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The phylogeny of ceutorhynchine weevils (Ceutorhynchinae, Curculionidae): mitogenome data improve the resolution of tribal relationships.
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Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics
<p>The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics</p> <p>Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.</p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
High-resolution microclimatic grids for the Bohemian Forest Ecosystem
<p>Here, we provide spatially continuous, high-resolution (5 m) microclimate grids covering all 923 km<sup>2</sup> of the Bohemian Forest Ecosystem (BFE), i.e. the complete area of the Šumava (Czech Republic) and Bavarian Forest (Germany) National Parks.</p> <p>To derive these grids, we have established a dense network of 288 microclimatic stations that continuously measured air, near-surface, and soil temperature every 15 minutes from 12th October 2019 to 11th October 2020. We combined the measured microclimate temperature with LiDAR derived land surface topography and forest structure through boosted spatial generalized additive models (GAMs).</p> <p>We validated the resulting microclimatic grids with an independent network of forest weather stations and compared these microclimatic grids with the SoilTemp (soil temperature), ForestTemp (near-ground forest understorey temperature), and downscaled ERA5-Land (air temperature). The developed BFE microclimatic grids were closer to independently measured temperatures than any other alternative and captured high microclimatic variability controlled jointly by land surface topography and forest structure. </p> <p>Our microclimatic grids represent accurate, high-resolution spatial variation of mean annual soil temperature, mean, maximum and minimum air temperature at two heights, and growing degree days at 200 cm. </p> <p><strong>The dataset contains 8 microclimatic grids (GeoTIFF format, coordinate system EPSG 31468, resolution 5 m).</strong></p> <p>Extent: 4587063, 5399139: 4646023, 5451289</p> <p>The name of the file is “name.tif”, where name represents the abbreviation of the microclimatic variable (see names below).</p> <p>The values are in °C (°C d for GDD), the data can be readily imported into standard geographical information system software (e.g., QGIS) or accessed in a statistical software (e.g., R). The datasets do not include colour schemes.</p> <p><strong>Measured variable (depth/height) </strong><br> - Microclimatic variable Abbreviation (Units)</p> <p><strong>Soil temperature (-8 cm) </strong> <br> - Mean temperature = mean temperature T.soil_8_cm.mean (°C)<br> <br><strong>Near-ground air temperature (15 cm) </strong> <br> - Mean temperature = mean temperature T.air_15_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_15_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_15_cm.min.5p (°C)<br> <br><strong>Air temperature (200 cm)</strong> </p> <p> - Mean temperature = mean temperature T.air_200_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_200_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_200_cm.min.5p (°C)<br> - Growing degree days = sum of degree days above base temperature (base 5°C) T.air_200_cm.GDD5 (°C d)</p> <p><strong>BFE_script_and_data.zip</strong> contains</p> <p> - Microclimatic variables, topography and forest structure variables for all stations used for modelling (BFE_data.RData).<br> - Script BFE_microclimate_maps_model_script.R used for statistical modelling and prediction. <br> - microclimate2predict.csv - list of microclimate variables for prediction used in the script.</p> <p>The data used for prediction cannot be made publically available. <br>The LIDAR data can be obtained from Administration of Bavarian Forest National Park and Šumava National Park Administration. The LIDAR-derived topography and forest structure rasters can be obtained upon request from the authors.</p> <p>Detailed description will be available in a manuscript.</p> <p><strong>Version 2</strong> insludes improved rasters, the GeoTIFFs include aplha channel for transparency of pixels with NA and full script used for processing.</p> <p> </p>
Multi-resolution X-Ray micro-CT images of Bentheimer Sandstones
<p>This dataset consists of multi-resolution X-Ray micro-tomography images of two Bentheimer sandstone rock cores. The rock cores were first used experimentally in [1] with further modelling in [2]. This new dataset is used directly in the publication [3] - preprint available at https://arxiv.org/abs/2111.01270. </p> <p>The original dataset from [1] (of the same rock cores) is hosted on the BGS National Geoscience Data Centre, ID #130625 at dx.doi.org/10.5285/5f899de8-4085-4370-a45e-e613f27e8f1d and there is also a subvolume image dataset, for easier download available on the Digital Rocks Portal, project 229, DOI:10.17612/KT0B-SZ28 at digitalrocksportal.org/projects/229. </p> <p>The images provided herein are from two distinct Bentheimer rock cores -- core 1 and core 2. The cores have diameter, 12.35mm, lengths 73.2mm and 64.7mm, core-averaged porosities of 0.203 and 0.223 and permeabilities of 1.636D and 0.681D for core 1 and 2, respectively. Core 2 has a clear low permeability lamination occurring at 2/3 of the total core length, whereas core 1 has a general fining towards the outlet of the core creating a reduction in porosity [1].</p> <p>The images were acquired with a Zeiss Versa 510 X-Ray CT scanner. We acquired images of two sub volumes from each core, at locations 1/3rd (subvolume 1) and 2/3rds (subvolume 2) of the way along the core length, at resolutions of 2, 6 and 18 microns. We refer to the 2 micron images as high-resolution (HR), the 6 micron images as low-resolution (LR) and the 18 micron images as very-low-resolution (VLR). There are also super-resolution (SR) images created at 2 micron resolution from the LR images, using a deep-learning algorithm. There are also cubic interpolation images created from the LR image - these are labels bicubic. These have a resolution of 2 microns, and size equal to the HR and SR images. Details of the SR and LR Bicubic generation are found in [3]. The following scanning protocols were used for the direct imaging:</p> <p>2 micron images:<br> --We use a 4x microscope objective, an exposure time of 8s, 2x averaged binning, 9001 projections, a scan voltage of 80kV and a power of 7W. Each scan takes approximately 24 hours.</p> <p>6 micron images:<br> --We use a flat panel detector, an exposure time of 0.7s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 14.46 degrees and the fan angle is 22.2 degrees. Each scan takes approximately 1 hour.</p> <p>18 micron images:<br> --We use a 0.4x microscope objective, an exposure time of 1s, 10x repeat frames, 1x averaged binning, 2401 projections, a scan voltage of 80kV and a power of 7W. The cone angle is 12.65 degrees and the fan angle is 12.65 degrees. Each scan takes approximately 2 hours.</p> <p>We present 4 sets of the images with different levels of processing. All images are mutual registered to each other. Each image filename has a Core#_Subvol#_resolution identifier, either with the actual resolution (e.g. 6) or the short form (e.g. LR). The following name endings are used</p> <p>(1) - '_16bit_LE.raw'. These are the .raw images of little-endian format. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(2) - '_16bit_LE_normalised.raw'. These are the .raw images of little-endian format with normalised greyscale values following the procedure in [1]. Preceding this filename is also the cubic image side length in voxels, e.g. _75cube. 12 images in total.</p> <p>(3) - 'Core1_Subvol1_HR' etc. These are the .tiff images of (2) above, which have been converted to 8 bit. Includes bicubic interpolation images and SR images, but no 16 micron images, since these were not used in the analysis of [3]. 16 images in total. </p> <p>(4) - 'Core1_Subvol1_HR_filtered' etc. These are the .tiff images from (3) above, which have filtered using non-local means filtering. More details are found in [3]. Note there are no SR images here since they are already essentially filtered, and included in (3) above. 12 images in total.</p> <p><br> <strong>References</strong><br> <br> [1] Jackson, S.J., Lin, Q. and Krevor, S. 2020. Representative Elementary Volumes, Hysteresis, and Heterogeneity in Multiphase Flow from the Pore to Continuum Scale. Water Resources Research, 56(6), e2019WR026396</p> <p>[2] Zahasky, C., Jackson, S.J., Lin, Q., and Krevor, S. 2020. Pore network model predictions of Darcy‐scale multiphase flow heterogeneity validated by experiments. Water Resources Research, 56(6), e e2019WR026708.</p> <p>[3] Jackson, S.J, Niu, Y., Manoorkar, S., Mostaghimi, P. and Armstrong, R.T. 2021. Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling. Under review, preprint available at https://arxiv.org/abs/2111.01270 </p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Parameter fields for the Hydrological Discharge (HD) model at 0.5° and 5 Min. horizontal resolution
<p><strong>HD model parameter files</strong></p> <p>This dataset comprises global parameter data that are necessary to run the Hydrological Discharge (HD) model Vs. 5.1, which has been published on <a href="http://doi.org/10.5281/zenodo.5707587">Zenodo</a>. The HD model calculates the lateral transport of water over the land surface to simulate discharge into the oceans. The HD model parameter dataset comprises parameter fields at 0.5° global resolution and at 5 Min. resolution (global, Europe). Details for both resolutions are provided below. </p> <p><strong>Authors</strong>: Stefan Hagemann, Tobias Stacke <br> <strong>Copyright 2021</strong>: Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon<br> <strong>License</strong>: under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0; https://creativecommons.org/licenses/)</p> <p><br> <strong>HD model parameter file at 5 Min resolution: hdpara_vs5_1.nc</strong></p> <p>River directions and digital elevation data were provided by Bernhard Lehner (pers. comm., 2014) and were derived from the HydroSHEDS (Lehner et al., 2006) database and from the Hydro1K dataset for areas north of 60°N (https://lta.cr.usgs.gov/HYDRO1K).<br> For a number of rivers (most of them north of 60°N), flow directions and model orography were manually corrected based on available GIS data, such as from DIVA (https://www.diva-gis.org/gdata), CCM River and Catchment Database<br> (Vogt et al. 2007), SMHI (Swedish Meteorological and Hydrological Institute), NVE (Norges vassdrags- og energidirektorats), SYKE (Finnish Environment Institute).</p> <p>This corrected dataset is referred to as HDvs5 in the following.<br> The HD model parameters for overland flow, base flow and river flow are generated as described in Hagemann and Dümenil (1998) and Hagemann et al. (2020). However, different to the HD model vs. 4 described in Hagemann et al. (2020), Vs5 utilizes inland water fractions from the ESA CCI Water Bodies Map v4.0 (Lamarche et al. 2017) and wetland fractions from the Global Lakes and Wetlands Database (Lehner and Döll 2004) instead of the previously used lake and wetlands fractions. Changes from Vs. 5.0 to Vs. 5.1 are provided in the file history_data.md that should be previewed below.</p> <p>The HD parameter dataset contains 14 variables which are shortly described in the following table.</p> <ul> <li> FLAG | Land sea mask | -</li> <li> FDIR | Flow direction | - | defined as written below</li> <li> ALF_K | HD model parameter Overland flow k | d-1</li> <li> ALF_N | HD model parameter Overland flow n | -</li> <li> ARF_K | HD model parameter Riverflow k | d-1</li> <li> ARF_N | HD model parameter Riverflow n | -</li> <li> AGF_K | HD model parameter Baseflow flow k | d-1</li> <li> AREA | Grid cell area | m-2 | based on own computation</li> <li> FILNEW | River flow target indices for longitudes | -</li> <li> FIBNEW | River flow target indices for latitudes | -</li> <li> DISTANCE | Distance between gridboxes in flow direction | m</li> <li> RIVERLENGTH | Distance between gridbox and the river mouth (or final sink) | km</li> <li> CAT_AREA | Upstream catchment area of gridbox | km²</li> <li> CAT_ID | Catchment ID of gridbox | -</li> </ul> <p> <em> Flow directions in variable FDIR are defined as on the Num Pad of a PC keyboard: </em> </p> <ul> <li> 7 8 9</li> <li> \ | /</li> <li> \|/</li> <li> 4--5--6</li> <li> /|\</li> <li> / | \</li> <li> 1 2 3</li> </ul> <p> Special directions: 5 = Sink point, i.e. no outflow<br> 0 = River mouth point in the ocean <br> -1 = Ocean point, but no river mouth</p> <p>Forcing data masks file: masks_5min.nc</p> <p>In the offline HD model version, this file is usually only used to obtain the grid information of the forcing data, i.e. of surface runoff and drainage (subsurface runoff). However, it contains four variables that are read in by the model, and that are actually used un coupled applications within the MPI-ESM. Even though these variables are not used in the HD model offline version, it was decided to keep them in order to allow future developments regarding the usage of these data and to keep some consistency with the HD model code implemented in MPI-ESM.</p> <ul> <li> ALAKE | Lake fraction within a grid box | Lamarche et al. 2017</li> <li> GLAC | Glacier fraction within a grid box | Hagemann 2002</li> <li> SLF | Land fraction within a grid box | Lamarche et al. 2017</li> <li> SLM | Land Sea Mask | Lamarche et al. 2017</li> </ul> <p>For simplicity, the data provided at the HD model resolution. Hence, these masks can be used when the forcing data are interpolated to the HD model resolution before they are read during the model run.</p> <p>This tar archive also include a subset of this global dataset for the European domain, hdpara_vs5_0_euro5min.nc.</p> <p> </p> <p><strong>HD model parameter file at 0.5° resolution: hdpara_vs1_11.nc</strong></p> <p>In addition, a global 0.5° HD parameter file hdpara_vs1_11 included. This is an update of the previous version 1.10 that was consistent to the parameter files used in previous offline and coupled applications of the HD model at 0.5° resolution (see, e.g. studies cited in Sect. 2.1 of Hagemann et al., 2020). Compared to the previous version 1.10, Vs. 1.11 now also utilzes the ESA water bodies and GLWD wetlands database such as in the 5 Min vs. 5.1 (see above). In additon, some flow directions have been updated. Except for DISTANCE and RIVERLENGTH, it comprises the same variables as for the 5 Min. version, but flow directions and parameters are generated as described in Hagemann and Dümenil (1998) and Hagemann and Dümenil Gates (2001). Here, the 0.5 degree mask file mask_05.nc comprises those masks that were utilized in the HD parameter generation. Only the land fraction is taken from Hagemann (2002) where the HD land sea mask indicates land.</p> <p><br> <strong>References</strong></p> <ul> <li>Hagemann, S., L. Dümenil (1998) A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 14 (1), 17-31</li> <li>Hagemann, S., L. Dümenil Gates (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J. Geophys. Res. 106, 1503-1510</li> <li>Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models, MPI Report No. 336, Max Planck Institute for Meteorology, Hamburg, Germany</li> <li>Hagemann, S., T. Stacke and H. Ho-Hagemann (2020) High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci., 8:12. doi: 10.3389/feart.2020.00012.</li> <li>Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O. (2017) Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), p.36.</li> <li>Lehner, B., P. Döll (2004) Development and validation of a global database of lakes, reservoirs and wetlands.</li> <li>J. Hydrol., 296: 1-22, doi:10.1016/j.jhydrol.2004.03.028.</li> <li>Vogt, J.V. et al. (2007): A pan-European River and Catchment Database. European Commission - JRC, Luxembourg, (EUR 22920 EN) 120 pp.</li> </ul> <p> </p>
Global forest management data at a 100m resolution for the year 2015
<p>We provide four data records:</p> <p>1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: </p> <ul> <li> <p>“ID” is a unique location identifier </p> </li> <li> <p>“Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. </p> </li> </ul> <ul> <li> <p>“Land_use_ID “is a land use class: </p> <ul> <li>11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests. </li> <li>20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc. </li> <li>31 - Planted forest. </li> <li>32 - Short rotation plantations for timber. </li> <li>40 - Oil palm plantations. </li> <li>53 - Agroforestry. </li> </ul> </li> <li> <p>“Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach.</p> </li> </ul> <p>2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ".</p> <p>3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif"</p> <p>4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: </p> <ul> <li> <p>“ID” is a unique location identifier </p> </li> <li> <p>“pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection </p> </li> <li> <p>“first_landuse_class “is a land use class, as in (1). </p> </li> </ul> <ul> <li> <p>“second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence. </p> </li> </ul> <p>5. Original crowdsourced data set as a .csv table.</p> <p>6. Compiled FAO FRA forest statistics and mapped classes by countries into one table (.csv format).</p> <p> </p>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
Decadal time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2021) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds.<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; dD = number of decade):<br> <code>ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Decadal</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7427010">https://zenodo.org/record/7427010</a></p>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
Copernicus Digital Elevation Model (DEM) for Europe at 3 arc seconds (ca. 90 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 3 arc seconds (approx. 90 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Copernicus Digital Elevation Model (DEM) for Europe at 30 arc seconds (ca. 1000 meter) resolution derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 30 arcsec (0:00:30 = 0.0083333333 ~ 1000 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reduce the spatial resolution to 30 arc seconds, weighted resampling was performed in GRASS GIS (using <code>r.resamp.stats -w</code> and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p> </p>
Copernicus Digital Elevation Model (DEM) for Europe at 1000 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 1000 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 1000 m, bilinear resampling was performed in GRASS GIS (using <code>r.proj</code> and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> ETRS89-extended / LAEA Europe (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 1000 m</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Copernicus Digital Elevation Model (DEM) for Europe at 100 meter resolution (EU-LAEA) derived from Copernicus Global 30 meter DEM dataset
<p>Overview:<br> The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.</p> <p>The Copernicus DEM for Europe at 100 meter resolution (EU-LAEA projection) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).</p> <p>Processing steps:<br> The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in <a href="https://gdal.org/drivers/raster/vrt.html">VRT</a> format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:</p> <p><code>gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt </code></p> <p>In order to reproject the data to EU-LAEA projection while reducing the spatial resolution to 100 m, bilinear resampling was performed in GRASS GIS (using <code>r.proj</code> and the pixel values were scaled with 1000 (storing the pixels as Integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using <code>r.relief</code>, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.</p> <p>Projection + EPSG code:<br> ETRS89-extended / LAEA Europe (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 100 m</p> <p>Pixel values:<br> meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.proj; r.relief)</p> <p>Original dataset license:<br> <a href="https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf">https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
PROBA-V Super-Resolution dataset
<p>The PROBA-V Super-Resolution dataset is the official dataset of <strong>ESA's Kelvins</strong> <strong>competition for "PROBA-V Super Resolution"</strong>. It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at <strong>300m</strong> and <strong>100m</strong> resolution in Plate Carrée projection. The <strong>300m</strong> resolution data is delivered as <strong>128x128</strong> grey-scale pixel images, the <strong>100m</strong> resolution data as <strong>384x384</strong> grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.).</p> <p>The goal of the challenge can be described as <strong>Multi-Image Super-resolution</strong>: Construct a single high-resolution image out of a series of more frequent low resolution images.</p> <p>Detailed information about the related competition can be found at <a href="https://kelvins.esa.int/proba-v-super-resolution">https://kelvins.esa.int/proba-v-super-resolution</a>.</p> <p>A publication about the generation of this dataset exists as well: </p> <ul> <li><strong>Märtens M., Izzo D., Krzic A. and Cox D.</strong> "Super-resolution of PROBA-V images using convolutional neural networks." Astrodynamics 3.4 (2019): 387-402. (<a href="https://arxiv.org/pdf/1907.01821.pdf">arxiv version</a>)</li> </ul>
Remote Sensing VQA - Low Resolution (RSVQA LR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page concerns the low resolution dataset.</p>
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.