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144 results for “Deviation”
Dataset for Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization
<p>Dataset for the paper <a href="https://www.algohex.eu/publications/bimdf-quantization/"><strong>Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization</strong></a>, <em>Martin Heistermann, Jethro Warnett, David Bommes</em>, ACM Transactions on Graphics, Volume 42, Issue 4 (2023), <a href="https://doi.org/10.1145/3592437">DOI 10.1145/3592437</a></p> <p>Both archive files (.zip and .tar.zst) have identical contents.</p> <p>Contents:</p> <ul> <li>Results of running various algorithmus variants on the "300" dataset <ul> <li>Intermediate data (e.g., patch decompositions)</li> <li>Final meshes</li> <li>Logfiles</li> <li>Metadata in json format (e.g., utilized settings, achieved energies, detailed runtimes)</li> </ul> </li> <li>Meshes used in all figures</li> </ul> <p> </p>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
Frequencty deviations seen in RWM carrier wave
<p>The RWM (Moscow, Russia) carrier wave was measured using FLEX-1500 (with PowerSDR software) and fldigi frequency analysing software. The location of the receiver was at Lochem, The Netherlands. More information on the measurements and some analysis see: http://www.archaeocosmology.org/eng/04122021eclipse.htm</p> <p> </p>
Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150)
<p>Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150). Details are reported in: Kriston L, Schäfer J, Jacob GA, Härter M, Hölzel LP. Reliability and validity of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3). <em>Eur J Psychol Assess</em> 2013; 29: 205-212.</p> <p>IMPORTANT: This is an opportunity sample that is not representative of any well-defined population. Accordingly, the values should not be used as reference or norm values for the German version of the YSQ-S3.</p>
Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats
<p><span>S</span><span>pecies</span><span> and their interactions are more dynamic over time and space</span> <span>in</span><span> fragmented habitats </span><span>than</span><span> in continuous habitats</span><span>.</span> <span>In fragmented habitats,</span><span> the</span> <span>low </span><span>nestedness</span> <span>of </span><span>mutualistic</span><span> networks may be related to the</span> <span>position</span><span> change</span> <span>of stable (high persistence over time/space) species and interactions in </span><span>the</span><span> network</span><span>s.</span><span> Previous studies</span> <span>have shown that </span><span>s</span><span>table species </span><span>and</span><span> interactions tend to </span><span>be in</span><span> the core position </span><span>of</span> <span>mutualistic</span><span> networks</span><span>. </span><span>H</span><span>owever</span><span>, </span><span>in fragmented habitats</span><span>, </span><span>it remains unknown whether </span><span>stable species or interactions still </span><span>tend to </span><span>be in</span><span> the core position.</span><span> </span><span>To address this gap,</span> <span>here</span><span> we evaluated </span><span>the correlation between the position of proximity to the network core and the temporal/spatial stability of </span><span>species and interactions</span><span>, </span><span>using</span> <span>the </span><span>observation of 42 plant-pollinator networks conducted in a fragmented island landscape over 3 years</span><span>.</span> <span>We showed that temporally/spatially </span><span>stable </span><span>species </span><span>and</span><span> interactions </span><span>deviated from the network core</span><span> to varying degrees</span><span>. Temporally stable plants</span><span> were</span> <span>most likely to deviate from the network core, followed by</span> <span>pollinators and</span> <span>interactions</span><span>, while only </span><span>spatially stable </span><span>pollinators</span><span> tend to </span><span>deviate from the network core</span><span>. </span><span>When unstable species (</span><span>present in few time/space points</span><span>, </span><span>typically specialists) and interactions occupy the network core,</span> <span>they cannot interact with most species in the network </span><span>as</span><span> generalists</span> <span>do</span><span>, </span><span>result</span><span>ing</span> <span>in</span> <span>the</span> <span>decrease of network nestedness. Therefore, from the perspective of</span><span> position and stability,</span><span> s</span><span>table species and interactions </span><span>deviate from the network core</span> <span>in</span> <span>fragmented habitats</span><span>, which </span><span>is an important reason for</span><span> the</span><span> decrease of</span><span> nestedness in </span><span>mutualistic</span><span> networks</span><span>.</span><span> </span><span>Our study</span><span> suggests that protecting</span> <span>plants that</span><span> occupy the core in large plant-pollinator networks is </span><span>essential for</span> <span>maintaining the network persistence in fragmented habitats.</span></p>
The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning
<p>The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts.</p> <p>The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows:</p> <ul> <li>Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。</li> </ul>
FIGURE A4 in Refined methods for estimating spirals and spiral deviations
FIGURE A4. Upper colossal squid beak with wavelet maxima and minima located on the hood. Circle and cross symbol denote the spiral axis. Inset shows the spiral deviations from the L2QL fit, with vertical lines for the center of spiral change and wavelet reconstruction with maxima (solid circles) and minima (open circles). Note the reversal of the inset horizontal scale to match the direction of growth from the rostral tip. See text for detail on wavelet scale and reconstruction.
FIGURE 6 in Refined methods for estimating spirals and spiral deviations
FIGURE 6. Patterns of spiral deviations with accretionary growth (arc length) for four spirals. Apart from the L1 spiral, deviation patterns are similar and have the same vertical scale. Vertical lines and shade (red) locate estimates of where changes in spiral shape occur on the outline.
FIGURE 4 in Refined methods for estimating spirals and spiral deviations
FIGURE 4. Nearest nine spirals to the LR-F1ventral valve outline, as ranked by similarity ratio on both local and global features. Spirals along axes are in approximate order of complexity. The green line represents agreement between local and global features.
FIGURE 5 in Refined methods for estimating spirals and spiral deviations
FIGURE 5. Reducing then increasing error (residual standard deviation) for different spirals fitted to the LR-F1 ventral valve. Absolute least error is not always the best spiral. See text for abbreviations and explanation.
FIGURE 2. Eleven spirals that are more complex than the simple logarithmic spiral, L1 in Refined methods for estimating spirals and spiral deviations
FIGURE 2. Eleven spirals that are more complex than the simple logarithmic spiral, L1 of Figure 1. Outlines are plotted on a logarithmic scale for radius against angle turned about the axis. Vertical lines show spirals with abrupt change, and shaded areas show regions of transitional change between log-piecewise spirals representing accretionary growth (see text for explanation and abbreviations).
FIGURE 1 in Refined methods for estimating spirals and spiral deviations
FIGURE 1. Accretionary growth (blue line) as an outline (A), and as a graph of log e (radius, r) against angle, θ, about an axis (cross symbol) (B). The intercept is the loge(radius) when θ = 0, and the constant slope defines the expansion of the L1 spiral.
FIGURE A1 in Refined methods for estimating spirals and spiral deviations
FIGURE A1. Photograph of a colossal squid upper beak. Arrows indicate two of the high points in undulations along the hood. Scale near the rostral tip is in mm.
FIGURE A3 in Refined methods for estimating spirals and spiral deviations
FIGURE A3. Scree plot of the reduction in error (residual standard deviation) for different spirals fitted to the upper beak of the colossal squid. Generalized transition spirals are denoted by the lowercase letter g.
Figure 7. Results obtained for the mean, the mean standard deviations and the mean standard errors of the independent speech variable in dyslalic subjects (both the control and the experimental group)
<p>Following the logopedic assessment of the investigated subjects, we moved on to the statistic processing of the gathered data, and we analysed the results obtained from the tests administered to the two groups after one year of speech therapy. The conducted analysis was directed both at the overall effectiveness of the speech therapy, and at the importance of strategies for the language development and stimulation, through the use of the computer-assisted Terapers system. It was noticed that the results obtained for the dyslalic subjects (children with pronunciation disorders) from the experimental group improved significantly due to the computer-based therapeutic program, compared to the subjects in the control group (who underwent classical therapy). The mean, the standard deviations and the standard error of the mean obtained for the independent speech variable in dyslalic children (control group and experimental group) are presented in Figure 7.</p>
Fig. 3 in Standard deviation of carabid size in Western German forest succession - a complex picture
Fig. 3. Relationship between SD of elytrae length of male C. violaceus and age of the stands (circles – beech, triangles – Douglas fir).
Fig. 4 in Standard deviation of carabid size in Western German forest succession - a complex picture
Fig. 4. Relationship between SD of elytrae length of female C. violaceus and age of the stands (circles – beech, triangles – Douglas fir).
Fig. 2 in Standard deviation of carabid size in Western German forest succession - a complex picture
Fig. 2. Relationship between SD of elytrae length of male and female C. violaceus (circles – beech, triangles – Douglas fir).
FIGURE 8 in Application of shell spiral deviation methodology to fossil brachiopods: Implications for obtaining specimen ontogenetic ages
FIGURE 8. Example of two fossil brachiopod species, Rafinesquina sp. (left) and Pseudoatrypa sp. (right), which generated problematic spiral deviation graphs. 1, Ventral view 2, Longitudinal-sectional view 3, Spiral fitting 4, Spiral deviations graphs.
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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.