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500 results for “Regular”
Regular full triangulations of the 3-dilated 3-simplex
<p>This dataset contains the 21 125 102 regular full triangulations of the 3-dilated 3-simplex, computed by mptopcom. The data is given up to symmetry, i.e. we have one triangulation from every orbit. The file `3d3.dat` is the input file for mptopcom, which was called with the `--regular` and `--full` flags. The output is in the file `3d3_regular_full.result.xz`.</p>
PIE LTER salt marsh vegetation cover data from regularly monitored quadrats along transects in Rowley, MA.
Marsh vegetation cover data from quadrats along transects in salt marsh sites in Rowley, MA. The transects are intended to study long term changes in marsh vegetation. Four sites ((12 transects) were originally set up to study the impact of salt marsh haying. Two of these sites (labeled McH and EPH) were regularly hayed until 2002. The other two (PUH and CC) were reference sites. Two additional sites labeled RM and RR (8 transects) were originally set up to track invasion by Phragmites australis.
PIE LTER salt marsh vegetation frequency of occurrence from regularly monitored transects in Rowley, MA
Marsh vegetation presence-absence data along transects in salt marsh sites in Rowley, MA. The transects are intended to study long term changes in marsh vegetation. Four sites ((12 transects) were originally set up to study the impact of salt marsh haying. Two of these sites (labeled McH and EPH) were regularly hayed until 2002. The other two (PUH and CC) were reference sites. Two additional sites labeled RM and RR (8 transects) were originally set up to track invasion by Phragmites australis.
Regularized quantum periods for four-dimensional Fano manifolds
<p><strong>The database smooth_fano_4</strong></p> <p>This is a database of regularized quantum periods for four-dimensional Fano manifolds. The database will be updated as new four-dimensional Fano manifolds are discovered and new regularized quantum periods computed.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper [CK2021]. If you make use of this data, please cite that paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708307</p> <p><strong>Names</strong></p> <p>The database describes Fano varieties via names, as follows:</p> <table> <caption>Names of Fano manifolds</caption> <thead> <tr> <th scope="col">Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>P1</td> <td>one-dimensional projective space</td> </tr> <tr> <td>P2</td> <td>two-dimensional projective space</td> </tr> <tr> <td>dP(k)</td> <td>the del Pezzo surface of degree k given by the blow-up of P2 in 9-k points</td> </tr> <tr> <td>P3</td> <td>three-dimensional projective space</td> </tr> <tr> <td>Q3</td> <td>a quadric hypersurface in four-dimensional projective space</td> </tr> <tr> <td>B(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 8k</td> </tr> <tr> <td>V(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 1, and degree k</td> </tr> <tr> <td>MM(r,k)</td> <td>the k-th entry in the Mori-Mukai list of three-dimensional Fano manifolds of Picard rank r, ordered as in [CCGK2016]<br> </td> </tr> <tr> <td>P4</td> <td>four-dimensional projective space</td> </tr> <tr> <td>Q4</td> <td>a quadric hypersurface in five-dimensional projective space</td> </tr> <tr> <td>FI(4,k)</td> <td>the four-dimensional Fano manifold of Fano index 3 and degree 81k</td> </tr> <tr> <td>V(4,k)</td> <td>the four-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 16k</td> </tr> <tr> <td>MW(4,k)</td> <td>the k-th entry in Table 12.7 of [IP1999] of four-dimensional Fano manifolds of Fano index 2 and Picard rank greater than 1</td> </tr> <tr> <td>Obro(4,k)</td> <td>the k-th four-dimensional Fano toric manifold in Obro's classification [O2007]</td> </tr> <tr> <td>Str(k)</td> <td>the k-th Strangeway manifold in [CGKS2020]</td> </tr> <tr> <td>CKP(k)</td> <td>the k-th four-dimensional Fano toric complete intersection in [CKP2015]</td> </tr> <tr> <td>CKK(k)</td> <td>the k-th four-dimensional Fano quiver flag zero locus in Appendix B of [K2019]</td> </tr> </tbody> </table> <p>A name of the form "S1 x S2", where S1 and S2 are names of Fano manifolds X1 and X2, refers to the product manifold X1 x X2.</p> <p><strong>References</strong></p> <p>[CCGK2016] <em>Quantum periods for 3-dimensional Fano manifolds</em>; Tom Coates, Alessio Corti, Sergey Galkin, Alexander M. Kasprzyk; Geometry and Topology 20 (2016), no. 1, 103-256.</p> <p>[CGKS2020] <em>Quantum periods for certain four-dimensional Fano manifolds</em>; Tom Coates, Sergey Galkin, Alexander M. Kasprzyk, Andrew Strangeway; Experimental Math. 29 (2020), no. 2, 183-221.</p> <p>[CK2021] <em>Databases of quantum periods for Fano manifolds</em>; Tom Coates, Alexander M. Kasprzyk; 2021.</p> <p>[CKP2015] <em>Four-dimensional Fano toric complete intersections</em>; Tom Coates, Alexander M. Kasprzyk, Thomas Prince; Proc. Royal Society A 471 (2015), no. 2175, 20140704, 14.</p> <p>[IP1999] <em>Fano varieties</em>; V.A. Iskovskikh, Yu. G. Prokhorov; Encyclopaedia Math. Sci. vol. 47, Springer, Berlin, 1999, 1-247.</p> <p>[K2019] <em>Four-dimensional Fano quiver flag zero loci</em>; Elana Kalashnikov; Proc. Royal Society A 275 (2019), no. 2225, 20180791, 23. </p> <p>[O2007] <em>An algorithm for the classification of smooth Fano polytopes</em>; Mikkel Obro; arXiv:0704.0049 [math.CO]; 2007.</p>
Regularized quantum periods for one-dimensional Fano manifolds
<p><strong>The database smooth_fano_1</strong></p> <p>This is a database of regularized quantum periods for one-dimensional Fano manifolds. There is one entry 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.5708188</p>
Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
Regular unimodular triangulations of the 3-dilated 3-simplex
<p>This dataset contains the 14 373 645 regular unimodular triangulations of the 3-dilated 3-simplex, computed by mptopcom. The data is given up to symmetry, i.e. we have one triangulation from every orbit. The file `3d3.dat` is the input file for mptopcom, which was called with the `--regular` and `--full` flags. This output was then filtered by a perl script to receive the unimodular triangulations only. The output is in the file `3d3_regular_unimodular.result.xz`.</p>
Regular triangulations of the Hypersimplex(3,6)
<p>This dataset contains the 42 489 025 regular triangulations of the Hypersimplex(3,6) computed with mptopcom. The triangulations were computed up to symmetry and the result contains one representative from every orbit. The dataset is structured as follows:</p> <ul> <li>`hypersimplex_3_6.dat` contains the input points and the group acting.</li> <li>`hypersimplex_3_6.result.*.xz` contains the output from mptopcom.</li> <li>`hypersimplex_3_6.job.auto` is the cluster script used to produce these triangulations.</li> <li>`hypersimplex_3_6.checkp.*` are the checkpoints between the different result files.</li> </ul> <p>Since the computation was too large, it had to split up into four parts.</p>
Regular triangulations of the Hypersimplex(2,7)
<p>This dataset contains the 30 485 496 regular triangulations of the Hypersimplex(2,7) computed with mptopcom. The triangulations were computed up to symmetry and the result contains one representative from every orbit. The dataset is structured as follows:</p> <ul> <li>`hypersimplex_2_7.dat` contains the input points and the group acting.</li> <li>`hypersimplex_2_7_reg.result.*.xz` contains the output from mptopcom.</li> </ul> <p>Since the computation was too large, it had to split up into eleven parts.</p>
Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'
<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>
profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean
<p>The dataset includes profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean </p> <p>Data was collected from open archive (<em>Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) </em><a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a></p> <p>Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset.</p> <p>Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)).</p> <p>A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used.</p> <p>Then the data from all buoys were interpolated on regular 2-m grid and included in one dataset.</p>
Semi-regular Vase - Shell-lattice construction based on regular and semi-regular tiling via functional composition.
<p>This vase has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific vase model has been created using function composition of dual semi-regular trivariate tiles over the shell geometry of trivariate deformation function yielding a trivariate volumetric representation of the shell.</p> <p>This model is provided in STL and MSH file formats.</p>
Semi-regular Duck - Shell-lattice construction based on regular and semi-regular tiling via functional composition.
<p>This duck has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific duck model has been created using function composition of dual semi-regular tiles over the shell geometry of a B-spline bivariate duck. </p> <p>This model is provided in STL and OBJ file format.</p>
Observation of a regular structure formation on the surface of vibrated ball beds started from random lose packing
<p>Near 4,000 2-mm diameter plastic balls were poured 88 times into plexiglass cylinder of internal diameter 26 mm. Then, such initially random loose-packing systems/beds were vibrated vertically with 100 Hz frequency using the vibration table Vibrax (Renfert GmbH, Germany) working in sinusoidal mode until a regular structure was observed on the cylinder surface. The power levels of the vibrations in the recorded ordering of balls were selected to represent all four levels (1, 2, 3 or 4) of vibrations available in the table, where number 1 means the weakest vibration and 4 means the strongest one.</p> <p>Locations of the balls on all sides of a vibrated cylindrical bed were simultaneously recorded on one video frame thanks to the use of two perpendicular mirrors, which enables observation of four images: one of the real cylinder and three of its mirror reflections. View of the table with the attached cylinder containing balls and two mirrors is presented in Fig. 1, while an explanation of the scene, as seen by the recording camera, is given in the scheme in Fig. 2. Video names were given in a standard form explained in the README.txt file.</p>
Source code and simulation results for computing resonance expansions of quadratic quantities with regularized quasinormal modes
<p>This data publication supplements the article "Resonance expansion of quadratic quantities with regularized quasinormal modes" [1]. Tabulated data related to the figures in the manuscript is provided along with the Matlab scripts used to generate the results. The Riesz projection software package RPExpand [2] has been extended to support quasi normal modes (QNMs) and, in particular, the proposed method for quadratic quantities. A current version is contained in the directory <code>Code</code>. Furthermore, the input files required for scattering and resonance simulations with the finite element method (FEM) solver JCMsuite [3] are contained.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.1 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Felix Binkowski, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger: Resonance expansion of quadratic quantities with regularized quasinormal modes, Physica Status Solidi A <strong>220</strong>, 2370013 (2023)</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[3] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p>
Data for: Regularized sequence-context mutational trees capture variation in mutation rates across the human genome
<p>Additional data on output models from Bayer as reported in:</p> <p>Regularized sequence-context mutational trees capture variation in mutation rates across the human genome</p> <p>Adams CJ, Conery M, Auerbach BJ, Jensen ST, Mathieson I, Voight BF. BioRxiv https://doi.org/10.1101/2022.10.14.512160</p> <p>Accepted, PLoS Genetics. </p> <p>Code Available at: https://github.com/bvoightlab/Baymer</p>
Wife of Bath's Prologue Regularized Apparatus
<p>This is a regularized apparatus for the Wife of Bath's Prologue (Chaucer's Canterbury Tales). The regularization was carried out by Peter Robinson. I generated the file on June 27 2020 using the Textual Communities API. </p>
Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil
<p>Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics).</p> <p>The excel file includes Aphanomyces root rot disease visual scores (<em>Root_Rating</em>), unique identifier for each lentil accession/line (<em>Lentil_ID</em>), unique identifier for each experiment (<em>Experiment</em>), and unique identifier for each image (<em>Lab_ID</em>).</p>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
Dataset for "Difference of photometric properties between regular and non-regular Miras in the Magellanic Clouds"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Difference of photometric properties between regular and non-regular Miras in the Magellanic Clouds</em>", accepted to the Astronomical Journal (17 February 2022).</p> <p><strong>Description of contents: </strong>This repository contains the numerical values for all of the features for the data (including the training set) and classification results. The specific files included in this deposit are as follows:<br> <br> ReadMe.txt This Readme file.<br> Feature_table_with_training_data.txt Feature Table with training data<br> Feature_table_with_classify_data.txt Feature table with classify data<br> Classify_result_table.txt Mira Regular/Non-regular <br> machine-learning classification</p> <p><strong>System requirements:</strong> The files are formatted according to the machine readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p> AAS: <a href="https://journals.aas.org/mrt-overview/">https://journals.aas.org/mrt-overview/</a><br> CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx">http://cds.u-strasbg.fr/doc/catstd.htx</a></p> <p> These files can be read in python using the astropy package:</p> <pre><code class="language-python">from astropy.table import Table data = Table.read("Classify_result_table.txt", format="ascii.cds")</code></pre> <p> or with the most recent version of TOPCAT (> Version 4.8)</p> <p><strong>Additional comments: </strong>The features of training data and classify data were calculated by Python package Feature Analysis for Time Series (FATS), and the result was presented in the file names "Feature_table_with_training_data.txt" and "Feature_table_with_classify_data.txt". Then these two data set were used in our machine learning process. The classification result of Mira was presented in the file "Classify_result_table.txt".<br> </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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