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Annual primary productivity of Spartina alterniflora in control and fertilized plots at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA, 1999-2025.
Annual productivity is estimated from aboveground biomass in Spartina alterniflora-dominated salt marsh plots on the Rowley River within the Plum Island Ecosystems (PIE) LTER site. Aboveground biomass is determined non-destructively.
Plant heights at control and fertilized plots in a Spartina alterniflora-dominated marsh, Law's Point, Rowley River, Plum Island Ecosystem LTER, MA (1999-2025).
Plant heights are measured during the growing season in permanent plots at a Spartina alterniflora-dominated salt marsh on the Rowley River within the Plum Island Ecosystems (PIE) LTER site. Plant heights are converted to plant weight using an algorithm to generate a non-destructive estimate of aboveground plant biomass.
SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the cover of sessile invertebrates, understory macroalgae, and bottom substrate types as determined by a uniform point contact method. The presence of over 150 taxa of sessile invertebrates and macroalgae are recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Multiple species can be recorded at any given point. Percent cover of a given species on a transect can be estimated from UPC observations as the fraction of total points at which that species was present x 100. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. These data are part of SBC LTERs kelp forest monitoring program, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Data collection began in 2000 and this dataset is updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. The two tables in this data package include: 1) The percent cover of sessile invertebrate and understory macroalage; and 2) the percent cover of bottom substrate.
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.
SBC LTER: Reef: Long-term experiment: Kelp removal: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
Point-Quarter Distance and Dimension Measurements to Calculate Shrub Density and Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeated measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected, sorted, and weighed to develop regressions for biomass. Purpose: Data was collected initially to determine density and dimensions of creosote bush in the Five points area on core rodent webs and on burned and unburned plots following the 2003 prescribed burn. It was decided to expand the project by continuing measurements through time to quantify the change in shrub size and with simultaneous harvest of shrubs to measure NPP.
Wave data for Hog Island Bay, Fowling Point and Upsur Neck, Virginia, 2009
An RBR wave gauge was deployed at 3 locations on the coast of Virginia during 2009.
VCR Fowling Point Salt Marsh Flux Tower, 2019-2023
This dataset contains eddy covariance CO2 and H2O fluxes, sensible and latent heat fluxes, and meteorological measurements collected in an intertidal salt marsh. The flux tower is part of the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The marsh is on the Atlantic Ocean side of the Delmarva Peninsula and faces shallow coastal lagoons backed by barrier islands. No major rivers drain into the area. The tower is located 2 km from the shoreline and 85 m from a major creek edge. The marsh is dominated by Spartina alterniflora (also referred to as Sporobolus alterniflorus), with an average height of 0.6 m. The semidiurnal tidal cycle inundates the marsh platform twice daily, with 10% of time points having water above the mud platform (indicated by the MHHW dataset variable). Fluxes were measured at 10Hz and averaged over 30 minutes. The CO2 and energy fluxes were gap-filled using the REddyProc software package (doi: 10.5194/bg-15-5015-2018). Using the nighttime approach, the gross primary production and ecosystem respiration fluxes were partitioned from the net ecosystem exchange flux. The research was conducted at the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The flux tower site (37-deg 24'39.85"N, 75-deg 50'0.53"W) a lagoonal salt marsh which is located near the area of Fowling point. The site is located at about 2.2 kilometers away from the mainland and 10.7 kilometers away from Hog Island, the nearest barrier island. The flux tower is situated at about 80 meters away from the creek edge, on the lagoonal salt marsh.
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots
<p>This dataset contains manually segmented tree point clouds from Personal Laser Scanning (PLS) data, and additionally automatic segmented trees from the same point clouds. The raw point cloud data has been published in LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots (<a href="https://doi.org/10.5281/zenodo.3698956">https://doi.org/10.5281/zenodo.3698956</a>) and six of those plots were processed for this data. Purpose of this data is to serve as benchmarking for automatic tree segmentation algorithms.</p>
Correlated order at the tipping point in the kagome metal CsV3Sb5
<p>Data deposite for the manuscript entitled "Correlated order at the tipping point in the kagome metal CsV3Sb5". The manuscript will soon appear online. </p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
Density Layers of selected Points of Interest from Open Street Map
<p>This dataset contains a raster layer of 100*100m resolution showing the density of selected amenities from Open Street Map. The amenities are selected as points of interest, where electric vehicles owners are likely to stop for a moment and recharge their vehicles. This dataset covers Europe and was obtained with the Overpass API. This dataset can be used to identify possible charging location for electric vehicles or any other purpose requiring to quantify the number of amenities in an area.</p> <p> </p> <p>This dataset shows densities of a selection of Points of Interest in Europe from Open Street Map [1].</p> <p>Included countries are:</p> <p> </p> <p><em><strong>country_codes</strong> = ['AT', 'BE', 'BG', 'HR', 'CY', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR', 'HU', 'IE', 'IT','LV', 'LT', 'LU', 'MT', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI', 'ES', 'SE', 'AL', 'AD', 'AM', 'BY', 'BA', 'FO', 'GE', 'GI', 'IS', 'IM', 'XK', 'LI', 'MK', 'MD', 'MC', 'ME', 'NO', 'SM', 'RS', 'CH', 'TR', 'UA', 'GB', 'VA']</em></p> <p> </p> <p>The requests of Points of Interest have been performed with the Overpass API [2] (free of charge).</p> <p> </p> <p>The codes included in each density are listed below :</p> <p> </p> <p><strong><em>'highway'</em></strong><em> = ['"highway"="motorway"', '"highway"="rest_area"'];</em></p> <p><strong><em>'parkings'</em></strong><em> = ['"parking"="surface"', '"parking"="multi-storey"', '"parking"="street_side"', '"parking"="underground"' , '"park_ride"' ];</em></p> <p><strong><em>'school'</em></strong><em> = ['"amenity"="college"', '"building"="college"', '"building"="university"', '"amenity"="university"', '"amenity"="school"' , '"amenity"="school"', '"amenity"="kindergarten"', '"amenity"="library"'];</em></p> <p><strong><em>'health'</em></strong><em>= ['"amenity"="clinic"', '"amenity"="dentist"', '"amenity"="school"' , '"amenity"="doctors"', '"amenity"="hospital"', '"amenity"="pharmacy"','"amenity"="veterinary"']; </em></p> <p><strong><em>'cafe'</em></strong><em>= ['"amenity"="cafe"','"amenity"="ice_cream"', '"amenity"="internet_cafe"']; </em></p> <p><strong><em>'supermarket'</em></strong><em> = ['"shop"="supermarket"', '"shop"="mall"', '"shop"= "department_store"', '"shop"= "convenience"'];</em></p> <p><strong><em>'restaurant'</em></strong><em>= ['"amenity"="restaurant"'];</em></p> <p><strong><em>'fastfood'</em></strong><em> = ['"amenity"="fast_food"'];</em></p> <p><strong><em>'sport'</em></strong><em>= ['"sport"']; </em></p> <p><strong><em>'hotel'</em></strong><em> = ['"tourism"="hotel"', '"building"="hotel"', '"tourism"="guest_house"','"tourism"="apartment"','"tourism"="hostel"','"tourism"="motel"','"tourism"="camp_site"']; </em></p> <p><strong><em>'pubs'</em></strong><em> = ['"amenity"="bar"','"amenity"="pub"', '"amenity"="biergarten"'];</em></p> <p><em>'theatre'= ['"amenity"="theatre"', '"amenity"="cinema"', '"amenity"="music_venue"', '"leisure"="stadium"' ]; </em></p> <p><strong><em>'night'</em></strong><em> = ['"amenity"="nightclub"', '"amenity"="casino"','"amenity"="gambling"','"amenity"="stripclub"']; </em></p> <p><strong><em>'socio'</em></strong><em>= ['"amenity"="arts_centre"', '"amenity"="community_centre"', '"amenity"="social_centre"', '"amenity"="music_school"', '"amenity"="language_school"']; </em></p> <p><strong><em>'shop'</em></strong><em> = ['"shop"'];</em></p> <p><strong><em>'tourism'</em></strong><em> = ['"amenity"="exhibition_centre"', '"tourism"="attraction"','"tourism"="viewpoint"','"tourism"="aquarium "','"leisure"="beach_resort "','"tourism"="gallery"','"tourism"="museum"','"tourism"="theme_park"','"tourism"="zoo"','"tourism"="artwork"'];</em></p> <p> </p> <p>The pixel values are the sum of the number of POIs of each type located in the pixel.</p> <p> </p> <p><em>Limitations of the dataset</em></p> <p>- The dataset provides densities of only a selection of points of interests, regardless of its type. The complete list of amenity codes can be found on the OSM Wiki [3].</p> <p>- Ways are only considered through their centre points.</p> <p> </p> <p>[1] “Open Street Map.” <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a> (accessed Sep. 05, 2023).</p> <p>[2] “Overpass API.” <a href="https://wiki.openstreetmap.org/wiki/Overpass_API">https://wiki.openstreetmap.org/wiki/Overpass_API</a> (accessed Sep. 05, 2023).</p> <p>[3] “Open Street Map Wiki.” <a href="https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance">https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance</a> (accessed Sep. 05, 2023).</p>
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
New Challenges in Point Cloud Visual Quality Assessment: A Systematic Review (Dataset)
<p>This dataset is a collection of annotated information on the scientific papers screened and analyzed for the systematic review of the literature in Point Cloud Visual Quality Assessment. </p> <p>The data is structured as follows:</p> <ul> <li>General information <ul> <li>Document title</li> <li>Authors</li> <li>Year of publication</li> <li>Venue (Conference or Journal title)</li> <li>Citations (number)</li> <li>URL/DOI</li> </ul> </li> </ul> <ul> <li>About the content <br> <ul> <li>Content Type: Point clouds (PC), Colored Point clouds (CPC), Meshes, Dynamic Point Clouds (DPC)</li> <li>Content source: Source of the content used in a subjective QA test or the evaluation of one or more QA metrics</li> </ul> </li> </ul> <ul> <li>About metric benchmarks <ul> <li>Subjective Ground-truth Data: Dataset(s) Source of the subjective scores used as ground-truth in a QA metric benchmark</li> <li>Assessed Metrics: Types of metrics assessed in a benchmark (JPEG standards, IQM, NR, State-of-the-art, others)</li> <li>Performance Measures: PLCC, SROCC, KRCC, RMSE, OR, others</li> </ul> </li> </ul> <ul> <li>About Objective QA metrics <ul> <li>Metric: Name given to the metric introduced in this paper</li> <li>Base: 3D-based or Projection-based</li> <li>Categories: Categories that characterize the approach of the proposed metric (Feature-based, Learning-Based, Perceptual-based, IQM, others) </li> <li>Reference: Full-Reference (FR), Reduced-Reference (RR) or No-Reference (NR)</li> </ul> </li> </ul> <ul> <li>About Subjective QA experiments <ul> <li>Display: Type of display (2D, 3D, AR, MR, VR) and interaction approach (passive, interactive, 3DoF, 6DoF) used in the described experiment.</li> <li>Rendering: Type of rendering used to display the stimuli (Points, Squares, Cubes, Surface)</li> <li>Lab/Remote: The experiment was run in one or more lab environments, or remotely (Lab, Cross-Lab, Remote)</li> <li>Rating: Subjective rating methodology used in the experiment (ACR, DSIS, PWC, others)</li> <li>Dataset: Name of the new subjective dataset if the experiment's results were published.</li> <li>Observers: Number of observers </li> <li>Distortion type: Types of distortions applied to the stimuli and assessed in the experiment</li> </ul> </li> </ul>
LigPCDS: Labeled Dataset of X-ray Protein Ligand Images in 3D Point Cloud and Validated Deep Learning Models
<p>The difference electron density from X-ray protein crystallography was used to create the first dataset of labeled ligand images in 3D point clouds, named <strong>LigPCDS</strong>. The dataset contain 244,226 entries of free organic ligands containing 3D representations labeled with two major labeling approaches: SP-based and AtomSymbol-based.</p> <p> </p> <p>The data from free organic molecules (non-covalent ligands) was retrieved from the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) in december 2019 with resolutions ranging from 1.5 to 2.2 Å. The ligand images (blobs) were interpolated from their calculated difference electron density map in a 3D grid-like bounding box, around their atomic positions, and stored in point clouds. These ligand grid representations were further processed to retrive the final ligands representation in 3D point clouds using a mask of the shape of the ligand. A grid spacing of 0.5 Å gave the best results. The density value of the grid points was used as feature. The labeling approach used the structure of the ligands to propose vocabularies of chemical classes based on the chemical atoms themselves and their cyclic substructures. These structure annotations were applied pointwise to the ligand 3D representations using an atomic sphere model. Four proposed vocabularies were validated by successfully training good performance deep learning models for the semantic segmentation of a stratified dataset from LigPCDS, using 78902 entries.</p> <p>The four validated deep learning models are: (i) the LigandRegion, composed by generic atoms of any type; (ii) the AtomCycle, composed by generic atoms outside cycles and generic cycles; (iii) the AtomC347CA56, composed by generic atoms outside cycles, not aromatic cycles of size 3 to 7 and aromatic cycles of size 5 and 6; and (iv) the AtomSymbolGroups, composed by the atoms symbols with groupings. The mean accuracy of these models in their cross-validation was between 49.7% <span lang="EN-GB">[-19.4,20.</span><span lang="EN-GB">2]</span> and 77.4% <span lang="EN-GB">[-11.7,12.1]</span> in terms of Intersection over Union (mIoU) metric and between 62.4% <span lang="EN-GB">[-18.8,19.</span><span lang="EN-GB">7]</span> and 87.0% <span lang="EN-GB">[-8.4,8.8]</span> in F1-score (mF1), confidence interval between squared brackets. The models i, ii and iii and the used labeled representations in 3D point cloud are contained in the SP-based record; and model iv and its used labeled representations are contained in the AtomSymbol-based record.</p> <p>The dataset and validated models may be used to tackle problems regarding known and unknown ligand building to drug discovery and fragment screening pipelines. </p> <p>The code used to create and validated the LigPCDS is available at the following repository: https://github.com/danielatrivella/np3_ligand</p> <p>This repository also contains the NP³ Blob Label application for ligand building using the validated deep learning models from LigPCDS.</p>
Data and Workflow to: Three-dimensional buoyant hydraulic fracture growth: constant release from a point source (Möri and Lecampion, (2022))
<p>This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fracture growth: constant release from a point source" by Möri and Lecampion, (2022).</p>
Soil properties as point estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample ID</td> <td>String </td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real </td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand </td> <td>Real </td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real </td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real </td> <td>Silt fraction</td> </tr> <tr> <td>EC </td> <td>Real </td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real </td> <td>ph</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real </td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC </td> <td>Real </td> <td>Soil Organic Carbon</td> </tr> </tbody> </table>
Soil properties as point estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real</td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
Soil properties as point estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><br> <strong>Description of the information contained in the corresponding "LTH points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p><br> </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.