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Supplementary data for Development of Indicators of Social Vulnerability and Fishing Reliance in English Coastal Communities
<p>Supplementary data for Development of Indicators of Social Vulnerability and Fishing Reliance in English Coastal Communities by Tom Gibson, Chloe Lucas and Angela Muench. Full details are available via the publication and details of folder and file structures are available in the ReadMe file. </p>
Leaf fluctuating asymmetry is not a reliable indicator of stress
<p>Stress experienced during development in organisms with bilateral structures could result in developmental instability, which is expressed as subtle non-directional deviations from perfect symmetry, known as fluctuating asymmetry (FA). As such, FA has been proposed, and extensively used, as a trait indicating stress for many organisms with bilateral structures and many types of stress. However, while this concept may apply to animals, the evidence for plants' main vegetative structures, i.e., leaves, remains equivocal, and a comprehensive synthesis on this topic is still missing.</p> <p>We designed observational field and controlled greenhouse studies, combining different growth and leaf forms across multiple stress gradients, comprising 21 species and 80 populations. We measured FA as the difference between the left and right area of the leaf, an approach that accommodates diverse leaf forms. We used high-precision, blind, single-person measurements and tested for other forms of symmetry and the effect of leaf size. We further complemented our study with a systematic literature review of FA in plant leaves, compiling 51 studies comprising 72 species, 23 stress types, and 131 unique entries (species × stress type). </p> <p>We consistently found no effect of stress on leaf FA in any of the studied species in both our field and experimental gradients. In the systematic literature review, only 39% of the unique entries showed the expected increase in FA with stress, 53% showed no effect, 9% showed an opposite trend of a decrease in FA with stress, and 1% showed an unimodal relationship. Importantly, only 40% of all entries fulfilled the crucial step of controlling for a high-precision measurement, and of these 49% reported the expected increase of FA with stress. </p> <p>Both the results of our observational and experimental approaches and the systematic literature review failed to support a clear relationship between stress and FA in plant leaves. These results clearly show that FA in plant leaves cannot be used as a reliable trait indicating stress during development.</p>
Reddit's NetSec forum GitHub projects, typology, maturity and popularity indicators
<p>Dataset including information of GitHub projects shared on Reddit's forum /r/netsec. It includes information about the posts itselves, like the author, title, image, tags, number of upvotes and number of comments, as well as repository information, such as number of stars, forks, issues, collaborators and the repository URL and last modification date.</p>
SHOWCASE Task 2.7 Biodiversity Indicators and Predictors data set
<p>The dataset contains five biodiversity (0-1 interval normalised) indicators from five case study areas (HU, ES, PT, NL and CH): wild bees abundance (field "Ind_WBA"), wild bees species richness (field "Ind_WBR"), spiders abundance (field "Ind_SpA"), spider species richness (field "Ind_SpR"), and vascular plants species richnes (field "Ind_PlaR"). Data are georeffered (EPSG: 3035) and coordinates are provided (field:" xcoord_EPSG3035" and "ycoord_EPSG3035"). Data were collected in two sampling rounds (field "Round"). Additionally the data set contains a set<span> of predictors for biodiversity indicators encompassing 27 variables, belonging to four different groups: 1. </span><span><span><span> </span></span></span><span>Landscape elements: proximity to roads and proximity to Small Woody Features (SWF, Copernicus land Monitoring Services, CLMS 2018); 2.<span> </span></span><span>Terrain descriptors: elevation, aspect, slope, and their derivatives (8 variables); 3. </span><span>Spectral signatures and Remote Sensing Indicatora (RSI) from Copernicus Sentinel 2 (14 variables); and </span><span><span>4.<span> </span></span></span><span>Biodiversity management (3 variables, dummy coded 0,1). </span></p> <table> <tbody> <tr> <td> <p><strong><span>Group </span></strong></p> </td> <td> <p><strong><span>Predictor </span></strong></p> </td> <td> <p><strong><span>Unit</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> <p><strong><span>Source</span></strong></p> </td> <td> <p><strong><span>Resolution</span></strong></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Road proximity<sup>a</sup></span></p> </td> <td> <p><span>m </span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>SWF proximity<sup>b</sup></span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Elevation</span></p> </td> <td> <p><span>m a.s.l.</span></p> </td> <td> <p><span>Copernicus DEM</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Aspect</span></p> </td> <td> <p><span>degree from North</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Mod. Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Topographic. wetness Index </span></p> </td> <td> <p><span>m/rad</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Valley depth</span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>BI, bare Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Blue (B2, 490 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Green (B3, 560 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>IR, infra-red (B8, 842 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDBSI, Norm. Diff. Bare Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDSI, Normalized Diff. Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDVI, Norm. Diff. Vegetation Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NIR, Near Infra-Red (B8A, 865 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Red (B4, 665 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSa, Soil Salinity </span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI1, Soil Salinity Index1</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI2, Soil Salinity Index2</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI3, Soil Salinity Index3</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SWIR Short Wave IR (B11,1610 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Biodiversity Intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Year of intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Round </span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> </tbody> </table> <p><sup><span>a</span></sup><span> Source of vector data: Open Street Map. © OpenStreetMap contributors. Available under the Open Database License from: openstreetmap.org. </span></p> <p><sup><span>b</span></sup><span> Source of raster data (res. 5 m): </span><span><a href="https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018"><span>https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018</span></a></span><span>. </span><span><a href="https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018"><span>https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018</span></a></span><span> </span></p>
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
Compilation of 17 anthropogenic pressure gradients and 18 benthic indicators in the Baltic Sea, Atlantic Ocean and Mediterranean Sea
<p>Compilation of 17 benthic datasets that sampled benthic ecosystems over gradients of commercial bottom trawling intensity (n=14), eutrophication (n=1), oxygen depletion (n=1) and pollution (n=1) (Table 1).</p> <p>Compilation of 18 benthic indicators that were calculated for each gradient dataset. The indicators estimated were community biomass, abundance, richness, relative Margalef diversity, Shannon index, Simpson index, Inverse Simpson, AZTI’s Marine Biotic Index (AMBI), Multivariate AMBI (M-AMBI), BENTIX Biotic Index (BENTIX), Danish Quality Index (DKI), Trawling Disturbance Index (TDI), Modified TDI (mTDI), Modified vulnerability index (mT), Median longevity (Lm), Partial TDI (pTDI), Sentinels of Seabed (SoS), Long-lived fraction (Lf).</p> <p>The dataset has information on: 1) indicator outputs per sampling station; 2) combined data tables with gradient, station, and species information; 3) individual gradient information with replicate samples and the environmental variables reported in the original study.</p> <p>#----------------</p> <p>NOTE: The dataset was updated in February 2025 due to incorrect description of units used for two gradients (Silver Pit and Thames) in the "Individual Gradient Studies" folder.</p> <p>#---------------</p> <p>Table 1. Overview of anthropogenic gradient datasets</p> <table> <tbody> <tr> <th>Location</th> <th>Sampling method</th> <th>Pressure gradient</th> </tr> </tbody> <tbody> <tr> <td>Adriatic Sea – Italian EEZ (sand)</td> <td>Rapido trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Adriatic Sea – Italian EEZ (mud)</td> <td>Rapido trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Dutch EEZ</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Dogger Bank</td> <td>Hamon grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Fladen Ground</td> <td>Day grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Long Forties</td> <td>Hamon grab</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea - Silver Pit</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>North Sea – Thames</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>Northern Iberian Coast (sand)</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Northern Iberian Coast (mud)</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Baltic Sea - Gotland</td> <td>van Veen grab</td> <td>Bottom trawling</td> </tr> <tr> <td>Baltic Sea – Polish EEZ</td> <td>Box core</td> <td>Bottom trawling</td> </tr> <tr> <td>NW Atlantic - Flemish Cap</td> <td>Otter trawl</td> <td>Bottom trawling</td> </tr> <tr> <td>Irish Sea - Sellafield</td> <td>Day grab</td> <td>Bottom trawling</td> </tr> <tr> <td>Gulf of Finland</td> <td>van Veen grab</td> <td>Oxygen depletion</td> </tr> <tr> <td>Saronikos Gulf</td> <td>Box core</td> <td>Eutrophication</td> </tr> <tr> <td>Vigo Estuary</td> <td>Box core</td> <td>Contaminants</td> </tr> </tbody> </table>
FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution
<h2>Overview</h2> <p>This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. </p> <p><strong>Resolution</strong>: 30x30 arcsec<br><strong>Projection</strong>: EPSG 4326<br><strong>Extent for historical data</strong>: 10.67°W – 47.67°E, 33.68°N – 71.33°N<br><strong>Extent for scenario data</strong>: 10.67°W – <em>39.33°E</em>, 33.68°N – 71.33°N<br><strong>Periods for historical data</strong>: 1961-1990 and 1991-2020<br><strong>Periods for scenario data</strong>: 2036-2065 and 2071-2100<br><strong>Format:</strong> GeoTIFF</p> <p><strong>List of climatologies (climate indicators) </strong> </p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y) or monthly (M)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation</p> </td> <td> <p> </p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> </tbody> </table> <h2>Data sources</h2> <p>The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. </p> <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li><em>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</em></li> </ul> <p><strong>Climate models from EURO-CORDEX </strong>(doi.org/10.1007/s10113-013-0499-2<strong>)</strong>:</p> <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>
Dataset: 'Protected Designations of Origin' (PDO) per NUTS-3 regions plus PDO-scores and social-ecological indicators (v2)
<p>The dataset contains the original research data belonging to the research article "EU-wide mapping of ‘Protected Designations of Origin’ food products (PDOs) reveals correlations with social-ecological landscape values" (Flinzberger et al. 2022: <a href="https://doi.org/10.1007/s13593-022-00778-4">https://doi.org/10.1007/s13593-022-00778-4</a>).</p> <p>Based on the mapping of 638 food products registered within the EU as 'Protected Designation of Origin' (PDO), the first excel file <em>"EU28_all_PDO_products_per_NUTS3.xlsx"</em> shows the distribution of PDO-labeled products across the EU28 NUTS-3 regions. All NUTS-3 regions are listed once for each PDO product they contain (as by 30 June 2020).</p> <p>The second excel file <em>"PDO_scores_and_social_ecological_indicator_data.xlsx"</em> includes the accumulated numbers of PDOs per NUTS-3 region (called PDO score) as registered by 30 June 2020. The PDO score is presented for all food products combined, and for four major food categories separately. Further, the excel file contains all aggregated values of social-ecological indicators (including their sources), used for the correlation analysis between PDO scores and social-ecological characteristics in the above-mentioned article.</p> <p>Code explanations (meta data) can be found on the second sheet of each excel file.</p> <p>- - - </p> <p>second file updated to v2</p>
Pelt biting as a practical indicator of social and environment stress in farmed red deer
<p>Data sets used in the analyses of paper title "Pelt biting as a practical indicator of social and environment stress in farmed red deer"</p>
Data from: Multiple lines of evidence indicate ongoing allopatric and parapatric diversification in an Afromontane sunbird (Cinnyris reichenowi)
<p>Africa's montane ecosystems are noteworthy not only for their isolation, but for their morphologically similar bird populations that inhabit geographically disparate localities. Many species possess range disjunctions in excess of 2,000 km and appear to represent populations that have been isolated since at least the last Ice Age, including the Northern Double-collared Sunbird (<em>Cinnyris reichenowi</em>). Recent work on other Afromontane birds has demonstrated substantial phylogeographic structure can exist in phenotypically similar populations, with cryptic species occurring parapatrically within the same mountain range. We explored genetic, morphological, and ecological diversity within <em>C. reichenowi</em> to assess whether cryptic regional diversification occurs across the disjunct portions of this species' range. Within <em>C. reichenowi</em>, we find consistent patterns of morphological disparity that coincide with genetic diversification between xeric and wet montane populations within the Cameroon Line in the Western population, and clear genetic differentiation between Western and Eastern populations. Our research demonstrates that the geographically isolated populations of <em>Cinnyris reichenowi</em> represent different species, and that ecological diversification is shaping populations within Central Africa. We show here that two named populations should be recognized as members of a western species in the Northern Double-collared Sunbird complex: nominate <em>Cinnyris preussi preussi</em> in the Cameroon Line montane forests, and <em>C. p. genderuensis</em> in the more xeric hinterland of Cameroon and the Central African Republic, likely occurring in adjacent Nigeria as well.</p>
Determination of Natural Radioactivity Levels and Associated Radiation Hazard Indices of Wheat Flour Samples From Selected Ethiopian Markets.
<p>Wheat flour is a nutrient-dense food that is commonly consumed by various age groups in Ethiopia. In this study, natural radioactivity levels of <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K, as well as the related dangerous radiological characteristics, were successfully measured on 10 different brands of wheat flour samples using high-purity germanium (HPGe) gamma-ray spectrometry. Average activity concentrations of <sup>226</sup>Ra, <sup>232</sup>Th, and <sup>40</sup>K in wheat flour are found to be 0.26<strong> ±</strong>0.07 Bq.kg<sup>-1</sup>, 1.54<strong> ± </strong>0.43 Bq.kg<sup>-1</sup>, and 59.79<strong>±</strong>3.65 Bq.kg<sup>-1</sup>, respectively. Moreover, the average values of the corresponding radiological parameters, Ra<sub>eq</sub>, H<sub>int</sub>, and annual effective dose (E<sub>ave</sub>) (sum), were also found to be 7.07±0.96 Bq.kg<sup>-1</sup>, 0.019±0.003, and 0.109±0.019 mSvy<sup>-1</sup>, respectively. When the obtained results for all of the samples were compared to the internationally accepted norms, they were found to be below the permissible world average values. Furthermore, the lifetime cancer risk was discovered to be substantially below the permissible limit. According to the study, the risk of absorbing natural radionuclides from wheat flour consumption is negligible. The findings can be used to develop radiation protection rules and regulations for the use of wheat flour in food production and processing.</p>
Fig. 1 in Impact Of Coastal Wetland Restoration Strategies In The Chongming Dongtan Wetlands, China: Waterbird Community Composition As An Indicator
Fig. 1. Locationofthestudysites (A–D).
Fig. 5 in Hrabeiella Periglandulata (Annelida: "Polychaeta") Do Apparent Differences In Chaetal Ultrastructure Indicate The Existence Of Several Species In Europe?
Fig. 5. Hrabeiellaperiglandulata – structureofchaetaewithfinefibresvisible (secondfixation
Fire danger indices historical data from the Copernicus Emergency Management Service
<p>June, July August consolidated data.</p> <p>Generated using Copernicus Climate Change Service information 2021.</p>
Meteorological indicator for selected European NUTS 3 regions
<p>13 datasets have been concatenated into one dataset for the purpose to predict future temperature scenario from 13 European countries.</p> <p>The 13 datasets have been published by Angelova, Denitsa; Blanco, Norman (2020), “Meteorological indicator dataset for selected European NUTS 3 regions ”, Mendeley Data, V3, doi: 10.17632/sf9x4h5jfk.3</p> <p>Further details at https://data.mendeley.com/datasets/sf9x4h5jfk/3</p>
SESMG model scenarios of the study "Indicators for the optimization of sustainable urban energy systems based on energy system modeling"
<p>This folder contains the model scenarios belonging to the publication "<strong>Indicators for the optimization of sustainable urban energy systems based on energy system modeling</strong>" (<a href="https://doi.org/10.1186/s13705-021-00323-3">https://doi.org/10.1186/s13705-021-00323-3</a>).</p> <p>The individual scenarios can be executed and evaluated with the <strong>Spreadsheet Energy System Model Generator (<a href="https://github.com/chrklemm/SESMG">SESMG</a>)</strong> <a href="https://doi.org/10.5281/zenodo.5412027">v0.0.4</a>, respectively <a href="https://doi.org/10.5281/zenodo.5520513">v0.2.0</a>.</p> <p>The file names are to be understood as follows:</p> <p><em>"scenario name"_"(dispatch) optimization criterion"_"scenario concretization"_"further scenario concretization"_"associated program version"</em>.xlsx.</p> <p>For example, the title name "<em>Scenario3_C_4MW_Biogas_SESMGv0.0.4.xlsx</em>" contains the following information:<br> - This file belongs to scenario 3 (see main publication for details).<br> - Dispatch optimized according to energy costs C (see main publication for details).<br> - The scenario contains 4 MW biogas CHP capacity (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.0.4.</p> <p>Another example. The title name "<em>optimization_C_80PercentDemand_70PercentEmissions_SESMGv0.1.1.xlsx</em>" contains the following information:<br> - This file belongs to the optimization scenario (see main publication for details).<br> - The primary optimization criterion is energy costs C (see main publication for details).<br> - Energy demand was capped at 80 percent and emissions at 70 percent of baseline (see main publication for details)<br> - The scenario is to be executed with SESMG version v0.1.1.<br> </p> <p><strong>Acknowledgements:</strong></p> <p>The authors would like to thank Prof. Dr. Peter Vennemann (Münster University of Applied Sciences) for the constructive discussion regarding this article. This research has been conducted within the R2Q project, funded by the German Federal Ministry of Education and Research (BMBF) - grant number 033W102A and the junior research group energy sufficiency funded by the German Federal Ministry of Education and Research (BMBF) as part of its Social-Ecological Research funding priority, funding number 01UU2004A. </p>
Database of vulnerability indicators used in the holistic characterization of vulnerability to flash floods in the region of Castilla y León (Spain)
<p>Database containing the vulnerability indicators used in the holistic analysis of vulnerability to flash floods in the region of Castilla y León (Spain), considering all its dimensions (social, economic, ecosystem, physical, institutional and cultural) and components (exposure, susceptibility and resilience). The database contains a total of 496 variables, of which 216 characterize social vulnerability, 180 economic vulnerability, 49 ecosystem vulnerability, 22 physical vulnerability, 23 institutional vulnerability and 6 cultural heritage vulnerability. The Excel file contains two sheets for each vulnerability dimension. The first sheet (whose name is composed with the name of the dimension and the suffix '_Variables') contains the information of the variables, i.e., the names of the municipalities, the province to which they belong and the values of the variables for each municipality. The variables on this sheet are identified as codes, whose definition and description (unit in which they are expressed, reference year of the information, information source and the link to the information) are found on the second sheet of each dimension (suffix '_Data_sources').</p>
Psychomotor Indices
<p>data based on Indian Female Cricketrs </p>
agr-spectral-indices: v1.0.0
<p>Database of vegetation indices for crop and land monitoring</p>
Process Performance Indicators for IT Service Management: The PPI Dataset
<p>Set of process performance indicators defined in natural language, related to the IT service management processes of several public organizations in Spain as published in <em>Estrada-Torres, B., del-Río-Ortega, A., Resinas, M., & Ruiz-Cortés, A. (2021). Process Performance Indicators for IT Service Management: The PPI Dataset. In Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts (pp. 125-132). CRC Press. DOI: <a href="https://doi.org/10.1201/9781003168393-8">https://doi.org/10.1201/9781003168393-8</a></em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.