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10,119 results for “IT Support”
Figure 16 in Support polygons and symmetrical gaits in mammals
Figure 16. Possible adaptive value of diagonal-sequence walking gaits in primates. At the moment of forefoot touchdown, when weight is about to be transferred to a new and untested substrate, the line of gravity (grey arrow: the vertical through the body's centre of mass, estimated here as the vertical through the midpoint of an ischium-to-occiput line) passes approximately through the supporting hindfoot (grey tone) in the D-S walk of the baboon (top), but not in the L-S walk of the horse (bottom). In primates or other arboreal animals with marked grasping specializations of the hind foot, the primate support pattern allows the animal to draw back or regain its balance if the new support breaks or bends precipitously.
Figure 11 in Support polygons and symmetrical gaits in mammals
Figure 11. Modified Hildebrand diagram for the walking gaits (average duty factor ≥ 50) in our data set. The theoretically relevant duty factor in each case – Sh for diagonal-sequence walks, Sf for lateral-sequence walks – is plotted against diagonality. The diagonal lines show the distribution predicted by the support-polygon model as initially formulated.
Figure 12 in Support polygons and symmetrical gaits in mammals
Figure 12. Modified Hildebrand diagram for our data set: revised support-polygon model. The relevant duty factor in each case – which is now Sf for L-S, D-C gaits (Rule 2: D = 100 – Sf) and Sh for the others – is plotted against diagonality. The diagonal lines show the distribution predicted for walking gaits by the revised support-pattern model. Compare Figs 3 and 11.
Figure 8 in Support polygons and symmetrical gaits in mammals
Figure 8. Values of diagonality predicted from the support-polygon model. Duty factors and graphic conventions as in Figure 4. To minimize overall bipedality while maximizing diagonal bipedality ('2-diag'), a quadruped walking with a lateral-sequence gait (A) should adopt a Dvalue equal to 100 minus the forelimb duty factor (100 − Sf: horizontal arrows). A quadruped using a diagonal-sequence walk (B) should adopt a D-value equal to the hindlimb duty factor (Sh: horizontal arrows). In A, this entails that forefoot liftoffs and ipsilateral hind footfalls should be simultaneous; in B, fore footfalls and ipsilateral hindfoot liftoffs should be simultaneous (vertical arrows). In both A and B, diagonal bipedality ('2-diag') comprises the minimal percentage (64%) of the stride period possible for these duty factors. The animal is supported on 3 legs ('3-ped') for the remaining 36% of the stride period.
Figure 15 in Support polygons and symmetrical gaits in mammals
Figure 15. Values of diagonality predicted for lateralsequence, lateral-couplets walks from the revised supportpolygon model. Duty factors and graphic conventions as in Figure 4. To minimize overall bipedality while maximizing unilateral bipedality ('2-unilat'), a quadruped walking with an L-S, L-C gait should adopt a D-value equal to the hindlimb duty factor minus 50 (horizontal arrows). This entails that fore footfalls and contralateral hind liftoffs should be simultaneous (vertical arrows). Compare Figure 8.
Figure 7 in Support polygons and symmetrical gaits in mammals
Figure 7. Curves of unilateral bipedality (Bu) and diagonal bipedality (Bd) against diagonality (D) for the general case where the duty factors of the hind- and forelimbs (Sh, Sf) are unequal. Kh = Sh − 50; Kf = Sf − 50. All variables represent durations expressed as percentages of the stride period (time between two successive falls of the same foot). See text for explanation.
Figure 6. Diagrammatic quadruped seen from above. Points A-D in Support polygons and symmetrical gaits in mammals
Figure 6. Diagrammatic quadruped seen from above. Points A-D represent the animal's four feet; point G represents the vertical drawn through its centre of gravity. The animal will be more nearly balanced when standing on two diagonally opposite feet (A, C) than when standing on two ipsilateral feet (A, D), as long as G is closer to line AC than to line AD – which will be the case in most situations.
Figure 2 in Support polygons and symmetrical gaits in mammals
Figure 2. Plot of diagonality against (average) duty factor: the Hildebrand diagram. Positions of some mammalian gait types (two-letter codes) are plotted on the diagram. DS, diagonal-sequence walks; LS, lateral-sequence walks; RP, running pace; RT, running trot; WP, walking pace; WT, walking trot.
Figure 10 in Support polygons and symmetrical gaits in mammals
Figure 10. Running gaits (Sh <50> Sf) in our data, plotted on the Hildebrand diagram. The arrow ('trot line') indicates the diagonality predicted for running gaits (D = 50).
Figure 12. Supporting synapomorphies, mapped onto a in A new stereospondyl from the German Middle Triassic, and the origin of the Metoposauridae
Figure 12. Supporting synapomorphies, mapped onto a cladogram of the preferred phylogeny. Only unequivocal character distribution is counted here as synapomorphy.
Hard to catch: experimental evidence supports evasive mimicry
<p></p><p>Most research on aposematism has focused on chemically defended prey, but the signaling difficulty of capture remains poorly explored. Similar to classical Batesian and Müllerian mimicry related to distastefulness, such 'evasive aposematism' may also lead to convergence in warning colours, known as evasive mimicry. A prime candidate group for evasive mimicry areAdelphabutterflies, which are agile insects and show remarkable colour pattern convergence. We tested the ability of naive blue tits to learn to avoid and generalizeAdelphawing patterns associated with the difficulty of capture and compared their response to that of birds that learned to associate the same wing patterns with distastefulness. Birds learned to avoid all wing patterns tested and generalized their aversion to other prey to some extent, but learning was faster with evasive prey compared to distasteful prey. Our results on generalization agree with longstanding observations of striking convergence in wing colour patterns amongAdelphaspecies, since, in our experiments, perfect mimics of evasive and distasteful models were always protected during generalization and suffered the lowest attack rate. Moreover, generalization on evasive prey was broader compared to that on distasteful prey. Our results suggest that being hard to catch may deter predators at least as effectively as distastefulness. This study provides empirical evidence for evasive mimicry, a potentially widespread but poorly understood form of morphological convergence driven by predator selection.</p><p></p>
Interpersonal Support (ISEL) in Hungarian communities experienced past disasters
<p>The dataset was generated from answers collected among citizens living in Hungarian Settlements that experienced natural disasters in the recent decades. The basis of the dataset is the Interpersonal Support Evaluation List, extended with questions related to the disaster experiences, as well as social and demographic background.</p>
Data supporting "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study"
<p>Data and code supporting the article "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study".</p> <p> </p>
Research data supporting: "Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly"
<p>This repository contains the set of data shown in the paper "<em>Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly</em>", published on <em>ACS Nano</em> (DOI: 10.1021/acsnano.1c0500).</p> <p>All the input data needed to run the simulations and get the results are organized in 5 different folders:</p> <ul> <li> <p><code>1-MinimlisticCGModel/</code></p> <p>in this folder there are the Gromacs and PLUMED input files for reproducing the minimalistic coarse-grained model shown in the article.</p> </li> <li> <p><code>2-AAandFineCGModels/</code></p> <p>the folder contains the atomistic (<code>monomerAA/</code>) and the coarse-grained Martini "wet" (<code>monomerGCWet/</code>) input files for modelling the Original monomer with one carboxyl group. The coarse-grained Martini "dry" (<code>monomerCGDry/</code>) model is reported for the Original monomer at the three different charged states.</p> </li> <li> <p><code>3-UnbiasedMD/</code></p> <p>this folder includes the Gromacs input files needed to reproduce the Unbiased simulations as shown in the article.</p> </li> <li> <p><code>4-MetaD/</code></p> <p>this folder incorporates the Gromacs and PLUMED input files necessary to reproduce both the Infrequent CG Metadynamics simulations (<code>InfrequentMetaD/</code>) and the Multiple-walker CG Metadynamics simulations (<code>MultipleWalkerMetaD/</code>)</p> </li> <li> <p><code>5-InSilico/</code></p> <p>in this folder all the files used to run the simulations about the exoliation experiments (<code>Original/</code>, <code>Type-1/</code>, <code>Type-2/</code>, <code>Type-3/</code>, <code>Type-4/</code>) are reported. Every subfolder contains all input files for the three different charged monomers (<code>Olig1-/</code>, <code>Olig2-/</code>, <code>Olig3-/</code>).</p> </li> </ul>
Sabatier principle of metal–support interaction for design of ultrastable metal nanocatalysts
<p>Data for the figures of the publication "Sabatier principle of metal–support interaction for design of ultrastable metal nanocatalysts".</p>
Supporting Data for Human Factors in Developing Automated Vehicles:A Requirements Engineering Perspective
<p>This data set complements our manuscript in submission with the title:</p> <p>"Human Factors in Developing Automated Vehicles: A Requirements Engineering Perspective"</p> <p>We provide two files:</p> <p>a) the interview guide</p> <p>b) an overview that maps from themes to example quotes and codes derived from particular interview subjects</p>
Daily maximum VPD - supporting data for Jain et al. 2021, Nature Climate Change
<p>Global daily maximum Vapour Pressure Deficit (VPD) for 1979-2020 at 0.25 deg resolution. This data supports the analysis in "Observed increases in extreme fire weather driven by atmospheric humidity and temperature", Jain et al. 2021, accepted for publication in Nature Climate Change.</p> <p>VPD was calculated using the hourly ERA5 2m temperature and 2m dewpoint temperature using the Alduchov and Eskridge (1996) approximation as implemented in the R package ‘bigleaf’ (Knauer et al. 2018).</p> <p>Variables were processed using inputs from the ERA5 Reanalysis (hourly surface data from 1979–2020, available from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a>). </p> <p>References</p> <p>Alduchov, O. A. & Eskridge, R. E., 1996: Improved Magnus form approximation of saturation vapor pressure. Journal of Applied Meteorology, 35, 601-609</p> <p>Knauer, J., El-Madany, T. S., Zaehle, S., & Migliavacca, M. (2018). Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. <em>PloS one</em>, <em>13</em>(8), e0201114.</p> <p> </p> <p> </p>
"Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.
<p> "Decision support systems halve fungicide compared to calendar-based strategies without increasing disease risk". Supplementary Data 1.https://doi.org/10.1038/s43247-021-00291-8 | www.nature.com/commsenv</p> <p>Dataset includes the results of 80 independent experiments reported in 22 articles and it has a dimension of 329 rows x 42 columns. Further information in "Description Supplementary Data 1.pdf" file. </p> <p>This dataset was assembled including also the data from the publication Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560.</p>
Data in support of the publication "First Detection and Thermal Characterization of Terminator CO2 Ice Clouds with ExoMars/NOMAD" on Geophysical Research Letters
<p>This archive contains the data underlying the 3 Figures in the main manuscript, including the vertical profiles of retrieved aerosols, vertical profiles of CO2 saturation ratio and spatial distribution of the retrieved clouds.</p>
Real data supporting HTTomolib/GPU libraries
<p>This is an archive containing real data for testing methods of <a href="https://github.com/DiamondLightSource/httomolib">HTTomoLib</a> and <a href="https://github.com/DiamondLightSource/httomolibgpu">HTTomoLibGPU</a> packages. Also see Python scripts and images. </p> <div> <h2>Data description:</h2> </div> <h3>I12 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>i12_dataset1.npz</strong> (119647.nxs)</td> <td>180-degrees scan of a sandstone rock</td> <td> <p>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The resolution of the sample is well-defined and the sample is fully within the field of view (FOV). This provides some freedom for experimentation using various preprocesing and reconstruction methods.</td> </tr> <tr> <td><strong>i12_dataset2.npz </strong>(146344.nxs)</td> <td>180-degrees scan</td> <td> <p>Data dimensions <code>[2050, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[51, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td> <p>The sample is larger than FoV and noisy. VoCentering method to find the centre of rotation can be tested on this data.</p> </td> </tr> <tr> <td> <p><strong>i12_dataset3.npz </strong>(119647.nxs)</p> </td> <td>180-degrees scan of a sandstone rock</td> <td> <p>projections, darks and flats are <code>[2050, 2560]</code> as <code>[detY, detX]</code>. Data type is <code>uint16</code>.</p> </td> <td>This data is suitable to test the centering method based on the phase cross-correlation</td> </tr> <tr> <td><strong>i12_dataset4.npz </strong>(<a href="https://zenodo.org/records/1443568">68067.nxs</a>)</td> <td>180-degrees scan</td> <td>Data dimensions <code>[2050, 30, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 30, 2560]</code> and darks are <code>[20, 30, 2560].</code> Data type is <code>uint16</code>.</td> <td>This data is well suitable for testing stripe (ring) removal artefacts and is a part of this <a href="https://zenodo.org/records/1443568">dataset</a>. </td> </tr> <tr> <td><strong>i12_dataset5.npz </strong>(162289.nxs)</td> <td>360-degrees scan</td> <td>Data dimensions <code>[3601, 15, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[101, 15, 2560]</code> and darks are <code>[101, 15, 2560].</code> Data type is <code>uint16</code>.</td> <td>360 degrees scan with the left rotation. Testing 360 to 180 conversion methods.</td> </tr> <tr> <td><strong>i12_dataset6.npz </strong>(164301.nxs)</td> <td>180-degrees scan, bio-sample</td> <td>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</td> <td>Biological sample of a very poor contrast. Phase contrast method needs to be applied to boost the contrast up and then reconstruction with the increased padding of the detector. Iterative methods can be tested. </td> </tr> </tbody> </table> <h3>I13 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td> <p><strong>i13_dataset1.npz </strong>(179623.nxs)</p> </td> <td>360-degrees scan of some biological sample</td> <td> <p>Projection data dimensions <code>[6001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector while reduced the angular dimension.</td> </tr> <tr> <td><strong>i13_dataset2.npz</strong> (179468.nxs)</td> <td>180-degrees scan of some biological sample.</td> <td> <p>Projection data dimensions <code>[2501, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>A well defined resolution for this biological sample.</td> </tr> <tr> <td><strong>i13_dataset3.npz </strong>(180622.nxs)</td> <td>360-degrees scan</td> <td> <p>Projection data dimensions <code>[6001, 3, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 3, 2560]</code> and darks are <code>[40, 3, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector, while reduced the angular dimension. </td> </tr> </tbody> </table> <h3>K11 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>k11_dataset1.npz </strong>(k11-37101)<strong><br></strong></td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[4001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[100, 10, 2560]</code> and darks are <code>[100, 10, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is within the FOV.</td> </tr> <tr> <td><strong>k11_dataset2.npz </strong>(k11-38750)</td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[521, 25, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[10, 25, 2560]</code> and darks are <code>[10, 25, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is larger than the FOV and <span>acquired with a</span> very quick (1ms) exposure. Great for testing with iterative methods. </td> </tr> </tbody> </table> <h3>Simulated data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>geant4_dataset1.npz</strong></td> <td>Geant4 simulated tomographic dataset in 180-degrees parallel-beam. </td> <td> <p>Data dimensions <code>[360, 540, 640]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The data is perfectly centered and within FOV. Various reconstruction methods can be tested, direct and iterative.</td> </tr> <tr> <td><strong>synth_tomophantom1.npz</strong></td> <td> <p><a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> simulated data of 3d Shepp-Logan with noise and stripe artefacts in 180-degrees parallel beam</p> </td> <td> <p>Data dimensions <code>[400, 314, 600]</code> as <code>[detY, angles, detX]</code>, data type is <code>float32</code>.</p> </td> <td>The data is perfectly centered and normalised with negative log taken. Quite a significant number of stripe artefacts present.</td> </tr> </tbody> </table>
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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)
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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.