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Synthetic Data for Neutrophil Analysis: Sets with regular shapes and Gaussian noise
<p><strong>Synthetic Datasets with regular shapes and Gaussian noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by Gaussian distributions of higher intensities than the background and <strong>Gaussian noise </strong>(check the corresponding irregular shapes with Poisson noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise). The orientation of the Gaussians varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Gaussians were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Gaussian noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 6):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData0_mat_Re </strong></li> <li><strong> First data set syntheticData1_mat_Re</strong></li> <li><strong> Second data set syntheticData2_mat_Re</strong></li> <li><strong> Third data set syntheticData3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData5_mat_Re</strong></li> <li><strong> Sixth data set syntheticData6_mat_Re</strong></li> </ul> <p> </p> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></p>
Research data supporting "Mechanical properties of semi-regular lattices"
<p>Research data supporting "Mechanical properties of semi-regular lattices" published in Materials & Design in 2022. This dataset includes:</p> <ul> <li>Processed data plotted in Figure 11 (Fig11.xlsx).</li> <li>Processed data plotted in Figure 19 (Fig19.xlsx).</li> <li>Finite element models used to compute the elastic and shear moduli of each semi-regular lattice. These can be opened with the commercial software Abaqus CAE version 2023 (periodic_boundary_model.cae).</li> </ul>
Research data supporting "The fracture toughness of demi-regular lattices"
<p>Research data supporting "The fracture toughness of demi-regular lattices" published in <em>Scripta Materialia</em> in 2023. This dataset includes:</p> <ul> <li>Processed data plotted in Figures 2 and 4 (Figs.xlsx).</li> <li>Python scripts used to generate the finite element models (*.py files). These have to be used with the commercial software Abaqus CAE.</li> </ul>
Research data supporting "Fracture toughness of semi-regular lattices"
<p>Research data supporting "Fracture toughness of semi-regular lattices" published in <em>International Journal of Solids and Structures</em> in 2023. This dataset includes:</p> <ul> <li>Processed data plotted in Figures 4, 5, 6, 10, and A2 (*.xlsx files).</li> <li>Raw data plotted in Figure 8 (Fig 8.xlsx).</li> <li>CAD files for all test specimens (*.STL files).</li> <li>Python scripts used to generate the finite element models (*.py files). These have to be used with the commercial software Abaqus CAE.</li> </ul>
Text-fig. 2. Soft-tissue imprints and traces of bioerosion on Middle Ordovician cephalopods from Estonia. a: GIT 819-1, Tragoceras falcatum (SCHLOTHEIM, 1820), drag bands; b: GIT 819-1, T. falcatum, pseudosutures; c: GIT 819-2, Estonioceras sp., drag bands; d: GIT 819-3, cf. Anthoceras vaginatum (SCHLOTHEIM, 1820), drag bands; e: GIT 819-4, cf. Orthoceras regulare SCHLOTHEIM, 1820, drag bands; f: Pits on the body chamber of GIT 819-1, T. falcatum. Specimens oriented with aperture downwards. Scale bars 1 mm. in Conch Structures, Soft-Tissue Imprints And Taphonomy Of The Middle Ordovician Cephalopod Tragoceras Falcatum From Estonia
Text-fig. 2. Soft-tissue imprints and traces of bioerosion on Middle Ordovician cephalopods from Estonia. a: GIT 819-1, Tragoceras falcatum (SCHLOTHEIM, 1820), drag bands; b: GIT 819-1, T. falcatum, pseudosutures; c: GIT 819-2, Estonioceras sp., drag bands; d: GIT 819-3, cf. Anthoceras vaginatum (SCHLOTHEIM, 1820), drag bands; e: GIT 819-4, cf. Orthoceras regulare SCHLOTHEIM, 1820, drag bands; f: Pits on the body chamber of GIT 819-1, T. falcatum. Specimens oriented with aperture downwards. Scale bars 1 mm.
Text-fig. 55. Scanning electron microscope (SEM) images of stamen fragments and pollen of Ibrahimia verminculata (a–h) and unnamed pantoporate pollen from pollen clump (i); Torres Vedras locality, Portugal. a) Holotype; stamen fragment that yielded the pollen in (b–f); b–f) Pantoporate pollen grains with vermiculate tectum and regularly spaced microechinate and pores with verrucate aperture membranes; g) Stamen fragment that yielded the pollen in (h); h) Abraded pantoporate pollen with vermiculate tectum and regularly spaced microechinate; i) Pantoporate pollen from coprolite with microreticulate-foveolate tectum and verrucate aperture membranes. Specimens, TV44-S148019 (holotype; a–f), TV44- S136782 (g, h), TV142-S170216 (i). Scale bars 300 Μm (a, g), 15 Μm (b), 6 Μm (d, e, h, i), 1.5 Μm (c, f). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 55. Scanning electron microscope (SEM) images of stamen fragments and pollen of Ibrahimia verminculata (a–h) and unnamed pantoporate pollen from pollen clump (i); Torres Vedras locality, Portugal. a) Holotype; stamen fragment that yielded the pollen in (b–f); b–f) Pantoporate pollen grains with vermiculate tectum and regularly spaced microechinate and pores with verrucate aperture membranes; g) Stamen fragment that yielded the pollen in (h); h) Abraded pantoporate pollen with vermiculate tectum and regularly spaced microechinate; i) Pantoporate pollen from coprolite with microreticulate-foveolate tectum and verrucate aperture membranes. Specimens, TV44-S148019 (holotype; a–f), TV44- S136782 (g, h), TV142-S170216 (i). Scale bars 300 Μm (a, g), 15 Μm (b), 6 Μm (d, e, h, i), 1.5 Μm (c, f).
Coordinates of CHP and non-CHP configurations in regular polygons
<p>We report the coordinates of the CHP configurations in several regular polygons, for different numbers of shells.</p>
Data Files of "A Multi-Year Photopolarimetric Study of the Semi-Regular Variable V CVn and Identification of Analogue Sources"
<p>The semi-regular variable star V Canum Venaticorum (V CVn) is well-known for its unusual linear polarization position angle (PA).<br> Decades of observing V CVn reveal a nearly constant PA spanning hundreds of pulsation cycles. This phenomenon has persisted<br> through variability that has ranged by 2 magnitudes in optical brightness and through variability in the polarization amplitude over<br> 0.3% and 6.9%. Additionally, the polarization fraction of V CVn varies inversely with brightness.<br> This paper presents polarization measurements obtained over three pulsation cycles. We find that the polarization maximum does<br> not always occur precisely at the same time as the brightness minimum. Instead, we observe a small lead or lag in relation to the<br> brightness minimum, spanning a period of a few days up to three weeks. Furthermore, the PA sometimes exhibits a non-negligible<br> rotation, especially at lower polarization levels.<br> To elucidate the unusual optical behavior of V CVn, we present a list of literature sources that also exhibit polarization variability<br> with a roughly fixed PA.We find this correlation occurs in stars with high tangential space velocities, i.e., “runaway” stars, suggesting<br> that the long-term constant PA is related to how the circumstellar gas is shaped by the star’s high-speed motion through the interstellar<br> medium.</p>
Delving into the relationship between regular physical exercise and cardiac interoception in two cross-sectional studies.
<p>This repository contains raw data from two studies corresponding to the article "No evidence of a relationship between regular physical exercise and cardiac interoception" by Yoris et al. In Study I, 45 resting EEG files are included for the Active (N = 24) and Inactive (N = 21) groups, both for the eyes closed and eyes open conditions. For Study II, there are 60 resting EEG files (30 Active/30 Inactive). Data are in EEGLAB format .set/fdt. The project is publicly available for free use and can be accessed at <a href="https://osf.io/xrsgn/">https://osf.io/xrsgn/</a>.</p>
CLDF dataset derived from Hruschka et al.'s "Detecting regular sound changes in linguistics as events of concerted evolution" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9.</p> </blockquote>
Reliable imputation of spatial transcriptome with uncertainty estimation and spatial regularization
<p>Imputation of missing features in spatial transcriptomics is urgently demanded due to technology limitations, while most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. <br> To fill the research gaps, we introduce a computational model, TransImp, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference. Uniquely, we derived a set of attributes that can accurately predict imputation uncertainty, hence enabling us to select reliably imputed genes. Also, we introduced a spatial auto-correlation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms have demonstrated that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImp offers a way towards a reliable spatial analysis of missing features for both matched and unseen modalities, e.g., nascent RNAs.</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>
The encoding of stochastic regularities is facilitated by action-effect predictions
<p>The data represent the raw EEG datasets generated and analysed during the current study, along with the Principal Component Analysis (PCA) solutions computed for the active and passive tasks, respectively. For details regarding the EEG preprocessing, statistical analyses, and results, please refer to the main manuscript body.</p>
Data for: Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)
<p>Data for our manuscript with title "Free-Breathing Water, Fat, R2∗ and B0 Field Mapping of the Liver Using Multi-Echo Radial FLASH and Regularized Model-based Reconstruction (MERLOT)"</p>
Data for Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
<p>Dataset substantiating the claims in <a href="https://arxiv.org/abs/2212.07957">[2212.07957] Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization (arxiv.org)</a></p>
Parallel Recognizer for Regular Texts
<p>parallel recognizer for regular texts</p>
Data set of a survey of people 6 months before reaching their regular retirement age
<p>This is an anonymized data set of a standardized survey of people about six months (+/- 3 months) before reaching their regular retirement age (n = 400). The survey is representative for the German-speaking part of Switzerland. Data was gained by telephone interviews, conducted by the market research institute DemoSCOPE in September 2019. The survey was part of the project “Identity Constructions for Retirement”, funded by the Swiss National Science Foundation (SNSF). Personal details that could lead back to the identity of the participants (among others postal code, profession, responses to open-ended questions) were removed from the data set.</p>
Regularized quantum periods for two-dimensional Fano manifolds
<p><strong>The database smooth_fano_2</strong></p> <p>This is a database of regularized quantum periods for two-dimensional Fano manifolds. There are ten entries in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708232</p>
Regularized quantum periods for three-dimensional Fano manifolds
<p><strong>The database smooth_fano_3</strong></p> <p>This is a database of regularized quantum periods for three-dimensional Fano manifolds. There are 105 entries in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708272</p>
Data for the article "Solution of the Thirring model in thimble regularization"
<p>Data set for the paper "Solution of the Thirring model in thimble regularization", arXiv:2109.02511 [hep-lat]. It includes the data required to generate the figures in the article.</p> <p> </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.