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Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
7 Tesla MRI of the ex vivo human brain at 100 micron resolution
Open the record for dataset details and reuse information.
Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI
Open the record for dataset details and reuse information.
Monthly Chlorophyll Concentration product of Mediterranean Sea at 1 km horizontal resolution 1998-2015
<p>Monthly Chlorophyll Concentration product of Mediterranean Sea at 1 km horizontal resolution, expressed as mg m^-3</p>
Temporal super-resolution microscopy using a hue-encoded shutter
<p>This dataset contains the data to reproduce the figures in our paper called "Temporal super-resolution microscopy using a hue-encoded shutter", <em>Biomedical Optics Express, 2019</em>.</p> <p>Together with the data, the code is available on <a href="https://github.com/idiap/hesm_distrib">Idiap's GitHub page</a>.</p>
Project files provided as supporting information to the manuscript "Ligand-protein interactions in lysozyme investigated through a dual-resolution model"
<p><strong>README file for the project files provided as supporting information to the manuscript "Ligand-protein interactions in lysozyme investigated through a dual-resolution model"</strong></p> <p>February 12, 2020</p> <p>Authors: Raffaele Fiorentini, Kurt Kremer and Raffaello Potestio</p> <p>================================</p> <p>Overview</p> <p>The dataset is organised in three (compressed) subfolders (see the tree diagrams in each section):</p> <p>- annihilation<br> - decoupling<br> - density</p> <p>The figure deltaG_binding_ann_dec_comparison.png shows the results of binding free energy calculations comparing the values obtained both for annihilation and decoupling.</p> <p>The figure deltaG_binding_annih_gromacs_espp.png displays the results for Binding FE, comparing the values obtained in GROMACS and ESPResSo++.</p> <p>The README.pdf file contains detailed information about these folders and their content.</p> <p>================================</p> <p>The "annihilation" folder contains all results concerning the calculation of binding free energy in case of annihilation and it is divided in two parts: </p> <p>- complex<br> - ligand</p> <p>In "complex" are reported the results of Ligand-Protein FE both in ESPResSo++ and GROMACS. All simulations are fully-atomistic. </p> <p>In "ligand" are reported the results of ligand solvation free energy both in ESPResSo++ and GROMACS. All simulations are fully-atomistic. </p> <p>====</p> <p>The "decoupling" folder contains all results concerning the calculation of binding free energy in case of decoupling and it is divided in three parts: </p> <p>- complex-DualRes<br> - complex-FullyAT<br> - ligand</p> <p>In "complex-DualRes" are reported the results of Ligand-Protein FE only in ESPResSo++ (GROMACS cannot do decoupling). The system is simulated in Dual-Resolution. It is possible to find the trajectory files in the sub-directories "lambdaindex-0" and "lambdaindex-30".</p> <p>In "complex-fullyAT" are reported the results of Ligand-Protein FE only in ESPResSo++. The system simulated is fully-atomistic. It is possible to find the trajectory file in the sub-directories "lambdaindex-0" and "lambdaindex-30".</p> <p>In "ligand" are reported the results of ligand solvation free energy only in ESPResSo++. All simulations are fully-atomistic. It is possible to find the trajectory file in the sub-directories "lambdaindex-0" and "lambdaindex-20".</p> <p>====</p> <p>The "density" folder contains the data for the tuning of the c parameter of the steric repulsion among residues. This parameter is tuned so that the water density attains the value computed in all-atom simulations.</p>
Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).
<p>Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".</p>
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
SLOCLIM: A high-resolution daily gridded precipitation and temperature dataset for Slovenia
<p>SLOCLIM is a new high-resolution daily gridded precipitation and temperature dataset for Slovenia, covering the whole territory of Slovenia from 1950 to 2018. A grid of 1x1 km spatial resolution consists of 20,998 points for which daily maximum and minimum temperature and precipitation was calculated. The observed climatic information was provided by Slovenian Environment Agency (ARSO).</p>
Air-Sea Ammonia Fluxes Calculated from High-Resolution Summertime Observations Across the Atlantic Southern Ocean
<p>This data set includes ocean ammonium concentrations, atmospheric ammonia gas concentrations, and calculated air-sea ammonia fluxes from the Atlantic sector of the Southern Ocean during summer. Associated with the folloiwng paper: </p> <p> https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020GL091963</p>
Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa
<p>Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a></p> <p>Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:</p> <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> <p>NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.</p>
High-resolution inundation dataset for coastal India and Bangladesh
<p>This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh.</p> <p><strong>Input data:</strong></p> <p>These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH.</p> <p><strong>Methods:</strong></p> <p>We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water.</p> <p>We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions.</p> <p><strong>Data set format:</strong></p> <p>The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.</p> <p><strong>Data set for download:</strong></p> <p>A. Three data layers for Odisha, India:</p> <ol> <li>OD_pre_binary.tif</li> <li>OD_post_binary.tif</li> <li>OD_inundation.tif</li> </ol> <p>These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.</p> <p>B. Three data layers for West Bengal, India:</p> <ol> <li>WB_pre_binary.tif</li> <li>WB_post_binary.tif</li> <li>WB_inundation.tif</li> </ol> <p>These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.</p> <p>C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region.</p> <ol> <li>BNG_L_pre_binary.tif</li> <li>BNG_L_post_binary.tif</li> <li>BNG_L_inundation.tif</li> <li>BNG_U_pre_binary.tif</li> <li>BNG_U_post_binary.tif</li> <li>BNG_U_inundation.tif</li> </ol> <p>The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.</p> <p>The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.</p>
Laser-free super-resolution microscopy
<p>Raw data for "Laser-free super-resolution microscopy"</p> <p>https://www.biorxiv.org/content/10.1101/121061v3.full.pdf</p>
ELC10: European 10 m resolution land cover map 2018
<p>Refer to preprint here: https://arxiv.org/abs/2104.10922</p> <p>A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018.</p> <p>The methodology is currently under review, but this will be updated as soon as the paper is available online. Please refer to the publication for accuracy estimates and usage guidelines.</p> <p>The map is split up into a number of raster tiles with the coordinate reference system "EPSG:3035 - ERTS89 / LAEA Europe".The filename of each tile is in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the entire ELC10 image.</p> <p>The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).</p>
Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy
<p>The dataset consists of a time series of the Sentinel-1 (S-1) surface soil moisture (SSM) product at 1 km spatial resolution validated in Balenzano et al. (2021 a) over the Southern Italy. The specifications of the S-1 SSM product are provided in Balenzano et al. (2021 b). The SSM time series was obtained in correspondence of the ascending (RON A146) S-1 Interferometric Wide swath (IW) acquisition dates from January 2015 to December 2018 with a temporal gap between consecutive of 6 days (when both S-1A and S-1B data are available) or 12 days. On each date (183 in total), two co-registered layers are provided: mean SSM [m3/m3] and its standard deviation [m3/m3], which provides the SSM uncertainty. The retrieval algorithm is a time series short term change detection (STCD) that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (Balenzano et al. 2013).</p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Tractography Challenge ISMRM 2015 High-resolution Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking an <em>in vivo</em> HCP-quality acquisition based on a simulated diffusion signal. </p>
Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands
<p><span lang="EN-GB">This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders:<strong> 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4</strong>). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: <strong>5_Maps</strong>). All 25 LiDAR metrics were calculated using Laserfarm workflow (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: <strong>6_Masks</strong>). To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: <strong>7_Auxiliary_data</strong>). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: <strong>8_Sensitivity_analysis</strong>). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: <strong>9_Use_case</strong>). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: <strong>10_Ground_classification</strong>). </span></p> <p><span lang="EN-GB">An overview of all the folders in the repository:</span></p> <p><strong><span lang="EN-GB">1. </span></strong><strong><span lang="EN-GB">AHN1</span></strong></p> <p><strong><span lang="EN-GB">2. </span></strong><strong><span lang="EN-GB">AHN2</span></strong></p> <p><strong><span lang="EN-GB">3. </span></strong><strong><span lang="EN-GB">AHN3</span></strong></p> <p><strong><span lang="EN-GB">4. </span></strong><strong><span lang="EN-GB">AHN4</span></strong></p> <p><strong><span lang="EN-GB">5. </span></strong><strong><span lang="EN-GB">Maps</span></strong></p> <p><span lang="EN-GB">Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset.</span></p> <p><strong><span lang="EN-GB">6. Masks</span></strong></p> <ul> <li><span lang="EN-GB">ahn3_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_building_road_water.tif</span></li> <li><span lang="EN-GB">ahn4_10m_mask_powerline.tif</span></li> <li><span lang="NL">ahn1_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn2_10m_NA_mask.tif</span></li> <li><span lang="NL">ahn3_10m_NA_mask.tif</span></li> <li><span lang="NL">a</span><span lang="NL">hn4_10m_NA_mask.tif</span></li> </ul> <p><span lang="NL"> </span></p> <p><span lang="EN-GB">It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0.</span></p> <p><strong><span lang="EN-GB">7. Auxiliary data</span></strong></p> <p><span lang="EN-GB">(1) Point_density</span></p> <ul> <li><span lang="EN-GB">ahn1_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn2_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn3_10m_point_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_point_density.tif</span></li> </ul> <p><span lang="EN-GB">(2) Pulse_density</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_pulse_density.tif</span></li> <li><span lang="EN-GB">ahn4_10m_pulse_density.tif</span></li> </ul> <p><span lang="EN-GB">(3) Flighttime</span></p> <ul> <li><span lang="EN-GB">ahn3_10m_flighttime.tif</span></li> <li><span lang="EN-GB">ahn4_10m_flighttime.tif</span></li> </ul> <p><span lang="EN-GB">(4) DTM_DSM</span></p> <ul> <li><span lang="EN-GB">ahn2_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn2_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn3_10m_dsm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dtm.tif</span></li> <li><span lang="EN-GB">ahn4_10m_dsm.tif</span></li> </ul> <p><span lang="EN-GB">It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2</span><span lang="EN-GB">–</span><span lang="EN-GB">AHN4. All raster layers are provide at 10 m resolution.</span></p> <p><strong><span lang="EN-GB">8. Sensitivity analysis</span></strong></p> <ul> <li><span lang="EN-GB">Dunes</span></li> <li><span lang="EN-GB">Marsh</span></li> <li><span lang="EN-GB">Grassland</span></li> <li><span lang="EN-GB">Shrubland</span></li> <li><span lang="EN-GB">Woodland</span></li> <li><span lang="EN-GB">Code</span></li> <li><span lang="EN-GB">Figure</span></li> </ul> <p><span lang="EN-GB">It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis.</span></p> <p><strong><span lang="EN-GB">9. Use_case</span></strong></p> <p><span lang="EN-GB">(1) Multi-temporal_AHN</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Usecase_multi-temporal_AHN.R</span></li> </ul> <p><span lang="EN-GB">It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). </span></p> <p><span lang="EN-GB">(2) Natura2000</span></p> <ul> <li><span lang="EN-GB">Data</span></li> <li><span lang="EN-GB">Natura2000_end2021_HABITATCLASS.csv</span></li> <li><span lang="EN-GB">Natura2000_NL_habitat_grouped.csv</span></li> <li><span lang="EN-GB">Usecase_Natura2000.R</span></li> </ul> <p><span lang="EN-GB">It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. </span></p> <p><strong><span lang="EN-GB">10. Ground classification</span></strong></p> <ul> <li><span lang="EN-GB">Raw_point_cloud</span></li> <li><span lang="EN-GB">Computed_metrics </span></li> <li><span lang="EN-GB">Plottings_and_code</span></li> <li><span lang="EN-GB">ArcGIS_project</span></li> </ul> <p><span lang="EN-GB">It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project).</span></p> <p><strong><span lang="EN-GB">Code availability</span></strong></p> <p><span lang="EN-GB">Jupyter Notebooks for processing AHN datasets: </span></p> <p><span lang="EN-GB"><a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a></span></p> <p><span lang="EN-GB">Laserfarm workflow repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a></span></p> <p><span lang="EN-GB">Laserchicken software repository: </span></p> <p><span lang="EN-GB"><a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a></span></p> <p><span lang="EN-GB">Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a></span></p> <p><span lang="EN-GB">Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a></span></p> <p><span lang="EN-GB">Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a></span></p> <p> </p>
Global drivers of forest loss at 1 km resolution
<p dir="ltr">The World Resources Institute and Google DeepMind created a global map of the dominant drivers of tree cover loss from 2001 to 2024 at 1 km spatial resolution (v1.2). We used a deep learning model to classify seven driver classes: permanent agriculture, hard commodities, shifting cultivation, logging, wildfires, settlements & infrastructure, and other natural disturbances. As part of the study, we collected a set of training samples through interpretation of very high resolution satellite imagery and developed a single world-wide customized residual convolutional neural network model (ResNet) using satellite data (Landsat and Sentinel-2) and ancillary biophysical and population data. In addition, we collected a stratified random sample of validation plots through interpretation of very high resolution satellite imagery to estimate the accuracy of the final classification map. </p> <p dir="ltr">For a full description of the methods, technical specifications, definitions, and limitations, please see the publication: <a href="https://doi.org/10.1088/1748-9326/add606" target="_blank" rel="noopener noreferrer">https://doi.org/10.1088/1748-9326/add606</a>.</p> <p dir="ltr">In this repository, we make the following available:</p> <ul> <li> <p dir="ltr">The <strong>training</strong> and <strong>validation </strong>data, available in two separate files. Both datasets were collected by a team of image interpreters and assessed for quality by two additional interpreters. Note that while for the validation data the quality of the primary driver was rigorously assessed, the secondary driver wasn’t subject to the same level of quality control. </p> </li> <li> <p dir="ltr">The <strong>global drivers of forest loss raster</strong> (drivers_forest_loss_1km.tif), including the <strong>discrete classification</strong> and <strong>probabilities</strong> for each class.</p> </li> </ul> <p><strong> </strong></p> <p dir="ltr"><strong>Creator & Contact</strong></p> <p dir="ltr">Created by World Resources Institute and Google DeepMind</p> <p dir="ltr">Contact: Michelle Sims: <a href="mailto:Michelle.Sims@wri.org">Michelle.Sims@wri.org</a></p> <p dir="ltr"> </p> <p dir="ltr"><strong>Annual updates</strong></p> <p dir="ltr">This dataset is updated annually with the Global Forest Change product from the University of Maryland <a href="https://www.science.org/doi/10.1126/science.1244693">(Hansen et al., 2013</a>). With each update, any training data plots containing the latest year of tree cover loss as the mode loss year are reinterpreted by image interpreters and the dominant driver label is updated, where necessary. An additional random sample of ~200 plots containing the latest year of tree cover loss is interpreted and added to the sample. Input data is updated for the Global Forest Change product, tree cover loss due to fires product (<a href="https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2022.825190/full">Tyukavina et al. 2022</a>), Sentinel 2, and Dynamic World (<a href="https://www.nature.com/articles/s41597-022-01307-4">Brown et al. 2022</a>) and the model is retrained with the updated training data and rerun to produce a new global map representative of the new time period.</p> <p dir="ltr">The files follow this specific naming convention:</p> <ul> <li> <p dir="ltr">Data type: `training` or `validation` or `drivers_forest_loss_1km` for global raster </p> </li> <li> <p dir="ltr">Start year: `2001`</p> </li> <li> <p dir="ltr">End year: `2022` or `2023` or `2024`</p> </li> <li> <p dir="ltr">Version: `v1` or `v1_1` or `v1_2`</p> </li> </ul> <p dir="ltr"> </p> <p dir="ltr"><strong>Definitions</strong></p> <p dir="ltr">A <em>driver</em> is defined as the direct cause of tree cover loss, and can include both temporary disturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to a non-forest land use (e.g., deforestation). The <em>dominant </em>driver is defined as the direct driver that caused the majority of tree cover loss within each 1 km cell over the time period. </p> <p dir="ltr">Classes are defined as follows:</p> <div dir="ltr"> <table><colgroup><col><col></colgroup> <tbody> <tr> <td> <p dir="ltr">Driver</p> </td> <td> <p dir="ltr">Definition</p> </td> </tr> <tr> <td> <p dir="ltr">Permanent agriculture</p> </td> <td> <p dir="ltr">Long-term, permanent tree cover loss for small- to large-scale agriculture. This includes perennial tree crops such as oil palm, cacao, orchards, nut trees, and rubber, as well as pasture and seasonal crops and cropping systems, which may include a fallow period. Agricultural activities are considered "permanent" if there is visible evidence that they persist following the tree cover loss event and are not a part of a temporary cultivation cycle. Clearing land for agricultural activities may involve use of fire. </p> </td> </tr> <tr> <td> <p dir="ltr">Hard commodities</p> </td> <td> <p dir="ltr">Tree cover loss due to the establishment or expansion of mining or energy infrastructure. Mining activities range from small-scale and artisanal mining to large-scale mining. Energy infrastructure includes power lines, power plants, oil drilling and refineries, wind and solar farms, flooding due to the construction of hydroelectric dams, and other types of energy infrastructure.</p> </td> </tr> <tr> <td> <p dir="ltr">Shifting cultivation </p> </td> <td> <p dir="ltr">Tree cover loss due to small- to medium-scale clearing for temporary cultivation that is later abandoned and followed by subsequent regrowth of secondary forest or vegetation. Clearing land for temporary cultivation may involve use of fire.</p> </td> </tr> <tr> <td> <p dir="ltr">Logging</p> </td> <td> <p dir="ltr">Forest management and logging activities occurring within managed, natural or semi-natural forests and plantations, often with evidence of forest regrowth or planting in subsequent years. This includes harvesting in wood-fiber plantations, clear-cut and selective logging, establishment of logging roads, and other forest management activities such as forest thinning and salvage or sanitation logging.</p> </td> </tr> <tr> <td> <p dir="ltr">Wildfire </p> </td> <td> <p dir="ltr">Tree cover loss due to fire with no visible human conversion or agricultural activity afterward. Fires may be started by natural causes (e.g. lightning) or may be related to human activities (accidental or deliberate).</p> </td> </tr> <tr> <td> <p dir="ltr">Settlements and infrastructure</p> </td> <td> <p dir="ltr">Tree cover loss due to expansion and intensification of roads, settlements, urban areas, or built infrastructure (not associated with other classes).</p> </td> </tr> <tr> <td> <p dir="ltr">Other natural disturbances</p> </td> <td> <p dir="ltr">Tree cover loss due to other non-fire natural disturbances, including storms, flooding, landslides, drought, windthrow, lava flows, sediment flow or meandering rivers, natural flooding, insect outbreaks, etc. If tree cover loss due to natural causes is followed by salvage or sanitation logging, it is classified as logging.</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) ensemble model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 1998 to 2017 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes. The input variables of this ANN ensemble model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m d–1, and μmol S m–2 d–1, respectively.</p> <p><strong>Update Note</strong></p> <ol> <li>In Version 4.0 and earlier versions, the sea ice cover data (from the OISST dataset) used to calculate Kt and DMS flux contained certain time periods with completely missing values, which were incorrectly replaced with zeros. This led to a significant overestimation of Kt and DMS flux in polar regions where sea ice coverage exists. The missing data periods include: November 27–28, 2011; January 7–9, 2016; April 18 to June 30, 2016; and January 7 to February 28, 2017. This issue was resolved beginning with Version 5.0 through the use of updated sea ice data.</li> <li>Compared to Version 5.0, the current version introduces a correction to a bug in the DMS concentration simulation. When input data were missing—primarily in polar regions—the DMS concentration should have been flagged as missing. However, it was previously assigned a value of 1.6031 nM in Version 5.0. In the current version, these values are now replaced with -999 to indicate missing data.</li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.