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FIGURE. Distribution and heat map of Carex brizoides L. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex brizoides L. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex praecox Schreb. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex praecox Schreb. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex arenaria L. in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex arenaria L. in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex curvata Knaf in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex curvata Knaf in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex repens Bellardi in Poland. Black dots—records before 2000, white dots—records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex repens Bellardi in Poland. Black dots—records before 2000, white dots—records since 2000.
FIGURE. Distribution and heat map of Carex pseudobrizoides Clavaud in Poland. Black dots—records before 2000, white dots— records since 2000. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Distribution and heat map of Carex pseudobrizoides Clavaud in Poland. Black dots—records before 2000, white dots— records since 2000.
Recordings of Artemis I LunaH-Map with the Allen Telescope Array on 2022-11-16
<p>This dataset contains IQ recordings of the X-band 8451.7 MHz MHz telemetry signal from the Artemis I LunaH-Map cubesat. The recording was done on 2022-11-16, about seven hours after launch. Antennas 1a and 5c from the Allen Telescope Array were used in dual XY (linear) polarization.</p> <p>The hardware configuration was as follows: the antennas were connected to RFCB LO d, which was tuned to a frequency of 8451.5 MHz and used an output IF of 512 MHz. A USRP N321 and a USRP N320 were connected to the IF output of the RFCB. The USRP N321 digitized both polarizations of antenna 1a, while the USRP N320 digitized both polarizations of antenna 5c. The USRPs used an external 10 MHz reference and PPS coming from the observatory distribution system, and LO sharing was set up among all the channels. The IQ sample rate of the USRPs was 1.92 Msps, and a GNU Radio flowgraph was used to record IQ data to disk. The GNU Radio flowgraph stored the IQ data for each channel as 16-bin integers in the <a href="https://wiki.gnuradio.org/index.php/Metadata_Information">GNU Radio metatata file format</a>, with detached headers.</p> <p>After recording, the data was converted to <a href="https://github.com/gnuradio/SigMF">SigMF</a> format. The detached headers are also provided in the dataset (they have the .hdr extension).</p> <p>There are two recordings in this dataset. In the first one, LunaH-Map is transmitting a PCM/PM/bi-phase-L signal at approximately 48 kbaud. In the second one, LunaH-Map is transmitting a PCM/PM/NRZ signal at approximately 255 kbaud.</p> <p>Since the second recording doesn't have good enough SNR for error-free decoding with a single antenna, the two antennas were beamformed by determining the correct complex amplitude, delay and frequency offsets for the two antennas, and by correcting these parameters before adding the signals. The beamformed signal is provided in the HMAP_2022-11-16T15:16:06_beamform SigMF dataset. In this dataset, the signal polarization has been synthesized by finding the correct amplitude and phase offset between the X and Y channels of each antenna, and adding coherently both channels before beamforming.</p>
Extraction Forms about Challenges and Problems of Software Processes in the Context of Global Software Development: A Systematic Mapping Study
<p>Extraction Forms of the Systematic Mapping Study about Challenges and Problems of Software Processes in the Context of Global Software Development. 50 papers were analized.</p>
Data for: Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction (Part 2/2)
<p>Magnetic Resonance Imaging measurement data used in our paper about "Free-Breathing Myocardial T1 Mapping using Inversion-Recovery Radial FLASH and Motion-Resolved Model-Based Reconstruction". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>)</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Optical genome mapping reveals and characterizes recurrent aberrations and new fusion genes in adult ALL
<p>Optical genome mapping results files on single patient level:</p> <p>SV (Structural Variants)</p> <p>CNV (Copy number Variants)</p> <p>Aneuploidies </p> <p>OGM raw data sets a are available from the corresponding author upon request.</p>
ECFAS Mapping products, Deliverable 5.5 – Mapping products – ECFAS Project (GA 101004211), www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p><p><i><strong>Description of the product</strong></i></p><p>The deliverable includes a document and a compressed directory with ready-to-print maps, the associated geospatial datasets and symbology files. All products were developed under Task 5.5 – Mapping products.<br>The document reports on the activities conducted during Task 5.5 – Mapping products that aimed at demonstrating the added value of the ECFAS products and propose adequate representation for communication and integration into the Copernicus Emergency Management Services. Five Demonstration cases, corresponding each to one recent coastal event and one location, were selected to highlight the ECFAS products resulting of the coastal Flood, Impacts and Shoreline position tools developed previously during the ECFAS activities. The Mapping products prepared for each demonstration case are provided along the document in cartographic formats (ready-to-print maps), along with the symbology files and geospatial datasets that were used to create the products.</p><p>This <strong>dataset</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of this dataset are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p><p>This <strong>Report</strong> is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p><p> </p><p> </p>
Shorelines dataset. Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset, ECFAS project (GA 101004211), www.ecfas.eu.
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><em><strong>Palomar-Vázquez, J.; Pardo-Pascual, J.E.; Almonacid-Caballer, J.; Cabezas-Rabadán, C. Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy. Remote Sens. 2023, 15, 3198. <a href="https://doi.org/10.3390/rs15123198">https://doi.org/10.3390/rs15123198</a></strong></em></p> <p><em><strong>J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, 104426, ISSN 0378-3839, <a href="https://doi.org/10.1016/j.coastaleng.2023.104426">https://doi.org/10.1016/j.coastaleng.2023.104426</a>.</strong></em></p> <p><em>Pardo-Pascual, J. E., Cabezas-Rabadán, C., Palomar-Vázquez, J., Fernández-Sarría, A., Almonacid-Caballer, J., Souto-Ceccon, P. E., Montes, J., Armaroli, C., and Ciavola, P.: Satellite-derived shorelines extracted using SAET for characterizing the effect of Storm Gloria in the Ebro Delta (W Mediterranean), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9856, <a href="https://doi.org/10.5194/egusphere-egu22-9856">https://doi.org/10.5194/egusphere-egu22-9856</a>, 2022.</em></p> <p>In relation to <strong>SAET</strong>, additional information, instructions and the <strong>open-source code</strong> are available here <a href="https://doi.org/10.5281/zenodo.5807710"><strong>https://zenodo.org/records/10256957</strong></a> and also in <strong>GitHub </strong>here<strong> <a href="https://github.com/jpalomav/SAET_master">https://github.com/jpalomav/SAET_master</a></strong></p> <p> </p> <p><strong>Description of the containing files inside the Dataset</strong></p> <p>The deliverable includes two different files: the dataset of shorelines and the accompanying report.</p> <p>The report describes the structure of the dataset of shorelines produced in Task 3.2 - Shoreline mapping validation and calibration and described in Deliverable 3.2 - Algorithms for satellite derived shoreline mapping and shorelines dataset (DOI: 10.5281/zenodo.5807711).</p> <p>The dataset is composed of three folders.</p> <ol> <li>The first folder ("SDS_vs_VHR_shoreline") contains the SDSs extracted at each test site by the different algorithms tested in ECFAS (SHOREX, Cabezas-Rabadán et al., 2021; Sánchez-García et al., 2020; CoastSat, Vos et al., 2019a,b and SAET, Palomar-Vázquez et al., 2021; Pardo-Pascual et al., 2021) to assess their performance through the comparison with shorelines photo-interpreted on coincident VHR satellite images. The SDSs obtained at all the test sites using SAET and CoastSAT are included, as well the SDSs obtained using SHOREX at the Spanish test sites. The photo-interpreted shorelines are also provided. For all the test sites it is provided the line separating the instantaneous shoreline (water/land boundary), obtained by photo-interpretation. For the sites in the Netherlands, also the wet/dry line is provided.</li> <li>The second folder ("SDS_vs_video-monitored_shorelines") presents for each of the three test sites the SDSs obtained using the different algorithms and video-derived shorelines. The SDSs at the beaches of la Victoria and Cala Millor were obtained using SAET, SHOREX and CoastSAT. In the case of Gerakas Beach, the shorelines were obtained using SAET and CoastSAT.</li> <li>The third folder ("SDS_SAET_Storm_cases") includes eight examples in different European coasts in which the SDSs before and after a storm have been obtained.</li> </ol> <p>This <strong>ECFAS dataset of shorelines</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the ECFAS dataset of shorelines are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>Report</strong> is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><em><strong>Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</strong></em></p> <p><strong>Description of the Dataset</strong></p> <p>The present database gathers flood and velocity maps for the European Union coast as well as their associated forcing parameters. The coast is divided into geographic regions embracing similar oceanographic conditions and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. For each coastal sector, flood models were developed using the LISFLOOD-FP model with a grid resolution of 100 m. The flood model configuration follows the recommendation highlighted in ECFAS Deliverable D5.2 - Validated LISFLOOD-FP model for coastal areas. The flood and velocity maps are associated with synthetic storms that are characterised by a specific extreme water level and storm duration. These parameters were derived from Extreme Value Analyses performed on the ECFAS ANYEU-SSL hindcast (ECFAS D4.1 - Report on the calibration and validation of hindcasts and forecasts of TWL and D4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding). Five extreme water level values for each coastal point of the hindcast, and three durations (12, 24 and 36 h) were identified leading to 15 scenarios for each coastal sector. The flood and velocity maps are gathered into a NetCDF file for each coastal sector indicating the scenario parameters as attributes. In addition, the extreme water level values used for each coastal sector are contained in a complementary NetCDF file.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>- The ECFAS Flood Catalogue was used to produce the associated ECFAS Pan-EU Impact Catalogue:</strong></p> <p><strong>Impact Catalogue in Zenodo</strong>: Duo, E., Montes Pérez, J., Le Gal, M., Souto Ceccon, P.E., Cabrita, P., Fernández Montblanc, T., and Ciavola, P., 2022. ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778864">https://doi.org/10.5281/zenodo.677865</a></p> <p><em><strong>Impact Catalogue Reference literature</strong>: Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></p> <p> </p> <p>The Flood Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Flood Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Flood Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 124 GB.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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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.