Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.7.1
Dataset results
19 results for “scenario mapping”
EJPSOIL_SERENA: Maps of Soil Organic Carbon Loss Scenarios in Elva Parish, Estonia
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>The study examined the effects of winter cropping systems on long-term soil fertility and their potential to mitigate SOC (Soil Organic Carbon) loss compared to bare soil during the winter months. It analyzed changes in SOC stocks (0–30 cm) at the field level in Elva Parish over the period 2020–2040, under different land-use scenarios. The modeling was based on a SOC stock map layer for Estonian mineral arable soils, developed by the Centre of Estonian Rural Research and Knowledge, which represented the baseline conditions in 2020. SOC stock projections were made using the RothC model, which simulates soil carbon turnover. </p> <p>In the first scenario (Scenario 1), the average SOC stock in Elva Parish by 2040 was estimated assuming the land would remain bare, without vegetation, during the winter months from October to April. In the second scenario (Scenario 2), the SOC stock projection accounted for the presence of winter vegetation, which means the soil is covered with vegetation year-round. The dataset includes four files: a projected SOC stock map for Elva Parish in 2040 and the stock changes from 2020–2040 under Scenario 1, along with a projected SOC stock map for 2040 and the stock changes from 2020–2040 under Scenario 2. </p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Short description of the datase:</p> <p>This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Berlin, Germany
<p><strong>Berlin heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045).</strong></p> <p>Heat stress exposure maps for Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Scenario: </strong>Urban planning scenario (situation LULC today + integrated urban planning projects 2030)</p> <p><strong>Exposure mapping variable: </strong><br /> Total population 2030<br /> Population density inhabitants per hectare 2030</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Scenario: Urban Planning</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Almada, Portugal
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong><br /> Total population 2011<br /> Population density inhabitants per hectare 2011<br /> Number of inhabitants aged 0 to 19 years 2011<br /> Number of inhabitants aged 20 to 65 years 2011<br /> Number of inhabitants aged +65 years 2011<br /> Number of childcare centres 2014<br /> Number of hospitals 2014<br /> Number of schools 2014<br /> Number of schools and universities 2014<br /> Number of universities 2014<br /> Number of resthomes 2014</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
Heat Stress Maps - Urban Planning Scenarios 1986-2005 / 2026 - 2045: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period 2026 - 2045 using the present land use / cover situation for the city but combined urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Antwerp, Belgium
<p><strong>Antwerp heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p><strong>Exposure mapping variable include:</strong><br /> * Total population 2030<br /> * Population density inhabitants per hectare 2030</p>
Heat Stress Exposure Maps - Urban Planning Scenario 2026 - 2045: Almada, Portugal
<p><strong>Almada heat stress exposure map: average number of heatwave days per year versus socio economic data - urban planning scenario (2026-2045)</strong></p> <p>Heat stress exposure maps for the city of Almada representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modeled over the reference period 2026-2045 using the present land use / cover situation for the city but combined with urban planning projects information until 2030. Hence, the urban morphology has been updated accordingly.</p> <p>Exposure mapping variable include:<br /> * Total population 2011<br /> * Population density inhabitants per hectare 2011</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Almada, Portugal
<p>Heat stress maps for the city of Almada representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Berlin, Germany
<p>Heat stress maps for Berlin representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p> <p>Scenario: Base scenario (situation LULC today)</p>
Heat Stress Maps - Base Scenarios 1986-2005 / 2026 - 2045 / 2081 - 2100: Antwerp, Belgium
<p>Heat stress maps for Antwerp representing<br /> * the average number of heatwave days<br /> (1986 – 2005 | 2026 – 2045 | 2081 – 2100)<br /> per statistical unit or per grid</p> <p>* The Urban Heat Island effect at 11pm per year<br /> (1986 - 2005) per statistical unit or per grid</p> <p>The heat stress parameter considered has been modelled over the reference period using the present land use / cover situation for the city.</p> <p>Please note that only the base scenario 1986-2005 has got maps with the 2 heat stress parameters:<br /> * Average number of heat wave days per year<br /> * Urban Heat Island effect at 11pm per year</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Antwerp, Belgium
<p>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</p> <p>Heat stress exposure maps for the city of Antwerp representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following: </strong></p> <p>Total population 2014</p> <p>Population density inhabitants per hectare 2014</p> <p>Number of inhabitants aged 0 to 4 years 2014</p> <p>Number of inhabitants aged 0 to 17 years 2014</p> <p>Number of inhabitants aged 18 to 65 years 2014</p> <p>Number of inhabitants aged +65 years 2014</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-2 & Map-3)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-2 & Map-3 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany
<p><strong>Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)</strong></p> <p>Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.</p> <p><strong>Exposure mapping variable include the following:</strong></p> <p>Total population 2013</p> <p>Population density inhabitants per hectare 2013</p> <p>Number of inhabitants aged 0 to 17 years 2013</p> <p>Number of inhabitants aged 18 to 65 years 2013</p> <p>Number of inhabitants aged +65 years 2013</p> <p>Number of schools 2014</p> <p>Number of childcare centers 2014</p> <p>Number of hospitals 2014</p> <p>Number of elderly stay facilities 2014</p>
Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region
<p>This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.</p>
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-1)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-1 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p> <p> </p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (REF to zenodo dataset, Pal J et al., 2024). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Description of the datase: This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Maps for Soil loss by water from climate change scenarios for Austria
<p><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion cookbook and are described in the respective project deliverables D3.3 and D3.4.</span></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.