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6,766 results for “project”
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>
Acer pseudoplatanus pot experiment measuring data of single leaves in LandKlif project
<p><span>We cultivated 168 saplings of Acer pseudoplatanus of four Bavarian provenances under varying shading (two treatments: sun-exposed and shaded) and watering (three treatments: regular watering / drought period in summer / drought periods in spring and summer). The experiment took place in a half-open greenhouse between March and August 2021. For each sapling, we measured the increase in height and stem diameter, as well as specific leaf area (SLA) and leaf dry matter content (LDMC) of three leaves. This dataset contains measuring data of fresh/dry weight and area of each single leaf.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Acer pseudoplatanus pot experiment measuring data of saplings in LandKlif project
<p><span>In LandKlif project we cultivated 168 saplings of Acer pseudoplatanus of four Bavarian provenances under varying shading (two treatments: sun-exposed and shaded) and watering (three treatments: regular watering / drought period in summer / drought periods in spring and summer). The experiment took place in a half-open greenhouse between March and August 2021. For each sapling, we measured the increase in height and stem diameter, as well as specific leaf area (SLA) and leaf dry matter content (LDMC) of three leaves.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Temperature data on forest plots recorded with hourly measurements in LandKlif Project
<p><span>Temperature data on forest plots in LandKlif project recorded with hourly measurements (EasyLOG USB, measured accurate to 0.5°C). Thermologgers were attached to the wildlife cameras within the forest. Due to battery leakage, corrosion, and programming errors, only 39 out of 56 loggers collected temperature data.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079
<div><strong>Summary:</strong></div> <div>One polymer-modified bitumen PMB 25/55-60 was tested in two stages: original and after the modified Thin-Film Oven Test (TFOT). The time ranges from 1h-5h, and temperatures from 120°C-200°C were used. The Fourier-Transform Infrared (FTIR) Spectroscopy and Dynamic Shear Rheometer (DSR) with parallel plates were conducted. Test temperatures range from 30–70°C for a 25 mm diameter plate and 0–30°C for an 8 mm plate with 10°C intervals and angular frequency range of 0.1, 1.0, and 10 Hz.</div> <div> </div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Basic characteristics of bituminous binder (R&B Temperatur, Penetration), CSV raw data:</div> <div> <ul> <li>01 - SP Pen.csv</li> </ul> </div> <div> </div> <div>Dynamic shear rheometer (Temperatures 0-70 °C, Angular Frequency 0.1Hz, 1.0Hz, 10 Hz, Complex Shear Modulus, Phase Angle):</div> <ul> <li>02.1 - DSR Rheology_Unaged.csv</li> <li>02.2 - DSR Rheology_2h_140C.csv</li> <li>02.3 - DSR Rheology_2h_200C.csv</li> <li>02.4 - DSR Rheology_5h_140C.csv</li> <li>02.5 - DSR Rheology_5h_200C.csv</li> </ul> <div> </div> <div>FTIR - Fourier-Transform Infrared Spectroscopy </div> <div> <ul> <li>OPUS Spectroscopy files.zip</li> </ul> </div> <div> </div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div>to open the OPUS files, please go to the © Bruker webpage and download the free OPUS Viewer.</div> <div>https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/downloads.html</div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div> </div>
Example dataset for a SpaDES compatible moduels' project
<p>This mock data is just an example to be used with an integrated SpaDES-compatible project available at https://github.com/tati-micheletti/EFI_webinar/tree/main. This current version has improved the datasets to make the analysis more interesting.</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
European cities with Geothermal District Heating and conventional District Heating - GeoDH project
<p>The dataset includes two shapefiles showing the location data for cities across Europe that use Geothermal District Heating and conventional District Heating. <br><br>This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the <strong>GeoDH project</strong> (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the<strong> state of the art as of 2014</strong> and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes. <br><br></p>
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>
Survey answers to identify barriers and enablers to climate change adaptation solutions (as part of the Adaptation AGORA project)
<p><span>This dataset s the result of collaborative work for Deliverable 4.1 (WP4; T4.1) of the Adaptation AGORA project. This survey aimed to capture the key factors supporting or hindering adaptation practitioners experienced with engaging citizens and stakeholders in climate change adaptation initiatives. </span></p> <p><span>The survey targeted <span>European adaptation practitioners, i.e., all professionals in charge of implementing climate change adaptation initiatives, and more particularly, those involved in collaborative processes engaging stakeholders and citizens </span><span>at the local and/or regional scale.</span></span></p> <p><span><span>The survev protocol can be found here: Euro-Mediterranean Center for Climate Change, University of Geneva, Stockholm Environment Institute, Barcelona Supercomputing Center, & Agenzia per la Promozione della Ricerca Europea. (2024). Protocol to carry out surveys to identify barriers and enablers to climate change adaptation solutions. Zenodo. <a href="https://doi.org/10.5281/zenodo.13385305" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13385305</a></span></span></p>
Meteorological Data from Chios: May 2024 Baseline Measurements for the MUSICA Project
<h2><strong>May 2024 – Chios (Chiostown)</strong></h2> <h3>Introduction</h3> <p>The present meteorological data is collected from the weather station in Chiostown, located in Chios, and is published on the Zenodo platform for open access. The station is positioned at an elevation of 32 meters, and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from May 1st to May 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data for May 2024 captures the transition from spring to early summer, offering insights into the warming trend and dry conditions typical for the region during this period.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for May 2024</h3> <ul> <li><strong>Highest temperature</strong>: 28.8°C, recorded on May 19th, 2024, at 18:20.</li> <li><strong>Lowest temperature</strong>: 12.3°C, recorded on May 15th, 2024, at 05:00.</li> <li><strong>Total rainfall</strong>: 1.2 mm, with the highest daily rainfall of 1.19 mm recorded on May 11th, 2024.</li> <li><strong>Highest wind speed</strong>: 56.3 km/h, recorded on May 27th, 2024, at 10:40.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena</p>
Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project
SV analysis of the long-read sequencing data of 1,019 samples from the 1000 Genomes Project. The data is hosted at the International Genome Sample Resource (IGSR) in the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/">1KG_ONT_VIENNA</a> directory. Please see the <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA.md">README</a> and <a href="https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1KG_ONT_VIENNA/README_1KG_ONT_VIENNA_datareuse_statement.md">data reuse statement</a> for further information about this dataset.
Global leaf inclination angle (LIA) and nadir leaf projection function (G(0)) products
<p>Leaf inclination angle (LIA), the angle between leaf surface normal and zenith directions, is a vital parameter in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. In the radiative transfer regime, LIA is generally represented by the leaf projection function (G(θ)), which is defined as the average projection ratio of unit leaf area in the illumination or viewing direction θ.</p> <p>This dataset (CAS-GLA) includes the first global mean LIA (MLA) and nadir leaf projection function (G(0)) products at the spatial resolutions of 500 m and 0.05 degrees. The products are recorded in GeoTIFF format under the WGS-84 geographic coordinate system. The global 500 m MLA product was generated by gap-filling LIA measurement data using a random forest regressor after a series of preprocessing, such as spatial expansion, LIA upscaling, and sample screening. Cross-validation shows that the predicted MLA presents a medium consistency (<em>r</em> = 0.75, RMSE = 7.15°) with the validation samples. The 500 m MLA product was further upscaled to 0.05 degrees by weighting the MODIS 500 m leaf area index. The G(0) product was derived from the MLA by assuming a single-parameter ellipsoidal leaf angle distribution. The G(0) product agrees moderately with high-resolution reference data (<em>r </em>= 0.62, RMSE = 0.15). Note the global MLA and G(0) products mainly represent the typical state during the growing season from 2001 to 2022. The MLA and G(0) products would enhance our knowledge about global LIA and should greatly facilitate remote sensing retrieval and land surface modeling studies.</p> <p>In the version 1.1, two quality layers were added to 500 m MLA product to represent the quality of input data and the prediction model. The input data quality was denoted by the proportion of high-quality BRDF inversions for each pixel. The prediction model quality was represented qualitatively for each pixel considering whether the MLA was predicted by extrapolating beyond the range of the training samples. In the 500 m MLA product, the three bands sequentially store MLA, input data quality (scale factor = 0.01), and model quality (1: predictions within the range of samples; 0: predictions out of the range of samples).</p>
Wittgenstein Center (WIC) Population and Human Capital Projections - 2023
<p>THIS IS VERSION WIC3.004 (Beta) (<strong>internal version V14,</strong> <strong>the last three versions were called V13, V12 and V11). </strong>This is a release version corresponding to the <a href="https://data.ece.iiasa.ac.at/ssp/#/login?redirect=%2Fworkspaces">SSP-Database 3.0</a> (will be updated) release and <a href="https://dataexplorer.wittgensteincentre.org/wcde-v3/">WIC-Data Explorer 2023</a></p> <p>Short Abstract</p> <p>We update the population and human capital components of the Shared Socio-Economic Pathways (SSPs) at the global level, considering the most recent baseline information. While the long-term assumptions based on extensive analysis and expert solicitations remain unchanged, we modify only the trend component. The first set of SSPs was based on demographic data through 2012. The population structures by age, sex, and education of the base year (2010) have since changed for most countries, mainly due to more recent data and reliable information. The mortality situation has improved in many countries affected by HIV/AIDS and among children in countries with higher mortality. The impact of COVID-19 on demographic trends must be addressed. In many countries, fertility rates have fallen faster than expected. International migration has been irregular and volatile as usual. These changes are reflected in the new update, with some improvements in operationalization.</p> <p>Changes in version "V14":</p> <p> We found a bug affecting the education distribution sex (interchanged) in 37 countries (with UN country code): 32 Argentina, 44 Bahamas, 48 Bahrain, 64 Bhutan, 76 Brazil, 108 Burundi, 132 Cabo Verde, 156 China, 158 China, Taiwan Province of China, 170 Colombia, 214 Dominican Republic, 218 Ecuador, 226 Equatorial Guinea, 288 Ghana, 296 Kiribati, 360 Indonesia, 392 Japan, 400 Jordan, 410 Republic of Korea, 414 Kuwait, 496 Mongolia, 528 Netherlands, 558 Nicaragua, 583 Micronesia (Fed. States of), 591 Panama, 626 Timor-Leste, 630 Puerto Rico, 634 Qatar, 643 Russian Federation, 662 Saint Lucia, 702 Singapore, 764 Thailand, 784 United Arab Emirates, 788 Tunisia, 792 Turkey, 840 United States of America, 894 Zambia. The impact on the total population is minimal.</p>
IFC Submarine Interconnection Projects Data Model
<p>Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.</p>
Hesperomys Project v25.3.0
<p>An export of data from the <a href="https://hesperomys.com">Hesperomys Project.</a> The Hesperomys Project is a database of taxonomy and nomenclature, focused on mammals but also covering some other groups, principally other fossil tetrapods. The database contains information such as:</p> <ul> <li>Taxonomic classification for all mammals, living and extinct</li> <li>References to original citations for the vast majority of names</li> <li>Type specimens and type localities for numerous names</li> </ul> <p>The full database is available online at hesperomys.com. This export contains:</p> <ul> <li>name.csv: Data on names, including taxonomic context, authority, citation, type locality, type specimen, and classification of the etymology.</li> <li>taxon.csv: Data on taxa, including classification and authority</li> <li>collection.csv: Data on collections that contain type specimens, including name, location, and number of type specimens in the database</li> <li>ce.csv: Data on published classifications that have been imported into the database</li> </ul> <p>The code used to generate the exports is on <a href="https://github.com/JelleZijlstra/taxonomy/blob/9a68bec908264ddfcb1d623b4c427ebfb689396f/taxonomy/db/export.py">GitHub</a>.</p> <p>Some previous releases are in separate Zenodo records:</p> <ul> <li>23.2.0 (https://zenodo.org/records/7654755)</li> <li>23.3.0 (https://zenodo.org/records/7730954)</li> <li>23.6.0 (https://zenodo.org/records/8049254)</li> <li>23.8.0 (https://zenodo.org/records/8260038)</li> <li>23.8.1 (https://zenodo.org/records/8298623)</li> <li>24.1.0 (https://zenodo.org/records/10481656)</li> <li>24.4.0 (https://zenodo.org/records/10969300)</li> </ul> <p>Future releases will be published as new versions of this record. Release notes for each release are available <a href="http://hesperomys.com/docs/release-notes">on hesperomys.com.</a></p>
Data from Glacier Model Intercomparison Project Phase 3 (GlacierMIP3)
<p>This dataset presents the data from the third phase of <a href="https://climate-cryosphere.org/glaciermip/">GlacierMIP</a> (GlacierMIP3: Equilibration of glaciers under different climate states). It includes regional glacier volume and area projections as submitted by the glacier modelling groups. Additionally, it features post-processed and aggregated data derived from GlacierMIP3, or in combination with other studies, which is used for the analyses and visualisations presented in the following manuscript: </p> <p><em>Zekollari*, H., Schuster*, L., Maussion, F., Hock, R., Marzeion, B., Rounce, D. R., Compagno, L., Fujita, K., Huss, M., James, M., Kraaijenbrink, P. D. A., Lipscomb, W. H., Minallah, S., Oberrauch, M., Van Tricht, L., Champollion, N., Edwards, T., Farinotti, D., Immerzeel, W., Leguy, G., Sakai, A. (under review): Glacier preservation doubled by limiting warming to 1.5°C. Preprint available at <a href="https://doi.org/10.31223/X51T5W">https://doi.org/10.31223/X51T5W</a>, 2024.</em><br><em>*Harry Zekollari and Lilian Schuster contributed equally to this dataset and the manuscript above.<br><br></em>If you use the data, please cite this Zenodo dataset and the above study. <em><br></em><br>More info in <em>README_data.pdf</em>. For information about the GlacierMIP3 experimental design, please refer to the<em> GlacierMIP3_protocol.pdf</em>. The code used to generate the postprocessed data and to conduct the analyses for the manuscript mentioned above is available at <a href="https://github.com/GlacierMIP/GlacierMIP3]">https://github.com/GlacierMIP/GlacierMIP3</a>.<br><br></p> <p>To assist potential data users, we have included a jupyter notebook (gmip3_data_example_use_cases.ipynb) that guides you through some simple use cases. This notebook can be directly run when clicking on this <a href="https://drive.google.com/file/d/1xbhXZwT3sQydAGi8rSjEXRKdKhFosW6d/view?usp=sharing">link</a>. Please note that you will need to log in to your Google account and, if you haven't already done so, install Google Colaboratory. The data will then be automatically downloaded to your account.</p> <p>We may adapt the data structure and improve the documentation during the review phase. If you have any questions or suggestions, please contact us (lilian.schuster@uibk.ac.at, harry.zekollari@vub.be).</p> <p>----<br>difference version v2 to v1.0: only the files <em>README_data.pdf</em> and the <em>lowess*_regional_glacier_temp_ch.csv</em> were changed according to the resubmission of the manuscript</p>
Worldwide Soundscapes project metadata and analysis scripts
<p>The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated passive acoustic monitoring meta-datasets (i.e. meta-data collections). This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description. Additionally, R scripts are provided to replicate the analysis published in [placeholder].</p> <p>The overview of all sampling sites and timelines can be found on the corresponding project on <a href="https://ecosound-web.de/ecosound_web/collection/index/106">ecoSound-web</a>, as well as a <a href="https://ecosound-web.de/ecosound_web/collection/show/49">demonstration collection</a> containing selected recordings. The recordings of this collection were annotated and analysed to explore macro-ecological trends.</p> <p>The audio recording criteria justifying inclusion into the meta-database are:</p> <ul> <li>Stationary (no transects, towed sensors or microphones mounted on cars)</li> <li>Passive (unattended, no human disturbance by the recordist)</li> <li>Ambient (no directional microphone or triggered recordings, non-experimental conditions)</li> <li>Spatially and/or temporally replicated (i.e. multiple sites sampled at the same time and/or multiple days - covering the same daytime - sampled at the same site)</li> </ul> <p>The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database. The data shared here only includes validated collections.</p> <p><strong>Changes from version 4.0.0</strong></p> <p>Added link to the published synthesis.</p> <p><strong>Meta-database CSV files</strong></p> <p><strong>collections</strong></p> <ul> <li>collection_id: unique integer, primary key</li> <li>name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.</li> <li>ecoSound-web_link: link of validated meta-collection on ecoSound-web</li> <li>primary_contributors: full names of people deemed corresponding contributors who are responsible for the dataset</li> <li>secondary_contributors: full names of people who are not primary contributors but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses</li> <li>date_added: when the datased was added (YYYY-MM-DD)</li> <li>URL_open_recordings: internet link for openly-available recordings from this collection</li> <li>URL_project: internet link for further information about the corresponding project</li> <li>DOI_publication: Digital Object Identifiers of corresponding publications</li> <li>core_realm_IUCN: The main, core realm of the dataset according to IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/</li> <li>medium: the physical medium the microphone is situated in</li> <li>locality: optional free text about the locality</li> <li>contributor_comments: free-text field for comments by the primary contributors</li> </ul> <p><strong>collections-sites</strong></p> <ul> <li>dataset_ID: primary key of collections table</li> <li>site_ID: primary key of sites table</li> </ul> <p><strong>sites</strong></p> <ul> <li>site_ID: unique integer, primary key</li> <li>site_name: internal name or code of sampling site as used in respective projects</li> <li>latitude_numeric: site's numeric degrees of latitude</li> <li>longitude_numeric: site's numeric degrees of longitude</li> <li>blurred_coordinates: whether latitude and longitude coordinates are inaccurate, boolean. Coordinates may be blurred with random offsets, rounding, snapping, etc. Indicate the blurring method inside the comments field</li> <li>topography_m: vertical position of the microphone relative to the sea level. for sites on land: elevation. For marine sites: depth (negative). in meters. Only indicate if the values were measured by the collaborator.</li> <li>freshwater_depth_m: microphone depth, only used for sites inside freshwater bodies that also have an elevation value above the sea level</li> <li>realm: Ecosystem type: main realm according to IUCN GET https://global-ecosystems.org/</li> <li>biome: Ecosystem type: main biome according to IUCN GET https://global-ecosystems.org/</li> <li>functional_group: Ecosystem type: main functional group according to IUCN GET https://global-ecosystems.org/</li> <li>contributor_comments: free text field for contributor comments</li> <li>GADM_0: Global ADMinistrative Database level 0 classification of terrestrial site or marine site that is within territorial waters. Source: https://gadm.org/download_world.html</li> <li>IHO: International Hydrographic Organization classification of marine site. Source: https://marineregions.org/downloads.php</li> <li>WDPA: World Database on Protected Areas classification of the site. Source: https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</li> </ul> <p><strong>deployments</strong></p> <ul> <li>dataset_ID: primary key of datasets table</li> <li>deployment: identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.</li> <li>subset_site_ID: If the deployment was not done in all the sites of the corresponding collection, site IDs where the deployment was conducted</li> <li>start_date: date of deployment start</li> <li>start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>permanent: whether the deployment is permanent, boolean</li> <li>end_date: date of deployment end (date when last scheduled operation starts)</li> <li>end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.</li> <li>operation_mode: continuous: recording takes place from the deployment start date-time to deployment end date-time.<br>periodical: recording takes place periodically (i.e., with duty cycle) from the deployment start date-time to deployment end date-time.<br>scheduled: recording takes place during scheduled daily time intervals (optionally with duty cycle)</li> <li>duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". empty if no duty cycle is used. For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes</li> <li>operation_start_time_mixed: only for scheduled recordings: start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>operation_duration_minutes: only for scheduled recordings: duration of operation in minutes, if constant</li> <li>operation_end_time_mixed: only for scheduled recordings: end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Only required if durations are variable. Do not use when end times are ambiguous (for instance, if a recording could be 1 hour or 25 hours long because the end is on the next day). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")</li> <li>high_pass_filter_Hz: frequency of the high-pass filter of the recorder if applied, in Hz. Otherwise, write "none". This may be called a "low-cut" filter too.</li> <li>bit_depth: sampling bit depth of the recordings. Often constant for a particular recorder</li> <li>channels: number of recorded audio channels</li> <li>sampling_frequency_kHz: frequency at which the microphone signal was sampled by the recorder (sounds of half that frequency will be recorded)</li> <li>recorder: recorder used for deployment</li> <li>microphone: microphone used for deployment</li> <li>target_taxa: main IUCN animal taxa that were studied with this deployment, using the exact IUCN Red list names (http://www.iucnredlist.org/), separated by commas. Only genera, families, orders, and classes are accepted. Empty if there was no taxonomic focus (i.e., general soundscapes were the study focus).</li> <li>contributor_comments: free text field for contributor comments</li> <li>exact_recordings: whether the deployment data here have been superseded by inserting more exact recording date-time ranges into the meta-collection on ecoSound-web</li> </ul> <p><strong>recordings (partial download from <a href="https://ecosound-web.de/">ecoSound-web</a>)</strong></p> <ul> <li>recording_id: primary key of the recordings table</li> <li>collection_id: ID of the collection the recording belongs to</li> <li>name: name of the recording</li> <li>site_id: site ID the recording belongs to:</li> <li>recorder_id: ID of the recorder used for the recording (internal ecoSound-web code)</li> <li>microphone_id: ID of the microphone used for the recording (internal ecoSound-web code)</li> <li>recording_gain:recording gain applied for amplifying the audio signal, in decibels</li> <li>duty_cycle_recording: fraction of the recording periode when the recorder is actively recording audio</li> <li>duty_cycle_period: period of the duty cycle, i.e., time between the starts of two subsequent recordings</li> <li>note: comments (contains the target taxon)</li> <li>file_date: date of the recording start</li> <li>file_time: local time of the recording start</li> <li>sampling_rate: audio sampling rate in Hz</li> <li>bitdepth: depth in bits for each audio sample</li> <li>channel_num: number of channels</li> <li>duration: duration of the recording in seconds. Note: duty-cycled recordings cover only a proportion of this duration<strong><br></strong></li> </ul> <p><strong>affiliations</strong></p> <ul> <li>affiliation_id: primary key of affiliations table</li> <li>lab_research_group: Laboratory or research group name</li> <li>department_school_institute: department, school, or institute name</li> <li>university_institution: University or institution name</li> <li>street_address: street address</li> <li>region_state_province_city: region, state, province, or city name</li> <li>postal_code: postal code</li> <li>country: country name</li> </ul> <p><strong>primary_contributors</strong></p> <ul> <li>First_name: First, given name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>Last_name: Last, family name, anonymised when contributor is technically accepted but has not yet given publication authorisation</li> <li>ORCiD</li> <li>affiliation_IDs: primary keys of the affiliations' table corresponding affiliations, separated by comma</li> <li>first_tier_position: Author position in first-tier</li> <li>publication_agreement: Has contributor explicitly agreed to share her/his meta-data in the collaboration agreement?</li> <li>co_author_first_synthesis: Has contributor confirmed co-authorship intention in the collaboration agreement?</li> </ul> <p>The following columns describe the contributor's role in the project accordint to <a href="https://credit.niso.org/">CRediT</a> taxonomy.</p> <p><strong>Auxiliary files for reproducing analysis</strong></p> <p><strong>R scripts</strong></p> <ul> <li><strong>acoustic analysis.R: </strong>reproduces the result of the soundscape case studies</li> <li><strong>metadata analysis.R:</strong> reproduces the metadata analysis results in the publication</li> </ul> <p><strong>Data from the demonstration collection (download from ecoSound-web)</strong></p> <ul> <li><strong>demo_recordings.csv:</strong> metadata of the recordings, see recordings table</li> <li><strong>demo_sites.csv: </strong>metadata of the sampling locations, see sites table</li> <li><strong>demo_tags.csv: </strong>data describing annotations made in demonstration recordings for the biophony, anthropophony, geophony, and unknown sound sources</li> <li><strong>spectrograms.zip:</strong> contains PNG format spectrograms used in generating Figure 5</li> </ul> <p><strong>Externally sourced data</strong></p> <ul> <li><strong>GET_areas_2.1.1.csv: </strong>raw data obtained from Keith et al. 2023 (https://doi.org/10.5281/zenodo.10081251), then summarized in QGIS to obtain areas per functional group</li> <li><strong>Havlik_sites.csv:</strong> data obtained from Havlik et al. 2022 supplementary material (https://www.frontiersin.org/articles/10.3389/fmars.2022.919418), originally named "Data Sheet 1.CSV"</li> <li><strong>Sugai_sites_updated.csv:</strong> data obtained from Sugai et al. 2019 (https://doi.org/10.1093/biosci/biy147), personal communication with permission</li> <li><strong>taxonomy.csv:</strong> raw data obtained from IUCN Red List for all animal taxa (https://www.iucnredlist.org/)</li> <li><strong>topography_range_latitude.csv:</strong> raw topography from GEBCO sub-ice data (https://www.gebco.net/data_and_products/gridded_bathymetry_data/), summarised by bins of 10 latitudinal rows</li> </ul>
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