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1,003 results for “darkness”
Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM
<p>This is the raw data for the manuscript:</p> <p>Characterization of extended defects in 2D materials using aperture-based dark-field STEM in SEM</p> <p> </p> <p>A readme file containing all descriptions can be found in the main folder. </p> <p> </p> <p>Abstract:</p> <p>Quantitative diffraction contrast analysis with defined diffraction vectors is a wellestablished method in TEM for studying defects in crystalline materials. A comparable transmission techniques is however not available in the more widely used SEM platforms. In this work, we transfer the aperture-based dark-field imaging method from the TEM to the SEM, thus enabling quantitative diffraction contrast studies at lower voltages in SEM. This is achieved in STEM mode by inserting a custom-made aperture between the sample and the STEM detector and centering the hole on a desired reflection. To select individual reflections for dark-field imaging, we use our Low Energy Nanodiffraction (LEND) setup [Schweizer et al., Ultramicroscopy 213, 112956 (2020)], which captures transmission diffraction patterns from a fluorescent screen positioned below the sample. The aperture-based dark-field STEM method is particularly useful for studying extended defects in 2D materials, where (i) stronger diffraction at the lower voltages used in SEM is advantageous, but at the same time (ii) two-beam conditions cannot be established, making quantitative diffraction contrast analysis with standard bright-field and annular dark-field detectors impossible. We demonstrate the method by studying basal plane dislocations in bilayer graphene, which have attracted considerable research interest due to their exceptional structural and electronic properties. Direct comparison of results obtained on identical dislocations by the established TEM method and by the new aperture-based dark-field STEM method in SEM shows that a reliable Burgers vector analysis is possible by applying the wellknown g·b=0 invisibility criterion. We further use the LEND setup to acquire 4D-STEM data and show that the virtual dark-field images match well with those in aperturebased dark-field STEM images for reliable Burgers vector analysis.</p> <p> </p>
Transcriptome analysis of dark-induced bleaching octocoral
<h3>Transcriptome of dark-induced bleaching soft coral, <em>Lobophytum hsiehi</em></h3> <p>The raw transcriptomic data has been archived in NCBI BioProject under accession number PRJNA1037697.</p> <p>The transcript reads (transcript_sequence.fasta) deposited here were processed using Trinity, TransDecoder, and CD-HIT.</p> <p>The dataset (transcript_and_deg_information.xlsx) provided transcript annotations and results from the differential expression analysis.</p>
Dark Patterns nos marketplaces: uma Investigação com Base nas Reclamações dos Consumidores
<p>Com a disseminação de sistemas interativos em ambientes de trabalho e sociais, a usabilidade é incorporada no desenvolvimento de novos sistemas, ao produzir a experiência do usuário. Este trabalho apresenta um estudo sobre Dark Patterns nos Marketplaces, a fim de identificar como a interface contribui para que ocorra a manipulação do usuário.</p>
Dark_Field_Compression_Paper
<p>This study investigates the effects of dark field compression on proton interactions using a series of simulations designed to model proton resonance and energy dissipation during high-energy collisions. By adjusting parameters such as compression strength, perturbation scale, initial separation, and time windows, the simulations reveal important insights into how dark field dynamics could replace traditional gravitational models and account for quantum phenomena in proton collisions, such as those observed in particle colliders like the Large Hadron Collider (LHC).</p>
DRET-DNA PAINT: using Dark dyes for fast super-resolution imaging
Open the record for dataset details and reuse information.
Data for "The protein structurome of Orthornavirae and its dark matter"
<p>Supplementary data for "The protein structurome of Orthornavirae and its dark matter" article</p>
pyEMAPS 4DSTEM Test Image - Annular Bright or Dark Field
<p>This file is required for one of pyEMAPS sample testing its 4DSTEM features: annular bright or dark field generation.</p> <p><strong>pyemaps</strong> package is a collection of python modules and libraries designed for transmission electron diffraction simulations and related crystallographic calculations for all crystal systems. The simulation is based on the theories of kinematical and dynamical electron diffraction, as described by Spence and Zuo in the book of Electron Microdiffraction (Plenum, New York, 1992). 4DSTEM features have also been added to the latest release. See <a title="pyEMAPS " href="https://emlab-solutions.github.io/pyemaps/overview.html" target="_blank" rel="noopener">this</a> for details.</p>
A "Dirty" Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils
<p>Amazonian rainforests once thought to hold an innate pristine wilderness, are increasingly known to have been densely inhabited by populations showing a diverse and complex cultural background prior to European arrival. To what extent these societies impacted their landscape is unclear. Amazonian Dark Earths (ADEs) are fertile soils found throughout the Amazon Basin, created by pre-Columbian societies as a result of more sedentary habits. Much is known of the chemistry of these soils, yet their zoology has been neglected. Hence, we characterised soil macroinvertebrate communities and activity in these soils at nine archaeological sites and adjacent reference soils in three Amazonian regions, totaling eighteen sampling sites. Furthermore, we characterized various soil chemical and physical properties associated with soil fertility.</p> <p>The current dataset contains data on soil macroinvertebrate biodiversity (26 taxa), with a special focus on termites, earthworms and ants. It also contains data on soil macromorphology, bulk density and porosity, soil carbon, nitrogen, macro and micronutrients, magnetic susceptibility and apparent electrical conductivity. The results show similar overall macrofauna morphospecies richness in ADE and adjacent soils, but distinct communities in each soil type. They also show higher soil fauna activity in ADEs when compared to adjacent reference soils, associated with greater earthworm populations. Finally, they also confirm the well-known high soil fertility in ADEs compared with adjacent soils. Land use was an important determinant of both macrofauna biodiversity and soil fertility. These findings support the idea that humans have built and sustained a contrasting high fertility ecosystem that persisted until our days, altering biodiversity distribution patterns in Amazonia.</p>
Correlation between halo properties and dark matter
<p>Pearson correlation coefficients between halo properties (panel a), the local dark matter density with halo virial mass<br> (panel b) and velocity dispersion (panel c), measured in bins of local density for different cosmic-web types, as obtained from the SLICS reference simulation. The error bars denote the standard<br> deviation obtained from the set of catalogs</p>
Slices of dark matter, halo and galaxy density fields
<p>Slices of 25 Mpc h−1 thick though different density fields involved in the calibration of the BAM products and its products. The<br> bottom panel shows the reconstruction of the halo number density field using different models of halo-bias (see §2.6). The rightmost<br> column shows galaxy density field from the reference and from BAM, built by populating halo catalogs with a model of halo occupation<br> distribution</p>
"Desinformación y relaciones públicas. Aproximación a los términos Black PR y Dark PR"
<p>Anexos del artículo "Desinformación y relaciones públicas. Aproximación a los términos Black PR y Dark PR"</p> <p>La desinformación ha alcanzado en los últimos años un alto grado de profesionalización. Las campañas de <em>Dark PR</em> o <em>Black PR</em>, son un nivel avanzado de las campañas negativas y en ellas se ficciona la prescripción y la influencia, normalmente con ánimo de desprestigiar al adversario político o el competidor.</p> <p>Se propone en este trabajo una revisión sistemática sobre los resultados de búsqueda de los términos <em>Black PR</em> y <em>Dark PR</em> en Google y Google Scholar. Se pretende identificar en primer lugar las definiciones y orígenes de ambos conceptos, conocer las estrategias utilizadas en estos trabajos, así como los casos de estudio que sirven para ejemplificar los mismos. Se observa que ambos conceptos son aún muy novedosos y son más empleados por medios de comunicación que por la literatura académica aunque presentan definiciones y usos comunes. Se recoge una tendencia creciente de prácticas de influencia negativa con numerosos ejemplos de <em>astroturfing</em>, campañas de desinformación y censura corporativa en distintos países. Esta circunstancia resulta problemática para el sector de las relaciones públicas pues difiere de los principios éticos de la profesión y obliga a quienes la ejercen a participar en prácticas poco honestas.</p>
Supplementary Data: Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT". The paper can be found at https://arxiv.org/abs/2209.13266.</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi.</p>
Supplementary animations for "Simulations of multi-field ultralight axion-like dark matter"
<p>Animations to supplement figures in "<a href="https://doi.org/10.48550/arXiv.2302.04302">Simulations of multi-field ultralight axion-like dark matter</a>", available at <a href="https://arxiv.org/abs/2302.04302">arxiv:2302.04302</a> or <a href="https://doi.org/10.1103/PhysRevD.107.063520">https://doi.org/10.1103/PhysRevD.107.063520</a>.</p>
Dark Skies Clasification of ISS images.
<p>The Dark Skies Citizen Science Project is indeed related to Cities at Night, a program that uses images taken from the International Space Station (ISS) to catalog and monitor light pollution in cities around the world. The project is a collaboration between the Complutense University of Madrid and other institutions.</p> <p>Citizen scientists can participate in the project by using the Cities at Night website to classify and identify the different sources of light in the images, such as streetlights, buildings, and illuminated signs. This information helps researchers create more accurate maps of light pollution and track changes in urban lighting over time.</p> <p>The Dark Skies Citizen Science Project is a great way for people to get involved in scientific research and contribute to a better understanding of the impact of light pollution on our cities and the environment.</p>
Profiled Likelihoods from A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data
<p>This repository contains the profiled likelihood ratios calculated in "A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data" [arXiv:2212.07435] for the unmasked analysis. Profiled likelihood ratios are provided for each of the 30 annuli considered in the work joined over the top three quartiles of data in energy dispersion.</p> <p>The data are provided in an hdf5 archive containing 532 datasets. Line rest energies in units of GeV are provided in the `Line_Energy` dataset, which is a length 531 1-D array. Each of the remaining 531 datasets contains a 3-D array of shape (30, 2, 1001) corresponding to the profiled likelihood ratio as a function of line intensity, in units of counts/cm<sup>2</sup>/s/sr. The log of the profiled likelihood ratio for the i<sup>th</sup> line rest energy in the j<sup>th</sup> annulus may be accessed by:</p> <pre><code class="language-python">archive = h5py.File('./Archive.h5', 'r') line_energy = archive['Line_Energy'][i] data = archive['Line_' + str(i)][j] line_intensity = data[0] log_profiled_likelihood_ratio = data[1] archive.close()</code></pre> <p>For details, see <a href="https://arxiv.org/abs/2212.07435">https://arxiv.org/abs/2212.07435</a>. </p>
A dark side of conservation biology: protected areas fail in representing subterranean biodiversity. Databases.
<p>We obtained distribution data for leiodids by digitizing the information provided by Fresneda and Salgado (2017). This information was updated and expanded with additional data from several sampling campaigns (unpublished data). For subterranean spiders, we included the entirety of the Alps range extending from France in the east westward through Switzerland, Italy, Liechtenstein, Austria, Germany, and Slovenia, by integrating available data in Global Biodiversity Information Facility (GBIF), Spider of Europe), Araneae.it and other literature sources.</p>
Figs 1, 2 in Vendaphaea, a new dark sac spider genus apparently endemic to the Soutpansberg Mountains, South Africa (Araneae: Corinnidae)
Figs 1, 2. Dorsal habitus of Vendaphaea lajuma sp. n., male (1) and female (2). Scale bars = 2 mm.
Supplementary animations for "Scalar dark matter vortex stabilization with black holes"
<p>Supplementary animations for <a href="https://doi.org/10.48550/arXiv.2301.13220">Scalar dark matter vortex stabilization with black holes</a>, <a href="https://arxiv.org/abs/2301.13220">arxiv:2301.13220, </a><a href="https://doi.org/10.1088/1475-7516/2023/07/004">https://doi.org/10.1088/1475-7516/2023/07/004</a></p>
Respiration patterns in the dark ocean
<p><strong>O2_utilization_rates.mat</strong></p> <p>Matlab data file containing the OUR data showed in Fig. 5. For each variable, the first dimension is for each of the 5000 Monte Carlo simulations, the second dimension is for each of the 10 regions, and the third dimension is for each of the defined isopycnals. </p> <p> </p> <p><strong>DOC_consumption_rates.mat</strong></p> <p>Matlab data file containing the OUR data showed in Fig. 5. For each variable, the first dimension is for each of the 5000 Monte Carlo simulations, the second dimension is for each of the 10 regions, and the third dimension is for each of the defined isopycnals. </p> <p> </p> <p><strong>Results_summary.xlsx</strong></p> <p>These are all the data included in Table S1, the data necessary to plot Fig. 10, and the data required to compute the spatially integrated values. </p> <p> </p> <p><strong>OUR_data_compil.xlsx</strong></p> <p>This is a short data compilation of oxygen utilisation rates, that was used to compare with results obtained in Sulpis et al. (in preparation): "Respiration patterns in the dark ocean". </p> <p>Data included in this file are from the following studies: </p> <p>Feely, R. A., Sabine, C. L., Schlitzer, R., Bullister, J. L., Mecking, S., & Greeley, D. (2004). Oxygen Utilization and Organic Carbon Remineralization in the Upper Water Column of the Pacific Ocean. Journal of Oceanography, 60, 45–52.</p> <p>Hinga, K. R. (1985). Evidence for a higher average primary productivity in the Pacific than in the Atlantic Ocean. Deep Sea Research Part A. Oceanographic Research Papers, 32(2), 117–126. https://doi.org/10.1016/0198-0149(85)90023-8</p> <p>Karstensen, J., Stramma, L., & Visbeck, M. (2008). Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Progress in Oceanography, 77(4), 331–350. https://doi.org/10.1016/j.pocean.2007.05.009</p> <p>Wang, W., Cai, M., Huang, P., Ke, H., Liu, M., Liu, L., Deng, H., Luo, B., Wang, C., Zheng, X., & Li, W. (2021). Transit Time Distributions and Apparent Oxygen Utilization Rates in Northern South China Sea Using Chlorofluorocarbons and Sulfur Hexafluoride Data—Wang—2021—Journal of Geophysical Research: Oceans—Wiley Online Library. Journal of Geophysical Research Oceans, 126(8). https://agupubs-onlinelibrary-wiley-com.proxy.library.uu.nl/doi/10.1029/2021JC017535</p> <p>Craig, H. (1971). The deep metabolism: Oxygen consumption in abyssal ocean water. Journal of Geophysical Research (1896-1977), 76(21), 5078–5086. https://doi.org/10.1029/JC076i021p05078</p> <p>Jenkins, W. J. (1998). Studying subtropical thermocline ventilation and circulation using tritium and 3He. Journal of Geophysical Research: Oceans, 103(C8), 15817–15831. https://doi.org/10.1029/98JC00141</p> <p>Jenkins, W. J. (1982). Oxygen utilization rates in North Atlantic subtropical gyre and primary production in oligotrophic systems. Nature, 300(5889), 246–248.</p> <p>Sarmiento, J. L., Thiele, G., Key, R. M., & Moore, W. S. (1990). Oxygen and nitrate new production and remineralization in the North Atlantic subtropical gyre. Journal of Geophysical Research: Oceans, 95(C10), 18303–18315. https://doi.org/10.1029/JC095iC10p18303</p> <p>Naqvi, S. W. A., Shailaja, M. S., Dileep Kumar, M., & Sen Gupta, R. (1996). Respiration rates in subsurface waters of the northern Indian Ocean: Evidence for low decomposition rates of organic matter within the water column in the Bay of Bengal. Deep Sea Research Part II: Topical Studies in Oceanography, 43(1), 73–81. https://doi.org/10.1016/0967-0645(95)00080-1</p> <p>Arı́stegui, J., Denis, M., Almunia, J., & Montero, M. F. (2002). Water-column remineralization in the Indian sector of the Southern Ocean during early spring. Deep Sea Research Part II: Topical Studies in Oceanography, 49(9–10), 1707–1720. https://doi.org/10.1016/S0967-0645(02)00008-5</p> <p>Broecker, W. S., Blanton, S., Smethie, W. M., & Ostlund, G. (1991). Radiocarbon decay and oxygen utilization in the Deep Atlantic Ocean. Global Biogeochemical Cycles, 5(1), 87–117. https://doi.org/10.1029/90GB02279</p>
A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument: Data Repository
<p>This repository contains the synthetic catalogue for the DESI Bright Galaxy Survey produced wit the N-body code <em>gevolution</em>, which is analysed in the manuscript "<a href="https://arxiv.org/abs/2306.04213">A case study for measuring the relativistic dipole of a galaxy cross-correlation with the Dark Energy Spectroscopic Instrument</a>", as well as the raw data of the analysis results. The catalogue "catalogue.csv.bz2" is in the CSV format and can be directly read using the pandas library of python, for example. The columns in the catalogue contain the following information:</p> <p>0. Column index<br> 1. Comoving coordinate x (in units of Mpc/h)<br> 2. Comoving coordinate y (in units of Mpc/h)<br> 3. Comoving coordinate z (in units of Mpc/h)<br> 4. Observed redshift<br> 5. Cosine of the observed polar angle measured with respect to the axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with <span class="math-tex">\(\phi_1 = \pi/4\)</span>, then rotating along the new y axis with <span class="math-tex">\(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\)</span> and setting the final rotation angle to zero, <span class="math-tex">\(\phi_3 = 0\)</span>; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix)<br> 6. Observed azimuthal angle phi measured with respect to axis pointing in the direction (1,1,1) along the box diagonal (the original comoving coordinate system has been rotated with an intrinsic z-y-z Euler rotation, first rotating along the z-axis with <span class="math-tex">\(\phi_1 = \pi/4\)</span>, then rotating along the new y axis with <span class="math-tex">\(\theta_2 = \mathrm{arccos}(1/\sqrt{3})\)</span> and setting the final rotation angle to zero, <span class="math-tex">\(\phi_3 = 0\)</span>; hence to get the unperturbed mu and phi coordinates, one needs to rotate the comoving x, y and z coordinates with the corresponding inverse Euler rotation matrix)<br> 7. Logarithm of the luminosity in units of solar luminosity <span class="math-tex">\(L_\odot\)</span><br> 8. Observed flux (in units of <span class="math-tex">\(L_\odot/\mathrm{Mpc}^2\)</span>)<br> 9. Number of particles in each object, plus a uniform noise between 0 and 1. This quantity is the proxy of the mass that was used to assign luminosity to the objects.<br> 10. Flag that identifies the selected objects within each redshift bin. The Flag is 0 for objects not included in the catalogue, and equal to the mean redshift of the bins <span class="math-tex">\(\bar{z} = 0.25, 0.35, 0.45\)</span> for the selected objects. <br> 11. Flag that identifies the bright and faint objects for case 1 (50% bright, 50% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 12. Flag that identifies the bright and faint objects for case 2 (90% bright, 10% faint, no flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 13. Flag that identifies the bright and faint objects for case 3 (50% bright, 50% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.<br> 14. Flag that identifies the bright and faint objects for case 4 (90% bright, 10% faint, with flux limit). Flag = 0 for non-selected objects, Flag = 1 for bright objects, Flag = 2 for faint objects.</p> <p>The example script "example-script.ipynb" demonstrates how to query the catalogue to extract e.g. the redshift distribution of the objects for the different cases considered in Table 4 of the manuscript.</p> <p>Additionally, the measured dipole data vectors with the jackknife covariance matrices (<span class="math-tex">\(\mathrm{cov}^\mathrm{JK}_{ij}\)</span>), as well as the theoretical data vectors with the theoretical measurement covariance (<span class="math-tex">\(\mathrm{cov}^\mathrm{th}_{ij}\)</span>) and the theoretical prediction covariance (<span class="math-tex">\(\mathrm{cov}^\mathrm{pred}_{ij}\)</span>) are provided within this repository:</p> <ul> <li>In the measurements.tar.gz archive, the measured data for the flux-limited case can be found in the /flux-limit subdirectory, while the data for the case without flux-limit is in /no-flux-limit. The data vectors are named "dipole_<redshift bin>_<% of bright galaxies>.txt. The first column in each of those files is the separation bin <span class="math-tex">\(d\)</span> in <span class="math-tex">\(\mathrm{Mpc}/h\)</span>, the second column is the mean two-point correlation function dipole of the 100 jackknife subsamples, and the third column is the square root of the diagonal part of the jackknife covariance matrix (<span class="math-tex">\(\mathrm{cov}^\mathrm{JK}_{ij}\)</span>). The corresponding jackknife covariance matrices are named "cov_<redshift bin>_<% of bright galaxies>.txt.</li> <li>In the theory.tar.gz archive, the theoretical predictions are found in /flux-limit for the case with flux limit and in /no-flux-limit for the case without flux limit. The theoretical data vectors are named "dipole_<% of bright galaxies>B_z<redshift bin>_gevol.dat". The first column in each of those files is the separation bin <span class="math-tex">\(d\)</span> in <span class="math-tex">\(\mathrm{Mpc}/h\)</span>, the second column the theoretical two-point correlation function dipole and the third column is the square root of the diagonal part of the theoretical prediction covariance matrix (<span class="math-tex">\(\mathrm{cov}^\mathrm{pred}_{ij}\)</span>) . The theoretical measurement covariance matrices are named "covariance_Lp6_<% of bright galaxies>B_z<redshift bin>_gevol.dat", and the theoretical prediction covariance matrices are named "covtheo_<% of bright galaxies>B_z<redshift bin>_gevol.dat". </li> </ul> <p>The example script also demonstrates how to use these data files to reproduce plots of the dipole measurement vs the theoretical prediction like in Figures 6, 7, C1 and C2. The archives need to be unpacked before using the example script to access the data.</p>
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