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228 results for “uniformity”
SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the cover of sessile invertebrates, understory macroalgae, and bottom substrate types as determined by a uniform point contact method. The presence of over 150 taxa of sessile invertebrates and macroalgae are recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Multiple species can be recorded at any given point. Percent cover of a given species on a transect can be estimated from UPC observations as the fraction of total points at which that species was present x 100. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. These data are part of SBC LTERs kelp forest monitoring program, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Data collection began in 2000 and this dataset is updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. The two tables in this data package include: 1) The percent cover of sessile invertebrate and understory macroalage; and 2) the percent cover of bottom substrate.
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.
SBC LTER: Reef: Long-term experiment: Kelp removal: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
Dataset for the publication entitled "An exact system of generation for face-milled hypoid gears with uniform depth taper: application to hypoid gear drives with high gear ratio"
Open the record for dataset details and reuse information.
Dataset for the publication "Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper"
<p>This dataset contains geometric and graphics data associated with the referenced paper, enabling the reproduction of the conducted research. </p>
Third Uniform California Earthquake Rupture Forecast (UCERF3) Fault System Solutions
<p>Data files for the Third Uniform California Earthquake Rupture Forecast (UCERF3), as described in <a href="https://doi.org/10.1785/0120130164">https://doi.org/10.1785/0120130164</a>.<br> <br> These data are stored in the original UCERF3 Fault System Solution file format, which uses binary files within zip containers. This format is being revised, and updates to this dataset will be published when the new and more user friendly format is finalized. See <a href="https://opensha.org/File-Formats">https://opensha.org/File-Formats</a> for more information.<br> <br> File descriptions:<br> <br> <strong>Branch Averaged Files</strong></p> <p>These files contain branch-averaged fault system solutions, where rupture properties (magnitude, rake, rate of occurrence, etc) are averaged across all UCERF3 logic tree branches, according to each branch's weighting in the final model. This is the simplest version of the model, and can be used as a quick approximation to mean hazard. One file exists for each fault model, and these files are compatible with the time-dependent version of UCERF3.</p> <ul> <li><em>branch_averaged_ucerf3_sol_FM3_1.zip</em> - fault model 3.1 branch averaged fault system solution</li> <li><em>branch_averaged_ucerf3_sol_FM3_2.zip</em> - fault model 3.2 branch averaged fault system solution</li> </ul> <p><strong>Full Model (Compound Solutions)</strong></p> <p>These files contain the full UCERF3 logic tree, and can be used to extract data for individual logic tree branches (e.g., for use in hazard calculations that consider all epistemic uncertainties).</p> <ul> <li><em>full_ucerf3_compound_sol.zip</em> - full compound solution file with information on all 1,440 time-independent logic tree branches</li> <li><em>full_ucerf3_compound_sol_with_individual_runs.zip</em> - same as above, but also containing rates for each of 10 simulated annealing inversion runs for each logic tree branch (total of 14,400 inversions)</li> </ul> <p><strong>True Mean Solutions</strong></p> <p>A different type of branch averaged solution, the “true mean” solution, is also available. They are similar to the branch averaged fault system solution described above, but instead use duplicate versions of each rupture whenever a key property (rake, magnitude, area) changes. This retains all variability allowing for quick reproduction of mean UCERF3 results with a minimum set of ruptures. The MeanUCERF3 ERF implemented in <a href="https://opensha.org">OpenSHA</a> uses these files and also allows the user to apply various approximations to further reduce the rupture count.</p> <p>Note: These solutions are not compatible with time dependent UCERF3 calculations as multiple instances of each subsection may exist, resulting in rate partitioning between instances and incorrect recurrence intervals for renewal model calculations.</p> <ul> <li><em>true_mean_ucerf3_sol.zip</em> - true mean fault system solution, across both fault models</li> <li><em>true_mean_ucerf3_sol_FM3_1.zip</em> - true mean fault system solution, only for fault model 3.1</li> <li><em>true_mean_ucerf3_sol_FM3_2.zip</em> - true mean fault system solution, only for fault model 3.2</li> </ul> <p><strong>Metadata</strong></p> <p>A copy of the original file format description is included in <em>file_format.md</em>, and is also <a href="https://opensha.org/File-Formats">available online here</a>. A CSV file that includes information on each gridded seismicity location is also included (<em>relm_gridded_region.csv</em>).</p>
The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating
<p>This dataset contains the raw data used for the publication:</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------<br> "The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating"<br> by C. Reiner-Rozman, R. Hasler, J. Andersson, T. Rodrigues, A. Bozdogan and P. Aspermair<br> --------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><br> It consists of the SEM images (in .tif format) and the determined surface coverages (in .dat format) as well as the measured electrical data (in .dat format) of the prepared graphene field-effect transistor chips. Headers/information in the data files are in English. When using this data in any form please refer to the above-mentioned publication.</p> <p>The data is structured according to the figures of the paper. Each folder contains the data relevant to validate the results presented in the respective figure of the publication. The files are labeled according to the following description:</p> <p>"measurement-type"_"chip-number"_"GO-concentration"_"spin-coating speed"</p> <p>"measurement-type": SEM, IDVG, baseline<br> "chip-number": an increasing number of fabricated device (only used when needed)<br> "GO-concentration": 143/214/285 µg/mL of graphene oxide (GO) in solution<br> "spin-coating speed": in rpm</p>
Supplemental Data for "Eyewall Asymmetries and Their Contributions to the Intensification of an Idealized Tropical Cyclone Translating in Uniform Flow"
<p>The repository contains a set of files required to reproduce the idealized tropical cyclone simulation analyzed in the manuscript entitled "Eyewall asymmetries and their contributions to the intensification of an idealized tropical cyclone translating in uniform flow", submitted to the Journal of the Atmospheric Sciences. See the README file for brief descriptions about the content of each file within this repository.</p> <p>The simulation was produced with the Cloud Model 1 (CM1) version 19.7, and CM1 can be downloaded at https://www2.mmm.ucar.edu/people/bryan/cm1/. </p>
Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"
<p>Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in <em>Nature Ecology & Evolution</em>, 2022, https://doi.org/10.1038/s41559-022-01888-8).</p> <p>Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years.</p> <p>Scripts written by Tonke Strack</p> <p>DATA SOURCES<br>* WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019).<br>* LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>* MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005).<br>* planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv</p> <p>DATA<br>1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt<br>2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv<br>3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv</p> <p>CODE<br>1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2)<br>2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series<br>3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4)<br>4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5)</p> <p>*Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber albus</em>, because some studies only reported them together as <em>Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>. In total, 41 species of planktonic foraminifera were included in this study.</p> <p>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. <em>Sci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).</p>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
Benchmarking (multi)wavelet-based dynamic and static non-uniform grid solvers for flood inundation modelling (Simulation results)
<p>Simulation result data for Environment Agency benchmark test 5, Thamesmead hypothetical flood, and Carlisle 2005 case studies, using uniform DG2, adaptive MWDG2, adaptive HWFV1, non-uniform DG2, non-uniform FV1 and non-uniform ACC solvers. </p> <p>Model results are archived in 3 zip files:</p> <ul> <li>EA5.zip contains results of Environment Agency test 5 (Néelz and Pender, 2013)</li> <li>Thamesmead.zip contains results of Thamesmead hypothetical flood (Liang et al., 2008)</li> <li>Carlisle.zip contains results of Carlisle 2005 flooding (Neal et al., 2009)</li> </ul> <p>The results are stored with the following file extensions:</p> <ul> <li>".wd" for 2D flood inundation maps in ESRI ASCII format</li> <li>".stage" for water depth or water level time-series at staging points in tabulated text format</li> <li>".velocity" for velocity time-series at staging points in tabulated text format</li> </ul> <p>Model outputs are stored under directories named for each solver.</p> <p><strong>References</strong></p> <p>Néelz, S., & Pender, G. (2013). Benchmarking the latest generation of 2D hydraulic modelling packages. <em>Environment Agency: Bristol, UK</em>.</p> <p>Liang, Q., Du, G., Hall, J. W., & Borthwick, A. G. (2008). Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver. <em>Journal of Hydraulic Engineering</em>, <em>134</em>(11), 1603–1610. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603)</p> <p>Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., & Horritt, M. S. (2009). Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations. <em>Journal of Hydrology</em>, <em>368</em>(1–4), 42–55. https://doi.org/10.1016/j.jhydrol.2009.01.026</p> <p> </p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Complex k-Uniform Tilings by a Simple Bitopic Precursor Self-Assembled on Ag(001)_experimental dataset
<p>This experimental dataset contains the raw underlying data for the article "Complex k-Uniform Tilings by a Simple Bitopic Precursor Self-Assembled on Ag(001) Surface" by Lukáš Kormoš, Pavel Procházka, Anton O. Makoveev, and Jan Čechal. </p>
Large Uniform Random SAT Samples
<p>Large Random SAT samples generated with the following <em>samplers</em>:</p> <ul> <li>BDDSampler</li> <li>Spur</li> <li>QuickSampler </li> <li>KUS</li> <li>Unigen2</li> <li>Smarch </li> </ul>
Model Counting and Uniform Sampling Instances
<p>These instances mainly consist of the formulas that have been used in the evaluation of recent model counting techniques. A significant set of benchmarks involving sampling set, i.e., they are meant for projected model counting.<br> <br> The specification for reading such files can be found at <a href="https://github.com/meelgroup/approxmc">https://github.com/meelgroup/approxmc</a><br> <br> Here is list of some of the papers that have reported results on these instances:</p> <p>1. BIRD: Engineering an Efficient CNF-XOR SAT Solver and its Applications to Approximate Model Counting<br> Mate Soos and Kuldeep S. Meel<br> Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2019.</p> <p>2. Accelerating Approximate Techniques for Counting and Sampling Models Through Refined CNF-XOR Solving<br> Mate Soos, Stephan Gocht, and Kuldeep S. Meel<br> Proceedings of International Conference on Computer-Aided Verification (CAV), 2020.<br> </p>
- Metasoma usually uniformly colored (a); clypeus with a distinct subapical tubercle (b); body and ovipositor smaller (B <25mm; OT <6) ……………………………………………………………6 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Metasoma usually uniformly colored (a); clypeus with a distinct subapical tubercle (b); body and ovipositor smaller (B <25mm; OT <6) ……………………………………………………………6
A Facial Motion Capture System Based on Neural Network Classifier Using RGB- Figure 4. An example of the uniform LBP operator (Huang et al., 2011)
<p>The original LBP operator labels the pixels of an image by means of decimal numbers called Local Binary Patterns or LBP codes, which encode the local structure around each pixel. It proceeds thus as illustrated in figure 4: Each pixel is compared with its eight neighbors in a 3x3 neighborhood by subtracting the center pixel value. The resulting strictly negative values are encoded with 0 and the others with 1. A binary number is obtained by concatenating all these binary codes in a clockwise direction starting from the top-left one and its corresponding decimal value is used for labeling. The derived binary numbers are referred to as Local Binary Patterns or LBP codes.</p>
JWST-TST DREAMS: Non-Uniform Dayside Emission for WASP-17b from MIRI/LRS
<p>Data and models accompanying the publication "JWST-TST DREAMS: Non-Uniform Dayside Emission for WASP-17b from MIRI/LRS". Here we include:</p> <ul> <li>ExoTiC-MIRI reductions of JWST MIRI LRS eclipse observations</li> <li>Eureka! reductions of JWST MIRI LRS eclipse observations</li> <li>PICASO forward atmospheric models </li> <li>ThERESA eclipse mapping outputs</li> <li>PHOENIX stellar model for WASP-17A</li> </ul> <p>Manuscript DOI: [10.3847/1538-3881/ad5c61] and [<a href="https://iopscience.iop.org/article/10.3847/1538-3881/ad5c61">Paper Link</a>]</p>
The surface deformation induced by thermal expansion of bedrock, based on the the uniform elastic sphere model
<p>The surface deformation induced by thermal expansion of bedrock(TEB), based on the the uniform elastic sphere model in the manuscript submitted to JGR: Solid Earth, including:</p> <p>1. Input data of TEB model:</p> <p><strong>Spherical harmonics coefficients of land surface temperature:</strong> Cosine terms (detrended); Sine terms (detrended) </p> <p>(The coefficients are based on temperature data provided by Physical Sciences Laboratory (PSL) of the National Oceanic and Atmospheric Administration; <u>https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html</u>)</p> <p>2. Output data of TEB model: </p> <p><strong>The 3-dimensional TEB displacements </strong> <strong>on the 0.5</strong><strong>°×</strong><strong>0.5</strong><strong>°</strong><strong>global grid:</strong> annual variations of East, North, Up components</p>
RDF2Vec DBpedia uniform embeddings in HDF5 file format
<p>This dataset contains the vectors from computing RDF2vec embeddings from a uniformly weighted DBpedia 2016-04 graph.</p> <p>The file has a group called "Vectors" which contains a dataset for each entity in the graph. The dataset name is the entity name and the dataset content is the embedded vector (length 200).</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In <em>Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics</em> (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
ScienceDex guides
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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.