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3,937 results for “number”
LYCEUM: Learning to call copy number variants on low coverage ancient genomes
<p>Copy number variants (CNVs) are pivotal in driving phenotypic variation that facilitates species adaptation. They are significant contributors to various disorders, making ancient genomes crucial for uncovering the genetic origins of disease susceptibility across populations. However, detecting CNVs in ancient DNA (aDNA) samples poses substantial challenges due to several factors: (i) aDNA is often highly degraded; (ii) contamination from microbial DNA and DNA from closely related species introduce additional noise into sequencing data; and finally, (iii) the typically low coverage of aDNA renders accurate CNV detection particularly difficult. Conventional CNV calling algorithms, which are optimized for high coverage read-depth signals, underperform under such conditions. To address these limitations, we introduce LYCEUM, the first machine learning-based CNV caller for aDNA. To overcome challenges related to data quality and scarcity, we employ a two-step training strategy. First, the model is pre-trained on whole genome sequencing data from the 1000 Genomes Project, teaching it CNV-calling capabilities similar to conventional methods. Next, the model is fine-tuned using high- confidence CNV calls derived from only a few existing high-coverage aDNA samples. During this stage, the model adapts to making CNV calls based on the downsampled read depth signals of the same aDNA samples. LYCEUM achieves accurate detection of CNVs even in typically low-coverage ancient genomes. We also observe that the segmental deletion calls made by LYCEUM show correlation with the demographic history of the samples and exhibit patterns of negative selection inline with natural selection.</p>
Data for the article 'Love numbers for Io with a magma ocean'
<p>Data sets for the article 'Love numbers of Io with a magma ocean'. The given file has the following structure and can be used to generate figures 2-5 in the manuscript</p> <p>Depth l (km) log10(visocisty (Pa s)) Ocean thickness (km) Dissipation (TW) k20 k22c k22s Delta_t20 (h) Delta_t22c (h) Delta_t22s (h).</p> <p>In case of any inquiries, please send an email to aygun@karel.troja.mff.cuni.cz.</p>
Spoiled for choice: Number of signalers constrains mate choice based on acoustic signals
<p>In many taxa, receivers use signals to detect and discriminate among mates. Signal detection and discrimination thus has important fitness consequences for individuals. Noise is defined as any factor that prevents detection or discrimination of signals. The noise produced by groups of signaling animals is a well-known impediment to signal detection and discrimination in animals, but how many signals produce the emergent, masking effects of noise? This dataset was generated to explore how receivers discriminate among signals in noisy, multi-choice environments. Subjects were female Australian field crickets, <em>Teleogryllus oceanicus. </em>We performed a series of phonotaxis (movement toward sound) assays in which we manipulated the number of long chirps in the signal. First, we assessed female preferences for the number of long chirp pulses and found that receivers preferred more long chirp pulses to fewer. Then we gave receivers a choice between a preferred, 7-pulse signal and either 1, 3, 5, or 7 presentations of the non-preferred, 2-pulse signal ("the multi-choice experiment"). We observed the probability that subjects left the release point, the probability that subjects responded to playback, and the probability of choosing the preferred stimulus. We also recorded the subject's latency to leave the release point and the latency to respond to playback. Because the angular separation between speakers decreased with increasing number of playback speakers in the multi-choice experiment, we then conducted an experiment ("the angular separation experiment") to determine whether observed effects were due to the spatial configuration of speakers or due to the emergent noise of multiple playback speakers. </p>
Particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles measured during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>This dataset consists of a 5-minute time series of particle number concentration of fluorescent and total aerosol particles that were measured by wideband integrated by aerosol sensor during the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017. For this dataset, aerosol particles within the optical diameter range of 0.5 μm to 14.5 μm were considered. In this dataset, the time periods when samples are likely contaminated by the ship’s exhaust were removed from the time series.</p> <p><strong>Dataset contents</strong></p> <ul> <li>PSD_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_fluorescent.csv , data file, comma-separated values</li> <li>PSD_hyper_tot.csv , data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This particle number size distribution of fluorescent, hyper-fluorescent, and total aerosol particles dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Fig. 5. – Sheet number 39 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 5. – Sheet number 39 of the Cupani Herbarium containing one specimen of Rosa L. (Rosaceae).
Fig. 6. – Sheet number 17 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 6. – Sheet number 17 of the Cupani Herbarium.
Fig. 3. – Sheet number 6 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 3. – Sheet number 6 of the Cupani Herbarium.
Fig. 7. – Sheet number 1 in Study of a pre-Linnaean herbarium attributed to Francesco Cupani (1657-1710)
Fig. 7. – Sheet number 1 of the Cupani Herbarium.
Runtime over 100 iterations with varying number of MPI processes
<p>Runtime over 100 iterations with varying number of MPI processes (np) and fixed block size <span class="math-tex">\(k=192\)</span> for matrix M5.</p>
More random numbers
Random numbers used to showcase showyourwork!<br><br>Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/rodluger/showyourwork-template/tree/80752fbc03784089b4485935a974f4de2751a252">this GitHub repo</a> using the following command:<br><br><pre><code class="language-bash">cd src/analysis && bash random.sh</code></pre><br>
Number of participations in projects
<p>Simple dataset of the distribution of project participations per participant (wood industry projects, Horizon 2020)</p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 21 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 21\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong><span class="math-tex">\(v_s\)</span></strong></td> <td><strong><span class="math-tex">\(B\)</span></strong></td> </tr> <tr> <td>R1</td> <td>2.20</td> <td>10</td> </tr> <tr> <td>R2</td> <td>0</td> <td>0</td> </tr> <tr> <td>R3</td> <td>2.20</td> <td>30</td> </tr> <tr> <td>R4</td> <td>2.20</td> <td>50</td> </tr> <tr> <td>R5</td> <td>1.10</td> <td>10</td> </tr> <tr> <td>R6</td> <td>0.22</td> <td>10</td> </tr> <tr> <td>R7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>R8</td> <td>1.76</td> <td>10</td> </tr> <tr> <td>R9</td> <td>2.85</td> <td>10</td> </tr> <tr> <td>R10</td> <td>6.58</td> <td>10</td> </tr> <tr> <td>R11</td> <td>2.20</td> <td>20</td> </tr> <tr> <td>R12</td> <td>5.49</td> <td>10</td> </tr> <tr> <td>R13</td> <td>3.29</td> <td>10</td> </tr> <tr> <td>R14</td> <td>3.60</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 59 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 59\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td>Item</td> <td><span class="math-tex">\(v_s\)</span></td> <td>B</td> </tr> <tr> <td>Tr</td> <td>0</td> <td>0</td> </tr> <tr> <td>T13</td> <td>1</td> <td>10</td> </tr> <tr> <td>T16</td> <td>3</td> <td>10</td> </tr> <tr> <td>T18</td> <td>5</td> <td>10</td> </tr> <tr> <td>T19</td> <td>10</td> <td>0.3</td> </tr> <tr> <td>T25</td> <td>10</td> <td>1</td> </tr> <tr> <td>T27</td> <td>10</td> <td>3</td> </tr> <tr> <td>T29</td> <td>10</td> <td>10</td> </tr> <tr> <td>T30</td> <td>30</td> <td>10</td> </tr> <tr> <td>T31</td> <td>10</td> <td>30</td> </tr> <tr> <td>T33</td> <td>10</td> <td>50</td> </tr> <tr> <td>T38</td> <td>20</td> <td>10</td> </tr> <tr> <td>T43</td> <td>15</td> <td>10</td> </tr> <tr> <td>T50</td> <td>25</td> <td>10</td> </tr> <tr> <td>T56</td> <td>8</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <p> </p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes characterization data (H<sub>2</sub>-O<sub>2</sub> titration and N<sub>2</sub> adsorption t-plot) associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments performed on high-throughput setup over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6. The dispersion of platinum over HUSY zeolite was determined by H<sub>2</sub>-O<sub>2</sub> titration, while the specific surface area was determined by t-plot N<sub>2</sub> adsorption. These data are classified based on the platinum loading and the characterization method.</p> <p>0.3 wt%Pt (H2-O2 titration) : The dispersion of 0.3 wt%Pt/HUSY</p> <p>0.3 wt%Pt (N2 ads t-plot) : The specific surface area of 0.3 wt%Pt/HUSY</p> <p>0.1 wt%Pt (H2-O2 titration) : The dispersion of 0.1 wt%Pt/HUSY</p> <p>0.1 wt%Pt (N2 ads t-plot) : The specific surface area of 0.1 wt%Pt/HUSY</p> <p>0.07 wt%Pt (H2-O2 titration) : The dispersion of 0.07 wt%Pt/HUSY</p> <p>0.07 wt%Pt (N2 ads t-plot) : The specific surface area of 0.07 wt%Pt/HUSY</p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes experimental data associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments on high-throughput setup by feeding equimolar mixtures of n-octane and tert-butylcyclohexane, and n-decane and methylcyclohexane. In order to investigate the above-mentioned impact, the kinetic behavior was examined at various experimental conditions, over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6.</p> <p>The process conditions which were used for every feed are summarized:</p> <ul> <li>Pure n-octane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-octane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-octane and tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> <li>Pure n-decane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-decane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-decane and methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> </ul> <p>The kinetics of hydroconversion of different alkane/cycloalkane feeds were compared based on conversion of reactants and yields to isomers. The data are classified based on figures in the Article.</p> <p>Figure 5 : <em>n</em>-Octane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure 6 : Octane isomer yields as a function of <em>n</em>-octane conversion - comparison of experiments with pure <em>n</em>-octane and in mixture with tert-butylcyclohexane</p> <p>Figure 7 : tert-Butylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 8 : Butylcyclohexane isomer yields as a function of tert-butylcyclohexane conversion comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 9 : <em>n</em>-Decane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 10 : Decane isomer yields as a function of <em>n</em>-decane conversion - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 11 : Methylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S9 : <em>n</em>-Octane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S10 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S13 : tert-Butylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S14 : tert-Butylcyclohexane conversion as a function of space time - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S15 : <em>n</em>-Decane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S16 : <em>n</em>-Decane conversion as a function of space time - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S17 : Methylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S18 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p>
Optics Continuum: F-Number and Focal Length of Light Field Systems
<p>Reference:</p> <p>Ivo Ihrke, <em>„F-Number and Focal Length of Light Field Systems: A Comparative Study of Field of View, Light Efficiency, Signal to Noise Ratio, and Depth of Field“</em>, OSA Continuum, 2022</p> <p>Contact:</p> <p>Ivo Ihrke [at] uni [minus] siegen [dot] de</p> <p>Content:</p> <p>The data set contains the raw images that have been used in generating the experimental Figures (4 and 6) in the article as well as a code snippet (see README.md) to extract the Lytro data.</p>
The population genetics of adaptation through copy-number variation in a fungal plant pathogen
<p>Supplementary Tables S1-S8 for the manuscript "The population genetics of adaptation through copy-number variation in a fungal plant pathogen"</p>
Dataset: Nuclear moments of indium isotopes reveal abrupt change at magic number 82
<p>Dataset for the figures in the paper 'Nuclear moments of indium isotopes reveal abrupt<br> change at magic number 82'</p> <p>Preprint DOI: https://doi.org/10.21203/rs.3.rs-611360/v1</p>
Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
<p>Studies characterizing the effect of a century of plant breeding on physiological traits are highly needed to identify candidate traits for future improvement in maize (<em>Zea mays</em> L.). A global evaluation of kernel weight progress over time requires the assembly of large and reliable data documenting genetic improvements in this trait across commercial breeding programs in different regions. We compiled a global dataset of yield and yield components from 34 published and unpublished studies comparing two or more maize cultivars from different decades of commercial release under field conditions. The dataset includes 750 entries of kernel weight data (requirement to be included in the systematic review), of which 642 and 666 include data entries of grain yield and kernel number, respectively. We also extracted the metadata describing experimental site information, agronomic management practices, and genotypic information. This dataset can be useful to identify trends of yield improvement across management conditions, with proper consideration of the trade-off between kernel number and kernel weight in maize.</p>
Water residence time and Damköhler number for DOC cycling in global watersheds
<p>The relative capacity for watersheds to eliminate or export reactive constituents has important implications on aquatic ecosystem ecology and biogeochemistry. Removal efficiency depends on factors that affect either the reactivity or advection of a constituent within river networks. In this dataset, we characterized instream water residence time and Damköhler number (Da) for dissolved organic carbon (DOC) uptake in global watersheds.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.