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3,655 results for “Structural data”
Research data for "Predicting Dynamics from Structure in a Sodium Silicate Glass"
<p>This dataset supports the paper "Predicting Dynamics from Structure in a Sodium Silicate Glass".</p> <p>The following files are provided.</p> <p>File: dataset_800.zip</p> <p>- Pickle files for:</p> <ul> <li>400 Sodium silicate glass structures of 3000 atoms</li> <li>30 Trajectories sampled eight times at various timescales up to 1 ns for each if the 400 glass structures</li> </ul> <p>File: in.comb</p> <p>- Lammps inputfile used to generate simulation from with the data in dataset was sampled</p>
Data and code for: A supergene controlling social structure in Alpine ants also affects the dispersal ability and fecundity of each sex
<p>Social organisation, dispersal and fecundity co-evolve, but whether they are genetically linked remains little known. Supergenes are prime candidates for coupling adaptive traits and mediating sex-specific trade-offs. Here, we test whether a supergene that controls social structure in <em>Formica selysi</em> also influences dispersal-related traits and fecundity within each sex. In this ant species, single-queen colonies contain only the ancestral supergene haplotype <em>M</em> and produce<em> MM</em> queens and <em>M</em> males, while multi-queen colonies contain the derived haplotype <em>P</em> and produce <em>MP </em>queens, <em>PP</em> queens, and <em>P</em> males. By combining multiple experiments, we show that the <em>M </em>haplotype induces phenotypes with higher dispersal potential and higher fecundity, for both sexes. Specifically, <em>MM</em> queens, <em>MP</em> queens, and <em>M </em>males are more aerodynamic and more fecund than <em>PP </em>queens and <em>P</em> males, respectively. Differences between <em>MP</em> and <em>PP</em> queens from the same colonies reveal a direct genetic effect of the supergene on dispersal-related traits and fecundity. The derived haplotype <em>P</em>, associated with multi-queen colonies, produces queens and males with reduced dispersal abilities and lower fecundity. More broadly, similarities between the <em>Formica </em>and <em>Solenopsis</em> systems reveal that supergenes play a major role in linking behavioural, morphological, and physiological traits associated with intraspecific social polymorphisms.</p>
(U)SAXS data (ID02 beamline, ESRF): Effects of pH on the fibrous structure formation of plant proteins during high-moisture extrusion
<p>Due to health and environmental factors, the food industry is looking for ways to introduce meat replacers made from plant-based proteins to consumer markets. The presence of structural anisotropy in the form of fibre is a prerequisite for meat analogues. Structure formation ability depends on the protein ingredients used, which leads to plant protein products with varying texture hardness and extent of fibre alignment. In the current study, we will test if it is possible to tune these properties based on the hypothesis that plant proteins have different structure formation abilities under varying pH conditions.</p>
Data_paper_Influence of buffer/protective layers on the structural and magnetic properties of SmCo films on Silicon
<p>Integration of Samarium Cobalt hard magnets on silicon requires buffer/protective layers that can enhance the magnetic properties of the magnet while preserving its structure and chemical composition after post-annealing treatments needed for the formation of the magnetically hard phase. In this work, a comparison of Samarium-Cobalt films for five different buffer/protective layers, namely Ti, W, TiW, Ta, Cr and two different annealing temperatures, 650°C and 750°C, is presented. Depending on materials and annealing temperatures, magnetic properties such as saturation and coercivity of the SmCo film can be finely tuned. We show that coercivity up to 3.65 T or saturation magnetization up to 0.95 T can be reached by proper choice of the relevant process parameters: deposition temperature, material for the buffer/protective layer and annealing temperature. Such value of coercivity is among the highest found in literature for thin films of SmCo.</p>
Supplemental data for: Structural polymorphism and diversity of human segmental duplications
<p>Data used for figure generation and analysis in: Structural polymorphism and diversity of human segmental duplications</p> <p> </p> <p>Code used for data analysis is on https://github.com/hrrsjeong/pangenome_SD</p>
Raw diffraction data for CCDC 2364133 – crystal structure of 2,2'-[ethane-1,2-diylbis(sulfanediyl)]bis(2-methyl-1,4-dithiane)
<p>X-ray diffraction data from a single crystal of 2,2'-[ethane-1,2-diylbis(sulfanediyl)]bis(2-methyl-1,4-dithiane)</p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance - Data
<p>Datasets and R code related to manuscript entitled, "Scale dependent spatial structuring of mountain river large bed elements maximizes flow resistance". See '0_READ_ME.rtf' file for additional description of available files.</p>
Data supporting "Slowest-first translation scheme: Structural asymmetry along protein sequences and co-translational folding"
<p>Contains data for a set of 16,200 non-redundant protein structures taken from the Protein Data Bank. Associated code can be found at https://github.com/jomimc/FoldAsymCode.</p>
Data from: Disentangling the drivers of ground-dwelling macro-arthropod metacommunity structure at two different spatial scales
<p>The goal of this study was to explore the community assembly rules at local and regional scales.</p> <p> </p> <p><strong><em>Site description </em></strong></p> <p>All sampling locations were selected within the black soil region (Fig. 1), which is predominantly located in the temperate continental monsoon climatic zone in North China. It is characterized by a dry and cold winter and warm and humid summer. The soil was classified as black soil following the Chinese Soil Classification System, which is equivalent to a Typic Hapludoll in the USDA Soil Taxonomy. More specific details for this soil (such as black soil coverage area, geographical and ecological resources, etc.) can be obtained from Wen and Liang (2001). Samples were collected from three municipal districts: Bei'an, Hulan and Dehui.</p> <p> </p> <p><strong><em>Sampling design and setup</em></strong></p> <p>We conducted field sampling of ground-dwelling macro-arthropods and measured a set of environmental and spatial variables across all sampling locations three times: in May, July and September 2015. In total, 15 plots (five plots in each of the three municipal districts) were selected and sampled. At each plot, we further selected five sampling sites (approximately 10 m away from each other).</p> <p>We collected additional samples for estimating soil abiotic parameters at each site. Soil samples (5 × 5 cm and 10 cm depth) were collected near each pitfall trap site. The exact geographic coordinates of each sampling site were obtained by GPS.</p> <p>Ground-dwelling macro-arthropods were sampled by a pitfall trapping method. For pitfall traps, we used plastic cups (7 cm in diameter and 12 cm deep), which were partially filled with saturated salt water. The traps were exposed for one week in each sampling month. All collected ground-dwelling macro-arthropods were removed from the pitfall traps, sorted and preserved in a 95% alcohol solution. All adult macroarthropods from pitfalls were identified at the species or genus level using appropriate keys (e.g., Simon (1879), Martens (1978) and Barrientos (2004) for Opiliones; Roberts (1993, 1995) for Lycosidae; and Forel and Leplat (2001) and Ortuño and Marcos (2003) for Carabidae) and then were counted. Juvenile ground-dwelling arthropods were excluded from all analyses due to difficulties with their identification (Gao et al., 2016).</p> <p> </p> <p><strong><em>Environmental and spatial variables</em></strong></p> <p>Environmental variables used in our analysis included soil organic matter, soil total nitrogen, water content, pH, temperature. Soil water content (SWC%) was measured in the laboratory after the fresh soil was loaded into an aluminium box. Prior to estimating soil total nitrogen (TN) (Kjeldahl's method described by Duchaufour (1975)), soil organic matter (SOM) (Anne's method described by Duchaufour (1975)) and pH (Pansu and Gautheyrou, 2003), the collected soil samples were air-dried at 25℃ for one week and sieved (1 mm mesh size). Local temperature values were obtained from the publicly available datasets (The Local Chronicles of Bei’an, Hulan and Dehui). Geographic coordinates were recorded for further spatial modelling analysis.</p> <p> </p> <p>We have seven data files:</p> <p>env BAHLDH may.csv</p> <p>env BAHLDH july.csv</p> <p>env BAHLDH september.csv</p> <p>sp BAHLDH may.csv</p> <p>sp BAHLDH july.csv</p> <p>sp BAHLDH september.csv</p> <p>Geospatial coordinates.csv</p> <p> </p> <p>Explanation of the variables in the datasets:</p> <p>Site: Bei’an, Hulan, Dehui represent sampling district; I-V represent sampling plot; 1-5 represent replicate</p> <p>SOM: soil organic matter</p> <p>pH: soil pH</p> <p>SWC: Soil water content</p> <p>TN: soil total nitrogen</p>
Data from: Using model analysis to unveil hidden patterns in tropical forest structures
<p>Data set of the article entitled: <strong>Using model analysis to unveil hidden patterns in tropical forest structures</strong></p> <p>This data set gives the following structural attributes for 133 forest plots at 9 sites in the tropics:</p> <ul> <li>tree density (ha<sup>-1</sup>)</li> <li>basal area (m<sup>2</sup> ha<sup>-1</sup>)</li> <li>mean diametere (cm)</li> <li>equivalent diameter (cm)</li> <li>density of trees in the dbh class 10-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-60 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 60 cm (ha<sup>-1</sup>)</li> <li>aboveground dry biomass (Mg ha<sup>-1</sup>)</li> <li>fraction of the biomass of trees with dbh ≥ 60 cm</li> <li>weighted mean wood density (g cm<sup>-3</sup>)</li> <li>density of trees in the dbh class 10-20 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 20-30 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 30-40 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 40-50 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 50-60 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 60-70 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 70-80 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 80-90 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 90-100 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 100-110 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 110-120 cm (ha<sup>-1</sup>)</li> <li>density of trees in the dbh class 120-130 cm (ha<sup>-1</sup>)</li> <li>density of trees with dbh ≥ 130 cm (ha<sup>-1</sup>)</li> </ul>
AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p>AutoDock 4.2 and CB-Dock data for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> with M<sup>pro</sup> from SARS-CoV-2 from PDB Id: 6LU7. </p>
NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p><sup>1</sup>H, <sup>13</sup>C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> (NPA = 2-(phenylamino) benzoate) in DMSO-<em>d</em><sub>6.</sub></p>
Supporting data (structural data, sample descriptions, microprobe data)
<p>Supporting data for the manuscript entitled "Tectonic evolution of the Nevado-Filábride complex (Sierra de los Filábres, Southeastern Spain): insights from new structural and geochronological data", submitted to Tectonics.</p>
Data supplement for "Topological magnon band structure of emergent Landau levels in a skyrmion lattice"
<p>Collection of the data sets for our paper, <a href="https://doi.org/10.1126/science.abe4441"><em>Topological magnon band structure of emergent Landau levels in a skyrmion lattice</em></a>. (The source code supplement can be found <a href="https://doi.org/10.5281/zenodo.5718363">here</a>.)</p> <p> </p> <p><strong>Contents</strong></p> <table> <caption>Data files used for the paper's figures.</caption> <thead> <tr> <th scope="col">Scan</th> <th scope="col">Figure</th> <th scope="col">File(s)</th> </tr> </thead> <tbody> <tr> <td>(i)</td> <td>2</td> <td>ill_thales/exp_4-01-1621/rawdata/025280<br> ill_thales/exp_4-01-1621/rawdata/025281</td> </tr> <tr> <td>(ii)</td> <td>S17</td> <td>ill_thales/exp_INTER-436/rawdata/022169</td> </tr> <tr> <td>(iii)</td> <td>2</td> <td>ill_thales/exp_4-01-1597/rawdata/023454</td> </tr> <tr> <td>(iv)</td> <td>3</td> <td>mlz_reseda/*</td> </tr> <tr> <td>(v)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020778<br> ill_thales/exp_INTER-413/rawdata/020779</td> </tr> <tr> <td>(vi)</td> <td>4</td> <td>ill_thales/exp_INTER-413/rawdata/020777</td> </tr> <tr> <td>(vii)</td> <td>S16</td> <td>ill_thales/exp_INTER-436/rawdata/022168</td> </tr> <tr> <td>(viii)</td> <td>S16</td> <td>ill_thales/exp_INTER-413/rawdata/020793</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023488</td> </tr> <tr> <td> </td> <td>S10</td> <td>ill_thales/exp_4-01-1597/rawdata/023489</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023453</td> </tr> <tr> <td> </td> <td>S11</td> <td>ill_thales/exp_4-01-1597/rawdata/023553<br> ill_thales/exp_4-01-1597/rawdata/023559</td> </tr> <tr> <td> </td> <td>S12</td> <td>ill_thales/exp_INTER-436/rawdata/022213<br> ill_thales/exp_INTER-436/rawdata/022216<br> ill_thales/exp_INTER-436/rawdata/022217</td> </tr> </tbody> </table> <p> </p> <table> <caption>Overview of experimental data sets.</caption> <thead> <tr> <th scope="col">Instrument</th> <th scope="col">Proposal</th> <th scope="col">Directory</th> </tr> </thead> <tbody> <tr> <td><a href="http://doi.org/10.1080/10448632.2015.1057050">THALES (ILL)</a></td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-413">INTER-413</a></td> <td>ill_thales/exp_INTER-413/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-436">INTER-436</a></td> <td>ill_thales/exp_INTER-436/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1597">4-01-1597</a></td> <td>ill_thales/exp_4-01-1597/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.INTER-477">INTER-477</a></td> <td>ill_thales/exp_INTER-477/</td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5291/ILL-DATA.4-01-1621">4-01-1621</a></td> <td>ill_thales/exp_4-01-1621/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2011.01.173">LET (RAL)</a></td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1620412">RB1620412</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td> </td> <td><a href="http://dx.doi.org/10.5286/ISIS.E.RB1720033">RB1720033</a></td> <td><em>Impossible to include in archive due to size.</em></td> </tr> <tr> <td><a href="https://www.psi.ch/en/sinq/tasp">TASP (PSI)</a></td> <td>20181324 (part 1)</td> <td>psi_tasp/exp_20181324_1/</td> </tr> <tr> <td> </td> <td>20181324 (part 2)</td> <td>psi_tasp/exp_20181324_2/</td> </tr> <tr> <td> </td> <td>20151888</td> <td>psi_tasp/exp_20151888/</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2017.09.063">MIRA (MLZ)</a></td> <td>13511</td> <td>mlz_mira/exp_13511</td> </tr> <tr> <td> </td> <td>15633</td> <td>mlz_mira/exp_15633</td> </tr> <tr> <td><a href="http://doi.org/10.1016/j.nima.2019.05.056">RESEDA (MLZ)</a></td> <td>P00745-01</td> <td>mlz_reseda/</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank E. Villard and P. Chevalier for technical support and J. Locatelli for IT support during the <em>THALES</em> experiments; and J. Frank for technical support during the <em>MIRA</em> experiments. We thank J. K. Jochum for support with the <em>RESEDA</em> experiment. We thank M. Kugler for his early experiments on skyrmion dynamics in MnSi.</p> <p> </p> <p>► Please see the <strong>readme.txt</strong> file in the archive for details.</p> <p> </p>
Data from: Context matters: the landscape matrix determines the population genetic structure of temperate forest herbs across Europe
<p>Context. Plant populations in agricultural landscapes are mostly fragmented and their functional connectivity often depends on seed and pollen dispersal by animals. However, little is known about how the interactions of seed and pollen dispersers with the agricultural matrix translate into gene flow among plant populations.</p> <p>Objectives. We aimed to identify effects of the landscape structure on the genetic diversity within, and the genetic differentiation among, spatially isolated populations of three temperate forest herbs. We asked, whether different arable crops have different effects, and whether the orientation of linear landscape elements relative to the gene dispersal direction matters.</p> <p>Methods. We analysed the species' population genetic structures in seven agricultural landscapes across temperate Europe using microsatellite markers. These were modelled as a function of landscape composition and configuration, which we quantified in buffer zones around, and in rectangular landscape strips between, plant populations.</p> <p>Results. Landscape effects were diverse and often contrasting between species, reflecting their association with different pollen- or seed dispersal vectors. Differentiating crop types rather than lumping them together yielded higher proportions of explained variation. Some linear landscape elements had both a channelling and hampering effect on gene flow, depending on their orientation.</p> <p>Conclusions. Landscape structure is a more important determinant of the species' population genetic structure than habitat loss and fragmentation <i>per se</i>. Landscape planning with the aim to enhance the functional connectivity among spatially isolated plant populations should consider that even species of the same ecological guild might show distinct responses to the landscape structure.</p>
Annotation dataset for the article titled "On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind"
<p>This is the resulting dataset from the text annotation exercise in the article titled "<strong>On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind</strong>" that will appear in the journal Open Archeology in a special issue titled Archaeological Practice on Shifting Grounds (edited by Åsa Berggren and Antonia Davidovic-Walther). The article is accepted for publication and the annotations are final. CIDOC CRM is used for text annotations.</p>
Dataset containing DTS-data used in Karttunen et al. "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland"
<p>This record contains DTS-data used in the following study:</p> <p>Karttunen et al. (2021): Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland, submitted to AMTD</p> <p> </p> <p>DTS_highfreq_SMEARIII_Karttunen_et_al.zip contains continuous high frequency potential temperature profiles measured along the SMEAR III 31-metre tall mast. See more information on the data in the netCDF-file attributes and on the measurement setup in the related manuscript.</p> <p>DTS_statistics_SMEARIII_Karttunen_et_al.nc contains profiles for the turbulence temperature statistics calculated from the continuous DTS potential temperature profiles.See more information in the netCDF-file attributes and the related manuscript.</p> <p> </p>
Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN | Software, datasets, and results
<p>Software, datasets, and results referenced in "Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN"</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.