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Dataset: Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled Cluster Theory for GPU-accelerated Computer Architectures
<p>This dataset collects the data (outputs, coordinate files) for the calculations presented in the manuscript "Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled<br> Cluster Theory for GPU-accelerated Computer Architectures".</p>
Dataset for On the regular linear spaces up to order 16
<p>This dataset contains, up to isomorphism, all (15_4,20_3) and (15_5,25_3) configurations, all (16_6,32_3) configurations with nontrivial automorphisms, as well as all 4-regular graphs on 15 vertices, 6-regular graphs on 15 vertices, 3-regular graphs on 16 vertices, and 4-regular graphs on 17 vertices. The configurations uniquely give regular linear spaces with parameters (15|2^45,3^20), (15|2^30,3^25), and (16|2^24,3^32). All files are compressed with gzip.</p> <p>The dataset supplements the publication "On the Regular Linear Spaces up to Order 16" by Anton Betten, Dieter Betten, Daniel Heinlein, and Patric R. J. Östergård.</p> <p>In the files containing configurations, each line is a configuration with the syntax<br> <number of points> <number b of blocks> <B1> <B2> ... <Bb> A<number of automorphisms><br> where<br> Bi is a block for all i=1,...,b and represented as a hex-encoded<br> (with alphabet 0123456789abcdef) characteristic vector of points.<br> The least significant bit is the rightmost bit.</p> <p>Example:<br> Assuming a total of 15 points labeled with {0,...,14}, the characteristic vector of a block {1,3,14} is<br> (0)100|0000|0000|1010<br> The first bit is padding as each hexadecimal number encodes four bits. Vertical bars designate groups of four bits. Consequently, the block is encoded as<br> 400a</p> <p>The following example shows the first line of one of the files:<br> $ zcat conf_15_4_20_3.txt.gz | head -n1<br> 15 20 1081 4101 2201 0c01 0026 004a 0092 4402 008c 0054 0a04 0038 2108 1110 0160 0620 08c0 5200 3400 6800 A1</p> <p>For the files containing graphs, we apply the graph6 file format but we extend each line by the corresponding number of automorphisms as described for configurations above, without the letter A. Programs for manipulating graphs in the graph6 format can be found in the gtools package that comes with the graph isomorphism program nauty (https://pallini.di.uniroma1.it/). Details regarding the graph6 format can be found in the documentation of nauty (https://pallini.di.uniroma1.it/Guide.html).</p> <p>For graphs with a most 62 vertices, which holds in all cases here, a line in graph6 format is the ASCII converted equivalent of<br> <number n of vertices + 63><ADJ><br> where ADJ is the upper triangle of the adjacency matrix read column-wise (that is, using the ordering 01, 02, 12, 03, 13, 23, ...) and of length n*(n-1)/2, encoded in the following way:<br> - pad on the right to make the length a multiple of 6<br> - split into groups of 6 and convert each group to a decimal number<br> - add 63 to each decimal number and convert to ASCII<br> We further extend any graph6 line by the nonstandard<br> <space><order of automorphism group></p> <p>Example:<br> Assume a graph with 5 vertices and edges: 02, 04, 13, 34 (the path 2-0-4-3-1), which has the adjacency matrix<br> 00101<br> 00010<br> 10000<br> 01001<br> 10010<br> Hence, the upper triangle read column-wise is<br> 0100101001<br> After padding we get<br> 010010100100<br> and after grouping<br> 010010|100100<br> Converting to decimal and adding 63 gives<br> 63+16+2|63+32+4<br> that is<br> 81|99<br> The number of vertices is 5, so we prepend 5+63=68:<br> 68 81 99<br> The line in graph6 format is therefore<br> DQc<br> and our nonstandard appending of the order of the automorphism group gives<br> DQc 2</p> <p>The first line of one of the files is as follows:<br> $ zcat graph_15_4.txt.gz | head -n1<br> Ns_???BAwjDoTOY_M_? 2</p> <p>The orders of the automorphism groups and the numbers of isomorphism classes are as follows. The (up to isomorphism) 114711393113 (16_6,32_3) regular linear spaces with no nontrivial automorphisms are not stored.</p> <table> <thead> <tr> <th> </th> <th>(15_4,20_3)</th> <th>(15_5,25_3)</th> <th>(16_6,32_3)</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>251712191</td> <td>1442354689</td> <td>114711393113</td> </tr> <tr> <td>2</td> <td>94229</td> <td>180367</td> <td>1125379</td> </tr> <tr> <td>3</td> <td>1129</td> <td>2178</td> <td>17287</td> </tr> <tr> <td>4</td> <td>915</td> <td>936</td> <td>3054</td> </tr> <tr> <td>5</td> <td>29</td> <td>33</td> <td> </td> </tr> <tr> <td>6</td> <td>142</td> <td>180</td> <td>240</td> </tr> <tr> <td>8</td> <td>85</td> <td>36</td> <td>50</td> </tr> <tr> <td>9</td> <td> </td> <td>4</td> <td> </td> </tr> <tr> <td>10</td> <td>4</td> <td>4</td> <td> </td> </tr> <tr> <td>12</td> <td>10</td> <td>13</td> <td>30</td> </tr> <tr> <td>15</td> <td>1</td> <td> </td> <td> </td> </tr> <tr> <td>16</td> <td>7</td> <td> </td> <td>3</td> </tr> <tr> <td>18</td> <td>4</td> <td>3</td> <td>2</td> </tr> <tr> <td>20</td> <td>2</td> <td>2</td> <td> </td> </tr> <tr> <td>24</td> <td>10</td> <td>5</td> <td>2</td> </tr> <tr> <td>30</td> <td>1</td> <td> </td> <td> </td> </tr> <tr> <td>32</td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>36</td> <td>4</td> <td> </td> <td>2</td> </tr> <tr> <td>40</td> <td>2</td> <td>1</td> <td> </td> </tr> <tr> <td>48</td> <td>4</td> <td> </td> <td>1</td> </tr> <tr> <td>72</td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>96</td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>120</td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>600</td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>720</td> <td>1</td> <td> </td> <td> </td> </tr> <tr> <td>total</td> <td>251808770</td> <td>1442538454</td> <td>114712539165</td> </tr> </tbody> </table> <table> <thead> <tr> <th> </th> <th>4-regular graphs with 15 vertices</th> <th>6-regular graphs with 15 vertices</th> <th>3-regular graphs with 16 vertices</th> <th>4-regular graphs with 17 vertices</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>656794</td> <td>1396131168</td> <td>1547</td> <td>76356249</td> </tr> <tr> <td>2</td> <td>119881</td> <td>69928313</td> <td>1261</td> <td>8665624</td> </tr> <tr> <td>3</td> <td>17</td> <td>630</td> <td>2</td> <td>127</td> </tr> <tr> <td>4</td> <td>21500</td> <td>3848635</td> <td>667</td> <td>997704</td> </tr> <tr> <td>5</td> <td> </td> <td>14</td> <td> </td> <td> </td> </tr> <tr> <td>6</td> <td>409</td> <td>55060</td> <td>15</td> <td>27213</td> </tr> <tr> <td>8</td> <td>4789</td> <td>274294</td> <td>330</td> <td>131662</td> </tr> <tr> <td>10</td> <td>10</td> <td>35</td> <td> </td> <td> </td> </tr> <tr> <td>12</td> <td>352</td> <td>21334</td> <td>11</td> <td>12577</td> </tr> <tr> <td>14</td> <td> </td> <td> </td> <td> </td> <td>4</td> </tr> <tr> <td>16</td> <td>1020</td> <td>23435</td> <td>147</td> <td>19786</td> </tr> <tr> <td>18</td> <td>1</td> <td>10</td> <td> </td> <td>2</td> </tr> <tr> <td>20</td> <td>7</td> <td>12</td> <td> </td> <td> </td> </tr> <tr> <td>24</td> <td>210</td> <td>5596</td> <td>11</td> <td>4344</td> </tr> <tr> <td>28</td> <td> </td> <td> </td> <td> </td> <td>18</td> </tr> <tr> <td>30</td> <td>4</td> <td>7</td> <td> </td> <td> </td> </tr> <tr> <td>32</td> <td>243</td> <td>2463</td> <td>51</td> <td>3320</td> </tr> <tr> <td>34</td> <td> </td> <td> </td> <td> </td> <td>3</td> </tr> <tr> <td>36</td> <td>1</td> <td>128</td> <td> </td> <td>53</td> </tr> <tr> <td>48</td> <td>106</td> <td>1453</td> <td>33</td> <td>1500</td> </tr> <tr> <td>56</td> <td>1</td> <td> </td> <td> </td> <td>15</td> </tr> <tr> <td>60</td> <td>2</td> <td>2</td> <td> </td> <td> </td> </tr> <tr> <td>64</td> <td>54</td> <td>285</td> <td>16</td> <td>639</td> </tr> <tr> <td>68</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>72</td> <td>6</td> <td>165</td> <td>2</td> <td>96</td> </tr> <tr> <td>96</td> <td>41</td> <td>309</td> <td>24</td> <td>504</td> </tr> <tr> <td>112</td> <td> </td> <td> </td> <td> </td> <td>7</td> </tr> <tr> <td>120</td> <td>5</td> <td> </td> <td> </td> <td>692</td> </tr> <tr> <td>128</td> <td>10</td> <td>48</td> <td>4</td> <td>132</td> </tr> <tr> <td>140</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>144</td> <td>10</td> <td>74</td> <td>3</td> <td>82</td> </tr> <tr> <td>168</td> <td>1</td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>192</td> <td>14</td> <td>77</td> <td>20</td> <td>193</td> </tr> <tr> <td>216</td> <td> </td> <td>2</td> <td> </td> <td>3</td> </tr> <tr> <td>224</td> <td>2</td> <td> </td> <td> </td> <td>6</td> </tr> <tr> <td>240</td> <td>18</td> <td>1</td> <td>2</td> <td>497</td> </tr> <tr> <td>256</td> <td>1</td> <td>6</td> <td>1</td> <td>24</td> </tr> <tr> <td>280</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>288</td> <td>5</td> <td>36</td> <td>9</td> <td>53</td> </tr> <tr> <td>320</td> <td> </td> <td>4</td> <td> </td> <td> </td> </tr> <tr> <td>384</td> <td>6</td> <td>26</td> <td>11</td> <td>58</td> </tr> <tr> <td>432</td> <td> </td> <td>9</td> <td>3</td> <td>2</td> </tr> <tr> <td>448</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>480</td> <td>15</td> <td> </td> <td> </td> <td>191</td> </tr> <tr> <td>512</td> <td> </td> <td>1</td> <td>2</td> <td>5</td> </tr> <tr> <td>576</td> <td>6</td> <td>12</td> <td>8</td> <td>22</td> </tr> <tr> <td>672</td> <td>1</td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>720</td> <td> </td> <td>2</td> <td> </td> <td>7</td> </tr> <tr> <td>768</td> <td>4</td> <td>7</td> <td>4</td> <td>18</td> </tr> <tr> <td>864</td> <td>3</td> <td>5</td> <td>2</td> <td>7</td> </tr> <tr> <td>896</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>960</td> <td>7</td> <td> </td> <td> </td> <td>83</td> </tr> <tr> <td>1056</td> <td> </td> <td> </td> <td> </td> <td>2</td> </tr> <tr> <td>1152</td> <td>1</td> <td> </td> <td>4</td> <td>10</td> </tr> <tr> <td>1200</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>1296</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>1440</td> <td>1</td> <td> </td> <td>3</td> <td>8</td> </tr> <tr> <td>1536</td> <td>1</td> <td>3</td> <td>1</td> <td>5</td> </tr> <tr> <td>1728</td> <td> </td> <td>4</td> <td> </td> <td>3</td> </tr> <tr> <td>1920</td> <td>6</td> <td>2</td> <td> </td> <td>32</td> </tr> <tr> <td>2016</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>2304</td> <td>1</td> <td>1</td> <td>1</td> <td>6</td> </tr> <tr> <td>2400</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>2592</td> <td> </td> <td>1</td> <td> </td> <td>1</td> </tr> <tr> <td>2880</td> <td> </td> <td> </td> <td> </td> <td>8</td> </tr> <tr> <td>3072</td> <td> </td> <td> </td> <td> </td> <td>2</td> </tr> <tr> <td>3360</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>3456</td> <td>1</td> <td> </td> <td>1</td> <td>1</td> </tr> <tr> <td>3840</td> <td>1</td> <td> </td> <td> </td> <td>6</td> </tr> <tr> <td>4480</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>4608</td> <td> </td> <td>1</td> <td>2</td> <td>2</td> </tr> <tr> <td>5760</td> <td>1</td> <td> </td> <td> </td> <td>10</td> </tr> <tr> <td>6912</td> <td> </td> <td>1</td> <td>1</td> <td>2</td> </tr> <tr> <td>7680</td> <td>1</td> <td> </td> <td> </td> <td>6</td> </tr> <tr> <td>8640</td> <td> </td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>9216</td> <td> </td> <td> </td> <td> </td> <td>3</td> </tr> <tr> <td>10368</td> <td> </td> <td>2</td> <td> </td> <td>1</td> </tr> <tr> <td>11520</td> <td> </td> <td>1</td> <td> </td> <td>2</td> </tr> <tr> <td>13824</td> <td> </td> <td> </td> <td>1</td> <td>3</td> </tr> <tr> <td>15360</td> <td> </td> <td> </td> <td> </td> <td>3</td> </tr> <tr> <td>16128</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>17280</td> <td>1</td> <td> </td> <td> </td> <td>2</td> </tr> <tr> <td>18432</td> <td> </td> <td> </td> <td>2</td> <td>2</td> </tr> <tr> <td>20736</td> <td> </td> <td> </td> <td>1</td> <td>2</td> </tr> <tr> <td>28800</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>36864</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>38400</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>55296</td> <td>1</td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>77760</td> <td> </td> <td>1</td> <td> </td> <td> </td> </tr> <tr> <td>82944</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>92160</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>248832</td> <td> </td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>403200</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>552960</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>1382400</td> <td> </td> <td> </td> <td> </td> <td>1</td> </tr> <tr> <td>1935360</td> <td> </td> <td>1</td> <td> </td> <td> </td> </tr> <tr> <td>7962624</td> <td> </td> <td> </td> <td>1</td> <td> </td> </tr> <tr> <td>10368000</td> <td>1</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>total</td> <td>805579</td> <td>1470293676</td> <td>4207</td> <td>86223660</td> </tr> </tbody> </table>
REDVID Collision Event Data – Linear Tracks and Hits
<p>An example, representative data set is generated using the REDuced VIrtual Detector (REDVID) simulation framework and contains complexity-reduced subatomic particle collision event data. Particle trajectory information and hit coordinates from interactions with reduced-order virtual detector models is included. The data is generated in 3D domain and follows the cylindrical coordinate system for hit point coordinates in space and trajectory function parameters.</p> <p>The included five tarballs each belong to a different data generation recipe. While all recipes include 10000 collision events, the number of tracks included in events varies from 1 track per event to 10000 tracks per event. This is noticeable from the tarball names.</p> <p>The data set is intended to be used as synthesised input for research involving ML-assisted pipeline design exploration, as well as ML model design exploration, e.g., Neural Architecture Search (NAS). To understand the data and its generation in detail, refer to the provided README file, as well as the related publication.</p>
Linear Acoustics 4: PAC-MAN
<p>The PAC-MAN geometry is the two-dimensional equivalent to the three-dimensional cat’s eye geometry. As shown in Fig. 1, it is a circle of radius with an angular cut-out ranging from (measured from the x-axis) and. An analytical solution of the sound field inside the cut-out and outside the PAC-MAN has been derived in <a href="http://www.sciencedirect.com/science/article/pii/S0021999117304631?via%3Dihub">Ziegelwanger-Reiter-JCP</a>. The PAC-MAN problem is suited for radiation and scattering.</p> <p>The description with references can be found in the PDF.</p> <p>H. Ziegelwanger and P. Reiter, Analytic formulation of radiated and scattered sound, can be found in the ZIP file.</p>
Linear Acoustics 2: Cat's eye
<p>This benchmark problem consists of a vibrating surface, which coincides with the spherical one. The plain surfaces of the missing octant are assigned a zero admittance. Thereby, the radiator allows construction of a smooth solution that will make it easy to identify solution failures caused by the ill–conditioning of the integral operator for techniques that solve the Helmholtz equation in an integral formulation, i.e. the irregular frequencies. Furthermore, the cat’s eye structure is a more complicated shape than a sphere and hence, the solution is expected to expose more irregular frequencies in a BEM solution than the sphere.</p> <p>A detailed description with references can be found in the PDF description.</p> <p><strong>Available solutions:</strong></p> <p>Steffen Marburg's solution using the Boundary Element Method: LA_Case2_CatEye_SteffenMarburg.pdf</p>
Linear Acoustics 3: Radiatterer
<p>A radiatterer is a geometry that both acts as a radiator of sound as well as it scatterers the radiated sound waves. In addition to the problem of irregular frequencies, it is well suited to compare resonance amplitudes yielded by different methods and different types of finite and boundary elements. Furthermore, the numerical methods should investigate whether mesh refinement in regions with large gradients is necessary or can be neglected.</p> <p>A detailed description with references can be found in the PDF description.</p> <p><strong>Available solutions:</strong></p> <p>Steffen Marburg's solution using the Boundary Element Method: LA_Case2_Radiatterer_SteffenMarburg.pdf</p>
Datasets for 2D Vertical Convection: Base States and Leading Linear Modes using Snek5000-cbox
<p>This repository contains two types of datasets related to 2D vertical convection analysis, generated using the snek5000-cbox simulation framework. The first dataset includes base states computed with the Selective Frequency Damping (SFD) method, considering various aspect ratios and Prandtl numbers. The second dataset provides the decomposed amplitude, phase, frequency, and omega of the leading linear mode, accompanied by the corresponding base states for different aspect ratios and Prandtl numbers. All datasets are stored in the .h5 file format for easy access and analysis. The scripts used to produce the datasets are provided in the repository https://github.com/snek5000/snek5000-cbox/tree/main/doc/scripts/2022sidewall_conv_instabilities.</p>
Linear Acoustics 1: Duct 01
<p>This benchmark problem may be considered as waves traveling through a duct. Although a smooth solution is expected over the entire frequency range, the numerical solution may be unstable if modes perpendicular to the traveling waves occur.</p> <p>This benchmark can be used to study the eigenvalue problem with arbitrary admittance boundary conditions, discusses the accuracy of mode superposition for reconstruction of the solution in frequency domain, as well as study the convergence of your formulation towards h- and p-refinement.</p> <p>A detailed description with references can be found in the PDF.</p>
Linear Spaces associated to 3R/P kinematic image
<p>The kinematic image of a 2R/P (open) chain is the intersection of a 3-space and the Study quadric. For a 3R/P chain (8 combinations: RRR, RRP, etc.) this is a parametrized 3-space (by one of the parameterizing joints). In our paper we have written a thorough description and algorithm on how to obtain these parameterized linear spaces. This result is documented in this dataset (text file, zipped).</p>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
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Data for: Top-down control and species composition non-linearly influence the short-term response of experimental food webs to a nutrient pulse perturbation
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Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation
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Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach
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Data from: Experimental validation of a linear momentum and bluff-body model for high-blockage cross-flow turbine arrays
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Incorporating effects of age on energy dynamics predicts non-linear maternal allocation patterns in iteroparous animals
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Data of Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
<pre>Data from title = "Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network", journal = "Computer Methods in Applied Mechanics and Engineering", pages = "112693", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2019.112693", author = "Wu, Ling and Zulueta, Kepa and Major, Zoltan and Arriaga, Aitor and Noels, Ludovic" </pre>
Molecular recognition and dynamics of linear poly-ubiquitins: integrating coarse-grain simulations and experiments
<p>Poly-ubiquitin chains are flexible multidomain proteins, whose conformational dynamics enable their molecular recognition by a large number of partners in multiple biological pathways. By using alternative linkage, it is possible to obtain poly-ubiquitin molecules with different dynamical properties. This flexibility is further increased by the possibility to tune the length of poly-ubiquitin chains. Characterizing the dynamics of poly-ubiquitins as a function of their length is thus relevant to understand their biology. Structural characterization of poly-ubiquitin conformational dynamics is challenging both experimentally and computationally due to increasing system size and conformational variability. Here, by developing highly efficient and accurate small-angle X-ray scattering driven Martini coarse-grain simulations, we characterize the dynamics of linear M1-linked di-, tri- and tetra-ubiquitin chains. Our data show that the behavior of the di-ubiquitin subunits is independent of the presence of additional ubiquitin modules. We propose that the conformational space sampled by linear poly-ubiquitins, in general, may follow a simple self-avoiding polymer model. These results, combined with experimental data from small angle X-ray scattering, biophysical techniques and additional simulations show that binding of NEMO, a central regulator in the NF-κB pathway, to linear poly-ubiquitin obeys a 2:1 (NEMO:poly-ubiquitin) stoichiometry in solution, even in the context of four ubiquitin units. Eventually, we show how the conformational properties of long poly-ubiquitins may modulate the binding with their partners in a length-dependent manner.</p>
Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
<p>This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model.</p> <p>Dataset includes following files:</p> <p>1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model.</p> <p>2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ).</p> <p>3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.</p> <p> </p>
JET-ILW Linear Pedestal ETG Data
<p>Data associated with paper '<a href="https://arxiv.org/abs/2004.13634">Toroidal and slab ETG instability dominance in the linear spectrum of JET-ILW pedestals</a>.' See attached Readme file for instructions.</p>
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</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.