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5,023 results for “landing”
CESM2 land output data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land output data from CESM2 which was generated in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2015-2100. The files are labelled according to the experiments they were generated from (base, MF (Max Forest) and No LULCC). Output fields are as follows:</p> <p>discharge_plus_runoff: surface water availability (river discharge plus surface runoff), units m^-3 s^-1</p> <p>EFLX_LH_TOT: total latent heat flux from land to atmosphere, units W m^-2</p> <p>QFLX_EVAP_TOT: total evapotranspiration (canopy evaporation plus canopy transpiration plus soil evaporation), units kg m^-2 s^-1</p> <p>SOILLIQ: soil liquid water content, units kg m^-2</p> <p>SW_surface_albedo: surface albedo, units fraction</p> <p>TSA: 2m air temperature, units K</p> <p>VEGWP: vegetation water potential, units m</p> <p> </p> <p>All data were generated and processed by James A. King.</p>
CESM2 land use data for study on hydrological impacts of large-scale forest expansion
<p>This repository contains the land use/ land cover input data for CESM2 which was used in the study investigating the hydrological impacts of global-scale forestation. The datasets cover the period 2000-2100. The files correspond to experiments described in the study as follows:</p> <p> </p> <p>Base: landuse.timeseries_0.9x1.25_SSP1-2.6_78pfts_CMIP6_simyr2000-2100_c220715.nc</p> <p>Max Forest: landuse.timeseries_0.9x1.25_hist_78pfts_SSPRFAFRS_SSP1_edit_xarray_4_simyr2000-2100_c221024.nc</p> <p>No LULCC: landuse.timeseries_0.9x1.25_hist_78pfts_SSPNOLULCC_3_simyr2000-2100_c221025.nc</p> <p> </p> <p>Files were created in collaboration by James A. King, James Weber, Peter Lawrence, and Stephanie Roe.</p> <p> </p>
Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers
<p><strong>Context</strong>:<em> </em>There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied.</p> <p><strong>Objectives</strong>: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season.</p> <p><strong>Methods</strong>: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales.</p> <p><strong>Results</strong>:<em> </em>We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. </p> <p><strong>Conclusions</strong>:<em> </em>Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.</p>
Land use change converts temperate dryland landscape into a net methane source
<p>Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12% respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally.</p>
Database for: Meta-analysis of the impact of land use intensification on earthworms in global agroecosystems
<p>The dataset comprises a compilation of studies investigating the impact of land use intensification on earthworms across global agroecosystems. Extracted from peer-reviewed publications, the dataset includes various fields such as climate characteristics are described using the Köppen-Geiger climate classification system. Soil properties such as type, texture, pH, and organic content are documented. Additionally, details regarding experimental parameters like replicates, sampling depth, and extraction methods are provided. Furthermore, the dataset encompasses information on agricultural practices including herbicide, insecticide, pesticide usage, fertilizer type and rate, grazing, tillage methods, and days after tillage for earthworm collection. Abundance, diversity, and their associated metrics are recorded for both control and treatment sites.</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2011_2015)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2016_2020)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (LAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Fig. 6 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 6. Orbital regions of species of the family Gecarcinidae in Japan. A, Gecarcoidea lalandii, RUMF-ZC-8137, male (73.7 × 54.6 mm); B, Cardisoma carnifex, RUMF-ZC-3710, male (53.5 × 45.0 mm); C, Epigrapsus politus, RUMF-ZC-1342, male (19.6 × 15.6 mm); D, Epigrapsus notatus, RUMF-ZC-4028, male (31.0 × 25.3 mm); E, Tuerkayana hirtipes, RUMF-ZC-4721, female (50.2 × 41.3 mm); F, Tuerkayana rotundum, RUMF-ZC-4004, female (52.2 × 40.2 mm). Abbreviations: EOA, external orbital angle; IFS, inferior sinus; PS, pterygostomial region; SC, suborbital crista; SM, suborbital margin; SO, suborbital region.
Fig. 5. Relationships between carapace length and P4 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 5. Relationships between carapace length and P4 (A) merus, (B) carpus, and (C) propodus lengths for three Discoplax species. Symbols: closed, males; open, females; stars and circles, D. gracilipes from Japan and Philippines, respectively; triangles, D. longipes; squares, D. michalis.
Fig. 3 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 3. Orbital region and left cheliped of Discoplax gracilipes, RUMF-ZC-5727, male (36.1 × 31.7 mm). Abbreviations: EOA, external orbital angle; PS, pterygostomial region; SC, suborbital crista; SO, suborbital region. Arrowhead indicates plectrum of stridulatory mechanism.
Fig. 2 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 2. Discoplax gracilipes from Hateruma Island, Japan, RUMF-ZC 5727, male (36.1 × 31.7 mm). A, Dorsal view; B, ventral view.
Fig. 1 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 1. Distribution map of Discoplax gracilipes (star, from Japan; circles, from Philippines), D. longipes (triangles), and D. michalis (square). Symbols with black margin indicate records with mitochondrial DNA sequences; symbols without margin indicate records based on both presently observed specimens and previous studies. Type locality of each species is shown with underbars.
Fig. 4 in A Distant Record of the Cavernicolous Land Crab, Discoplax gracilipes (Crustacea: Decapoda: Gecarcinidae) from Japan
Fig. 4. Male left first gonopods (G1s) of three Discoplax species. A–D, Discoplax gracilipes, Hateruma, Japan (A, B: RUMF-ZC-5727, 36.1 × 31.7 mm), and Libaong, Philippines (C, D: ZRC 2001.2302, 35.8 × 30.9 mm); E, F, D. longipes, Lifou Island (ZRC 2001.1150, 43.6 × 38.5 mm); G, H, D. michalis (RUMF-ZC-1329, 50.9 × 43.3 mm). A, C, E, F, Ventral view; B, D, F, H, dorsal view. Setae of all G1s were partially removed.
Figure 10 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 10: PCA biplot of environmental parameters and abundance of microarthropods with month as grouping factor. sav ctrl: savanna; mb: maize bare; mbnpk: maize with chemical NPK fertilizer; mmdom: maize with dead organic matter.
Figure 9 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 9. PCA biplot of environmental parameters and abundance of microarthropods with treatment as grouping factor. sav ctrl: savanna, mb: maize bare, mbnpk: maize with chemical NPK fertilizer, mmdom: maize with dead organic matter.
Figure 8 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 8. Oribatida and Gamasina abundance (tsd. ind./m2) in plots (mb: maize bare, mmdom: maize with DOM). Abundances being significantly different from each other are marked by different letters for each microarthropod group (Wilcoxon signed rank test [p ˂ 0.05]). Error bars = standard error.
Figure 7 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 7. Collembola, Oribatida and Gamasina abundance (tsd. ind./m2) in plots (mb: maize bare, mbnpk: maize with chemical NPK). Abundances being significantly different from each other are marked by different letters (Wilcoxon signed rank test [p ˂ 0.05]). Error bars = standard error.
Figure 6 in Effect of three different land use types on the temporal dynamics of microarthropod abundance in the high Guinean savanna of Ngaoundéré (Adamawa, Cameroon)
Figure 6. Effect of Maize cultivation on Collembola, Oribatida and Gamasina in control plots (sav ctrl: savanna, mb: maize bare). Abundances being significantly different from each other are marked by different letters for each microarthropod group (Wilcoxon signedrank test [p ˂ 0.05]). Error bars = standard error.
ScienceDex guides
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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.