short-term dynamics of corporate shares market value during periods of decline and return to the original values of shares market
<p>The determinants of short-term dynamics of corporate shares market value during periods of decline and return to the original values of shares market</p>
Determinants of short-term dynamics of corporate shares market value during periods of decline and return to the original values of shares market
<p>The determinants of short-term dynamics of corporate shares market value during periods of decline and return to the original values of shares market</p>
FIG. 4. Mean values and 95 in Reproductive dynamics of the nectarivorous Geoffroy's tailless bat Anoura geoffroyi (Glossophaginae) in a highland Neotropical area of Brazil, with evidence of a mating period
FIG. 4. Mean values and 95% confidence intervals related to gonadosomatic index, epididymis-somatic index and Sertoli cell efficiency obtained during rainy and dry seasons and mating and non-mating periods of A. geoffroyi. Confidence intervals overlap indicates non-significant difference between the estimated values
FIG. 1 in Reproductive dynamics of the nectarivorous Geoffroy's tailless bat Anoura geoffroyi (Glossophaginae) in a highland Neotropical area of Brazil, with evidence of a mating period
FIG. 1. Histological sections of ovaries, uteri and uterine tubes of the nectarivorous bat A. geoffroyi. A) primordial follicle (Pf) and unilaminar primary follicle (Ulf). B) primordial follicle (Pf), unilaminar primary follicle (Ulf) and multilaminar primary follicle (Mlf). C) primordial follicle (Pf) and antral follicle (Af). D) mature follicle (Mf), scale bar: 22 µm. E) polarized ovary presenting primordial follicles (Pf) located in the antimesometrial cortex area and corpus luteum (Cl), scale bar: 88 µm. F) spermatozoa (Sp) housed in uterine crypts, scale bar: 45 µm. G) uterine tube containing an embryo in initial stage of development (identified with the help of P. H. Krutzsch, University of Arizona; Tucson, Arizona, EUA), scale bar: 42 µm. H) gravidic uterus showing an embryo in initial stage of development with neural tube evident, scale bar: 25 µm. All sections H–E stained
Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure
<p>These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows:</p> <p>Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.</p>
Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure
<p>These datasets accompany the Zemuhe fault surface coordinates that were used for construction of the fault geometry model, the observational fault surface slip of the 1850 Xichang M 7.5 earthquake and the intensity distribution in Estimation of the nucleation location and rupture extent of the 1850 Xichang, Sichuan, China, earthquake by dynamic rupture simulations on a multi-segment stepover structure submitted to Earth and Space Science by Yu et al. (2023). The data is structured as follows:</p> <p>Once unzipped the data within the archive are six txt files: fault_surface_slip_Feng.txt, which is from Feng et al. (2000); fault_surface_slip_Ren.txt, which is from Ren and Lin (2010), fault_surface_slip_Yu.txt, which is from Yu et al., (2001); intensity.txt, which is from Feng et al. (2000); landslides.txt, which is from Guo et al. (2014) and Cao (2015); zemuhe_fault_surface_coordinates.txt, which is from Working group for geologic mapping on Zemuhe fault (2000). The file folder stress_configuration contains the initial stress setup for the model in Figure 5c.</p>
Estimation of worker population size-density by nest counting in the Asian weaver ant, Oecophylla smaragdina (Hymenoptera: Formicidae) and it's dynamic in oil palm plantations industry
<p>Supplementary material-information supporting the article of research related to the Asian weaver ant population size-density in the oil palm plantations. The findings suggested an abundant numerical amount of individual workers per colony. The weaver ant were self-sustainable surviving during long years i.e. more than 20 years. This is the first study carried out on a large scale in oil palm plantation directly in the field by gathering only empirical data and monitor the population dynamic on a long term basis. </p>
Deciphering the structural dynamics in molten salt-promoted MgO-based CO2 sorbents and their role in the CO2 uptake. Open data access
<p><strong>Open data for publication "Deciphering the structural dynamics in molten salt-promoted MgO-based CO<sub>2</sub> sorbents and their role in the CO<sub>2</sub> uptake" </strong></p>
FIG. 3 in Breeding Population Dynamics of Threatened Crawfish Frogs Inform Targets for Habitat Management
FIG. 3. Correlation between tadpole survival and the instantaneous population growth rate (k) for 1,000 points drawn randomly from 1,000 simulations of stochastic population simulations of Nate's Pond with (left panel) and without (right panel) immigration included in the simulation model. The white line is a third order polynomial function fitted to the data, and the gray band represents the 95% confidence interval. The horizontal dashed line indicates a k of 1. The distributions of tadpole survival rates and k values are provided as marginal plots.
FIG. 2 in Breeding Population Dynamics of Threatened Crawfish Frogs Inform Targets for Habitat Management
FIG. 2. Percent change in the instantaneous population growth rate (k) in response to a percent change in female Crawfish Frog tadpole, juvenile, or adult survival rates for a population located in southwest Indiana, USA, between 2009–2013. Percent change in each vital rate represents the general range of that vital rate observed in this study. Change was estimated by varying the focal rate while holding all other rates constant.
FIG. 1 in Breeding Population Dynamics of Threatened Crawfish Frogs Inform Targets for Habitat Management
FIG. 1. (A) Post-birth, females-only, stage matrix model of Crawfish Frogs that accommodates age and state-specific transition probabilities and the ''temporary emigration'' of individuals that skip breeding or breed at other sites. (B) Projection matrix of the model used to estimate elasticities and sensitivities of vital rates. Parameters in the matrix model: embryonic survival (Se), tadpole survival (St), metamorph and juvenile survival (Sj), adult survival following the first breeding event (Sa1), adult survival following two or more breeding events (Sa2), probability that a one-year-old juvenile matures at age 2 (p), probability that a two-year-old juvenile matures at age 3 (q), the probability that a mature individual emigrates to become a non-breeding individual (c22), probability that a non-breeding individual remains a non-breeding individual (c'), and clutch size multiplied by 0.5 to represent half of all eggs developing into females (C). We assumed that all individuals mature by age 4. Individuals that emigrate to become a nonbreeding individual may either skip reproduction or breed at another site.
Data from: Two forms of asynchronous release with distinctive spatiotemporal dynamics in central synapses
<p>Asynchronous release is a ubiquitous form of neurotransmitter release that persists for tens to hundreds of milliseconds after an action potential. How asynchronous release is organized and regulated at the synaptic active zone (AZ) remains debatable. Using nanoscale-precision imaging of individual release events in rat hippocampal synapses, we observed two spatially distinct subpopulations of asynchronous events, ~75% of which occurred inside the AZ and with a bias towards the AZ center, while ~25% occurred outside of the functionally defined AZ, i.e., ectopically. The two asynchronous event subpopulations also differed from each other in temporal properties, with ectopic events occurring at significantly longer time intervals from synchronous events than the asynchronous events inside the AZ. Both forms of asynchronous release did not, to a large extent, utilize the same release sites as synchronous events. The two asynchronous event subpopulations also differ from synchronous events in some aspects of exo-endocytosis coupling, particularly in the contribution from the fast calcium-dependent endocytosis. These results identify two subpopulations of asynchronous release events with distinctive organization and spatiotemporal dynamics.</p>
Ab initio multiple spawning simulations for "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>60 ICs a(0.82)-2SA-cas(6,4)-SCF/6-31G* using AIMS/uPBE0-D3 for 1st ps of alpha-terpinene photochemistry. 20 for each<br> conformer upon rotation around the isopropyl group. Each initial condition (IC) is saved in a<br> separate folder labeled after conformers m, p, t, and with the index number of the initial condition.<br> The folder contains the positions and amplitudes of all trajectory basis functions (TBF) arising from<br> the initial condition as well as information about their coupling. The TBF index 1 always refers to<br> the TBF launched in the Franck-Condon region of the excited state based on an initial condition<br> sampled from a ground state Wigner distribution. </p> <p>Description of the folders and subsequent files in each IC folder:</p> <p>Simulation_data <br> X-YYYY : X is the isomer and YYYY is the initial condition number<br> "Positions.x.xyz" : Files containing the geometries of each TBF in cartesian coordinates in Angstroms<br> at each time step. The "x" in the filename corresponds to the index of the TBF.<br> "Amp.x" : Files containing the TBF amplitudes for each timestep.<br> "Spawn.log" : File containing timing information about the spawning events.<br> "S.dat" : File containing coupling matrices between the TBFs for every time step.<br> "ext_x" : Folders containing extensions of TBFs on DFT level. Each folder contains a file<br> "coors.xyz" with cartesian coordinates in Angstroms at each time step. The<br> timestep size of the DFT trajectories is uniformly 0.5 femtoseconds. Only TBFs in<br> the groundstate are extended on DFT level. Therefore, there is no folder "ext_1".</p> <p>Simulation_Parameters : Parameters for FMS and TeraChem nonadiabatic dynamics <br> X-rotamer : x is isomer containing parameter files for all X rotamers<br> "c0.casscf" : Binary file with the alpha(0.82)-SA2-CAS(6,4)-SCF/6-31G* orbitals<br> "Geometry.dat" : Initial condition (position and momentum) to start the FMS/TeraChem nonadiabatic dynamics<br> "Control.dat" : Parameter file for running FMS<br> "misc_options" : Parameter file for running TeraChem <br> "DFT-tc.in" : TeraChem adiabatic dynamics on ground electronic state (ext_x)<br> </p>
Data and code to create figures of "Rehybridization dynamics into the pericyclic minimum of an electrocyclic reaction imaged in real-time"
<p>Instruction of making Fig 4 in the main text:<br> 04/07/2023, SLAC, CA<br> Yusong (Liu), on behalf of Thomas (Wolf)</p> <p>1. Making Figure 1:<br> Figure 1 is fully vectorized with file 'Figure-1.svg', also saved in a png version, 'Figure-1.png'</p> <p>2. Making Figure 2:<br> The data plots in panels a and b are produced from a Matlab script: 'MakingFig2MainText.m'<br> This script loads data saved in 'TerpNatCommFig2.mat' and produce several files if using the saving condition:<br> 'PDFStaticTwoDelaysExpSim_22.fig'<br> 'PDFStaticTwoDelaysExpSim_22.png'<br> 'PDFStaticTwoDelaysExpSim_22.svg'<br> The final Figure 2, 'Figure-2.svg', then was assembled using 'PDFStaticTwoDelaysExpSim_22.svg' and 'Molskeleton.svg'.</p> <p>3. Making Figure 3:<br> The data plots in panels a, b, and c are produced from a Matlab script: 'MakingFig3MainText.m'<br> This script loads data saved in 'TerpNatCommFig3.mat' and produce several files if using the saving condition:<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.fig'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.png'<br> 'Fig3PDFFalseCMapLineOutExpSim_V10.svg'<br> The final Figure 3, 'Figure-3.svg', then was assembled using 'Fig3PDFFalseCMapLineOutExpSim_V10.svg' and 'MolCartoonsFig3.svg'.</p> <p>4. Making Figure 4:<br> (1). Drawing the signal in Fig. 4 panel a<br> Fig. 4a was generated from a py script: 'MakingFig4aMainText.ipynb'<br> Run this script and it will load the data set 'MainFig4aData.npy'generate a figure showing Fig. 4a.<br> Change the figure saving condition to decide whether or not save the figure to both .svg and .png files as below:<br> 'Figure-4a.png'<br> 'Figure-4a.svg'<br> (2). Drawing the signals in Fig. 4 panels b and c<br> These two panels, the signals are drew from a Matlab script: 'MakingFig4bcMainText.m'<br> Run this script and it will load data set 'TerpNatCommFig4.mat' and plot panels b and c<br> Change the figure saving conditions to decide whether or not save to both .svg and .png files as below:<br> 'Fig4DataPanelsbc_v11.fig'<br> 'Fig4DataPanelsbc_v11.png'<br> 'Fig4DataPanelsbc_v11.svg'<br> (3). Fig 4 is then assembled with the 3 data plot panels, molecular cartoons, and equations:<br> Data plot panels:<br> 'Figure-4a.svg'<br> 'Fig4DataPanelsbc_v11.svg'<br> Molecular cartoons:<br> 'Fig4aCartoon.svg' 'Fig4bCartoon.svg' 'Fig4cCartoon.svg'<br> And equations:<br> 'Fig4bEquPhi.svg' 'Fig4cEquPsi.svg'<br> Fig 4 is fully vectorized with file: 'Figure-4.svg' and also saved as a .png version 'Figure-4.png'</p> <p>5. Making Figure-5:<br> The whole Fig. 5 is produced from a single .py script: 'MakingFig5MainText.ipynb'<br> Run the script and it will plot Figure-5. If choosing the save conditions, then it will save Figure-5 to 'Figure-5.svg' and 'Figure-5.png'.</p>
Dataset for "Population-level transposable element expression dynamics influence trait evolution in a fungal crop pathogen"
<p><strong>Supplementary Tables</strong></p> <p><strong>Supplementary Table S1: </strong>SRA accession list of RNAseq reads.</p> <p><strong>Supplementary Table S2:</strong> Genomic localization of TEs in gene elements and 10 kb windows upstream and downstream of the transcription start site (TSS) in the reference genome IPO323.</p> <p><strong>Supplementary Table S3:</strong> Genome-wide TE insertion polymorphism (TIPs) in the pathogen population. 0 represents TE absence and 1 represents TE presence.</p> <p><strong>Supplementary Table S4:</strong> Gene expression (log-transformed RPKM) values across the population.</p> <p><strong>Supplementary Table S5:</strong> Locus-specific transcript abundance at individual TE loci (FPKM) across individuals.</p> <p><strong>Supplementary Table S6:</strong> Percent of expressed copies within each TE family in the reference genome IPO323 and percent expressed TE copies in each TE family across the population.</p> <p><strong>Supplementary Table S7:</strong> Linkage disequilibrium of TIP in the genome and neighboring SNPs within the 600bp distance from the TE loci.</p> <p><strong>Supplementary Table S8:</strong> Genome-wide association mapping of the virulence-associated trait (PLACP: percent leaf area covered by pycnidia) and TE insertion polymorphisms in the genome.</p> <p><strong>Supplementary Table S9:</strong> TIPs in the genome significantly associated with metabolite peak intensity variation in the pathogen population (filtered by Bonferroni threshold).</p>
Cone-beam Gel phantom data for dynamic X-ray tomography
<p>The gel phantom was constructed to simulate the flow of iodine-based contrast agents in plant stems, whilst being more tolerant to the high radiation dose of X-ray tomography scanning. A thorough explanation of the phantom, its motivation and the measurement setup can be found in <a href="https://arxiv.org/abs/2003.02841">arXiv:2003.02841</a>. Attached is a brief documentation which focuses primarily on the higher dimensional 3D files and the key differences between the two data sets.</p> <p>The primary measurements consist of 17 consecutive time frames, with an initial state of no contrast agent followed by a steady increase thereof, primarily via downward diffusion into the gel body over time. Each time frame consists of 360 projections acquired with a cone-beam micro-CT scanner, resulting in a sequence of 3D cone-beam sinograms. Moreover, the first time frame is provided with higher angular sampling of 720 projections, and an additional 18th time frame with 1600 projections.</p> <p>The data set contains the following MATLAB files:</p> <ul> <li><strong>3dGelPhantomData_b4.mat</strong></li> <li><strong>3dGelPhantomData_b8.mat</strong></li> <li><strong>3dGelPhantomData_b16.mat</strong></li> <li><strong>3dGelPhantomExtraFrames_b4.mat</strong></li> <li><strong>3dGelPhantomExtraFrames_b8.mat</strong></li> <li><strong>3dGelPhantomExtraFrames_b16.mat</strong></li> </ul> <p>And a more detailed documentation: <strong>ConeBeamGelPhantomDocumentation.pdf </strong>(v1.0)</p> <p>The proper use of these files requires the <a href="https://www.astra-toolbox.com/">ASTRA Toolbox</a>, <a href="https://www.cs.ubc.ca/labs/scl/spot/">the Spot Linear-Operator Toolbox</a>, and <a href="https://www.mathworks.com/matlabcentral/fileexchange/74417-heltomo-university-of-helsinki-ct-data-toolbox">the HelTomo Toolbox</a>. In addition the operators and reconstruction algorithms provided by ASTRA for 3D geometry <strong>require CUDA capable graphics card </strong>(GPU) due to large amounts of (parallel) computations. The older <a href="https://doi.org/10.5281/zenodo.3696816">2D data set</a> provides a computationally lighter and more user-friendly starting point for inexperienced users.</p>
Dynamic interplay between target search and recognition for a Type I CRISPR-Cas system
<p>CRISPR-Cas effector complexes enable the defense against foreign nucleic acids and have recently been exploited as molecular tools for precise genome editing at a target locus. To bind and cleave their target, the CRISPR-Cas effectors have to interrogate the entire genome for the presence of a matching sequence. Here we dissect the target search and recognition process of the Type I CRISPR-Cas complex Cascade by simultaneously monitoring DNA binding and R-loop formation by the complex. We directly quantify the effect of DNA supercoiling on the target recognition probability and demonstrate that Cascade uses facilitated diffusion for its target search. We show that target search and target recognition are tightly linked and that DNA supercoiling and limited 1D diffusion need to be considered when understanding target recognition and target search by CRISPR-Cas enzymes and engineering more efficient and precise variants. </p>
Data and codes for "Dynamical Quantum Phase Transitions Following Double Quenches"
<p>Codes for producing figures and data in the paper "Dynamical Quantum Phase Transitions Following Double Quenches" (<a href="https://arxiv.org/abs/2302.01661">https://arxiv.org/abs/2302.01661</a>). Included are the data for the return rates and python codes for producing this data and the figures. A Mathematica file is included which was used for analysing the conditions for DQPTs in the paper, and for producing the remaining figures.</p>
Files associated with coordination of actin plus-end dynamics paper.
<p>Files associated with coordination of actin plus-end dynamics paper.</p>
Online survey data: Exploring the socio-economic dynamics and innovation capacities of rural food and farming microbusinesses
<p>Online survey data which, in compilation with focus group data, formed the basis for our NICRE-funded report into the socio-economic dynamics and innovation capacities of rural food and farming microbusinesses. Full report is available here: https://eprints.glos.ac.uk/11449/ </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.