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5,023 results for “landing”
ERA5-Land selected indicators daily aggregates for the Latin America region, 2000
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2000.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1999
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1999.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 1998
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 1998.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2013
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2013.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2012
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2012.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2007
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2007.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2004
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2004.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2008
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2008.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2015
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2015.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2010
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2010.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 1950-2022
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p><p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p><p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, from 1950 to 2022.</p><p> </p><p> </p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2022
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2022.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2021
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2021.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2020
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2020.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2018
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2018.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2019
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2019.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Typology of agricultural land systems of Germany at a resolution of 100 hectares.
The decline of farmland biodiversity has widely been recognized in society and politics. Many factors that negatively affect biodiversity are associated with agriculture. European policy instruments and measures, which aimed at mitigating these impacts, have not been successful in counteracting the negative trends of farmland biodiversity. There is a growing recognition that conservation policy instruments need to be spatially targeted, given the heterogeneity of agricultural landscapes and extent of agricultural intensification in Europe. For Germany, we developed a typology of agricultural land systems (ALS) that captures the regional characteristics of agricultural intensification. For this purpose, we applied a cluster-analysis integrating indicators for land cover, landscape structure, land-use intensity, climate and relief at a resolution of with a spatial resolution of 1 km². As a result, we present a typology of eight ALS ranging from large-scale, intensive arable farming to extensive grassland/forest mosaics in mountains. The data included in this package contain the typology ALS and the corresponding values for the indicators for each hexagonal grid cell of 1 km²cell size. The typology of ALS could be used as a spatial framework for regional targeting of conservation policy instruments and for monitoring regional-specific trends of biodiversity indicators and their drivers. The data are supplement to the publication: Pingel, M; Sietz, D; Röder, N; Klimek, S and Golla, B. (2025) Typology of Agricultural Land Systems to Support Tailored Agri-Environmental Schemes for Farmland Biodiversity: A Case Study from Germany. [Preprint]. http://dx.doi.org/10.2139/ssrn.5162566.
Valuing multiple ecosystem services under contrasting land use scenarios, Grand River Basin (Iowa and Missouri, USA), 2016
This dataset includes spatial and tabular data used to evaluate ecosystem service outcomes under three land-use scenarios (baseline, buffered, and productivity-based) within the Grand River Basin, spanning southwest Iowa and northwest Missouri. The dataset comprises geospatial layers and model inputs for land cover, soil characteristics, and hydrology, as well as outputs from the InVEST modeling suite for nutrient delivery ratio, sediment delivery ratio, carbon storage, and pollinator abundance. It also includes financial data used to estimate the net present value of bioenergy grassland systems, including enterprise budgets and biomass yield assumptions. The scenarios simulate the conversion of cropland to native grassland either adjacent to streams (Buffered) or on low-productivity soils (Productivity-based), and the dataset captures associated changes in land cover, ecosystem service indicators, and potential biomass production. This dataset is intended to support further research and planning related to landscape-scale conservation, ecosystem service valuation, and bioenergy development in agricultural regions.
Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis
Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increa
Land use designations, Andrews Experimental Forest, 1998-2011
Land use designations for the Andrews Experimental Forest including the headquarters site, late successional reserves (LSR) from the Northwest Forest Plan designations, designated control areas, quarry locations, and areas potentially suitable for experiments. The designated control areas and potentially suitable for experiences areas were identified in 1998. Quarry locations were updated in 2011. The data were gathered from a variety of source data (NW Forest Plan designations for Willamette National Forest, identification of sites from digital orthophotography, and from maps drawn by Andrews staff).
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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