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761 results for “impact assessment”
Data from: An environmental impact assessment of different management regimes in eucalypt plantations in southern China using Landscape Function Analysis
<p>There are global concerns regarding the detrimental environmental impacts of industrial forest plantations developed over the past 30 years. To address this concern, the Landscape Function Analysis methodology was used to rapidly assess indices of soil stability, water infiltration, and nutrient cycling within eucalypt plantations at different growth stages and under different management regimes in Guangxi Province, China. Results showed that these plantations under both regimes were approaching an ecologically functional state by the time of harvest. However, within the plantation management that included the burning of post-harvest biomass residues, indices of water infiltration, and nutrient cycling were significantly lower than within the plantation that retained post-harvest residues. Indicators of rain splash protection, perennial vegetation cover, and litter accumulation were all lower in the plantation that practiced residue burning and pre-planting cultivation. Retention of post-harvest residues improves landscape functionality at the time of re-planting. Our results indicate that burning and extensive cultivation prior to re-planting should be minimized.</p>
d5-6-assessment-of-impacts-sosnowiec
<p>Data output from the tool set application for the assessment of the environmental (emissions, carbon footprint, ambient air concentrations), health (exposure and health effects), and economic impacts (e.g. health-related costs).</p> <ul> <li><strong>Sosnowiec shapefile</strong><br> file: sosnowiec.7z<br> Shapefile with the urban scale domain over Sosnowiec with 20 km x 20 km</li> <li><strong>2_SOS_Agriculture_baseline</strong><br> file: claircity_agricultureemissions_sos_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for livestock (emissions from agriculture are not available) at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Sosnowiec by farms, meadows, vineyards land uses classified in the Open Street Map database.</li> <li><strong>3_SOS_Air Quality_Baseline (mesoscale/NO2 concentrations)</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_SOS_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_SOS_Air Quality_Baseline (mesoscale/PM10 concentrations)</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_SOS_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: sos_aq2app_no2.txt<br> Annual NO2 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_SOS_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: sos_aq2app_pm10.txt<br> Annual PM10 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_SOS_Air Quality_Baseline (urban scale/ PM2.5 concentrations)</strong><br> file: sos_aq2app_pm2.txt<br> Annual PM2.5 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_SOS_Air Quality_Baseline (mesoscale-report)</strong><br> file: sos_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Sosnowiec region, which includes a detailed description of the air quality modelling system WRF-CAMx (section 1.1.) and a description of the methodology applied to evaluate the model performance (section 1.2.). It also includes results of concentration fields and a source apportionment for NO2, PM10 and PM2.5.</li> <li><strong>6_SOS_CarbonFootprint_Baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> Carbon footprint methodologies and estimation for the baseline year for Sosnowiec</li> <li><strong>3_SOS_Air Quality_Baseline (urbanscale-report)</strong><br> file: sos_aq_urbanscale_report.pdf<br> This report provides a brief overview oh the methodology used. It presents an analysis of concentration fields for NO2, PM10, PM2.5 of the total and by modeled sectors, it also includes an analysis of the source contribution and for the maximum values.</li> <li><strong>2_SOS_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf</li> <li><strong>2_SOS_IRCI_Scenarios</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf</li> <li><strong>2_SOS_temporal_profiles</strong><br> file: sosnowiec-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of Sosnowiec's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables.</li> <li><strong>2_SOS_Transport_baseline_map</strong><br> file: roadnetwork_sos.zip<br> Map with lines, link with emissions using filed "ID"</li> <li><strong>2_SOS_Transport_baseline_values</strong><br> file: emission_values_sos.zip<br> Part 2 of 2 files that make the Sosnowiec transport emissions baseline: total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" shapefile with road links units: g emissions at link level consisting of a zip file with 2 .csv-files, in the following format: first column: link-ID (link with shapefile of the road network) second column: pollutant (PM, PM non-exhaust or NOx) third column: mode ("BESTEL"= van or light freight, "MIDZWVR" = medium freight, "MOTOR" = motorcycles, "OVBUS" = bus, "PERSAUTO" = passenger cars, "ZWAARVR" = heavy freight columns D-AY: hourly intervals for weekday ("WD") and weekend ("WE") all units in g 2 files with values: aggregates over type, by type of day, per hour of day and a separate file for annual totals (at link level, per pollutant (including FC))</li> <li><strong>2_SOS_transport_scenarios</strong><br> file: copy-of-190915_sos_scenario_results_summary_with_ups.xlsx<br> This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: %</li> <li><strong>5_SOS_health_statistics </strong><br> file: sos_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>5_SOS_health_scenarios</strong><br> file: summary_results_sos.xlsx<br> Health-related impacts (selected mortality and morbidity endpoints) related to exposure to PM2.5, NO2, and PM10, considering concentration-response functions and baseline concentrations recommended by WHO.</li> <li><strong>3_SOS_NO2_AQ_latlong</strong><br> file: sos_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_SOS_PM2_AQ_latlong</strong><br> file: sos_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_SOS_PM10_AQ_latlong</strong><br> file: sos_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>6_SOS_CarbonFootprint_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> Carbon footprint business as usual and scenario projections for Sosnowiec</li> <li><strong>3_SOS_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: sos_psat.xlsx<br> Time series of daily average contributions for each source group for PM10, PM2.5 and NO2 concentrations from WRF-CAMx modelling system with the SA tool, for the Sosnowiec urban area.</li> <li><strong>3_SOS_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: sos_no2_scenarios.mat<br> Annual NO2 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_SOS_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: sos_pm10_scenarios.mat<br> Annual PM10 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_SOS_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: sos_pm2_scenarios.mat<br> Annual PM2.5 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_SOS_Exposure_EU_Baseline NO2 matrix</strong><br> file: sos_no2_exposureeu_baseline.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_SOS_Exposure_EU_Baseline PM10 matrix</strong><br> file: sos_pm10_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_SOS_Exposure_WHO_Baseline PM10 matrix</strong><br> file: sos_pm10_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for the baseline year.</li> <li><strong>4_SOS_Exposure_EU_Baseline PM2 matrix</strong><br> file: sos_pm2_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for the baseline year.</li> <li><strong>4_SOS_Exposure_WHO_Baseline PM2 matrix</strong><br> file: sos_pm2_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for the baseline year.</li> <li><strong>4_SOS_Exposure_EU_Scenarios NO2 matrix</strong><br> file: sos_no2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_SOS_Exposure_EU_Scenarios PM10 matrix</strong><br> file: sos_pm10_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_SOS_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: sos_pm10_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_SOS_Exposure_EU_Scenarios PM2 matrix</strong><br> file: sos_pm2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_SOS_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: sos_pm2_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for BAU, scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> </ul>
d5-6-assessment-of-impacts-aveiro
<p>Data output from the tool set application for the assessment of the environmental (emissions, carbon footprint, ambient air concentrations), health (exposure and health effects), and economic impacts (e.g. health-related costs).</p> <ul> <li><strong>CIRA shapefile</strong><br> file: cira.7z<br> Shapefile with the urban scale domain over CIRA/ Aveiro region with 40 km x 55 km</li> <li><strong>2_CIRA_ Natural _baseline</strong><br> file: claircity_naturalemissions_cira_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for nature at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of CIRA by forest, grass, parks and nature reserve areas classified in the Open Street Map database.</li> <li><strong>2_CIRA_Agriculture_baseline</strong><br> file: claircity_agricultureemissions_cira_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for agriculture and livestock at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of CIRA by farms, meadows, vineyards land uses classified in the Open Street Map database</li> <li><strong>3_CIRA_Air Quality_Baseline (mesoscale/NO2 concentrations)</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_CIRA_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_CIRA_Air Quality_Baseline (mesoscale/PM10 concentrations)</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_CIRA_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: cira_aq2app_no2.txt<br> Annual NO2 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_CIRA_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: cira_aq2app_pm10.txt<br> Annual PM10 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_CIRA_Air Quality_Baseline (urban scale/ PM2.5 concentrations)</strong><br> file: cira_aq2app_pm2.5.txt<br> Annual PM2.5 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>6_CIRA_CarbonFootprint_baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> Carbon footprint methodologies and estimation for the baseline year for Aveiro Region</li> <li><strong>3_CIRA_Air Quality_Baseline (mesoscale-report)</strong><br> file: cira_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Aveiro region, which includes a detailed description of the air quality modelling system WRF-CAMx (section 1.1.) and a description of the methodology applied to evaluate the model performance (section 1.2.). It also includes results of concentration fields and a source apportionment for NO2, PM10 and PM2.5.</li> <li><strong>3_CIRA_Air Quality_Baseline (urbanscale-report)</strong><br> file: cira_aq_urbanscale_report.pdf<br> This report provides a brief overview oh the methodology used. It presents an analysis of concentration fields for NO2, PM10, PM2.5 of the total and by modeled sectors, it also includes an analysis of the source contribution and for the maximum values.</li> <li><strong>2_CIRA_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf</li> <li><strong>2_CIRA_IRCI_Scenarios</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf</li> <li><strong>2_CIRA_temporal_profiles</strong><br> file: aveiro-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of CIRA's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables.</li> <li><strong>2_CIRA_Transport_baseline_map</strong><br> file: roadnetwork_cira.zip<br> Map with lines, link with emissions using filed "ID" import as text delimited file, use string for georeference. WGS84 Distinction is made between the core area and the periphery</li> <li><strong>2_CIRA_Transport_baseline_values</strong><br> file: emission_values_cira.zip<br> Part 2 of 2 files that make the CIRA transport emissions baseline: total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" shapefile with road links units: g emissions at link level consisting of a zip file with 4 .csv-files, in the following format: first column: link-ID (link with shapefile of the road network) second column: pollutant (PM, PM non-exhaust or NOx) third column: mode ("BESTEL"= van or light freight, "MIDZWVR" = medium freight, "MOTOR" = motorcycles, "OVBUS" = bus, "PERSAUTO" = passenger cars, "ZWAARVR" = heavy freight columns D-AY: hourly intervals for weekday ("WD") and weekend ("WE") all units in g First distinction is made between core area and the periphery. data files need to be linked with the correct map-file from part 1 Second distinction is made between aggregates (over type, by type of day, per hour of day) and a separate file for annual totals (at link level, per pollutant (including FC))</li> <li><strong>2_CIRA_transport_scenarios_CITY</strong><br> file: 191001_aveicity_scenario_results_summary_withups.xlsx<br> This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: % These values are valid for the city-area's of Aveiro</li> <li><strong>2_CIRA_transport_scenarios_REGION</strong><br> file: 191001_aveiregion_scenario_results_summary_withups.xlsx<br> This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: % These values are valid for the region of Aveiro, excluding the city area's</li> <li><strong>5_CIRA_health_statistics</strong><br> file: cira_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>5_CIRA_health_scenarios</strong><br> file: summary_results_cira.xlsx<br> Health-related impacts (selected mortality and morbidity endpoints) related to exposure to PM2.5, NO2, and PM10, considering concentration-response functions and baseline concentrations recommended by WHO.</li> <li><strong>3_CIRA_NO2_AQ_latlong</strong><br> file: cira_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_CIRA_PM2_AQ_latlong</strong><br> file: cira_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_CIRA_PM10_AQ_latlong</strong><br> file: cira_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>6_CIRA_CarbonFootprint_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> Carbon footprint business as usual and scenario projections for Aveiro region</li> <li><strong>3_CIRA_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: cira_psat.xlsx<br> Time series of daily average contributions for each source group for PM10, PM2.5 and NO2 concentrations from WRF-CAMx modelling system with the SA tool, for the Aveiro Region urban area.</li> <li><strong>3_CIRA_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: cira_no2_scenarios.mat<br> Annual NO2 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_CIRA_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: cira_pm10_scenarios.mat<br> Annual PM10 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_CIRA_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: cira_pm2_scenarios.mat<br> Annual PM2.5 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_CIRA_Exposure_EU_Baseline NO2 matrix</strong><br> file: cira_no2_exposureeu_baseline.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_CIRA_Exposure_EU_Baseline PM10 matrix</strong><br> file: cira_pm10_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_CIRA_Exposure_WHO_Baseline PM10 matrix</strong><br> file: cira_pm10_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for the baseline year.</li> <li><strong>4_CIRA_Exposure_EU_Baseline PM2 matrix</strong><br> file: cira_pm2_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for the baseline year.</li> <li><strong>4_CIRA_Exposure_WHO_Baseline PM2 matrix</strong><br> file: cira_pm2_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for the baseline year.</li> <li><strong>4_CIRA_Exposure_EU_Scenarios NO2 matrix</strong><br> file: cira_no2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_CIRA_Exposure_EU_Scenarios PM10 matrix</strong><br> file: cira_pm10_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_CIRA_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: cira_pm10_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_CIRA_Exposure_EU_Scenarios PM2 matrix</strong><br> file: cira_pm2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_CIRA_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: cira_pm2_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for BAU, scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> </ul>
d5-6-assessment-of-impacts-amsterdam
<p>Data output from the tool set application for the assessment of the environmental (emissions, carbon footprint, ambient air concentrations), health (exposure and health effects), and economic impacts (e.g. health-related costs).</p> <ul> <li><strong>Amsterdam shapefile</strong><br> file: amsterdam.7z<br> Shapefile with the urban scale domain over Amsterdam with 25 km x 20 km</li> <li><strong>2_AMS_Agriculture_baseline</strong><br> file: claircity_agricultureemissions_ams_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for agriculture and livestock at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Amsterdam by farms, meadows, vineyards land uses classified in the Open Street Map database</li> <li><strong>3_AMS_Air Quality_Baseline (mesoscale/NO2 concentrations</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_AMS_Air Quality_Baseline (mesoscale/PM10 concentrations</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_AMS_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_AMS_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: ams_aq2app_no2.txt<br> Annual NO2 average concentrations from URBAIR model</li> <li><strong>3_AMS_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: ams_aq2app_pm10.txt<br> Annual PM10 average concentrations from URBAIR model</li> <li><strong>3_AMS_Air Quality_Baseline (urban scale/PM2.5 concentrations)</strong><br> file: ams_aq2app_pm2.txt<br> Annual PM2.5 average concentrations from URBAIR model</li> <li><strong>6_AMS_CarbonFootprint_Baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> Carbon footprint methodologies and estimation for the baseline year for Amsterdam</li> <li><strong>3_AMS_Air Quality_Baseline (mesoscale-report)</strong><br> file: ams_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Amsterdam region, which includes a detailed description of the air quality modelling system WRF-CAMx (section 1.1.) and a description of the methodology applied to evaluate the model performance (section 1.2.). It also includes results of concentration fields and a source apportionment for NO2, PM10 and PM2.5.</li> <li><strong>3_AMS_Air Quality_Baseline (urbanscale-report)</strong><br> file: ams_aq_urbanscale_report.pdf<br> This report provides a brief overview oh the methodology used. It presents an analysis of concentration fields for NO2, PM10, PM2.5 of the total and by modeled sectors, it also includes an analysis of the source contribution and for the maximum values.</li> <li><strong>2_AMS_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf</li> <li><strong>2_AMS_IRCI_Scenarios</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf</li> <li><strong>2_AMS_temporal_profiles</strong><br> file: ams-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of Amsterdam's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables.</li> <li><strong>2_AMS_Transport_source_apportionment</strong><br> file: 191125_adam_v2.xlsx<br> describes the share of road transport emissions by behavior-properties and population properties. Pure data & pivot-table for easy use: male/female 3 income-classes 5 age groups 3 car-ownership groups (0, 1, 2 or more) Time of day: morning-, evening peak, midday and night Type of day (weekend/weekday) Motive: 6 classes mode:</li> <li><strong>2_AMS_Transport_baseline_map</strong><br> file: roadnetwork_ams.zip<br> The road network - link with emissions using "linkID"</li> <li><strong>2_AMS_Transport_baseline_emissions</strong><br> file: ams_emissions.zip<br> Part 2 of 2 files that make the Amsterdam transport emissions baseline: total emissions per link in .csv per pollutant (NOx, PM non-exhaust and PM): To be linked to the road network file using the identifier "uniqueID" format: first column: link-ID (link with shapefile of the road network) second column: pollutant (PM, PM non-exhaust or NOx) third column: mode ("BESTEL"= van or light freight, "MIDZWVR" = medium freight, "MOTOR" = motorcycles, "OVBUS" = bus, "PERSAUTO" = passenger cars, "ZWAARVR" = heavy freight columns D-AY: hourly intervals for weekday ("WD") and weekend ("WE") all units in g</li> <li><strong>2_AMS_transport_scenarios</strong><br> file: copy-of-190429_adam_scenario_results_summary_v2.xlsx<br> This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: %</li> <li><strong>5_AMS_health_scenarios</strong><br> file: summary_results_ams.xlsx<br> Health-related impacts (selected mortality and morbidity endpoints) related to exposure to PM2.5, NO2, and PM10, considering concentration-response functions and baseline concentrations recommended by WHO.</li> <li><strong>5_AMS_health_statistics</strong><br> file: ams_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>3_AMS_NO2_AQ_latlong</strong><br> file: ams_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_AMS_PM2_AQ_latlong</strong><br> file: ams_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_AMS_PM10_AQ_latlong</strong><br> file: ams_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>6_AMS_CarbonFootprint_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> Carbon footprint business as usual and scenario projections for Amsterdam</li> <li><strong>3_AMS_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: ams_psat.xlsx<br> Time series of daily average contributions for each source group for PM10, PM2.5 and NO2 concentrations from WRF-CAMx modelling system with the SA tool, for the Amsterdam urban area.</li> <li><strong>3_AMS_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: ams_no2_scenarios.mat<br> Annual NO2 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_AMS_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: ams_pm10_scenarios.mat<br> Annual PM10 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_AMS_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: ams_pm2_scenarios.mat<br> Annual PM2.5 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_AMS_Exposure_EU_Baseline NO2 matrix</strong><br> file: ams_no2_exposureeu_baseline.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_AMS_Exposure_EU_Baseline PM10 matrix</strong><br> file: ams_pm10_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_AMS_Exposure_WHO_Baseline PM10 matrix</strong><br> file: ams_pm10_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for the baseline year.</li> <li><strong>4_AMS_Exposure_EU_Baseline PM2 matrix</strong><br> file: ams_pm2_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for the baseline year.</li> <li><strong>4_AMS_Exposure_WHO_Baseline PM2 matrix</strong><br> file: ams_pm2_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for the baseline year.</li> <li><strong>4_AMS_Exposure_EU_Scenarios NO2 matrix</strong><br> file: ams_no2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_AMS_Exposure_EU_Scenarios PM10 matrix</strong><br> file: ams_pm10_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_AMS_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: ams_pm10_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_AMS_Exposure_EU_Scenarios PM2 matrix</strong><br> file: ams_pm2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_AMS_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: ams_pm2_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for BAU, scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> </ul>
d5-6-assessment-of-impacts-ljubljana
<p>Data output from the tool set application for the assessment of the environmental (emissions, carbon footprint, ambient air concentrations), health (exposure and health effects), and economic impacts (e.g. health-related costs).</p> <ul> <li><strong>Ljubljana shapefile</strong><br> file: ljubljana.7z<br> Shapefile with the urban scale domain over Ljubljana with 20 km x 20 km</li> <li><strong>2_LJB_Agriculture_baseline</strong><br> file: claircity_agricultureemissions_ljb_jan2019.pdf<br> Emissions (in kg/year) were based on EMEP emission inventory for agriculture and livestock at 0.1x0.1 degrees resolution (~ 10 km) for the year 2015 disaggregated for the urban domain of Ljubljana by farms, meadows, vineyards land uses classified in the Open Street Map database</li> <li><strong>3_LJB_Air Quality_Baseline (mesoscale/NO2 concentrations)</strong><br> file: no2_2010010100_2010123123_lcc.png<br> Annual NO2 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LJB_Air Quality_Baseline (mesoscale/PM10 concentrations)</strong><br> file: pm10_2010010100_2010123123_lcc.png<br> Annual PM10 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LJB_Air Quality_Baseline (mesoscale/PM2.5 concentrations)</strong><br> file: pm2.5_2010010100_2010123123_lcc.png<br> Annual PM2.5 average concentrations (µg/m^3) from WRF-CAMx modelling system, for the mesoscale domain D2.</li> <li><strong>3_LJB_Air Quality_Baseline (urban scale/ NO2 concentrations)</strong><br> file: lju_aq2app_no2.txt<br> Annual NO2 average concentrations from URBAIR model</li> <li><strong>3_LJB_Air Quality_Baseline (urban scale/ PM10 concentrations)</strong><br> file: lju_aq2app_pm10.txt<br> Annual PM10 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_LJB_Air Quality_Baseline (urban scale/ PM2.5 concentrations)</strong><br> file: lju_aq2app_pm2.txt<br> Annual PM2.5 average concentrations from URBAIR model Coordinate system: LCP clicurb (meters)</li> <li><strong>3_LJB_Air Quality_Baseline (mesoscale-report)</strong><br> file: ljb_mesoscale.pdf<br> This report provides an overview of the modelling approach used to characterize the air quality in the Ljubljana region, which includes a detailed description of the air quality modelling system WRF-CAMx (section 1.1.) and a description of the methodology applied to evaluate the model performance (section 1.2.). It also includes results of concentration fields and a source apportionment for NO2, PM10 and PM2.5.</li> <li><strong>6_LJB_Carbon Footprint_Baseline</strong><br> file: ech.ma.15-fr2-wp5-carbon-footprint-ed2.pdf<br> Carbon footprint methodologies and estimation for the baseline year for Ljubljana</li> <li><strong>6_LJB_CarbonFootprint_Scenarios</strong><br> file: ech.ma.15-fr4-wp5-carbon-footprint-future-ed3-.pdf<br> Carbon footprint business as usual and scenario projections for Ljubljana.</li> <li><strong>3_LJB_Air Quality_Baseline (urbanscale-report)</strong><br> file: lju_aq_urbanscale_report.pdf<br> This report provides a brief overview oh the methodology used. It presents an analysis of concentration fields for NO2, PM10, PM2.5 of the total and by modeled sectors, it also includes an analysis of the source contribution and for the maximum values.</li> <li><strong>3_LJB_IRCI_baseline</strong><br> file: ech.ma.15-fr1-wp5-irc-ed5.3.pdf</li> <li><strong>2_LJB_IRCI_Scenarios</strong><br> file: ech.ma.15-fr3-wp5-irc-future-ed5.pdf</li> <li><strong>2_LJB_temporal_profiles</strong><br> file: ljubljana-daily_hourlytd_res_comm_emi.xlsx<br> Temporal profiles of Ljubljana's residential sector and commercial sector scale in %: Daily emissions, typical days emissions and hourly typical days emissions of PM10 and NOX variables.</li> <li><strong>2_LJB_Transport_baseline_map</strong><br> file: roadnetwork_lju.zip<br> Map with lines, link with emissions using filed "ID"</li> <li><strong>2_LJB_Transport_baseline_values</strong><br> file: emission_values_lju.zip<br> Part 2 of 2 files that make the Ljubljana transport emissions baseline: total emissions per link in .csv: To be linked to the road network file using the identifier "uniqueID" shapefile with road links units: g emissions at link level consisting of a zip file with 2 .csv-files, in the following format: first column: link-ID (link with shapefile of the road network) second column: pollutant (PM, PM non-exhaust or NOx) third column: mode ("BESTEL"= van or light freight, "MIDZWVR" = medium freight, "MOTOR" = motorcycles, "OVBUS" = bus, "PERSAUTO" = passenger cars, "ZWAARVR" = heavy freight columns D-AY: hourly intervals for weekday ("WD") and weekend ("WE") all units in g 2 files with values: aggregates over type, by type of day, per hour of day and a separate file for annual totals (at link level, per pollutant (including FC))</li> <li><strong>2_LJB_transport_scenarios</strong><br> file: 191023_lju_scenario_results_summary_v2.xlsx<br> This data-set reflects the relative changes of road transport emissions in different years and scenario's compared to the baseline. 2 sets of scenario's are given, one per tab: "SDW": BAU & scenario's established in the stakeholder dialogue workshop "UPS": updated BAU (if applicable) & final Unified Policy Scenario (UPS) selected in the policy workshop. reported for 3 future years compared to the 2015 baseline: 2025, 2035 and 2050 reported for NOx & PM for 6 modes: "MIDZWR": medium truck "ZWVR": heavy truck "BUS": busses "MOTO": motorcycles "CAR": passenger cars "VAN": light freight, assumed to be a mix of passenger cars and medium trucks all units: %</li> <li><strong>5_LJB_health_statistics </strong><br> file: lbj_health-analysis.xlsx<br> demographics and population data to calculate the health statistics</li> <li><strong>5_LJB_health_scenarios</strong><br> file: summary_results_lbj.xlsx<br> Health-related impacts (selected mortality and morbidity endpoints) related to exposure to PM2.5, NO2, and PM10, considering concentration-response functions and baseline concentrations recommended by WHO.</li> <li><strong>3_LJB_NO2_AQ_latlong</strong><br> file: lju_no2_latlong.rar<br> The shapefile includes total NO2 concentrations (µg/m^3) , as well as concentrations by sector (transport, shipping, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_LJB_PM2_AQ_latlong</strong><br> file: lju_pm2_latlong.rar<br> The shapefile includes total PM2 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_LJB_PM10_AQ_latlong</strong><br> file: lju_pm10_latlong.rar<br> The shapefile includes total PM10 concentrations (µg/m^3) , as well as concentrations by sector (transport, IRCI and industrial). Coordiante system: WGS1984</li> <li><strong>3_LJB_Air Quality_Baseline (mesoscale/SourceApportionment)</strong><br> file: ljb_psat.xlsx<br> Time series of daily average contributions for each source group for PM10, PM2.5 and NO2 concentrations from WRF-CAMx modelling system with the SA tool, for the Ljubljana urban area.</li> <li><strong>3_LJB_Air Quality Scenarios_urban scale NO2 matrix</strong><br> file: lbj_no2_scenarios.mat<br> Annual NO2 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU, scenarios low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_LJB_Air Quality Scenarios_ urban scale PM10 matrix</strong><br> file: lbj_pm10_scenarios.mat<br> Annual PM10 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>3_LJB_Air Quality Scenarios_urban scale PM2 matrix</strong><br> file: lbj_pm2_scenarios.mat<br> Annual PM2.5 average concentrations (µg/m^3) from URBAIR model, considering the impact of all scenarios in all the emission sectors: transport, industrial and IRCI, together with the background concentrations. The file contains X, Y coordinates, together with the total annual average concentrations for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_LJB_Exposure_EU_Baseline NO2 matrix</strong><br> file: lbj_no2_exposureeu_baseline.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_LJB_Exposure_EU_Baseline PM10 matrix</strong><br> file: lbj_pm10_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for the baseline year.</li> <li><strong>4_LJB_Exposure_WHO_Baseline PM10 matrix</strong><br> file: lbj_pm10_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for the baseline year.</li> <li><strong>4_LJB_Exposure_EU_Baseline PM2 matrix</strong><br> file: lbj_pm2_exposureeu_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for the baseline year.</li> <li><strong>4_LJB_Exposure_WHO_Baseline PM2 matrix</strong><br> file: lbj_pm2_exposurewho_baseline.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for the baseline year.</li> <li><strong>4_LJB_Exposure_EU_Scenarios NO2 matrix</strong><br> file: lbj_no2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual NO2 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_LJB_Exposure_EU_Scenarios PM10 matrix</strong><br> file: lbj_pm10_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 40 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_LJB_Exposure_WHO_Scenarios PM10 matrix</strong><br> file: lbj_pm10_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM10 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 20 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_LJB_Exposure_EU_Scenarios PM2 matrix</strong><br> file: lbj_pm2_exposureeu_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the EU annual legal limit value of 25 ug/m^3 for BAU scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> <li><strong>4_LJB_Exposure_WHO_Scenarios PM2 matrix</strong><br> file: lbj_pm2_exposurewho_scenarios.mat<br> Population potentially exposed to the annual PM2.5 average concentrations. The file contains X, Y coordinates, together with the total number of inhabitants, and the total number of inhabitants in each grid cell with an annual concentration exceeding the WHO annual guideline value of 10 ug/m^3 for BAU, scenarios, low and high ambition scenarios from the SDW, as well as the FUPS for the time-window 2025, 2035, and 2050.</li> </ul>
Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy
<p>Replication materials for "The Impact of a National Research Assessment on the Publications of Sociologists in Italy" to appear in Science and Public Policy</p> <p>DOI: 10.1093/scipol/scab013</p> <p><strong>More information on data on GitHub page</strong>: https://github.com/akbaritabar/SPP-ANVUR-sociologists-2021</p> <p> </p> <p><strong>Introduction to data</strong></p> <p>Data were collected from Scopus in September 2016 and included all records published by Italian sociologists between 2006 and 2015. This period covered five full years before and after ANVUR, whose original call for participation was on 7 November 2011. By considering five years before and after the call, we aimed to trace pre-existing behaviour and examine scientists’ reactions to institutional policies.</p>
Supplementary material 2 from: Shivambu N, Shivambu TC, Downs CT (2020) Assessing the potential impacts of non-native small mammals in the South African pet trade. NeoBiota 60: 1-18. https://doi.org/10.3897/neobiota.60.52871
Table S2
Supplementary material 1 from: Shivambu N, Shivambu TC, Downs CT (2020) Assessing the potential impacts of non-native small mammals in the South African pet trade. NeoBiota 60: 1-18. https://doi.org/10.3897/neobiota.60.52871
Table S1
Code for Individual Based Model to assess impact of KHV release on carp population
<p><b>1: </b>Common carp (<i>Cyprinus carpio</i>) is one of the top global invasive vertebrates and can cause significant ecological damage. The Australian Government's National Carp Control Program (NCCP) proposes to release Koi Herpesvirus (KHV) to eradicate feral carp in one of the largest ecological interventions ever attempted. Ecological and human health risks have been highlighted regarding the release of a highly pathogenic viral biocontrol for an aquatic species. The efficacy of KHV has also been questioned, and it has not been demonstrated to produce lasting population reductions.</p> <p> </p> <p>2. We developed an individual-based model (IBM) to examine the ecological and evolutionary response of a carp population after KHV release. This simulated the interaction between fish life history, viral epidemiology, host genetic resistance and population demography to critically evaluate the impact of KHV release under optimal conditions and a "best case scenario" for disease transmission.</p> <p> </p> <p>3. KHV will rarely result in prolonged reductions or population extinctions. Crucially, realistic scenarios result in a rapidly rebounding population of resistant individuals. Additional measures aimed to reduce carp population recovery rate (e.g. with genetic engineering) require rapid efficacy to significantly reduce carp numbers alongside KHV.<br> </p> <p>4. Fish fecundity has an overwhelming influence on viral efficacy as a biocontrol agent when combined with genetic resistance within a population. A high probability of population extinction is only met when carp fecundity is reduced to 1% of biological observations.<br> </p> <p><span>5.<i> Synthesis and applications. </i></span>We use an individual-based model to evaluate the efficacy of Koi Herpesvirus biocontrol in Common <a>Ca</a>rp, and find that high host fecundity combined with genetic resistance results in rapid population rebound after initial large fish-kills. Biocontrol approaches relying on natural selection lose efficacy over successive generations as resistance genes increases in frequency. Given the intense logistical effort and risks to ecosystems and human health associated with large fish kills after viral release, we suggest that sustained manual removal, alongside ecological restoration to favour recovery of native species, provides a risk-free approach to reducing populations.<br> </p>
Supplementary material 1 from: Measey J, Wagener C, Mohanty NP, Baxter-Gilbert J, Pienaar EF (2020) The cost and complexity of assessing impact. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 279-299. https://doi.org/10.3897/neobiota.62.52261
Questionnaire
Supplementary material 2 from: Measey J, Wagener C, Mohanty NP, Baxter-Gilbert J, Pienaar EF (2020) The cost and complexity of assessing impact. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 279-299. https://doi.org/10.3897/neobiota.62.52261
Amphibian EICAT & SEICAT scores
Supplementary material 3 from: Evans T, Blackburn TM, Jeschke JM, Probert AF, Bacher S (2020) Application of the Socio-Economic Impact Classification for Alien Taxa (SEICAT) to a global assessment of alien bird impacts. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 123-142. https://doi.org/10.3897/neobiota.62.51150
Nine additional tables
Supplementary material 2 from: Evans T, Blackburn TM, Jeschke JM, Probert AF, Bacher S (2020) Application of the Socio-Economic Impact Classification for Alien Taxa (SEICAT) to a global assessment of alien bird impacts. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 123-142. https://doi.org/10.3897/neobiota.62.51150
Appendix B
Supplementary material 1 from: Evans T, Blackburn TM, Jeschke JM, Probert AF, Bacher S (2020) Application of the Socio-Economic Impact Classification for Alien Taxa (SEICAT) to a global assessment of alien bird impacts. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 123-142. https://doi.org/10.3897/neobiota.62.51150
Appendix A
Dataset: Using taxonomic treatments to assess an author's taxonomic range: the impactful career of Jocelia Grazia
<p>Here we present the dataset of the descriptive analysis of the bibliographic production of the world renowned heteropterist Dr. Jocélia Grazia. We analyzed a total of 220 published documents, including scientific papers, scientific notes, and book chapters. Additionally, we applied the Plazi workflow to extract taxonomic treatments, images, tables, and references from 75 different documents in accordance with the FAIR (Findability, Accessibility, Interoperability, and Reuse). The extracted treatments are available on TreatmentBank.</p> <p> </p> <p><strong>grazia-festschrift-basic_metadata.csv</strong>: this file contains basic metadata information about all 219 publications published up to 2020 (not inclusive), such as: File, Authors, Journal, Year, Volume, Issue, Pagination, doi.</p> <p><strong>grazia-festschrift-coauthors_data.csv</strong>: this file contains information on the co-authors, separated in different columns, with the country of their affiliation as well. Includes: filename, title, Author 1, Author 1 Country... Author 7, Author 7 Country.</p> <p><strong>grazia-festschrift-manual_counts.csv</strong>: contains manual counts on all of the 219 publications considered in this dataset: filename, title, number of treats, number of new species, number of genera, number of new family.</p> <p><strong>grazia-festschrift-preanalyses.csv</strong>: contains parameters assessed during the pre-analyses of the 219 publications considered in this dataset, including: File, Title, PDF Status, PDF Origin, OCR Quality, Was open-access?, Extracted, Has treatments, Includes Taxonomy?, Description of?, Species name as header?, Has keys, Has tables, Has materialCitation?, Has treatmentCitation?</p> <p><strong>grazia-festschrift-tb_stats-collecting_country.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Number of Materails Citation, Collecting Country.</p> <p><strong>grazia-festschrift-tb_stats-collecting_year.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Number of Materials Citations, Collecting Year. This wasn’t used in the dashboards included in the paper.</p> <p><strong>grazia-festschrift-tb_stats-collector_name.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Number of Materials Citations, Collector Name.</p> <p><strong>grazia-festschrift-tb_stats-overall.csv</strong>: results from the API call described in the paper, on 2020209, including general stats on the liberated data for the project: Number of Articles, Document Author, Number of Treatments, Number of Treatment Citations, Number of Materials Citations, Number of Figures, Number of Tables, Number of Bibliographic References.</p> <p><strong>grazia-festschrift-tb_stats-taxon_authority_names.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Taxon Authority Name.</p> <p><strong>grazia-festschrift-tb_stats-taxonomic_ranks.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Document UUID, Rank of Taxon.</p> <p><strong>grazia-festschrift-tb_stats-taxonomic_status.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Document UUID, Taxonomic Status.</p> <p><strong>grazia-festschrift-tb_stats-treatCit_cited_authors.csv</strong>: results from the API call described in the paper, on 2020209: Number of Treatments, Number of Treatment Citations, Cited Authors.</p> <p><strong>grazia-festschrift-to_Zenodo.csv</strong>: this was the file used to batch upload publications to Zenodo, including the resulting DOI, minted in case of absence of this PID. Includes: filename, motivation, upload_type, publication_type, publication_date, title, original_doi, journal_title, journal_volume, journal_issue, journal_pages, part_of_title, partof_pages, thesis_supervisor_01, thesis_01_affiliation, thesis_university_01, language, creator_1_name, creator_1_affiliation… creator_8_name, creator_8_affiliation, description, access_right, license, Keyword_1… Keyword_16, contributor_1_name, contributor_1_type, community_1, doi.</p>
Data from: Health impact assessment of air pollution in Valladolid, Spain
Objective: to estimate the attributable and targeted avoidable deaths of outdoor air pollution by ambient PM10, PM2.5 and O3 according to specific WHO methodology. Design: health impact assessment. Setting:City of Valladolid, Spain (around 300.000 residents). Data sources: demographics; mortality; pollutant concentrations collected 1999-2008. Main outcome measures: attributable fractions (AFs); attributable and targeted avoidable deaths (ADs; TADs) per year for 1999 – 2008. Results: Higher TADs estimates (shown here) were obtained when assuming as "target" concentrations WHO Air Quality Guidelines instead of Directive 2008/50/EC. ADs are considered relative to pollutant background levels. All-causemortality associated to PM10 (all ages): 52 ADs (95% CI: 39-64); 31 TADs (95% CI: 24 – 39). All-causemortality associated to PM10 (<5 years): 0 ADs (95% CI: 0-1); 0 TADs (95% CI: 0 –1). All-causemortality associated to PM2.5 (> 30 years): 326 ADs (95% CI: 217-422); 231 TADs (95% CI: 153 - 301). Cardiopulmonary and lung cancer mortality associated to PM2.5 (>30 years): o Cardiopulmonary: 186 ADs (95% CI: 74-280) ; 94 TADs (95% CI: 36 – 148). o Lung cancer : 51 ADs (95% CI: 21-73); 27 TADs (95% CI: 10 – 41). All-cause, respiratory and cardiovascular mortality associated to O3(all ages): o All-cause: 52ADs (95% CI: 25-77) ; 31 TADs (95% CI: 15 – 45). o Respiratory : 5 ADs (95% CI : -2 – 13) ; 3 TADs (95%% CI : -1 – 8). o Cardiovascular: 30 ADs (95% CI: 8-51) ; 17 TADs (95% CI: 5 – 30). Negative estimates which should be read as zero were obtained when pollutant concentrations were below counterfactuals or assumed risk coefficients were below 1. Conclusions: Our estimates suggest a not negligible negative impact on mortality of outdoor air pollution. The implementation of WHO methodology provides critical information to distinguish an improvement range in air pollution control.
Data from: Assessing impact of exogenous features on biotic phenomena in the presence of strong spatial dependence: a lake sturgeon case study in natural stream settings
Modeling spatially explicit data provides a powerful approach to identify the effects of exogenous features associated with biological processes, including recruitment of stream fishes. However, the complex spatial and temporal dynamics of the stream and the species' reproductive and early life stage behaviors present challenges to drawing valid inference using traditional regression models. In these settings it is often difficult to ensure the spatial independence among model residuals---a key assumption that must be met to ensure valid inference. We present statistical models capable of capturing complex residual anisotropic patterns through the addition of spatial random effects within an inferential framework that acknowledges uncertainty in the data and parameters. Proposed models are used to explore the impact of environmental variables on Lake sturgeon (Acipenser fulvescens) reproduction, particularly questions about patterns in egg deposition. Our results demonstrate the need to apply valid statistical methods to identify relationships between response variables, e.g., egg counts, across locations, and environmental covariates in the presence of strong and anisotropic autocorrelation in stream systems. The models may be applied to other settings where gamete distribution or, more generally, other biotic phenomena may be associated with spatially dynamic and anisotropic processes.
Data from: The assessment of science: the relative merits of post-publication review, the impact factor and the number of citations
Background: The assessment of scientific publications is an integral part of the scientific process. Here we investigate three methods of assessing the merit of a scientific paper: subjective post-publication peer review, the number of citations gained by a paper and the impact factor of the journal in which the article was published. Methodology/principle findings: We investigate these methods using two datasets in which subjective post-publication assessments of scientific publications have been made by experts. We find that there are moderate, but statistically significant, correlations between assessor scores, when two assessors have rated the same paper, and between assessor score and the number of citations a paper accrues. However, we show that assessor score depends strongly on the journal in which the paper is published, and that assessors tend to over-rate papers published in journals with high impact factors. If we control for this bias, we find that the correlation between assessor scores and between assessor score and the number of citations is weak, suggesting that scientists have little ability to judge either the intrinsic merit of a paper or its likely impact. We also show that the number of citations a paper receives is an extremely error-prone measure of scientific merit. Finally, we argue that the impact factor is likely to be a poor measure of merit, since it depends on subjective assessment. Conclusions: We conclude that the three measures of scientific merit considered here are poor; in particular subjective assessments are an error-prone, biased and expensive method by which to assess merit. We argue that the impact factor may be the most satisfactory of the methods we have considered, since it is a form of pre-publication review. However, we emphasise that it is likely to be a very error-prone measure of merit that is qualitative, not quantitative.
Data from: Global assessment of the impact of type 2 diabetes on sleep through specific questionnaires. A case-control study
Type 2 diabetes (T2D) is an independent risk factor for sleep breathing disorders. However, it is unknown whether T2D affects daily somnolence and quality of sleep independently of the impairment of polysomnographic parameters. Material and Methods: A case-control study including 413 patients with T2D and 413 non-diabetic subjects, matched by age, gender, BMI, and waist and neck circumferences. A polysomnography was performed and daytime sleepiness was evaluated using the Epworth Sleepiness Scale (ESS). In addition, 135 subjects with T2D and 45 controls matched by the same previous parameters were also evaluated through the Pittsburgh Sleep Quality Index (PSQI) to calculate sleep quality. Results: Daytime sleepiness was higher in T2D than in control subjects (p=0.003), with 23.9% of subjects presenting an excessive daytime sleepiness (ESS>10). Patients with fasting plasma glucose (FPG ?13.1 mmol/l) were identified as the group with a higher risk associated with an ESS>10 (OR 3.9, 95% CI 1.8-7.9, p=0.0003). A stepwise regression analyses showed that the presence of T2D, baseline glucose levels and gender but not polysomnographic parameters (i.e apnea-hyoapnea index or sleeping time spent with oxigen saturation lower than 90%) independently predicted the ESS score. In addition, subjects with T2D showed higher sleep disturbances [PSQI: 7.0 (1.0-18.0) vs. 4 (0.0-12.0), p<0.001]. Conclusion: The presence of T2D and high levels of FPG are independent risk factors for daytime sleepiness and adversely affect sleep quality. Prospective studies addressed to demonstrate whether glycemia optimization could improve the sleep quality in T2D patients seem warranted.
Datasets and codes for "Exploiting high-resolution ADS-B data for flight operation reconstruction towards environmental impact assessment"
<p>Here you can find all the datasets and Python codes used to obtain the results illustrated in "Exploiting high-resolution ADS-B data for flight operation reconstruction towards environmental impact assessment" (authors: Marco Pretto, Lorenzo Dorbolò, Pietro Giannattasio).</p><p>Folders are in the following order:</p><ul><li>0_files: flight tracking data (from the OpenSky Network) and open databeses</li><li>1_preproc: flight separation algorithm</li><li>2_preproc: runway assignment algorithm</li><li>3_proc: ground track reconstruction algorithm</li><li>4_postproc: postprocessing codes (for figures)</li></ul>
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.