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7,031 results for “marine”
Height and density data for marine animal forest forming species
<p>Data file (.csv) of values of height and density for marine animal forest (MAF) forming species collected from the literature. Fields are for discrete observations:</p> <table> <tbody> <tr> <td>Species</td> <td>Species name</td> </tr> <tr> <td>Santavy</td> <td> <p>Morphology class using categories in </p> <p><span>Santavy DL, Lee A. Courtney, William S. Fisher, Robert L. Quarles, Stephen J. Jordan (2013) Estimating surface area of sponges and gorgonians as indicators of habitat availability on Caribbean coral reefs. Hydrobiologia 707:1-16. <span>https://doi.org/10.1007/s10750-012-1359-7</span><br></span></p> </td> </tr> <tr> <td>Height</td> <td>Mean colony height (cm)</td> </tr> <tr> <td>Density</td> <td>Mean colony density m-2</td> </tr> <tr> <td>Btemp</td> <td>Average bottom temperature for species based on OBIS records (K)</td> </tr> <tr> <td>Depth</td> <td>Average depth for species based on OBIS records (m)</td> </tr> <tr> <td>Phylum</td> <td>Taxonomy</td> </tr> <tr> <td>Class</td> <td>Taxonomy</td> </tr> <tr> <td>Order</td> <td>Taxonomy</td> </tr> <tr> <td>Family</td> <td>Taxonomy</td> </tr> <tr> <td>Genus</td> <td>Taxonomy</td> </tr> <tr> <td>Source</td> <td>Publication source for data</td> </tr> <tr> <td>Title</td> <td>Publication title</td> </tr> <tr> <td>DOI/link</td> <td>DOI for source data</td> </tr> </tbody> </table>
Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model
<p>The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.</p>
FESOM-REcoM model data: Severe 21st-century ocean acidification in Antarctic Marine Protected Areas
<p>This repository contains all post-processed model output used in the paper "Severe 21st-century ocean acidification in Antarctic Marine Protected Areas". It contains the data underlying the figures in the paper, such as regional averages, as well as masks for the marine protected areas and the grid information file of the original model output.</p><p>The data were created using python scripts provided at <a href="https://doi.org/10.5281/zenodo.10295920">https://doi.org/10.5281/zenodo.10295920</a>. </p><p>Original model output, including full fields of computed pH and saturation states with respect to aragonite and calcite, is available at the World Data Center for Climate (WDCC) under the following DOIs:</p><ul><li>simA, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_hist_vA_vC</a></li><li>simA, ssp126: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s126_vA_vC</a></li><li>simA, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s245_vA_vC</a></li><li>simA, ssp370: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s370_vA_vC</a></li><li>simA, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_A_s585_vA_vC</a></li><li>simB: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_B_1921_cA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_hist_vA_cC</a></li><li>simC, ssp245: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s245_vA_cC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_C_s585_vA_cC</a></li><li>simC, historical: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_hist_cA_vC</a></li><li>simC, ssp585: <a href="https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC">https://doi.org/10.26050/WDCC/FESOM14-REcoM2_D_s585_cA_vC</a></li></ul><p> </p>
Analyzing marine biofilms developed on carbon nanotube-modified surfaces by 3D OCT approach
<p>Glass, epoxy resin, and carbon nanotubes (CNT) composite were analyzed regarding wettability by water contact angle measurement, and roughness by atomic force microscopy. Cyanobacterial biofilms formed by Nodosilinea cf. nodulosa LEGE 10377 were developed on these surfaces for seven weeks and under controlled hydrodynamic conditions. Biofilm wet weight and structural parameters such as biofilm thickness, contour coefficient, biovolume, porosity, and average size of non-connected pores obtained from Optical Coherence Tomography (OCT) were assessed.</p>
Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023
<p>Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions.</p> <p>The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, fjord masks and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2.</p> <p>Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder.</p> <p>Table 1. The layer structure of the Svalbard calving front data product.</p> <table> <tbody> <tr> <td> <p><strong>Layer Name</strong></p> </td> <td> <p><strong>Details</strong></p> </td> </tr> <tr> <td> <p>traces</p> </td> <td> <p>Line geometries recording the terminal traces of all the glaciers (EPSG:3995).</p> </td> </tr> <tr> <td> <p>centrelines</p> </td> <td> <p>Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995).</p> </td> </tr> <tr> <td> <p>domains</p> </td> <td> <p>Polygon geometries recording the glacier domains (EPSG:3995).</p> </td> </tr> <tr> <td> <p>fjord_masks</p> </td> <td> <p>Polygon geometries recording the fjord masks (EPSG:3995).</p> </td> </tr> <tr> <td> <p>front_change_time_series</p> </td> <td> <p>Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326).</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 2. The feature attribute table of the data layer.</p> <table> <tbody> <tr> <td> <p><strong>Data Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Glacier</p> </td> <td> <p>The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id.</p> </td> </tr> <tr> <td> <p>Sensor</p> </td> <td> <p>The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”.</p> </td> </tr> <tr> <td> <p>ImageId</p> </td> <td> <p>The image id of the satellite image used in mapping the glacier calving front.</p> </td> </tr> <tr> <td> <p>DateString</p> </td> <td> <p>The datetime string of the satellite image in the format of “YYYYMMDD”.</p> </td> </tr> <tr> <td> <p>CFL_Change</p> </td> <td> <p>The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series.</p> </td> </tr> <tr> <td> <p>glacier_lat</p> </td> <td> <p>The latitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> <tr> <td> <p>glacier_lon</p> </td> <td> <p>The longitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> </tbody> </table>
Conductivity–Temperature–Depth (CTD) and dissolved oxygen profile data from shipboard surveys collected within Olympic Coast National Marine Sanctuary, 2005-2023
<p>This data set includes Conductivity-Temperature-Depth (CTD) and dissolved oxygen profile data that were collected along Washington State’s outer coast within Olympic Coast National Marine Sanctuary towards the northernmost extent of the California Current System. Measurements were made at fourteen hydrographic stations during mooring deployment, recovery, and maintenance cruises between the months of May and October from 2005–2023. The 792 CTD profiles were acquired using Sea-Bird Scientific 19 SeaCAT or 19plus SeaCAT CTD profilers with associated SBE-43 (Sea-Bird Electronics) or Beckman or YSI-type (Yellow Springs Instruments) dissolved oxygen sensors. The data were processed via Sea-Bird Scientific’s SBE Data Processing application using six of the modules in the following order: <em>Data Conversion, Filter, Align CTD, Loop Edit, Derive, and Bin Average</em>. These processing steps and associated methods are the same as those used to process CTD data that make up the <a href="../records/5814071">Newport Hydrographic Line time series</a> located off the central Oregon coast thus allowing for a direct comparison between the two regions.</p> <table> <tbody> <tr> <td><strong>Station Name </strong></td> <td><strong>Latitude</strong></td> <td><strong>Longitude</strong></td> <td><strong>Water Depth (m, MLLW)</strong></td> </tr> <tr> <td><strong>Makah Bay (MB)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>MB015</td> <td>48.3254oN</td> <td>124.6768oW</td> <td>15</td> </tr> <tr> <td>MB042</td> <td>48.3240oN</td> <td>124.7354oW</td> <td>42</td> </tr> <tr> <td><strong>Cape Alava (CA)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CA015</td> <td>48.1663oN</td> <td>124.7568oW</td> <td>15</td> </tr> <tr> <td>CA042</td> <td>48.1660oN</td> <td>124.8234oW</td> <td>42</td> </tr> <tr> <td>CA065 </td> <td>48.1659oN</td> <td>124.8949oW</td> <td>65</td> </tr> <tr> <td><strong>Teahwhit Head (TH)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>TH015</td> <td>47.8761oN</td> <td>124.6195oW</td> <td>15</td> </tr> <tr> <td>TH042</td> <td>47.8762oN</td> <td>124.7334oW</td> <td>42</td> </tr> <tr> <td>TH065 </td> <td>47.8767oN</td> <td>124.7967oW</td> <td>65</td> </tr> <tr> <td><strong>Kalaloch (KL)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>KL015</td> <td>47.6008oN</td> <td>124.4284oW</td> <td>15</td> </tr> <tr> <td>KL027</td> <td>47.5946oN</td> <td>124.4971oW</td> <td>27</td> </tr> <tr> <td>KL050 </td> <td>47.5933oN</td> <td>124.6112oW</td> <td>50</td> </tr> <tr> <td><strong>Cape Elizabeth (CE)</strong></td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>CE015</td> <td>47.3568oN</td> <td>124.3481oW</td> <td>15</td> </tr> <tr> <td>CE042</td> <td>47.3531oN</td> <td>124.4887oW</td> <td>42</td> </tr> <tr> <td>CE065 </td> <td> 47.3528oN</td> <td>124.5669oW</td> <td>65</td> </tr> </tbody> </table>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula
<p>The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).<br> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”.</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast
<p>The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Bay of Biscay
<p>The ensemble provides future projections of key marine variables under climate change for the Bay of Biscay region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the North Sea
<p>The ensemble provides future projections of key marine variables under climate change for the North Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p> </p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Mediterranean Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Mediterranean region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p> <br>Analogue datasets are provided in separate zenodo entries for the regions of the North Sea, the Baltic Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p> <p> </p>
An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Baltic Sea
<p>The ensemble provides future projections of key marine variables under climate change for the Baltci Sea region. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).</p> <p>The following Earth System Models were used in building the ensemble:</p> <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul> <p>A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>.</p> <p>Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Bay of Biscay, the Chilean coast and the area around the Yucatán Peninsula, see “Related identifiers”.</p> <p>We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>, <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>.</p> <p><br><strong>This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</strong></p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Automotive Environment
<p>This dataset contains the files corresponding to which results have been included in the journal paper. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the University of Birmingham using distributed radar sensors installed on the mobile laboratory. The data will be used to develop algorithms to extract the information needed for high-resolution multi-modal and multi-perspective sensing.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars.</p> <p>This report describes the measurement scenarios and data structure of INRAS Radarlog (76 GHz – 81 GHz) used for the data collection campaign.</p> <p>Contact: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com, or m.s.gashinova@bham.ac.uk</p>
Dataset for 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea'
<p>This dataset complements the paper 'Meteorological Conditions Influence the Migration of a Marine Dune Field in the Southern North Sea' accepted <span>for publication in Journal of Geophysical Research - Earth Surface</span>.</p> <p> </p> <p>This dataset contains Digital Terrain Maps (DTMs) of specific areas offshore from Dunkirk, on the northern coast of France, opening to the Southern Bight of the North Sea. These areas host marine dunes (sand waves) that have been numerically investigated in this research to identify the parameters influencing their migration. The openTELEMAC system (version v8p4) can be downloaded from <a href="https://opentelemac.org/">https://opentelemac.org/</a>. The model development, calibration and validation is described in Durand (2024).</p> <p> </p> <p>The site-specific data collected by France Energies Marines (2021) are currently proprietary. To protect these data, DTMs of bathymetric changes are provided, calculated as the difference in metres between the final and initial seabed levels. Negative values indicate lowering of the seabed (erosion) and positive values indicate rising (accretion).</p> <p>The initial and final periods are:</p> <ul> <li>S1: 17-Nov-2019</li> <li>S2: 17-Mar-2020</li> <li>S5: 5-Dec-2020</li> </ul> <p>The DTMs are provided for two areas (refer to paper for locations):</p> <ul> <li>Tile #1</li> <li>Tile #3</li> </ul> <p> </p> <p>Included in the dataset are observations, Case I model output (without wind and atmospheric pressure), and Case II model output (with wind and atmospheric pressure).</p>
Identifying South African Marine Protected Areas at risk from marine heatwaves and cold spells
<p>This data reflects information on marine heatwaves (MHWs) and marine cold spells (MCSs) that occurred along the South African coast from January 1982 to April 2022, with special focus on Marine Protected Areas. Thermal metrics for MHW and MCS events were obtained using the HeatwaveR package (Schlegel and Smit, 2018) and the associated Marine Heatwave Tracker (Schlegel, 2020). </p> <p> </p> <p>THis data stems from Courtailac et al (in review) Indentifying South AFrican Marine Protected Areas at risk of marine heatwaves and cold-spells </p>
Coastal and Marine Ecological Classification Standard (CMECS) Catalog
<p>The <strong>Coastal and Marine Ecological Classification Standard (CMECS) Catalog</strong> is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).</p> <p>The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, <a href="https://www.go-fair.org/fair-principles/" rel="nofollow">https://www.go-fair.org/fair-principles/</a>). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via <a href="https://github.com/NOAA-OCM/cmecs" target="_blank" rel="noopener">the NOAA-OCM/cmecs GitHub</a> as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the <a href="https://github.com/NOAA-OCM/cmecs/wiki/CMECS-Thesaurus-Quick-Link"><strong>CMECS Thesaurus</strong></a>, is also available in PDF and MD formats.</p> <p>This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the <a href="https://github.com/NOAA-OCM/cmecs/releases/tag/v1.1.1" target="_blank" rel="noopener">CMECS GitHub v1.1.1 Release Page.</a></p> <p><strong>Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov</strong></p> <p>For more information about the CMECS Catalog, see the <a href="https://github.com/NOAA-OCM/cmecs/wiki">https://github.com/NOAA-OCM/cmecs/wiki.</a></p> <p>For more information about CMECS, including technical guidance and classification examples, visit the <a href="https://iocm.noaa.gov/standards/cmecs-home.html" rel="nofollow">NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage</a>.</p> <p>CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the <a href="https://www.ncei.noaa.gov/products/coastal-marine-ecological-classification-standard" rel="nofollow">NOAA National Centers for Environmental Information (NCEI) CMECS webpage</a> under the <strong>Vocabulary Maintenance</strong> section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.</p>
Dataset for marine vessel detection from Sentinel 2 images in the Finnish coast
<p>This dataset contains annotated marine vessels from 15 different Sentinel-2 product, used for training object detection models for marine vessel detection. The vessels are annotated as bounding boxes, covering also some amount of the wake, if present.</p> <h2>Source data</h2> <div> <div>Individual products used to generate annotations are shown in the following table:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> </tr> </tbody> </table> </div> <div> <div> </div> <div>Even though the reference data IDs are for L1C products, L2A products from the same acquisition dates can be used along with the annotations. However, Sen2Cor has been known to produce incorrect reflectance values for water bodies.</div> <div> </div> <div>The corresponding L2A product identifiers are:</div> </div> <div> </div> <div> <table style="width: 58.034%; height: 411.47px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"><strong>Location</strong></td> <td style="width: 79.3617%; height: 19.5938px;"><strong>Product name</strong></td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Archipelago sea</td> <td style="width: 79.3617%; height: 39.1875px;">S2A_MSIL2A_20220515T100031_N0400_R122_T34VEM_20220515T141508.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEM_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T34VEM_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEM_20240717T165127.SAFE</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.0296%; height: 39.1875px;">Gulf of Finland</td> <td style="width: 79.3617%; height: 39.1875px;">S2B_MSIL2A_20220606T095029_N0510_R079_T35VLG_20240619T162121.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220626T095039_N0510_R079_T35VLG_20240620T063951.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220703T094039_N0510_R036_T35VLG_20240702T130032.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220721T095041_N0510_R079_T35VLG_20240713T035445.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Bay</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220627T100611_N0510_R022_T34WFT_20240628T095704.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34WFT_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220828T095549_N0510_R122_T34WFT_20240708T091048.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Bothnian Sea</td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20210714T100029_N0500_R122_T34VEN_20230224T182455.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220619T100029_N0510_R122_T34VEN_20240628T011619.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220624T100041_N0510_R122_T34VEN_20240714T162313.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220813T095601_N0510_R122_T34VEN_20240717T165127.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;">Kvarken</td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220617T100611_N0510_R022_T34VER_20240627T130404.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2B_MSIL2A_20220712T100559_N0510_R022_T34VER_20240718T063657.SAFE</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.0296%; height: 19.5938px;"> </td> <td style="width: 79.3617%; height: 19.5938px;">S2A_MSIL2A_20220826T100611_N0510_R022_T34VER_20240705T120522.SAFE</td> </tr> </tbody> </table> </div> <div><br> <div>The raw products can be acquired from <a href="https://dataspace.copernicus.eu" target="_blank" rel="noopener">Copernicus Data Space Ecosystem.</a> The products listed above can be unavailable due to e.g. processing level updates and old versions being deleted. In those cases, try searching with the tile identifier and acquisition date in order to get the correct product ID.</div> <br> <h2>Annotations</h2> <br> <div>The annotations are bounding boxes drawn around marine vessels so that some amount of their wakes, if present, are also contained within the boxes. The data are distributed as geopackage files, so that one geopackage corresponds to a single Sentinel-2 tile, and each package has separate layers for individual products as shown below:</div> <br> <blockquote> <div>T34VEM</div> <div>|-20220515</div> <div>|-20220619</div> <div>|-20220721</div> <div>|-20220813</div> </blockquote> <br> <div>All layers have a column <strong>id</strong>, which has the value <strong>b</strong><strong>oat</strong> for all annotations.</div> <br> <div>CRS is EPSG:32634 for all products except for the Gulf of Finland (35VLG), which is in EPSG:32635. This is done in order to have the bounding boxes to be aligned with the pixels in the imagery.</div> <br> <div>As tiles 34VEM and 34VEN have an overlap of 9.5x100 km, 34VEN is not annotated from the overlapping part to prevent data leakage between splits.</div> <br> <h3>Annotation process</h3> The minimum size for an object to be considered as a potential marine vessel was set to 2x2 pixels. Three separate acquisitions for each location were used to detect smallest objects, so that if an object was located at the same place in all images, then it was left unannotated. The data were annotated by two experts. <div> </div> <table style="width: 63.327%; height: 391.876px;"> <tbody> <tr style="height: 39.1875px;"> <td style="width: 72.7285%; height: 39.1875px;"><strong>Product name</strong></td> <td style="width: 23.0224%; height: 39.1875px;"><strong>Number of annotations</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220515T100031_N0510_R122_T34VEM_20240617T162344.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">183</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEM_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">519</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T34VEM_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1518</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEM_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1371</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220606T095029_N0510_R079_T35VLG_20240619T111429.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">277</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220626T095039_N0510_R079_T35VLG_20240620T013500.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">1205</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220703T094039_N0510_R036_T35VLG_20240702T075354.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">746</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220721T095041_N0510_R079_T35VLG_20240712T224506.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">971</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220627T100611_N0510_R022_T34WFT_20240628T041908.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">122</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34WFT_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">162</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220828T095549_N0510_R122_T34WFT_20240708T035231.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">98</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20210714T100029_N0500_R122_T34VEN_20230224T120043.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">450</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220619T100029_N0510_R122_T34VEN_20240627T204751.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">66</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220624T100041_N0510_R122_T34VEN_20240714T110124.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">424</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220813T095601_N0510_R122_T34VEN_20240717T115958.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">399</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2A_MSIL1C_20220617T100611_N0510_R022_T34VER_20240627T094433.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">83</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;"> <div> <div>S2B_MSIL1C_20220712T100559_N0510_R022_T34VER_20240718T033027.SAFE</div> </div> </td> <td style="width: 23.0224%; height: 19.5938px;">184</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 72.7285%; height: 19.5938px;">S2A_MSIL1C_20220826T100611_N0510_R022_T34VER_20240705T062429.SAFE</td> <td style="width: 23.0224%; height: 19.5938px;">88</td> </tr> </tbody> </table> <br><br> <h3>Annotation statistics</h3> <br>Sentinel-2 images have spatial resolution of 10 m, so below statistics can be converted to pixel sizes by dividing them by 10 (diameter) or 100 (area).</div> <div> <table> <tbody> <tr> <td> </td> <td><strong>mean</strong></td> <td><strong>min</strong></td> <td><strong>25%</strong></td> <td><strong>50%</strong></td> <td><strong>75%</strong></td> <td><strong>max</strong></td> </tr> <tr> <td><strong>Area (m²)</strong></td> <td>5305.7</td> <td>567.9</td> <td>1629.9</td> <td>2328.2</td> <td>5176.3</td> <td>414795.7</td> </tr> <tr> <td><strong>Diameter (m)</strong></td> <td>92.5</td> <td>33.9</td> <td>57.9</td> <td>69.4</td> <td>108.3</td> <td>913.9</td> </tr> </tbody> </table> <br><br> <div>As most of the annotations cover also most of the wake of the marine vessel, the bounding boxes are significantly larger than a typical boat. There are a few annotations larger than 100 000 m², which are either cruise or cargo ships that are travelling along ordinal directions instead of cardinal directions, instead of e.g. smaller leisure boats.</div> <br> <div>Annotations typically have diameter less than 100 meters, and the largest diameters correspond to similar instances than the largest bounding box areas.</div> <br> <h3>Train-test-split</h3> <br> <div>We used tiles 34VEN and 34VER as the test dataset. For validation, we split the other three tile areas into 5x5 equal sized grid, and used 20 % of the area (i.e 5 cells) for the validation. The same split also makes it possible to do cross-validation.</div> <div> </div> <div> </div> <div> </div> </div> <div> <h3>Post-processing</h3> </div> <div><br> <div>Before evaluating, the predictions for the test set are cleaned using the following steps:</div> <br> <div>1. All prediction whose centroid points are not located on water are discarded. The water mask used contains layers `jarvi` (Lakes), `meri` (Sea) and `virtavesialue` (Rivers as polygon geometry) from the Topographical database by the National Land Survey of Finland. Unfortunately this also discards all points not within the Finnish borders.</div> <div>2. All predictions whose centroid points are located on water rock areas are discarded. The mask is the layer `vesikivikko` (Water rock areas) from the Topographical database.</div> <div>3. All predictions that contain an above water rock within the bounding box are discarded. The mask contains classes `38511`, `38512`, `38513` from the layer `vesikivi` in the Topographical database.</div> <div>4. All predictions that contain a lighthouse or a sector light within the bounding box are discarded. Lighthouses and sector lights come from Väylävirasto data, `ty_njr` class ids are 1, 2, 3, 4, 5, 8</div> <div>5. All predictions that are wind turbines, found in Topographical database layer `tuulivoimalat`</div> <div>6. All predictions that are obviously too large are discarded. The prediction is defined to be "too large" if either of its edges is longer than 750 meters.</div> </div> <div> </div> <div>Model checkpoint for the best performing model is available on Hugging Face platform: <a href="https://huggingface.co/mayrajeo/marine-vessel-detection-yolov8">https://huggingface.co/mayrajeo/marine-vessel-detection-yolo</a><br> <h2>Usage</h2> The simplest way to chip the rasters into suitable format and convert the data to COCO or YOLO formats is to use <a href="https://github.com/mayrajeo/geo2ml">geo2ml</a>. First download the raw mosaics and convert them into GeoTiff files and then use the following to generate the datasets. <div> </div> To generate COCO format dataset run</div> <div> </div> <div> <pre><code>from geo2ml.scripts.data import create_coco_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_coco_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, dataset_name='<name_of_dataset>', gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> <div><br> <div>To generate YOLO format dataset run</div> <div> <pre><code>from geo2ml.scripts.data import create_yolo_dataset raster_path = '<path_to_raster>' outpath = '<path_to_save_the_dataset>' poly_path = '<path_to_gpkg>' layer = '<date_of_raster>' create_yolo_dataset(raster_path=raster_path, polygon_path=poly_path, target_column='id', gpkg_layer=layer, outpath=outpath, save_grid=False, gridsize_x=320, gridsize_y=320, ann_format='box', min_bbox_area=0)</code></pre> </div> </div>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Automatic time step adjustment for shortening the runtime of the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. More specifically, steady annual cycles mostly are used to assess and validate the models against<br> observational data and to identify relevant biogeochemical processes. Offline simulations based on the transport matrix method already reduce the high computational effort significantly. Furthermore, they facilitate the application<br> of larger time steps in a simple way. In this paper, we present two different methods that automatically adjust the time step during the simulation of a steady state using transport matrices. The algorithms use either an adaptive<br> step size control or decreasing time steps. Their aim is to apply always the time step as large as possible but without any manual selection. We applied the methods for a variety of ecosystem models of different complexity, using Latin<br> hypercube samples of size 100 for the model parameters of each model. We showed that both methods computed an approximation of the steady annual cycle that was of the same accuracy as solutions obtained with a fixed time step. Both algorithms lowered the runtime of the steady annual cycle computation significantly. The performance gain depended on the complexity of the models. Moreover, the adaptive method has a certain overhead that might lead to higher computational cost in special cases.</p> <p><strong>Content:</strong></p> <ul> <li>Tracer concentrations of a reference solution for all parameter vectors and biogeochemical models</li> <li>SQLite database including the results using the decreasing time steps algorithm</li> <li>Tracer concentrations of the results using the decreasing time steps algorithm</li> <li>SQLite database including the results using the step size control algorithm</li> <li>Tracer concentrations of the results using the step size control algorithm</li> </ul>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.