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1,049 results for “height”
Plant heights from permanent plots in a Spartina alterniflora-dominated marsh, Nelson Island, Parker River National Wildlife Refuge, Plum Island Ecosystems LTER, MA (2019-2025).
Plant heights are measured during the growing season in permanent plots at a Spartina alterniflora-dominated salt marsh on Nelson Island, Parker River National Wildlife Refuge, 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: Daily averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)
From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
SEV-LTER quadrat plant species cover and height all sites and experiments
This dataset includes plant species cover and height data measured in 1 m x 1 m quadrats at several sites and experiments under the Sevilleta LTER program. Quadrat locations span four distinct ecosystems and their ecotones: creosotebush dominated Chihuahuan Desert shrubland (est. winter 1999), black grama-dominated Chihuahuan Desert grassland (est. winter 1999), blue grama-dominated Plains grassland (est. winter 2002), and pinon-juniper woodland (est. winter 2003). Data on plant cover and height for each plant species are collected per individual plant or patch (for clonal plants) within 1 m x 1 m quadrats. These data inform population dynamics of foundational and rare plant species. In addition, using plant allometries, these non-destructive measurements of plant cover and height can be used to calculate net primary production (NPP), a fundamental ecosystem variable that quantifies rates of carbon consumption and fixation. Estimates of plant species cover, total plant biomass, or NPP can inform understanding of biodiversity, species composition, and energy flow at the community scale of biological organization, as well as spatial and temporal responses of plants to a range of ecological processes and direct experimental manipulations. The cover and height of individual plants or patches are sampled twice yearly (spring and fall) in permanent 1m x 1m plots within each site or experiment. This dataset includes core site monitoring data (CORE, GRIDS, ISOWEB, TOWER), observations in response to wildfire (BURN), and experimental treatments of extreme drought and delayed monsoon rainfall (EDGE), physical disturbance to biological soil crusts on the soil surface (CRUST), interannual variability in precipitation (MEANVAR), intra-annual variability via additions of monsoon rainfall (MRME), additions of nitrogen as ammonium nitrate (FERTILIZER), additions of nitrogen x phosphorus x potassium (NutNet), and interacting effects of nighttime warming, nitrogen addition, and El Ni
Scarp height data and topographic profiles from the Zomba Graben, southern Malawi
<p>Measurements of fault scarp height from five faults in the Zomba Graben, southern Malawi (Table S1-S6). Topographic profiles used to measure the height of the scarp are in Tables S7-S11.</p> <p>For more details of this dataset please refer to Wedmore, L. N. J., Biggs, J., Williams, J. N., Fagereng, Å. Dulanya, Z., Mphepo, F., & Mdala, H. (2020). Active fault scarps in southern Malawi and their implications for the distribution of strain in incipient continental rifts. <em>Tectonics</em>, 39(3), <a href="https://doi.org/10.1029/2019TC005834">https://doi.org/10.1029/2019TC005834</a></p> <p> </p> <p>Please contact the author, Luke Wedmore (luke.wedmore@bristol.ac.uk) for more details.</p>
Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations
<p><strong>Overview:</strong></p> <p>The dataset of <strong>Thermal Bridges on Building Rooftops (TBBR dataset)</strong> consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to <strong>2680x3370</strong> to remove empty borders. See the "Usage" section below for details about the stored formats made available here.</p> <p>The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p>The dataset contains <strong>926 images</strong> with a total of <strong>6,927 annotations</strong> of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles.</p> <p>For the annotation of the thermal images the image processing program <em>VGG Image Annotator </em>from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated.</p> <p><strong>Usage:</strong></p> <p>Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height].</p> <p>Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g.</p> <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> <p>these will be decompressed into the file structure:</p> <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> <p>Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below.</p> <p>Note: The annotation files contain <em>relative</em> paths to numpy files, in case of problems please convert to <em>absolute</em> paths (i.e. insert the containing directory before each file path in the JSON annotation files).</p> <p>We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet"><strong>TBBRDet software</strong></a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries.</p> <p>We recommend the following folder structure for use:</p> <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> <p><strong>Metadata:</strong></p> <p>The experimental metadata was structured with the <strong>Spatio Temporal Asset Catalog (STAC)</strong> specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis.</p> <p>One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded.</p> <p>This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the<br> corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata.</p> <p>Metadata of the archived NumPy files for each image was structured using the <strong>Data Package</strong> schema from the <strong>Frictionless Standards</strong>. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file.</p> <p>Note that <strong>camera1</strong> corresponds to the <strong>RGB camera</strong> and <strong>camera2</strong> the <strong>thermal</strong>.</p> <p><strong>FAIR Digital Objects:</strong></p> <p>All files are represented in a standardized way as <strong>FAIR Digital Objects<br> (FAIR DOs)</strong> to enable machine actionable decisions on the data in spirit of<br> the FAIR principles.</p> <p><strong>Persistent Identifier (PID):</strong></p> <p>Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>.</p> <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>
L2C - Canopy height models across the Brazilian Amazon
<p>Canopy height models derived from LiDAR data collected across the Brazilian Amazon. The files are provided in .tiff format in 7 zip folders. A full description of the data set is available here: https://zenodo.org/record/4968706#.YzB693ZKg5s</p> <p>We also provide the summary data used for statistical analysis in the associated publication: </p> <p>Reis and Jackson et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology.</p> <p>Each transect covered 375 ha (12.5 km × 300 m) by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per square meters, the field of view was equal to 30°, the flying altitude was 600 m, and transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was set to be below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and under 0.5 m, respectively.</p> <p>The data collection was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1)</p> <p>The research project was funded by the UK Natural Environment Research Council project number <strong>NE/S010750/1</strong></p>
Data for "Effects of External Water on Volcanic Column Height and Collapse"
<p>Generate data for the publication "Effects of External Water on Volcanic Column Height and Collapse" (in prep). Please cite Carrillo, E.L. (2024) if any data in this repository is used.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Proteins required for stereocilia elongation during mammalian hair cell development ensure precise and steady heights during adult life
<p>This dataset contains all source data for Hartig <em>et al </em>2024, PNAS, including:</p> <p>Data files</p> <p>Raw images and TDT ABR/DPOAE files</p> <p>ROIS and raw measurements from quantifications in ImageJ</p> <p>R scripts for data visualization and statistics</p> <p>Reports of statistical analyses including diagnostic qq plots and distributions</p>
Maximum height for the native vegetation in Minas Gerais State, Brazil
<p>Maximum height for native vegetation of Minas Gerais State (Brazil) based on GEDI measurements and environmental factors. The environmental layers included annual average temperature, annual average precipitation, terrain elevation, slope, number of cloud free days, number of months with precipitation below 100 mm. The GEDI height records were overlapped to the environmental layers, and filtered, considered the efficiency frontier.</p>
Comparison of high-resolution global canopy height maps and their applicability to biodiversity modelling - dataset
<p>This repository was created to provide datasets related with an article comparing high-resolution global canopy height maps and exploring their applicability to biodiversity modeling in temperate biomes.</p> <p>EBR stands for Entlebuch Biosphere Reserve, MRF stands for Mount Richmond Forest and TAW stands for Trinity Alps Wilderness.</p> <p>The original airborne laser scanning point clouds used for the generation of the canopy height models were sourced from the LINZ Data Service and OpenTopography, and licensed for reuse under the CC BY 4.0 licence (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fdoi.org.mcas.ms%2F10.5069%2FG97D2SB0%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://doi.org/10.5069/G97D2SB0</a>); Federal Office of Topography swisstopo (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fwww.swisstopo.admin.ch.mcas.ms%2Fen%2Fgeodata%2Fheight%2Fsurface3d.html%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://www.swisstopo.admin.ch/en/geodata/height/surface3d.html</a>); and U.S. Geological Survey (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fapps.nationalmap.gov.mcas.ms%2Fdownloader%2F%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://apps.nationalmap.gov/downloader/</a>).</p> <p>The Global Forest Canopy Height Map - GFCH (Potapov et al. 2021; https://glad.umd.edu/dataset/gedi) and the high-resolution canopy height model of the Earth - HRCH (Lang et al. 2022, https://langnico.github.io/globalcanopyheight/) are provided free of charge, without restriction of use under Creative Commons Attribution 4.0 International License. Publications, models, and data products that make use of these datasets must include proper acknowledgement.</p> <p><em>P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165. <a href="https://doi.org/10.1016/j.rse.2020.112165">https://doi.org/10.1016/j.rse.2020.112165</a></em></p> <p><em>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322.</em></p> <p>R scripts related with this datasets are available at Github (https://github.com/lukasgabor/Comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability; <a href="https://doi.org/10.5281/zenodo.7332716">DOI: 10.5281/zenodo.7332716</a>)</p> <p>In the previous version (1.0) the average was calculated for the canopy height. In this version (1.1), the maximum height is calculated for the canopy height.</p>
CNBH-10 m: A first Chinese building height at 10 m resolution
<p>Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/macao">Macau</a> has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.</p>
White spruce trees tagged measured for total height and girth at 10 centimeter height, and leader length, Coldfoot, Alaska 2015, 2016
White spruce seedlings have colonized the site of the Coldfoot transplant garden (CF, 67°15′32″N, 150°10′12″W) since the original garden was established in 1982. Some trees are 2-3 meter tall. All seedlings and trees within the current (2014) garden were tagged, located with a Global Positioning System (GPS) receiver, and measured in 2015 and 2016 for total height and girth at 10 centimeter height and leader length.
Tussock height and diameter in moist acidic tussock tundra at the site of the 2007 Anaktuvuk River fire scar, and nearby unburned tundra measured in 2016
This dataset consists of Eriophorum vaginatum tussock height and width (diameter) measurements, and was used to evaluate differences in physical strucutre of previously burned tundra (2007 Anaktuvuk River fire) and nearby unburned tundra. At each site, all tussocks that intersected four 100 meter transects were measured from soil surface to tussock top in four cardinal directions, and diameter was measured in two directions. These data were used to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Tree regeneration after fire: Wickersham Dome long-term vegetation study, birch height data
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Lists heights of all individual paper birch (Betula papyrifera) present in a plot.
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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.