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741 results for “decay”
Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal faces".
<p>Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal facets".</p> <p>David John1,2, Zdeněk Remeš1, Radim Novák1, Štěpán Remeš1, Jakub Volf1,3,4, Oleg Babčenko1, Egor Ukraintsev5, Bohuslav Rezek5, and Maksym Buryi3</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10/112, 162 00, Prague, Czech Republic<br>2 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University, Břehová 7, 115019, Prague, Czech Republic<br>3 Institute of Plasma Physics of the Czech Academy of Sciences, U Slovanky 2525/1a, 182 00, Prague, Czech Republic <br>4 Department of Inorganic Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28, Czech Republic<br>5 Faculty of Electrical Engineering of the Czech Technical University, Technická 2, 160 00, Prague, Czech Republic</p> <p> </p> <p>Dataset description:</p> <p>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00000 AFM data<br>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00003 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00001 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00003 AFM data<br>afm zn AFM data<br>data phase shift fit phase shift data<br>grafy phase shift fit phase shift data<br>ZnO faces optical images</p>
Fuτure - dataset for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons
<h1> Data description</h1> <h2>MC Simulation</h2> <p><br>The <strong>Fuτure</strong> dataset is intended for studies, development, and training of algorithms for reconstructing and identifying hadronically decaying tau leptons. The dataset is generated with Pythia 8, with the full detector simulation being performed by Geant4 with the CLIC-like detector setup CLICdet (CLIC_o3_v14) setup. Events are reconstructed using the Marlin reconstruction framework and interfaced with Key4HEP. Particle candidates in the reconstructed events are reconstructed using the PandoraPF algorithm.</p> <p>In this version of the dataset no γγ -> hadrons background is included.</p> <h2>Samples</h2> <p><br>This dataset contains e+e- samples with Z->ττ, ZH,H->ττ and Z->qq events, with approximately 2 million events simulated in each category.</p> <p>The following processes e+e- were simulated with Pythia 8 at sqrt(s) = 380 GeV:</p> <ul> <li>p8_ee_qq_ecm380 [Z -> qq events]</li> <li>p8_ee_ZH_Htautau [ZH -> Ztautau]</li> <li>p8_ee_Z_Ztautau_ecm380 [ZH -> Ztautau]</li> </ul> <p>The .root files from the MC simulation chain are eventually processed by the software found in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a> in order to create flat ntuples as the final product.</p> <h2><br>Features</h2> <p><br>The basis of the ntuples are the particle flow (PF) candidates from PandoraPF. Each PF candidate has four momenta, charge and particle label (electron / muon / photon / charged hadron / neutral hadron). The PF candidates in a given event are clustered into jets using generalized kt algorithm for ee collisions, with parameters p=-1 and R=0.4. The minimum pT is set to be 0 GeV for both generator level jets and reconstructed jets. The dataset contains the four momenta of the jets, with the PF candidates in the jets with the above listed properties.</p> <p>Additionally, a set of variables describing the tau lifetime are calculated using the software in <a href="https://github.com/HEP-KBFI/ml-tau-en-reg">Github</a>. As tau lifetime is very short, these variables are sensitive to true tau decays. In the calculation of these lifetime variables, we use a linear approximation.</p> <p>In summary, the features found in the flat ntuples are:</p> <p> </p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>reco_cand_p4s</td> <td>4-momenta per particle in the reco jet.</td> </tr> <tr> <td>reco_cand_charge</td> <td>Charge per particle in the jet.</td> </tr> <tr> <td>reco_cand_pdg</td> <td>PDGid per particle in the jet.</td> </tr> <tr> <td>reco_jet_p4s</td> <td>RecoJet 4-momenta.</td> </tr> <tr> <td>reco_cand_dz</td> <td>Longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dz_err</td> <td>Uncertainty of the longitudinal impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy</td> <td>Transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>reco_cand_dxy_err</td> <td>Uncertainty of the transverse impact parameter per particle in the jet. For future steps. Fill value used for neutral particles as no track parameters can be calculated.</td> </tr> <tr> <td>gen_jet_p4s</td> <td>GenJet 4-momenta. Matched with RecoJet within a cone of radius dR < 0.3.</td> </tr> <tr> <td>gen_jet_tau_decaymode</td> <td>Decay mode of the associated genTau. Jets that have associated leptonically decaying taus are removed, so there are no DM=16 jets. If no GenTau can be matched to GenJet within dR < 0.4, a fill value is used.</td> </tr> <tr> <td>gen_jet_tau_p4s</td> <td>Visible 4-momenta of the genTau. If no GenTau can be matched to GenJet within dR<0.4, a fill value is used.</td> </tr> </tbody> </table> <p>The ground truth is based on stable particles at the generator level, before detector simulation. These particles are clustered into generator-level jets and are matched to generator-level τ leptons as well as reconstructed jets. In order for a generator-level jet to be matched to generator-level τ lepton, the τ lepton needs to be inside a cone of dR = 0.4. The same applies for the reconstructed jet, with the requirement on dR being set to dR = 0.3. For each reconstructed jet, we define three target values related to τ lepton reconstruction:</p> <ul> <li> a binary flag <strong>isTau</strong> if it was matched to a generator-level hadronically decaying τ lepton. <strong>gen_jet_tau_decaymode</strong> of value -1 indicates no match to generator-level hadronically decaying τ.</li> <li> the categorical decay mode of the τ <strong>gen_jet_tau_decaymode</strong> in terms of the number of generator level charged and neutral hadrons. Possible <strong>gen_jet_tau_decaymode</strong> are {0, 1, . . . , 15}.</li> <li> if matched, the visible (neglecting neutrinos), reconstructable pT of the τ lepton. This is inferred from the <strong>gen_jet_tau_p4s</strong></li> </ul> <h2>Contents:</h2> <ul> <li>qq_test.parquet</li> <li>qq_train.parquet</li> <li>zh_test.parquet</li> <li>zh_train.parquet</li> <li>z_test.parquet</li> <li> z_train.parquet</li> <li>data_intro.ipynb</li> </ul> <h2>Dataset characteristics</h2> <p> </p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong># Jets</strong></td> <td><strong>Size</strong></td> </tr> <tr> <td>z_test.parquet</td> <td> <pre>870 843</pre> </td> <td>171 MB</td> </tr> <tr> <td>z_train.parquet</td> <td> <pre>3 483 369</pre> </td> <td>681 MB</td> </tr> <tr> <td>zh_test.parquet</td> <td> <pre>1 068 606</pre> </td> <td>213 MB</td> </tr> <tr> <td>zh_train.parquet</td> <td> <pre>4 274 423</pre> </td> <td>851 MB</td> </tr> <tr> <td>qq_test.parquet</td> <td> <pre>6 366 715</pre> </td> <td>1.4 GB</td> </tr> <tr> <td>qq_train.parquet</td> <td> <pre>25 466 858</pre> </td> <td>5.6 GB</td> </tr> </tbody> </table> <p>The dataset consists of 6 files of 8.9 GB in total.</p> <h2>How can you use these data?</h2> <p>The .parquet files can be directly loaded with the Awkward Array Python library.<br>An example how one might use the dataset and the features is given in <strong>data_intro.ipynb</strong></p>
Datasets for "Turbulent magnetic decay controlled by two conserved quantities"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Turbulent magnetic decay controlled by two conserved quantities" by A. Brandenburg, & A. Banerjee. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Unit cells and resummed interactions for the calculations in "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions"
<p>This directory contains all the unit cells with the respective resummed interactions used for the optimisation procedure to obtain the results in the work "Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions[1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README file.</p> <p>[1]: J. A. Koziol et al., Systematic Analysis of Crystalline Phases in Bosonic Lattice Models with Algebraically Decaying Density-Density Interactions, <a href="https://10.21468/SciPostPhys.14.5.136">10.21468/SciPostPhys.14.5.136</a>, 2023</p>
IS471 - Decay spectroscopy data from the CRIS francium campaign 2014
<p>Decay spectroscopy data from the November 2014 campaign of the experiment IS471 on francium isotopes with the CRIS experimental setup at CERN ISOLDE.</p>
Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid
<p>This dataset contains measurement data from the following publication: Belt, T.; Kyyrö, S.; Kilpinen, A. T. (2023) Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid. Journal of Materials Science, 10.1007/s10853-023-08874-w. Small samples of Scots pine sapwood were modified using different concentrations of phenol formaldehyde (2.5, 5, 10, 20 and 30% resin solids content) and sorbitol-citric acid (5, 10, 20, 30 and 40% resin solids content) and then exposed to brown rot decay by <em>Coniophora puteana</em> and <em>Rhodonia placenta</em>. Sample masses and dimensions were measured at different points to determine their weight gain, anti-swelling efficiency and moisture exclusion efficiency due to modification, their mass loss due to decay and their moisture content at the end of the decay test. Fluorescence images were collected from decayed and control samples after the decay test. Further details on the experimental procedures can be found in the publication. </p> <p>The "Sample IDs and measurement data.csv" -file contains the sample IDs and all measured dimensions and mass data for every sample. Areas A<sub>dry0</sub>, Ad<sub>ry1</sub>, A<sub>wet</sub>, and A<sub>dry2</sub> are the cross-sectional areas of the samples in the dry state before modification, in the dry state after modification and before leaching, in the wet state during leaching, and in the dry state after leaching, respectively. Masses m<sub>dry0</sub>, m<sub>dry1</sub>, m<sub>dry2</sub>, m<sub>RH85</sub>, m<sub>wet</sub>, and m<sub>dry3</sub> are the masses of the samples in the dry state before modification, in the dry state after modification and before leaching, in the dry state after leaching, in the conditioned state at RH 85%, in the wet state at the end of the decay test, and in the dry state after the decay test, respectively.</p> <p>The "Fluorescence images" -folder contains fluorescence images collected from the samples. The image files are named according to the ID of the imaged sample, followed by additional tags. The samples modified using phenol formaldehyde were imaged using both green and UV excitation, and the file names contain the tag "green" or "UV" to denote the used excitation wavelengths. For all samples, the sample ID (and the excitation tag) are followed by a number to differentiate replicate images collected from the sample. </p>
Mesofauna community influences litter chemical trajectories during early-stage litter decay in compost
Decomposition of organic material is a fundamental ecosystem process, the rate of which is moderated by both litter chemistry and decomposer communities. Because litter chemistry changes throughout decomposition, we would expect the decomposer food web to interact with these changes in their basal resource to alter the trajectories of chemical changes during decay. To investigate how decomposer mesofauna influence patterns of litter chemical change throughout early stages of decay, we tracked mass loss, macro- and micronutrient elements, and fiber chemistry dynamics in Arizona sycamore (Platanus wrightii) leaves decomposed under optimal decay conditions in a biotically diverse compost pile. By using litterbags of two different mesh sizes to manipulate the mesofauna gaining access to the litter, we record how the complexity of the soil mesofauna community changes the trajectory of litter chemistry.
Data Files for Observations and simulations of dropout events and flux decays in October 2013
<p>This webpage provides access to diffusion coefficients and simulated electron distributions used in the article "Observations and simulations of dropout events and flux decays in October 2013: Comparing MEO equatorial with LEO polar orbit" by Pierrard V., J.-F. Ripoll, G. Cunningham, E. Botek, O. Santolik, S. Thaller, B. Kurth, M. Cosmides<span>.</span></p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
<ol> <li>Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture.</li> <li>Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every six months (end of wet and dry seasons) over a four-year period across 5 sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions.</li> <li>Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons, and simulated drought conditions. Wood blocks discovered by termites decomposed 16% to 36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates.</li> <li>Taken together, we found that spatial and seasonal variation in precipitation are important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes.</li> </ol>
Experimental Data for Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions
<p>This dataset includes wave amplitude decay data, force prediction and measurement data (organized in spreadsheets), and phase-averaged force measurement data stored in a MATLAB <code>.mat</code> file. The accompanying paper, titled <em>"Wave Decay by Submerged Rigid Vegetation under Orthogonal Wave-Current Conditions,"</em> will be published in <em>Geophysical Research Letters.</em> A detailed description of the variables is provided at the end of each spreadsheet. The detailed measurement methods are described in the paper. The data in the <code>.mat</code> file is arranged according to the experimental case order specified in the spreadsheet named <em>"force measurement."</em></p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells
<p>Source data for the publication: UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells.<br> Includes raw image data (e.g. agarose gels, western blots, northern blots), quantifications, qPCR raw Ct values and other supporting material.</p>
All data of the manuscript "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process" submitted to Geophysical Research Letters
<p>The data supports the manuscript entitled "A self-sustained charge neutrality lightning model containing the channel decay and reactivation process”. Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movies S1.avi, and it shows the entire development process of IC1 discharge.</p> <p>The data can be used freely for scientific purposes with the appropriate citation.</p>
Imprints of latitude, host taxon and decay stage on fungus-associated arthropod communities
<p>Interactions among fungi and insects involve hundreds of thousands of species. While insect communities on plants have formed some of the classic model systems in ecology, fungus-based communities and the forces structuring them remain poorly studied by comparison. We characterize the arthropod communities associated with fruiting bodies of eight mycorrhizal basidiomycete fungus species from three different orders along a 1200-km latitudinal gradient in northern Europe. We hypothesized that—matching the pattern seen for most insect taxa on plants—we would observe a general decrease of fungal-associated species with latitude. Against this backdrop, we expected local communities to be structured by host identity and phylogeny, with more closely related fungal species sharing more similar communities of associated organisms. As a more unique dimension added by the ephemeral nature of fungal fruiting bodies, we expected further imprints generated by successional change, with younger fruiting bodies harboring communities different from older ones. Using DNA metabarcoding to identify arthropod communities from fungal fruiting bodies, we find that latitude leaves a clear imprint on fungus-associated arthropod community composition, with host phylogeny and decay stage of fruiting bodies leaving lesser but still-detectable effects. The main latitudinal imprint is on a high arthropod species turnover, with no detectable pattern in overall species richness. Overall, these findings paint a new picture of the drivers of fungus-associated arthropod communities, suggesting that latitude will not affect <i>how many</i> arthropod species inhabits a fruiting body, but rather <i>what</i> species occur in it and <i>at w</i>hat relative abundances (as measured by sequence read counts). These patterns upset simplistic predictions regarding latitudinal gradients in species richness and in the strength of biotic interactions.</p>
Supporting data for "Modeling the albedo neutron decay source of radiation belt electrons and protons"
<p>Data sets are provided in support of the publication to appear in JGR-Space Physics. They include tabulated values of computed albedo neutron flux above the atmosphere, and of resulting radiation belt electron and proton source functions. Data format is described in the README files.</p>
Lowest Common Ancestor Generations (LCAG) Phasespace Particle Decay Reconstruction Dataset
<p>This record contains the corresponding dataset for the paper <a href="https://dx.doi.org/10.1088/2632-2153/ac8de0"><strong>Learning Tree Structures from Leaves For Particle Decay Reconstruction</strong></a>. The dataset contains the resulting simulated particle physics decays, with information about the detected particle (leaves) to be used as input, and Lowest Common Ancestor Generations (LCAGs) to be used as training targets. The code used for the paper's experiments, which contains the PyTorch dataset/dataloader, can be found at: <a href="https://github.com/Helmholtz-AI-Energy/BaumBauen">github.com/Helmholtz-AI-Energy/BaumBauen</a>.</p> <p>The dataset contains simulated synthetic particle decays, simulated using the <a href="https://github.com/zfit/phasespace">PhaseSpace</a> library.<br> All simulated decay topologies have a common root particle of mass 100 (arbitrary units). Intermediate particles are selected at random with replacement from the following masses: [90, 80, 70, 50, 25, 20, 10].<br> Final state particles, which make up the leaf nodes of generated topologies, are drawn with replacement from the following masses: [1, 2, 3, 5, 12]. For each intermediate particle (including the root), we limit the minimum number of children to two, and the maximum five.</p> <p>Tree topology creation to generate the dataset was as follows:<br> starting from the root particle a set of children are selected from the available intermediate and final state particles such that the sum of their masses totals less than the root, this process is then repeated for each child particle which is not a final state particle and so on until only final state particles remain.</p> <p>This dataset consists of 200 topologies (unique decay processes) in total, with 16,000 samples per topology. In the paper's experiments, 2000 topologies for each of training, validation, and testing were used. Leaf node features are not normalized. We have not enforced any ordering of the nodes and leave them unsorted as created in the dataset.</p> <p>When unpacked, the dataset archive will have the following structure, with the labelling pattern <em>[data]_[subset].[topology].npy</em></p> <pre><code>└── phasespace_dataset/ ├── lcas_train.000.npy ├── leaves_train.000.npy ├── ... ├── lcas_train.199.npy ├── leaves_train.199.npy ├── lcas_val.000.npy ├── leaves_val.000.npy ├── ... ├── lcas_val.199.npy ├── leaves_val.199.npy ├── lcas_test.000.npy ├── leaves_test.000.npy ├── ... ├── lcas_test.199.npy └── leaves_test.199.npy </code></pre> <p> </p>
Datasets for "Scaling of the Hosking integral in decaying magnetically-dominated turbulence"
<pre>This directory contains an index.html file with links to the run directories with secondary data for the figures and a Mathematica notebook for make the figures for the paper "Scaling of the Hosking integral in decaying magnetically-dominated turbulence" by H. Zhou, R. Sharma, and A. Brandenburg. If anything turns out to be incomplete, please email hongzhe.zhou@sjtu.edu.cn and brandenb@nordita.org. </pre>
Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i).
First observation of 229mTh decay
<p>Raw data of the first observation of internal conversion electrons emitted in the decay of 229mTh. Data was taken on 15.10.2014 and consists of 100 CCD camera images (4 s exposure time per image) of an MCP detector combined with a phosphor screen. The detection was performed during soft landing of triply charged 229Th ions on the MCP detector. The 229Th ion beam was generated by thermalization of alpha recoil ions originating from a 233U source. Therefore the isomeric state is populated to about 2%. A Matlab file used for image evaluation is provided in the file folder.</p>
Data for "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" by Zheng et al.
<p>All data of the manuscript "Numerical simulation study of the evolution of lightning channel decay and reactivation processes" submitted to Journal of Geophysical Research: Atmospheres.</p> <p>The data supports the manuscript entitled "Numerical simulation study of the evolution of lightning channel decay and reactivation processes”. Microsoft Notepad can open the *.txt files and the *.DAT files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at different channel segments. </p> <p>The data can be used freely for scientific purposes with the appropriate citation. </p>
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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.