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472 results for “Geometry”
Crown Geometry Measurements of 14 Species in the CTFS-ForestGEO Plot at Harvard Forest 2013
Tree crown geometry and height, especially when coupled with remotely sensed data, can aid in the characterization of tree and forest structure. In this study, we collected crown geometry data (tree height, crown radius, and crown depth) in order to develop mixed-effects model allometric equations. We leveraged the already existing Center for Tropical Forest Science (CTFS) and Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO) MegaPlot on Prospect Hill at Harvard Forest, Massachusetts to apply allometric equations. In total, we sampled 374 trees across 14 species. Developed allometry was applied to 2014 CTFS-ForestGEO census data to develop allometric canopy height models, which were compared to a lidar canopy height model acquired by NASA’s G-LiHT.
Explicit FE simulation results for orthopedic screw-bone interaction, for different screw geometries and bone quality
<p>The dataset disclosed herein was employed to train artificial neural network surrogate models, specifically for tasks related to screw optimization. Please read "_readMe.txt" before using.</p>
Dataset for "Impact of the flow-field distribution channel cross-section geometry on PEM fuel cell performance: stamped vs. milled channel"
<p>Experimental data comprises raw data from load curve characterisation of a PEM fuel cell used for the validation of the mathematical model. Model data comprise of space-dependent values of hydrogen and oxygen concentration, local current densities, gas pressures and gas velocities in the modelled cell. These data were used for the investigation of the effect of different geometric parameters of flow-field channels on the performance of a PEM fuel cell.</p>
Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux -- Supplemental Data Set: Sea Level Sensitivity Kernels
<p><strong>Quantifying the Sensitivity of Sea Level Change in Coastal Localities to the Geometry of Polar Ice Mass Flux<br> SUPPLEMENTAL DATA SET: SEA LEVEL SENSITIVITY KERNELS</strong></p> <p>To accompany</p> <p> Jerry X. Mitrovica, Carling C. Hay, Robert E. Kopp, Christopher Harig, and<br> Konstantin Laytchev (2018). Quantifying the Sensitivity of Sea Level Change<br> in Coastal Localities to the Geometry of Polar Ice Mass Flux. Journal of<br> Climate. doi: 10.1175/JCLI-D-17-0465.1.</p> <p>We provide sea level kernels for ~740 tide gauge sites in the Permanent Service for Mean Sea Level (PSMSL) database (Holgate et al., 2013). Kernels associated with sensitivities to Greenland and Alaskan glacier melt are given on a spatial grid covering the globe, with 512 latitude rows (i=1,512) and 1024 longitude (j=1,1024) columns.</p> <p>Longitude values are evenly spaced moving eastward from Greenwich (the jth grid point has an east longitude value of (j-1)×360°/1024). Latitude values are Gauss-Legendre points beginning close to the North Pole and ending near the South Pole. Kernels associated with sensitivities to Antarctic melt are given on a spatial grid covering the globe, with 256 (Gauss-Legendre) latitude rows (i=1,256) and 512 longitude (j=1,512) columns. Longitude values are evenly spaced moving eastward from Greenwich.</p> <p>The format of the files is: </p> <p> grid_sitenumber_region.txt</p> <p>where “region” is either “green” (Greenland), “ant” (Antarctic) or “Alaska” (Alaska). The list of sites (and site numbers) is provided in the sites.txt file. The first 8 sites in this list were test sites and can be ignored.</p>
Natural frequency of oscillations of a solid surface (without holes) and perforated sieve with holes of complex geometry in the shape of five-petal epicycloid
<p>The experimental determination of the structural function of the frequency response consists in identifying the natural frequencies of oscillation of the test surfaces, for which the laboratory equipment was developed, and the following methodology was used. </p> <p>To determine the structural function of the frequency response, it is necessary to obtain two data channels: the input force and the corresponding response of the test object (test surface). In impact measurement, the input force is provided by a modal impact hammer, and the output response of the test object (test surface) is measured using an accelerometer.<br>The basic elements of the scheme are a special impact pulse type hammer PCB 084A17 for creating excitations (oscillations); cables for signals transmission; accelerometer sensor PCB 352V10 with highly sensitive piezoelectric elements for fixing oscillations; signal amplifier SIEMENS model SCADAS Mobile; computer with Simcenter Testlab 2019.1 software for processing and visualization test results.</p> <p>The study was conducted according to the following algorithms:<br>1. Test setup: boundary conditions; determination of test scheme and parameters; frequency range; determination of excitation source and force level.<br>2. Testing: installation and control of accelerometers; object excitation and frequency response measurement; check of measurement quality and coherence.<br>3. Post-test: modal curve fitting; validation of the modality against the assurance criterion and modal synthesis.<br>The research was carried out using the following algorithm. </p> <p>The perforated surface prototype was rigidly fixed to the prefabricated frame. With this type of fixation, the investigated surface at the periphery is fixed and unable to move.<br>The surface of the prototype was marked by overlaying a coordinate grid with the specified step.<br>This data of natural frequency of oscillations of a solid surface under various modes, which are obtained experimentally. The obtained oscillation frequencies are needed to determine the difference between the construction of a solid plate and a perforated surface with holes of complex geometry.</p>
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets
<p>The WS22 database provides a collection of molecular datasets that explores a broad configurational space of flexible organic molecules with varying sizes and complexity. It includes several chemical properties calculated with a quantum chemical (QM) method. Complementary to the structured datasets, this repository also provides the molecular geometries for the equilibrium structures together with the corresponding output of the QM frequency calculations. Details about the methodology, content, and structure of the WS22 datasets are provided in the README file included in this repository.</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.
<p>Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques. </p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique. This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article. (Link to be added when available).</p>
HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous United States
<p>Regional- and continental-scale models predicting variations in the magnitude and timing of streamflow are important tools for forecasting water availability as well as flood inundation extent and associated damages. Such models must define the geometry of stream channels through which flow is routed. These channel parameters, such as width, depth, and hydraulic resistance, exhibit substantial variability in natural systems. While hydraulic geometry relationships have been extensively studied in the United States, they remain unquantified for thousands of stream reaches across the country. Consequently, large-scale hydraulic models frequently take simplistic approaches to channel geometry parameterization. Over-simplification of channel geometries directly impacts the accuracy of streamflow estimates, with knock-on effects for water resource and hazard prediction.</p> <p>Here, we present a hydraulic geometry dataset derived from long-term measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). This dataset includes (a) at-a-station hydraulic geometry parameters following the methods of Leopold and Maddock (1953), (b) at-a-station Manning's n calculated from the Manning equation, (c) daily discharge percentiles, and (d) downstream hydraulic geometry regionalization parameters based on HUC4 (Hydrologic Unit Code 4). This dataset is referenced in Heldmyer et al. (2022); further details and implications for CONUS-scale hydrologic modeling are available in that article (https://doi.org/10.5194/hess-26-6121-2022). </p> <p><strong>At-a-station Hydraulic Geometry</strong></p> <p>We calculated hydraulic geometry parameters using historical USGS field measurements at individual station locations. Leopold and Maddock (1953) derived the following power law relationships:</p> <p>\(w={aQ^b}\)</p> <p>\(d=cQ^f\)</p> <p>\(v=kQ^m\)</p> <p>where Q is discharge, w is width, d is depth, v is velocity, and a, b, c, f, k, and m are at-a-station hydraulic geometry (AHG) parameters. We downloaded the complete record of USGS field measurements from the USGS NWIS portal (https://waterdata.usgs.gov/nwis/measurements). This raw dataset includes 4,051,682 individual measurements from a total of 66,841 stream gages within CONUS. Quantities of interest in AHG derivations are Q, w, d, and v. USGS field measurements do not include d--we therefore calculated d using d=A/w, where A is measured channel area. We applied the following quality control (QC) procedures in order to ensure the robustness of AHG parameters derived from the field data:</p> <ol> <li>We considered only measurements which reported Q, v, w and A.</li> <li>For each gage, we excluded measurements older than the most recent five years, so as to minimize the effects of long-term channel evolution on observed hydraulic geometry relationships.</li> <li>We excluded gages for which measured Q disagreed with the product of measured velocity and measured area by more than 5%. Gages for which \( Q\neq vA\) are often tidally influenced and therefore may not conform to expected channel geometry relationships.</li> <li>Q, v, w, and d from field measurements at each gage were log-transformed. We performed robust linear regressions on the relationships between log(Q) and log(w), log(v), and log(d). AHG parameters were derived from the regressed explanatory variables. <ol> <li>We applied an iterative outlier detection procedure to the linear regression residuals. Values of log-transformed w, v, and d residuals falling outside a three median absolute deviation (MAD) envelope were excluded. Regression coefficients were recalculated and the outlier detection procedure was reapplied until no new outliers were detected.</li> <li>Gages for which one or more regression had p-values >0.05 were excluded, as the relationships between log-transformed Q and w, v, or d lacked statistical significance.</li> <li>Gages were omitted if regressed AHG parameters did not fulfill two additional relationships derived by Leopold and Maddock: \(b+f+m=1{\displaystyle \pm }0.1\) and \(a{\displaystyle \times }c{\displaystyle \times }k=1{\displaystyle \pm }0.1\).</li> </ol> </li> <li>If the number of field measurements for a given gage was less than 10, either initially or after individual measurements were removed via steps 1-4, the gage was excluded from further analysis.</li> </ol> <p>Application of the QC procedures described above removed 55,328 stream gages, many of which were short-term campaign gages at which very few field measurements had been recorded. We derived AHG parameters for the remaining 11,513 gages which passed our QC.</p> <p><strong>At-a-station Manning's n</strong></p> <p>We calculated hydraulic resistance at each gage location by solving Manning's equation for Manning's n, given by</p> <p>\(n = {{R^{2/3}S^{1/2}} \over v}\)</p> <p>where v is velocity, R is hydraulic radius and S is longitudinal slope. We used smoothed reach-scale longitudinal slopes from the NHDPlusv2 (National Hydrography Dataset Plus, version 2) ElevSlope data product. We note that NHDPlusv2 contains a minimum slope constraint of 10<sup>-5</sup> m/m--no reach may have a slope less than this value. Furthermore, NHDPlusv2 lacks slope values for certain reaches. As such, we could not calculate Manning's n for every gage, and some Manning's n values we report may be inaccurate due to the NHDPlusv2 minimum slope constraint. We report two Manning's n values, both of which take stream depth as an approximation for R. The first takes the median stream depth and velocity measurements from the USGS's database of manual flow measurements for each gage. The second uses stream depth and velocity calculated for a 50th percentile discharge (Q<sub>50</sub>; see below). Approximating R as stream depth is an assumption which is generally considered valid if the width-to-depth ratio of the stream is greater than 10<span>—</span>which was the case for the vast majority of field measurements. Thus, we report two Manning's n values for each gage, which are each intended to approximately represent median flow conditions.</p> <p><strong>Daily discharge percentiles</strong></p> <p>We downloaded full daily discharge records from 16,947 USGS stream gages through the NWIS online portal. The data includes records from both operational and retired gages. Records for operational gages were truncated at the end of the 2018 water year (September 30, 2018) in order to avoid use of preliminary data. To ensure the robustness of daily discharge percentiles, we applied the following QC:</p> <ol> <li>For a given gage, we removed blocks of missing discharge values longer than 6 months. These long blocks of missing data generally correspond to intervals in which a gage was temporarily decommissioned for maintenance.</li> <li>A gage was omitted from further analysis if its discharge record was less than 10 years (3,652 days) long, and/or less than 90% complete (>10% missing values after removal of long blocks in step 1.</li> </ol> <p>We calculated discharge percentiles for each of the 10,871 gages which passed QC. Discharge percentiles were calculated at increments of 1% between Q<sub>1</sub> and Q<sub>5</sub>, increments of 5% (e.g. Q<sub>10</sub>, Q<sub>15</sub>, Q<sub>20</sub>, etc.) between Q<sub>5</sub> and Q<sub>95</sub>, increments of 1% between Q<sub>95</sub> and Q<sub>99</sub>, and increments of 0.1% between Q<sub>99</sub> and Q<sub>100</sub> in order to provide higher resolution at the lowest and highest flows, which occur much less frequently.</p> <p><strong>HG Regionalization</strong></p> <p>We regionalized AHG parameters from gage locations to all stream reaches in the conterminous United States. This downstream hydraulic geometry regionalization was performed using all gages with AHG parameters in each HUC4, as opposed to traditional downstream hydraulic geometry--which involves interpolation of parameters of interest to ungaged reaches on individual streams. We performed linear regressions on log-transformed drainage area and Q at a number of flow percentiles as follows:</p> <p>\(log(Q_i) = \beta_1log(DA) + \beta_0\)</p> <p>where Q<sub>i</sub> is streamflow at percentile i, DA is drainage area and \(\beta_1\) and \(\beta_0\) are regression parameters. We report \(\beta_1\), \(\beta_0\) , and the r<sup>2</sup> value of the regression relationship for Q percentiles Q<sub>10</sub>, Q<sub>25</sub>, Q<sub>50</sub>, Q<sub>75</sub>, Q<sub>90</sub>, Q<sub>95</sub>, Q<sub>99</sub>, and Q<sub>99.9</sub>. Further discussion and additional analysis of HG regionalization are presented in Heldmyer et al. (2022).</p> <p><strong>Dataset description</strong></p> <p>We present the HyG dataset in a comma-separated value (csv) format. Each row corresponds to a different USGS stream gage. Information in the dataset includes gage ID (column 1), gage location in latitude and longitude (columns 2-3), gage drainage area (from USGS; column 4), longitudinal slope of the gage's stream reach (from NHDPlusv2; column 5), AHG parameters derived from field measurements (columns 6-11), Manning's n calculated from median measured flow conditions (column 12), Manning's n calculated from Q50 (column 13), Q percentiles (columns 14-51), HG regionalization parameters and r<sup>2</sup> values (columns 52-75), and geospatial information for the HUC4 in which the gage is located (from USGS; columns 76-87). Users are advised to exercise caution when opening the dataset. Certain software, including Microsoft Excel and Python, may drop the leading zeros in USGS gage IDs and HUC4 IDs if these columns are not explicitly imported as strings.</p> <p> </p> <p><strong>Errata</strong></p> <p>In version 1, drainage area was mistakenly reported in cubic meters but labeled in cubic kilometers. This error has been corrected in version 2.</p>
Growth of wild cucumber (Echinocystis lobata) in three variants of support (5, 20, 50 cm) and shoot geometry
<p><span>The experiments included three variants of support: 5 cm, 20 cm, and 50 cm. </span><span>The data was collected in the years 2020-2023. After developing their first pair of mature leaves, when the plants were about 20-35 cm in length, the plants were photographed. The shutter was released every 15 minutes. The resulting images were combined into a 10 fps video. The following were used for the analysis: 13 recordings of the 5 cm variant and 12 recordings of the 20 and 50 cm variants.</span></p> <p><span>Next, an analysis was performed based on the resulting video using Tracker</span><span> for kinetic analysis of video objects. The recordings were used to measure the plant growth parameters. The tape measure and point mass tools were used to determine the length of the shoots and to change the position of the apex relative to the areas of the X- and Y-axis photographed over time. To compensate for this, a trend line was drawn (polynomial of the 2nd degree, due to the very good fit, R<sup>2</sup> = 0,975–0,995) and new plant length parameters were calculated. </span></p> <p><span>To enhance the research, cross-sections of the shoots of ten plants were scanned. Samples were taken every 5 cm from the base of the plant. Using ImageJ</span><span> the following parameters were measured: shoot cross-sectional area, tissue area, perimeter, and circularity. </span></p>
Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm
<p><strong>CHARACTERISTICS</strong><br>Planet: <strong>Mars</strong><br>Radius: <strong>R<sub>M</sub> = 3389.5 km</strong> (volumetric mean planetary radius)<br>Spacecraft: <strong>NASA/Mars Atmosphere and Volatile Evolution (MAVEN)</strong><br>Spacecraft coordinates system: <strong>Mars Solar Orbital (MSO)</strong> equivalent to <em>Sun-State </em>coordinate system:</p> <ul> <li>+<em>X<sub>MSO</sub></em> points towards the Sun from the planet’s centre,</li> <li>+<em>Z<sub>MSO</sub></em> towards Mars’ North pole and perpendicular to the orbital plane defined as the <em>X<sub>MSO</sub></em>–<em>Y<sub>MSO</sub></em> plane passing through the centre of Mars,</li> <li><em>Y<sub>MSO</sub></em> completes the orthogonal system.</li> </ul> <p>Time span: <strong>01/11/2014 to 30/04/2024</strong> (Mars Years MY32 to MY36 included, part of MY37).<br>Total number N of candidate bow shock crossings in the database: <strong>N = 20107</strong></p> <p><strong>ORIGINAL DATASETS USED</strong><br>The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used.</p> <p><strong>METHOD</strong><br>To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in:<br>Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, <em>Journal of Geophysical Research</em>, <strong>127</strong>, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>. </p> <p>Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a></p> <p>These algorithms consist of two consecutive steps: </p> <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> <p><strong>REMARK ON VERSIONS</strong><br>From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that:</p> <ul> <li>45<sup>∘</sup><<em>θ</em><sub><em>B</em><em>n</em></sub><135<sup>∘</sup>: quasi-perpendicular shock condition</li> <li><em>θ</em><sub><em>B</em><em>n</em></sub>≤45<sup>∘</sup> and <em>θ</em><sub><em>B</em><em>n</em></sub>≥135<sup>∘</sup>: quasi-parallel shock condition</li> </ul> <p>Uncertainty on these angles is estimated to be ± 5º. </p> <p>From Version 4 onwards, we also added the solar longitude Ls (in degrees).</p> <p>For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper.</p> <p><strong>VARIABLES DESCRIPTION</strong><br>This database contains the following ASCII variables:</p> <ul> <li>Bow shock times in MAVEN's database (1-s resolution): <em>T</em><sub>bs</sub></li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius <em>R</em><sub><em>M</em> </sub>(<em>R<sub>M</sub></em> = 3389.5 km):<br><em>X<sub>MSO</sub></em>,<sub> </sub><em>Y<sub>MSO</sub></em>, <em>Z<sub>MSO</sub></em> and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in <em>R<sub>M</sub></em>)</li> <li>Solar Zenith angle in degrees: <em>SZA</em> = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn < 135 deg: quasi-⊥ shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> <p><strong>WARNING</strong><br>This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a <em>first indicator</em> of the shock location, and <em>with</em> <em>caution</em>: it <strong>CANNOT</strong>, and <strong>WILL NOT </strong>substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 R<sub>M</sub> (with R<sub>M</sub> = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind.</p> <p>Current formatting optimised for MATLAB.</p> <p><strong>ACKNOWLEDGEMENTS</strong><br>C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>. </p> <p><strong>LICENSE AND RIGHTS</strong><br>This database is shared under a Creative Commons CC-BY-4.0 license.</p> <p>Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF), <br> Austrian Academy of Sciences (ÖAW), 2021-09-08<br>Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA<br>Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn<br>Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.</p> <p> </p> <p><br>Contact email: cyril.simon.wedlund@gmail.com</p>
A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks
<p>This repository pertains to an integrative docking benchmark containing 40 binary protein-protein docking cases. The inputs were monomer structures and chemical crosslinks, varying the numbers, types, false positive rate, sources of input crosslinks, and sources of monomer structures. The discrete geometry method Wall-EASAL and IMP are compared.</p>
The importance of geometry in the corneal micropocket angiogenesis assay
<p>Dataset and software supporting the submitted and revised manuscript for the study: "The importance of geometry in the corneal angiogenesis micropocket assay." See the enclosed README and manuscript for further information.</p>
Global Reservoir Geometry Database
<p>This is a global-scale reservoir storage-area-depth dataset including 6,824 major reservoirs. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimizes the error of total storage and surface area estimation. This algorithm has been applied to 6,800 reservoirs included in the Global Reservoir and Dam database (GRanD). The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all GRanD reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies, and ground-truth measurements for 34 reservoirs in the United States and China.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
Reproduction package for: 'Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries'
<p>Data files, python scripts and notebooks to reproduce the code output comparisons performed in "Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/1538-4357/ad7255" target="_blank" rel="noopener"><em>ApJ</em> <strong>975</strong> 202</a>, <a href="https://doi.org/10.48550/arXiv.2406.07285" target="_blank" rel="noopener">arXiv.2406.07285</a>).</p> <p>Please refer to the README for detailed information.</p> <p>N.B. The neutral hydrogen column density (${\rm N}_{\rm H}$) value is mentioned in the paper to be $0.2 \times 10^{20} {\rm cm}^{-2}$, whereas all the analyses in the paper, as reflected in this Zenodo package, actually uses ${\rm N}_{\rm H} = 2 \times 10^{20} {\rm cm}^{-2}$.</p>
The IHA database of human geometries including torso, head and complete outer ears for acoustic research
<p>This is the first version of the IHA database, which is being created in the project HAPPAA C1 funded by the Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 352015383 - SFB 1330 C1. (https://uol.de/en/sfb-1330-hearing-acoustics)</p> <p>The database includes a subsample of 10 human geometries comprising the torso, head and the entire outer ear including the ear canal and eardrum. The data are available in two different 3D object formats: ply binary file, stl binary file.</p>
Geometry and tool motion planning for curvature adapted CNC machining
<p>Examples of 5-axis CNC machining tool paths and corresponding g-codes for a concave, convex and a freeform surface milling with a toroidal cutter (supported information for the paper "Geometry and tool motion planning for curvature adapted CNC machining", DOI 10.1145/3450626.3459837).</p> <p>In the 'path.txt' files, each line contains three numbers that are Euclidean coordinates of the contact points; in the 'positions.txt' files, each line contains six numbers: the first three being the coordinates of the centers of the torus and the other three being the coordinates of the unit axis vector of the tool, pointing outside the surface. </p>
ScienceDex guides
Understand access before you commit
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.