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1,049 results for “height”
IODP Expedition 379 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 371 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 360 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 397 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Airborne radar observation dataset of sea surface height on 8 December 2016
<p>This dataset contains the results of time-series sea surface height (SSH) observation data of flight No.1, 2, 3, 4, 7, and 8 on 8 December 2016 by airborne altimeter measurement using a frequency modulated continuous wave (FM-CW) radar. The observation flight were carried out south of Japan passed over the Kuroshio Current. The data files are written in CSV format, the columns are UTC date, time, latitude, longitude, flight altitude, observed SSH, 1 min moving averaged SSH values, geoid height, and tide height. The geoid height and the tide height are derived by the EGM 2008 model (Pavlis et al. 2012) and the Nao.99Jb model (Matsumoto et al. 2000), respectively. The original data sampling rate of 800 microseconds is resampled by 80 milliseconds in each file. The data comes from a paper under review for Geophysical Research Letter.</p>
Geographic variation of tree height of Pinus pinea L. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinea </em>L. planted in common gardens in France and Spain, between years 1993 and 1997. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimensions of this database is 56,624 individual tree height measurements <em> </em>with 9 common gardens and 55 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
Geographic variation of tree height of Pinus nigra Arn. gathered from common gardens in Europe
<p>This dataset collects individual georeferenced tree height data from <em>Pinus nigra</em> Arn. planted in common gardens in France, Germany and Spain, between years 1968 and 2009. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 194,642 individual tree height data measurements <em> </em>with 15 common gardens and 78 different provenances. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p> <p> </p>
Geographic variation of tree height of Pinus pinaster Aiton gathered from common gardens in Europe and North-Africa
<p>This dataset collects individual georeferenced tree height data from <em>Pinus pinaster</em> Aiton planted in common gardens in France, Morocco and Spain, between years 1966 and 1992. The experimental design varies depending on the common garden, from a randomized complete to incomplete block design, RCB or RIB, respectively. The final dimension of the dataset is 123,801 individual tree height data measurements <em> </em>with 14 common gardens and 182 different genetic units. The data can be used to assess genetic variation and phenotypic plasticity with further applications in biogeography and forest management. </p>
High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions
<p>This Zenodo repository contains the data presented in Spiekermann, K. A.; Pattanaik, L.; Green, W. H.* <a href="https://www.nature.com/articles/s41597-022-01529-6">High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions</a>, Sci. Data 9, 417, (2022). We recommend people refer to this dataset as RDB7 i.e. a diverse reaction database whose transition states contain up to 7 heavy atoms.</p> <p>Atom-mapped SMILES, barrier heights, reaction enthalpies, and Reaction Mechanism Generator (RMG) reaction family for each reaction are listed in the comma-separated values files <strong><em>b97d3.csv</em></strong>, <strong><em>wb97xd3.csv</em></strong>, <strong><em>ccsdtf12_dz.csv</em></strong>, and<em> <strong>ccsdtf12_tz.csv</strong></em>. <em><strong>ccsdtf12_dz_individual_heats_of_formation.csv</strong></em> containing the individual heats of formation for each stable species (i.e., reactant and product). The values in all of these files are in kcal/mol. Q-Chem output files from the reoptimized products are provided for 16,302 reactions at B97-D3/def2-mSVP and for 11,926 reactions at ωB97X-D3/def2-TZVP level of theory. For convenience, these also include the original log files for the reactant, transition state, and non-reoptimized products from Grambow et al. (10.5281/zenodo.3715478) since they were used to calculate barrier heights, enthalpies, and rate constants in this work. The numbering of reaction indices matches that from the originally published dataset to facilitate easy comparison. MOLPRO output files from the single point calculations are provided for 11,926 reactions at the CCSD(T)-F12/cc-pVDZ-F12 level of theory as well as for the 15 validation reactions run at CCSD(T)-F12/cc-pVTZ-F12. The raw log files for all calculations are stored in <strong><em>b97d3.tar.gz</em></strong>, <strong><em>wb97xd3.tar.gz</em></strong>, <strong><em>ccsdtf12_dz.tar.gz</em></strong>, and <strong><em>ccsdtf12_tz.tar.gz</em></strong>. Each archive contains a separate folder for each reaction, which contains log files for the reactant, transition state, and product/s. The Q-Chem log files contain the output from a geometry optimization and harmonic vibrational analysis while the MOLPRO log files contain output from an energy calculation. Transition state theory rate constants, fitted Arrhenius parameters, and average percentage error between the calculated and fitted rate constants can be found for the rigid reactions in <strong><em>ccsdtf12_dz_rigid.csv</em></strong>. The list of 50 temperatures (K) used during Arrhenius fitting is provided in <strong><em>arkane_temperatures.csv</em></strong>, and the raw Arkane outputs are provided in <strong><em>ccsdtf12_dz_rigid.tar.gz</em></strong>.</p> <p>The improvement from fitting bond additivity corrections at B97-D3/def2-mSVP, ωB97X-D3/def2-TZVP, CCSD(T)-F12/cc-pVDZ-F12//ωB97X-D3/def2-TZVP, and CCSD(T)-F12/cc-pVTZ-F12//ωB97X-D3/def2-TZVP is shown in <strong><em>b97d3_def2msvp_BAC.csv</em></strong>, <strong><em>wb97xd3_def2tzvp_BAC.csv</em></strong>, <strong><em>ccsdtf12_ccpvdzf12__wb97xd3_def2tzvp_BAC.csv</em></strong>, and <strong><em>ccsdtf12_ccpvtzf12__wb97xd3_def2tzvp_BAC.csv</em></strong> respectively. The files contain the experimental and calculated enthalpies for the reference species from the RMG-database used for fitting. The correction values are publicly stored on the RMG-database GitHub on the AEC_BAC branch, though they are also provided in <strong><em>fitted_corrections.pkl</em></strong> for convenience. Further validation of the BACs at the double zeta level was done by comparing to experimental values from the Pedley set since over half of these molecules were not in the RMG-database training set used for fitting. The comparison is shown in <strong><em>ccsdtf12_dz_vs_Pedley_experimental.csv</em></strong>.</p> <p> </p>
IODP Expedition 398 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 355 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)
<p>This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677. </p> <p><em>Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region.</em></p> <p><em>Coordinate system : Lambert_Belge_72.</em></p> <p><em>The CHM was built on </em><em>:</em></p> <ul> <li><em>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>. and;</em></li> <li><em>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. </em><em>Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></em></li> </ul> <p><em>Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. </em></p> <p><em>The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, </em><em>and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). </em><em>Green roofs were excluded.The CHM raster was then converted to polygon features. </em></p> <p><em>Classification :</em></p> <ul> <li><em>From 0 to 0.5 m (nDSM value) : gridcode 1 = </em><em>grass</em></li> <li><em>From 0.5 to 2 m (nDSM value): gridcode 2 = </em><em>low shrubs</em></li> <li><em>From 2 to 5 m (nDSM value): gridcode 3 =</em><em> high shrubs</em></li> <li><em>From 5 to 113.96 m (nDSM value): gridcode 4 = </em><em>trees</em></li> </ul>
Shrub inventory data such as shrub identity, height and biomass in a 20x5m core plot at Mt. Kilimanjaro
<p>This dataset describes position and sizes of all shrubs above 130 cm high in all plots, also fruiting and flowering events in KiLi project. -999999 represents NA in numeric variables. </p> <p>The shrub inventory was carried out within a 5 × 20 m subplot in the centre of each plot. Within this subplot, the shrub layer was defined as consisting of all woody stems exceeding 1.3 m in height, but below 10 cm dbh and thus not included in the tree inventory. We measured dbh at 1.3 m with a diameter tape (Forestry Suppliers; for dbh's above 3 cm) or a caliper (for dbh's below 3 cm) and the height of each shrub with a hypsometer.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Tree inventory data such as tree identity, position in the plot, height, architecture and biomass on Mt. Kilimanjaro
<p>This dataset describes position and sizes of all trees above 10 cm diameter at breast height in all plots, also fruiting and flowering events and if it is a canopy tree or not in KiLi project. -999999 represents NA in numeric variables. </p> <p>Within each plot, all trees wider than 10 cm diameter at breast height (dbh) were marked with aluminium tags and their dbh and height were measured. The dbh was measured with a diameter tape (Forestry Suppliers, USA) at 1.3 m for normally shaped trees and 20 cm below or above when branches or irregular shapes impeded measurement at that height. The 1.3 m height was measured from the highest ground level around the stem to standardize measurements taken on slopes. For trees which were strongly buttressed or too big to measure by hand, a laser dendrometer (Criterion RD 1000 with TruPulse 200/200, Centennial, USA) was used to measure the tree above the buttresses and at 1.3 m. Lianas above 10 cm in diameter were also marked and their dbh was measured. Tree height was measured using an ultra-sonic hypsometer (Vertex IV Hypsometer, Haglöf, Langsele, Sweden) or a laser rangefinder (TruPulse 200/200). The tree inventories were carried out between December 2010 and March 2013.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
IODP Expedition 353 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 359 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines
<p>Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.</p> <p>The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).</p> <p>The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":</p> <p> 0: Open land (OL) [#440154]<br> 1: Scrub (S) [#404387]<br> 2: Young regenerating forest (YRF) [#29788e]<br> 3: Low density forest (LDF) [#22a884]<br> 4: Medium density forest (MDF) [#7ad251]<br> 5: High density forest (HDF) [#fde725]<br> 10: Oil palm [#fcffa4]<br> 11: Coconut [#a4feff]<br> 50: Urban [#fa0000]<br> 255: No data</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research articles. These citations are as follows:</p> <p>Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.</p> <p>Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. <em>Remote Sensing of Environment</em>, <em>261</em>, 112479.</p> <p>Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448</p> <p> </p>
Database from: Orthometric, Normal and Geoid Heights In the Context of the Brazilian Altimetric Network
<p>This dataset is part of an article entitled "ORTHOMETRIC, NORMAL AND GEOID HEIGHTS IN THE CONTEXT OF THE BRAZILIAN ALTIMETRIC NETWORK" (https://doi.org/10.1590/s1982-21702022000100003), published in the Bulletin of Geodesic Sciences.</p> <p>This dataset includes 569 stations whose values for geodetic and normal height, gravity and geopotential numbers are provided by the IBGE (Brazilian Institute of Geography and Statistics). In addition, we included orthometric height data and differences between orthometric and normal height data calculated from the Hemlert and Mader methods, using constant and variable density data provided by Medeiros et al. (2021) (https://doi.org/10.1016/j.jsames.2021.103425) and Sheng et al. (2019) (https://doi.org/10.1016/j.tecto.2019.04.005).</p> <p> </p>
Canopy Height Model Dresden 2017
<p>The canopy height model (CHM) represents area-wide tree canopy heights within the City of Dresden (Germany). The CHM provides spatially explicit information on urban forest structure enabling the assessment of the small-scale impacts of urban trees and strategically managing the ecosystem services they provide. The high-resolution raster layer has a cell size of 0.5 meter and maps the height of the upper crown layer above the underlying ground.</p> <p>The CHM was derived from a classification of the urban forest in a LiDAR point cloud using a data fusion approach combining LiDAR with multispectral imagery and a 3D building model. LiDAR data were acquired in 2017. The classification is described in detail in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>The raster is available as a single-band GeoTIFF in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833).</p> <p>The source data used was made freely available by the “Landesamt für Geobasisinformation Sachsen” (GeoSN) under the license "Data license Germany - attribution - Version 2.0" and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></p>
Simulated thickness profiles of ALD film in a wide microchannel of 500 nm height published as Fig.4 in PCCP 24 (2022) 8645-8660
<p>A series of simulated thickness profiles of atomic layer deposition (ALD) film grown in a wide lateral high-aspect-ratio (LHAR) microchannel is archived as an Excel file. This dataset has been published as Figure 4 in the publication "Conformality of atomic layer deposition in microchannels: impact of process parameters on the simulated thickness profile" (Yim and Verkama et al., Phys. Chem. Chem. Phys. 24 (2022) 8645-8660. https://doi.org/10.1039/D1CP04758B). A diffusion-reaction model by Ylilammi et al. (Ylilammi et al., J. Appl. Phys. 123 (2018) 205301. https://doi.org/10.1063/1.5028178) was re-implemented for the simulation. For this simulation, a channel height of 500 nm, which is a typical height for microscopic PillarHallTM LHAR test chips (Yim and Ylivaara et al., Phys. Chem. Chem. Phys., 22 (2020) 23107-23120. https://doi.org/10.1039/D0CP03358H), was used.<br> The Excel file consists of 11 tabs in total: metadata, baseline thickness profile, and Fig4a to Fig4i. The baseline thickness profile and data of fig4a Fig4i are also available as a CSV file. The metadata page describes the data with its baseline conditions. The baseline conditions used in the simulation are: sticking coefficient = 0.01, temperature = 250 °C, initial partial pressure of Reactant A = 100 Pa, molar mass of Reactant A = 0.100 kg mol-1, hard-sphere diameter of Reactant A = 6 × 10-10 m, partial pressure of inert gas I = 500 Pa, molar mass of inert gas I = 0.028 kg mol-1, hard-sphere diameter of inert gas I = 3.74 × 10-10 m, mass density of deposited film = 3500 kg m-3, areal number density of metal M atoms in MyZx material = 4 nm-2, number of metal atoms in a Reactant A molecule = 1, number of metal atoms in a formula unit of growing film = 1, number of cycle = 250, desorption probability = 0.01 s-1, channel height = 500 nm, and channel width = 10 mm. The baseline thickness profile tab contains a thickness profile obtained in the baseline conditions as film thickness versus distance within a microchannel. The thickness profile stored from Fig4a to Fig4i tabs was obtained by varying individual parameters with other parameter values in baseline conditions: initial partial pressure of Reactant A (Fig4a), pulse time (Fig4b), molar mass of Reactant A (Fig4c), mass density of deposited film (Fig4d), adsorption density (Fig4e), desorption probability (Fig4f), sticking coefficient (Fig4g), temperature (Fig4h) and partial pressure of inert gas (Fig4i).</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.