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18 results for “Country-level”
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Climate Solutions Explorer - downscaled country-level IAM scenarios
<p><strong>This is a pre-release dataset and is subject to change.</strong></p> <p>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <strong><a href="https://www.climate-solutions-explorer.eu">www.climate-solutions-explorer.eu</a></strong></p> <p>The Mitigation (and Summary) Dashboards present mitigation information, i.e. emissions, energy and carbon sequestration, for over 200 countries and 10 regions. To present data for all countries, Integrated Assessment Model runs from the MESSAGEix-GLOBIOM model have been downscaled by using a methodology described in Sferra et al. 2021 <a href="#_ftn1">[1]</a>. The algorithm produces a range of pathways consistent with the underlying IAM-results, based on criteria such as historical data, planned capacities, country-available resource in the form of supply cost-curves, quality of governance as well as regional benchmarks based on IAM results. The data is provided from 2020 to 2070, for a limited set of variables used on the website.</p> <p>The scenarios included are:</p> <ul> <li><strong>Current Policies:</strong> Current Policies scenarios here are based on the implementation of national mitigation targets implemented by country without any further strengthening of action. Expected to lead to 2.7 °C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_CurPol_T45 scenario.</li> <li><strong>NDCs Delayed Action to 2030:</strong> Assumes trajectory based on the implemented NDCs until 2030, and then reduces emissions typically in line with a globally 2°C by 2100. The data is from the MESSAGEix-GLOBIOM_1.1 GP_NDC2030_T45 scenario.</li> <li><strong>Glasgow Pledges:</strong> "Glasgow Pledges" scenarios here are based on the pledges made by countries at the 2022 COP26 Glasgow Summit, and represent increased ambition, likely taking the world closer to below 2°C in 2100, but still some distance away from the aspirations of 1.5°C of the Paris Agreement. The data is from the MESSAGEix-GLOBIOM_1.1 GP_Glasgow scenario.</li> <li><strong>Glasgow Pledges+:</strong> "Glasgow Pledges+" scenarios drops the NDC pledges and expands mid-century strategy pledges to net-zero for all countries and regions. The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowP scenario.</li> <li><strong>Glasgow Pledges++:</strong> "Glasgow Pledges++" scenarios here aims at filling the gap between national mid-century strategies and the 1.5/2 °C global scenarios. This scenario builds upon the Glasgow+ scenario and anticipates the action (net-zero target year defined for each region) in 5 or 10 years (depending on the model’s time steps). The data is from the MESSAGEix-GLOBIOM_1.1 GP_GlasgowPP scenario.</li> </ul> <p><a href="#_ftnref1">[1]</a> Sferra, F. et al. 2021. Downscaling IAMs results to the country level – a new algorithm. IIASA Report. IIASA, Laxenburg, Austria. <a href="https://pure.iiasa.ac.at/17501">https://pure.iiasa.ac.at/17501</a>.</p> <p> </p> <p> </p> <p><strong>Release notes (v0.2)</strong></p> <p>This version brings improvements in:</p> <ul> <li>harmonization data source, now done for 2018 using PRIMAP</li> <li>calculation of Kyoto Gases for R10, and Kyoto Gases (incl. indirect AFOLU) for countries</li> <li>addition of R10 and EU27 region data</li> <li>Corrections to variable aggregation</li> </ul> <p> </p>
Figure 5 in Dragonflies from hot springs in Russia with a country-level checklist of species known to occur in geothermal environments
Figure 5. Odonata specimens from geothermal habitats of the Kunashir Island [RMBH]. (A) Mnais costalis, male, 29.vii.2011. (B) M. costalis, male, 29.vii.2011. (C) Anotogaster sieboldii, male, 26.vii.2011. (D) A. sieboldii, female, 24.vii.2011. (E) Orthetrum melania, male, 29.vii.2011. (F) O. melania, female, 29.vii.2011. (G) Sympetrum pedemontanum elatum, male, 26.vii.2011. (H) S. pedemontanum elatum, female, 24.vii.2011. Corresponding labels are presented below each specimen. (Photos: Yu. S. Kolosova).
Figure 4 in Dragonflies from hot springs in Russia with a country-level checklist of species known to occur in geothermal environments
Figure 4. Habitats, exuvium, and larva of Odonata in geothermal areas of the Kamchatka Peninsula. (A) Warm pool near the Karymshinsky hot springs, 12 June 2013. (B) Exuvium of Libellula quadrimaculata on the shore of this pool. (C) Lakelet Medvezhie in the Valley of Geysers, 13 August 2014. (D) Larva of Aeshna juncea collected from this lakelet. Scale bar = 2 mm. (Photos: O. V. Aksenova).
Figure 2. Hot spring habitats and a in Dragonflies from hot springs in Russia with a country-level checklist of species known to occur in geothermal environments
Figure 2. Hot spring habitats and a live dragonfly on the Kunashir Island. (A) Neskuchensky hot springs, a habitat of Sympetrum pedemontanum elatum, Anotogaster sieboldii, and Orthetrum melania, 26 July 2011. (B) Stolbovsky hot springs, a habitat of Mnais costalis, Anotogaster sieboldii, and Orthetrum melania, 29 July 2011. (C) Male of Orthetrum melania near the Neskuchensky hot springs, 26 July 2011. (Photos: Yu. S. Kolosova [A, C] and O. V. Aksenova [B]).
Figure 1 in Dragonflies from hot springs in Russia with a country-level checklist of species known to occur in geothermal environments
Figure 1. Map of sampling localities of Odonata in eastern Russia: Stolbovsky hot springs (1); Neskuchensky hot springs (2); Karymshinsky hot springs (3); and the Valley of Geysers (4).
Figure 3 in Dragonflies from hot springs in Russia with a country-level checklist of species known to occur in geothermal environments
Figure 3. Microhabitats in the Neskuchensky hot springs, Kunashir Island, and Odonata larvae collected from this geothermal source. (A) Scheme of microhabitats within the geothermal system with water and ground temperature measurements during the period of 24-26 July 2011 (before heavy monsoon rainfalls). The black symbols indicate collecting sites of Anotogaster sieboldii (circles) and Sympetrum pedemontanum elatum (squares) larvae. The color arrows indicate the oviposion sites of A. sieboldii before (green) and after (red) heavy monsoon rainfalls. (B) Larvae of Anotogaster sieboldii, 26 July 2011. Scale bar = 2 mm. (C) Larvae of Sympetrum pedemontanum elatum, 26 July 2011. Scale bar = 2 mm. (Photos: O. V. Aksenova).
Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
<p><span><span><span><span><span><span><span><span><span><span><span>In the context of growing societal demands for land based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested those hypotheses by investigating the bidirectional relations between changes in cropland area and intensity, using a global cross-country panel dataset over 1961-2016. We used a cointegration approach with additional tests to disentangle long and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, palm oil and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. Staple cereals such as wheat and rice manifested significant land sparing. In low-income countries, intensification driven by increases in total factor productivity was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yields increases for key tropical commodity crops. Our study design could allow addressing other complex long and short run causal dynamics in land and social-ecological systems.</span></span></span></span></span></span></span></span></span></span></span></p>
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
<p>The datasets contain country-level net and gross CO2 flux data for land use, land-use change and forestry (LULUCF) from various approaches as used in the paper "Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry" (<a href="https://doi.org/10.5194/essd-16-605-2024">Obermeier et al., 2024, <em>Earth System Science Data</em></a>).</p>
Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
Open the record for dataset details and reuse information.
Data for "Food demand displaced by global refugee migration1 has unequal effects on country-level water stress"
<p>Dataset associated with the paper "Food demand displaced by global refugee migration1<br> has unequal effects on country-level water stress" to appear in Nature Communications in 2023.</p>
GEDI L4B Country-level Summaries of Aboveground Biomass
This dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S). For comparison purposes, this dataset also includes national-scale National Forest Inventory (NFI) estimates of AGBD from the 2020 Global Forest Resources Assessment (FRA) published by the Food and Agriculture Organization (FAO, 2020) of the United Nations.The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth's surface. GEDI was launched on December 5, 2018, and is attached to the International Space Station (ISS). The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which consist of ~25 m footprint samples spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. The data are provided in comma-separated value (CSV) format.
Country-Level GDP and Downscaled Projections Based on the SRES A1, A2, B1, and B2 Marker Scenarios, 1990-2100
The Country-Level GDP and Downscaled Projections Based on the Special Report on Emissions Scenarios (SRES) A1, A2, B1, and B2 marker scenarios, 1990-2100, were developed using the 1990 base year GDP (Gross Domestic Product) from national accounts database available from the UN Statistics Division. SRES regional GDP growth rates were calculated from 1990 to 2100 based on the SRES marker model regional data and applied uniformly to each country that fell within the SRES-defined regions. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Country-Level Population and Downscaled Projections Based on the SRES A1, B1, and A2 Scenarios, 1990-2100
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Country-Level Population and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100
The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).
Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates
The Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates data set consists of country-level estimates of total, urban, and rural populations and land area, country-wide, that are in proximity to a nuclear power plant. This data set was created using a global data set of point locations of nuclear power plants, with buffer zones at 30km, 75km, 150km, 300km, 600km, and 1200km, and the Global Population Count Grid Time Series Estimates, Version 1 to estimate the population within each buffer zone for the years 1990, 2000, and 2010. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) Land and Geographic Unit Area Grids were used to estimate land area within each buffer zone. The GRUMPv1 Urban Extents Grid was used to further delineate population and land area estimates within urban and rural areas. All grids used for population, land area, and urban mask were of 1 km (30 arc-second) resolution.
Global Fire Emissions Indicators, Country-Level Tabular Data: 1997-2015
The Global Fire Emissions Indicators, Country-Level Tabular Data: 1997-2015 contains country tabulations from 1997 to 2015 for the total area burned (hectares) and total carbon content (tons). The annual total area burned is for all fire types per country. There are two groups of total carbon content (TCC), annual totals for all six fire types per country and annual totals for each of six fire types per country which include Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forest fires.
MERRA-2 avgM_2d_pm25_admin0, 2d, Single-Level, Country-Level Surface PM2.5 Monthly Mean Products V1 (M2_TMAX_PM25) at GES DISC
M2_TMAX_PM25 is a value-added product derived from the MERRA-2 aerosol monthly product M2TMNXAER_5.12.4 (or tavgM_2d_aer_Nx). The surface concentration of fine particulate matter (PM2.5) is calculated as the sum of individual aerosol components (organic carbon, black carbon, sulfate, sea salt, and dust) (Buchard et al., 2017) and is recast from the native MERRA-2 model grid. This data collection includes separate files for country-level (and territories) PM2.5 concentrations with and without population weighting applied. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
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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)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.