GeoTOP is a three-dimensional model of the layer structure and soil type (clay, sand, gravel and peat) of the shallow subsoil of the Netherlands to a depth of up to 50 m below NAP. In GeoTOP, the substrate is subdivided into a regular three-dimensional grid (grid) of contiguous voxels (volume cells) of 100 by 100 m in the horizontal direction and 0.5 m in the vertical. Each voxel is associated with its properties. These are the lithostratigraphic or geological unit (layer) to which a voxel belongs, the lithograph class (ground type) representative of the voxel and a number of attributes that together form a measure of model uncertainty. In addition to voxels, GeoTOP also includes a detailed layer model and the interpreted drilling descriptions used in making the model. For making GeoTOP, the mainland of the Netherlands is divided into a number of regions, called model areas. GeoTOP is not yet ready for all model areas. Furthermore, the subsoil of the Dutch part of the Continental Plate is not included in GeoTOP. GeoTOP is a (sub)regional model. It is not suitable for use on a local scale; to create a local subsurface model, additional data will always be required. For further information, please refer to the BRO website: https://basisregistratieondergrond.nl/

Aim: Vegetation structure is a key determinant of animal diversity and species distributions. The introduction of Light Detection and Ranging (LiDAR) has enabled the collection of massive amounts of point cloud data for quantifying habitat structure at fine resolution. Here, we review the current use of LiDAR-derived vegetation metrics in diversity and distribution research of birds, a key group for understanding animal-habitat relationships. Location: Global. Methods: We review 50 relevant papers and quantify where, in which habitats, at which spatial scales and with what kind of LiDAR data current studies make use of LiDAR metrics. We also harmonize and categorize LiDAR metrics and quantify their current use and effectiveness. Results: Most studies have been conducted at local extents in temperate forests of North America and Europe. Rasterization is currently the main method to derive LiDAR metrics, usually from airborne laser scanning data with low point densities (<10 points/m2) and small footprints (<1 m diameter). Our metric harmonization suggests that 40% of the currently used metric names are redundant. A categorisation scheme allowed to group all metric names into 18 out of 24 theoretically possible classes, defined by vegetation part (total vegetation, single trees, canopy, understory, and other single layers as well as multi-layer) and structural type (cover, height, horizontal variability, vertical variability). Metrics related to canopy cover, canopy height and canopy vertical variability are currently most often used, but not always effective. Main conclusions: LiDAR metrics play an important role in understanding animal space use. Our review and the developed categorization scheme may facilitate future studies in the selection, prioritization and ecological interpretation of LiDAR metrics. The increasing availability of airborne and spaceborne LiDAR data and the development of voxel-based and object-based approaches will further allow novel ecological applications, also for open habitats and other vertebrate and invertebrate taxa.

Three datasets are available, each consisting of 15 csv files. Each file containing the voxelised shower information obtained from single particles produced at the front of the calorimeter in the |η| range (0.2-0.25) simulated in the ATLAS detector. Two datasets contain photons events with different statistics; the larger sample has about 10 times the number of events as the other. The other dataset contains pions. The pion dataset and the photon dataset with the lower statistics were used to train the corresponding two GANs presented in the AtlFast3 paper SIMU-2018-04.

The information in each file is a table; the rows correspond to the events and the columns to the voxels. The voxelisation procedure is described in the AtlFast3 paper linked above and in the dedicated PUB note ATL-SOFT-PUB-2020-006. In summary, the detailed energy deposits produced by ATLAS were converted from x,y,z coordinates to local cylindrical coordinates defined around the particle 3-momentum at the entrance of the calorimeter. The energy deposits in each layer were then grouped in voxels and for each voxel the energy was stored in the csv file. For each particle, there are 15 files corresponding to the 15 energy points used to train the GAN. The name of the csv file defines both the particle and the energy of the sample used to create the file.

The size of the voxels is described in the binning.xml file. Software tools to read the XML file and manipulate the spatial information of voxels are provided in the FastCaloGAN repository.

Updated on February 10th 2022. A new dataset photons_samples_highStat.tgz was added to this record and the binning.xml file was updated accordingly.

Updated on April 18th 2023. A new dataset pions_samples_highStat.tgz was added to this record.