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4,107 results for “pointing”
Investigating the universality of five-point QCD scattering amplitudes at high energy
<p>We provide various analytic results for one- and two-loop five-point QCD scattering amplitudes in multi-Regge kinematics (MRK).<br>If you use the results distributed with this repository in your research work, please cite <a href="https://arxiv.org/abs/2411.14050">2411.14050</a>.<br><br>This repository contains two archives:</p> <ol> <li><strong>mrk_results.tar.gz</strong>: all the analytic results in Mathematica readable format are collected here. For a detailed description of their content and the notation adopted see README file in this archive.<br><br></li> <li><strong>expansions_n4lp.tar.gz</strong>: in this archive the expansions of the one- and two-loop massless pentagon functions up to N^4LP are provided (see section 2 of the <a href="https://arxiv.org/abs/2411.14050">paper</a> for a detailed description of the beyond leading-power expansion). Different expansions are performed in the upper and lower z-complex plane (see section of 2 of the <a href="https://arxiv.org/abs/2411.14050">paper</a>). It further contains a Mathematica script, README.wl, which serves both as a description and example file.</li> </ol>
Mangrove terrestrial laser scanning (TLS) point clouds and quantitative structural models (QSMs)
<p>Datasets for a publication entitled, "Terrestrial laser scanning for the estimation of above ground biomass of mangrove roots by modelling them as inverted trees."</p> <p>See the file "Data dictionary for Mangrove terrestrial laser scanning.pdf" for a description of the datasets included in the zipped folder. </p>
Shorelines, elevation transects and environmental data for Drew Point, Ak study area, 2019-2022
<p>This record consists of the shorelines and their derived products, elevation transects and environmental data used for the Cryosphere preprint: <em>Multiple modes of shoreline change along the Alaskan Beaufort Sea observed using ICESat-2 altimetry and satellite imagery</em>. There are three primary datasets:</p> <p><em>ERA-5 </em>contains hourly output ERA5 reanalysis dataset (Hersbach et al., 2020, https://doi.org/10.24381/cds.adbb2d47) from a single pixel (-153.83, 70.87) from May 1st through November 30th for 2019, 2020, 2021, and 2022. It also contians a list of derived open water days for each year.</p> <p><em>ICESat-2 </em>contains the subsetted ATL03 photons (Neuman et. al, 2023, https://doi.org/10.5067/ATLAS/ATL03.006) for RGT 137 ground tracks 1r, 2r and 3r on 07 April 2019, 04 January 2020, 02 July 2021, and 31 December 2021, elevation profiles derived from SlideRule (Shean et al., 2023), and shoreline boundaries picked from the elevation profiles and Planet imagery. It also includes cross-shore transects used to project ICESat-2-derived changes into the local shoreline-perpindicular direction.</p> <p><em>Planet_shorelines</em> contains shorelines drived from 3m multispectral imagery from Planet Labs (Planet Team, 2024) as well as their derivatiev products. This included shorelines used for year-to-year change (<em>annual_coastlines</em>) and for uncertainty analysis (<em>uncertainty_coastlines</em>). It also includes the derived year-to year-change <em>(shoreline_change),</em> the baseline and cross-shore transects used to derived these change estiamtes, and a table with our derived annual shoreline change estimates compared with previously published estimates in this region.</p> <p>More detailed information on each datasets can be found in each folder's README file.</p>
Time-lapse electrical resistivity tomography and seismic reflection imaging of a shallow ground-water aquifer (0-50 m): Mississippi River levee seepage across the Duncan Point bar, Baton Rouge, Louisiana, U.S.A.
<p>The electrical resisitivity raw data files are slightly processed to remove bad data points but can be inverted using tomographic inversion code. </p> <p>The seismic data were assembled in Seismic Unix format, a shortened version of the SEG-Y format (Society of Exploration Geophysicists Exchange Format-Y https: //seg. org/Publications/SEG-Technical-Standards), that has the 3200-byte EBCDIC and 400-byte tape header removed. The data uploaded online (<a href="https://zenodo.org/records/14776025">https://zenodo.org/records/14776025</a>) is a CMP brute-stacked seismic section. </p> <p>During data collection, shotpoint location changed proceeding along a 136-degree azimuth (south-easterly direction), and spaced every 1 m.</p> <p>A total of 48, horizontal-component 28-Hz nominal geophones were placed every one meter and shotpoints were located half-way between geophones. Geophones remained fixed at their locations throughout the survey and so the CMP spacing is nominally 0.5-m but fold varies linearly from a value of 1 from either side of the survey to a central maximum of 24. The seismic source consisted of a partially buried 20-lb steel I-beam struck repeatedly on either side three times by an 8-lb sledge hammer. Data of the same striking polarity were added in-phase in the field. Data with opposing polarity at each shotpoint location were subtracted later to enhance SH-wave data and suppress converted SH-to-P waves.</p> <p>Seismic processing is minimal and consists of standard surface-wave muting, elimination of bad seismic traces, normal moveout, bandpass filtering (between 12 Hz and 50 Hz) and preliminary stacking with trace mixing every 3 CMPs. The data were stacked with a single velocity throughout that ranged from 80 m/s (Vs) at 0.2 s, to 100 m/s at 0.35 s and reached 180 m/s at 0.5 s of two-way traveltime.</p> <p> </p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Point and Nonpoint Proportion of Potentially Contaminated Supply (PPCS) for 116 United States cities
<p>Point and nonpoint PPCS metrics (and additional metrics) computed for 116 United States cities using gamut (Geospatial Analytics for Multisectoral Urban Teleconnections)---<a href="https://doi.org/10.5281/zenodo.5590217">https://doi.org/10.5281/zenodo.5590217</a>.</p> <p>These results are described in the following publication:</p> <p>Turner, S.W.D., Rice, J., Nelson, K., Vernon, C., McManamay, R., Dickson, K., and Marston, L. (accepted manuscript) Comparison of potential drinking water source contamination across one hundred U.S. cities. <em>Nature Communications</em>.</p> <p> </p>
Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry
<p>Dataset for article "Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry" published in <em>Adv. Mater. Interfaces</em> 2021, <strong>8</strong>, 2001881. </p> <p>The data includes:</p> <ul> <li>XRD data of La<sub>1-x</sub>Sr<sub>x</sub>FeO<sub>3</sub> thin films</li> <li>Optical conductivity of LSF films as a function of oxygen partial pressure and Sr content</li> <li>Concentration of electronic holes in LSF thin films as a function of oxygen partial pressure and temperature</li> <li>Defect chemistry models employed for describing the concentration of point defects in LSF thin films.</li> <li>Equilibrium constants for oxygen incorporation reactions in LSF thin films at different temperatures</li> <li>Ellipsometry raw data of LSF50 thin film as a function of equivalent oxygen pressure </li> <li>Electrochemical impedance spectra of the LSF50 thin film at 400 ºC</li> </ul>
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit and Effects on the Point Cloud
<p>This data repository contains vibration tests of an Ouster OS1-64 lidar including a docker based python environment and documentation.</p> <p>It consists of movement data, Fotos, IMU data, pointclouds and a ground truth measurements with an Riegl VZ6000 laser scaner.</p> <p>For further details see the linked publication and the example.ipynb notebook.</p> <p><strong>Quick start</strong></p> <ul> <li> <p>download the repo</p> </li> <li> <p>unzip the archive</p> </li> <li> <p>install VS code with the remote development extension</p> </li> <li> <p>install docker desktop</p> </li> <li> <p>open the folder in a new VS code window</p> </li> <li> <p>Say "yes" to open the folder inside a docker container</p> </li> <li> <p>wait for the container to start</p> </li> <li> <p>open the example jupyter notebook</p> </li> </ul> <p><strong>Structure</strong></p> <blockquote> <pre>├── .devcontainer .... VS code devcontainer (arm64 and amd64)</pre> <pre>├── Acceleromenter_A_z_deflection .... Accelerometer data</pre> <pre>├── Foto .... Fotos of the setup</pre> <pre>│ ├── VZ6000 .... Fotos of the ground truth measurements</pre> <pre>│ └── test_setup .... Fotos of the test setup</pre> <pre>├── Notebook .... A jupyter notebook with examples</pre> <pre>├── OS1_64_IMU .... Ouster IMU data</pre> <pre>├── OS1_64_pointcloud .... point cloud data</pre> <pre>├── VZ6000_groundtruth .... Ground truth data from Riegl VZ6000</pre> <pre> └── targets .... contains 2 different sets of reference</pre> <pre>│ ├── scene_aligned_by_reflectors</pre> <pre>│ └── targets_aligned</pre> <pre>└── files.csv .... an overview of the files and meta data</pre> </blockquote>
Presence-Absence Points for Tree Species Distribution Modelling for Europe
<p>The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset.</p> <p>A set of <strong>50</strong> different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes:</p> <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> <p>Points were included in the dataset only if the probability value extracted for at least one of the above classes was <strong>≥ 50%</strong> for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.</p> <p>The final dataset contains <strong>4,359,999</strong> observations for and a total of <strong>630 </strong>columns. <br> <br> The first <strong>8 </strong>columns of the dataset contain metadata information used to uniquely identify the points:</p> <ul> <li><strong>id</strong>: unique point identifier,</li> <li><strong>year</strong>: year of observation,</li> <li><strong>postprocess</strong>: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li><strong>Tile_ID</strong>: contains the tile id from the eu_tiling_system (30 km grid),</li> <li><strong>easting</strong>: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>northing</strong>: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>Atlas_class</strong>: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li><strong>lc1</strong>: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> <p>The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of <strong>16 target species </strong>for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (<em>i.e.</em> <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (<em>i.e.</em> <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species.</p> <p>The <em>RDS </em>file is created from a data.table object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p>We provide <em>RDS </em>files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached <em>GeoPackage </em>("eu_tiling_system_30km"): open it in QGIS and filter by "ID".</p> <p>In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the <em>static</em> RDS file need to be bound with the respective year.</p> <p>To access our predictions (probabilities and uncertainties) produced for the target species access:</p> <ul> <li><strong>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></strong></li> <li>Check the <strong>Related identifiers </strong>section of this repository to access each species individually</li> </ul> <p>If you instead would like to know more about the creation of this dataset and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Internal morphology point clouds of lunar pits
<p>This archive contains point clouds showing the internal geometry of six pits on the Moon. These point clouds were generated via manual feature matching in "oblique stereo pairs": pairs of Lunar Reconnaissance Orbiter Narrow Angle Camera (LROC NAC) images at two different off-nadir angles observing one wall of a pit under similar lighting conditions. These images have pixel scales of ~0.3-2.2 m/pixel, allowing the creation of point clouds with point spacing on the order of 5-10 m, depending on the density of identifiable features on the pit walls.</p> <p>The manually-generated point clouds from multiple stereo pairs have been merged together, and aligned to and merged with dense digital terrain models (DTMs) from more nadir-looking NAC stereo images where available, to produce point clouds that cover the upper walls, floors, and immediate surroundings of the pits.</p> <p>For a full description of the processing method, see Wagner and Robinson (2022), linked in this archive's metadata.</p> <p>This archive contains models for the following pits:<br> Lacus Mortis Pit (LMP)<br> Mare Ingenii Pit (MIP)<br> Mare Tranquillitatis Pit (MTP)<br> Marius Hills Pit (MHP)<br> Schlüter Crater Pit (SCP)<br> Southwest Mare Fecunditatis Pit (SWFP)</p>
Point cloud data sets of real and virtual Chenopodium alba
<p>This data set contains:</p> <p>- 5 annotated point clouds of real Chenopodium alba plants obtained from multi-view 2D camera imaging. Annotations consist of 5 classes: leaf blade, petiole, apex, main stem, branch. .txt files contain both 3D coordinates and annotations. .ply files are also provided for raw 3D point data without annotations.</p> <p>- 24 annotated point clouds of virtual Chenopodium alba that were generated by a L-system simulation program. Annotations consist of 3 classes: leaf blade, petiole, stem. 3D coordinates and annotations are in separated .txt files. </p> <p>These files have been used in a companion paper.</p> <p> </p>
Nanoscale mapping of point defect concentrations with 4D-STEM
<p>The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. </p> <p>1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted <em>in situ</em> on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å<sup>-1</sup> was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p>2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted <em>in situ </em>on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration. With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ<sup>-2</sup> per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p>
Multimodal Dataset of 3D point clouds and CT-volumes
<p>The multimodal dataset for evaluating algorithms for aligning CT volumes and point clouds which is presented in 'Multimodal registration across 3D point clouds and CT-volumes'. (Saiti, E., and T. Theoharis. "Multimodal registration across 3D point clouds and CT-volumes." <em>Computers & Graphics</em> 106 (2022): 259-266.) The multimodal dataset consistsof real micro-CT scans and their synthetically generated 3D models (point clouds) .</p>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Confs N_f=2+1 physical point fullQCD T=230 MeV, lattice = 48^3 x 12
<p>README (Written by Claudio Bonanno: claudio.bonanno@fi.infn.it)</p> <p>Archives of thermalized and well-decorrelated gauge configurations</p> <p>Discretization: Symanzik-improved gauge action & N_f = 2+1 flavors of rooted stout staggered fermions with physical quark masses<br> and physical pion mass</p> <p>Theory details: T=230 MeV, 48^3 x 12 lattice with a lattice spacing a = 0.07149 fm.</p> <p>Algorithm: standard RHMC in the presence of a topological bias potential. For the details about the topological bias potential see https://arxiv.org/abs/1807.07954</p> <p>The topological bias potential can be removed afterwards through standard reweighting.</p> <p>Conf name: stored_conf.${conf_ID} where conf_ID is equal to the RHMC step the conf has been saved.</p> <p>Conf have been saved every 30 RHMC seps in binary files according to the ILDG format for standard C programs.</p> <p>For more details about the ILDG format see, e.g., https://www-zeuthen.desy.de/~pleiter/ildg/ildg-file-format-1.1.pdf</p> <p>The auto-correlation time of Q is about 8 RHMC steps in our setup.</p>
FGI ARVO VLS-128 LiDAR Point Cloud, Käpylä, 7th of September 2020
<p>This LiDAR point cloud dataset is collected with a research platform of Finnish Geospatial Research Institute (FGI), called Autonomous Research Vehicle Observatory (ARVO). The dataset was collected with Velodyne VLS-128 Alpha Puck LiDAR, 7th of September 2020 in a suburban environment in the area of Käpylä in Helsinki, the capital of Finland. The environment in the dataset consists of a straight two-way asphalt street, called Pohjolankatu, which starts from a larger controlled intersection at the crossing of Tuusulanväylä (60.213326° N, 24.942908° E in WGS84) and passes by three smaller uncontrolled intersections until the crossing of Metsolantie (60.215537° N, 24.950065° E). It is a typical suburban street with tram lines, sidewalks, small buildings, traffic signs, light poles, and cars parked on both sides of the streets. To collect a reference trajectory and to synchronize the LiDAR measurements, we have used a Novatel PwrPak7-E1 GNSS Inertial Navigation System (INS).</p> <p>The motion distortion of each individual scan has been corrected with a postprocessed GNSS INS trajectory and the scans have been registered with Normal Distributions Transform (NDT). Each point is provided with a semantic label probability vector and the final point cloud is averaged with a 1 cm voxel filter.</p> <p>The steps to create this preprocessed dataset have been described in more detail in the article "<a href="https://arxiv.org/abs/2301.03956">Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity</a>" published in IROS 2022. However, the number of points in each semantic segment in Table I in Section IV-A are different. The correct values are shown in the table below. This does not affect the results.</p> <table align="left"> <caption>TABLE I: RandLA-Net classified dataset label proportions.</caption> <tbody> <tr> <td><strong>Semantic label</strong></td> <td><strong>No. of points</strong></td> <td><strong>% of all</strong></td> <td><strong>% of used</strong></td> </tr> <tr> <td>Ground</td> <td>14,206,060</td> <td>32.3</td> <td>50.3</td> </tr> <tr> <td>Building</td> <td>7,782,757</td> <td>17.7</td> <td>27.6</td> </tr> <tr> <td>Tree Trunk</td> <td>3,736,775</td> <td>8.5</td> <td>13.2</td> </tr> <tr> <td>Fence</td> <td>2,201,851</td> <td>5.0</td> <td>7.8</td> </tr> <tr> <td>Pole</td> <td>206,983</td> <td>0.5</td> <td>0.7</td> </tr> <tr> <td>Traffic Sign</td> <td>85,316</td> <td>0.2</td> <td>0.3</td> </tr> <tr> <td>Labels used here</td> <td>28,219,742</td> <td>64.1</td> <td>100.0</td> </tr> <tr> <td>Others</td> <td>15,821,962</td> <td>35.9</td> <td> </td> </tr> <tr> <td>Total</td> <td>44,041,704</td> <td>100.0</td> <td> </td> </tr> </tbody> </table>
IODP Expedition 379 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
IODP Expedition 371 Magnetic susceptibility (point or contact system)
Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).
1 million cMSSM parameter space points with low-energy predictions from SPheno and MicrOMEGAs
<p>This dataset was produced and used in the paper <a href="https://arxiv.org/abs/2405.18471">Symbolically Regressing Beyond the Standard Model Physics</a>. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/symbolic-regression-bsm">here</a>.</p> <p>The dataset specifications:</p> <ul> <li>Randomly sampled 1 million points of the cMSSM parameter space and respective low-energy observables.</li> <li>Low-energy observables computed using using `SPheno` and `MicrOMEGAs`. <ul> <li>Only points that produced `SPheno` output and neutral LSP are processed by `MicrOMEGAs`.</li> <li>The dataset includes all points, even if they are "unphysical", i.e. points without `SPheno` output or neutral LSP. In the paper, this was used to train a classifier to filter out "unphysical" points.</li> </ul> </li> <li>The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory sampled in the priori <ul> <li>'m0': [0, 10] TeV</li> <li>'m12': [0, 10] TeV</li> <li>'A0': [-60,60] TeV</li> <li>'tanb': [1.5,50]</li> <li>The sign of the 'mu' parameter was fixed to positive (+1)</li> </ul> </li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>Flattened `SPheno` outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> <li>`MicrOMEGAs` outputs. These inlcude: 'dm_Omega', 'dm_spin', 'dm_candidate`, `mo_output`, `dm_c_{bino,wino,higgsino1,higgsino2}`, which are, respectively: dark matter relic density value, dark matter candidate spin, dark matter candidate, the whole `MicrOMEGAs` output, and the coefficient of `{bino,wino,higgsino1,higgsino2}` components of the dark matter state.</li> </ul> </li> </ul> <p>Versions:</p> <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>MicrOMEGAs 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> <p>The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.</p> <p> </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.