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5,023 results for “landing”
Land, carbon and biodiversity data for supply chain impact calculations
<p>Monitoring, halting and reversing land conversion is fundamental to meeting international biodiversity and climate targets, and agriculture is the major driver of land conversion. We present an open access set of global data for calculating land use change impacts of agricultural supply chains. These data, originally prepared for the LandGriffon service, include indicators of deforestation, conversion of natural ecosystems, greenhouse gas emissions, and loss of intact or high integrity ecosystems following international standards and guidelines for reporting and target setting in the agriculture, forestry, and land use sector. In order to assign impacts to agricultural production, we prepare data using a spatial adaptation of the statistical Land Use Change (sLUC) accounting approach distributing impact to human activities across the local area using a 50km radius. The results are high resolution global maps of impact per hectare of land occupation. These can then be combined with land footprint data, cropland extent, or productivity maps to calculate land use change related impacts for specific crop volumes sourced from specific regions. Carbon and deforestation results are validated against FAO statistics at the national level.</p>
Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt
<p>Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt in 2019/2020. Total of samples: 957. The process to collect them is described in Chaves, M., & Sanches, I. (2023). Improving crop mapping in Brazil's Cerrado from a data cubes-derived Sentinel-2 temporal analysis. Remote Sensing Applications: Society and Environment, 32, 101014. <a href="https://www.sciencedirect.com/science/article/pii/S2352938523000964">https://www.sciencedirect.com/science/article/pii/S2352938523000964</a> and Chaves, M., Soares, A., Mataveli, G., Sánchez, A., & Sanches, I. (2023). A semi-automated workflow for LULC mapping via Sentinel-2 data cubes and spectral indices. Automation, 4(1), 94-109. <a href="https://www.mdpi.com/2673-4052/4/1/7">https://www.mdpi.com/2673-4052/4/1/7</a>.</p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Data archive for journal paper "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments"
<p>The datasets archived here include data assimilation results presented in the journal paper, "Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments" (https://doi.org/10.1175/JHM-D-22-0198.1). The output was produced by combining land surface modeling (Noah-MP with HYMAP river routing) and Sentinel-1 backscatter data, applying a 1D Ensemble Kalman Filter using the NASA Land Information System. We provide Netcdf daily output files for 6 different experiments</p><p>- OLfd and OLgw: model-only (open-loop, OL) for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMfd and DASMgw: data assimilation (DA) with soil moisture (SM) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) <br>- DASMLAIfd and DASMLAIgw: data assimilation (DA) with soil moisture (SM) and leaf area index (LAI) updating for two different model settings (fd: free drainage and gw: SIMTOP groundwater option) </p><p>Each experiment directory contains five subdirectories (DAOBS, EnKF, ROUTING, RTM, SURFACEMODEL) with corresponding outputs as described in https://nasa-lis.github.io/LISF/LIS_users_guide/LIS_users_guide.html</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_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>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_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>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 2023
<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, for the year of 2023.</p>
Intra-urban variations in land surface phenology in a semi-arid environment
<p>Data repository for 'Intra-urban variations in land surface phenology in a semi-arid environment', ERL</p> <p>Contact: Ben Crawford, University of Colorado Denver (benjamin.crawford@ucdenver.edu)</p> <p>Data description:</p> <ul> <li>NDVI.zip: <ul> <li>MODIS NDVI geotif rasters for Denver study area</li> <li>Additional metadata provided in subdirectories</li> </ul> </li> <li>LST.zip: <ul> <li>Landsat LST geotif rasters for Denver study area</li> </ul> </li> <li>Tair.zip <ul> <li>Seasonal modeled air temperatures for Denver study area (as described in the manuscript and supplemental information)</li> </ul> </li> <li>Den470_LandCover_250m_WGS.tif <ul> <li>Denver study area 2018 land cover fractions, derived from 1 m data at https://data.drcog.org/</li> </ul> </li> </ul> <p> </p>
Sulfate Isotope and geochemical ice core data from Dronning Maud Land over the penultimate glacial termination
<p>Sulfur isotope data over the penultimate termination from the EPICA Dronning Maud Land (EDML) ice core as published in Fischer et al., Nature Geosciences, 2024. The file contains:</p> <ul> <li>measured geochemical and sulfur isotope data from the EDML ice core over the time interval 112-153 kyr before present</li> <li>deconvolution of different sulfur sources for the EDML ice core over the time interval 112-153 kyr before present</li> <li>reconstruction of atmospheric sulfate aerosol concentrations from the EDML ice core over the time interval 112-153 kyr before present</li> <li>measured geochemical and sulfur isotope data data from the shallow coastal B38 ice core over the time interval 1964-1968</li> </ul>
PROBA-V Land Use @100m
<p>The PROBA-V Land Use @100m map for 2015-2020 covers the pan-tropical zone (23°S to 23°N) and consists of six annual maps, each with four bands. Bands 1 (B1) and 2 (B2) represent the land use at the beginning and end of each year, classified into four categories: "oil palm plantation" (value 1), "other perennial plantation" (value 2), "tropical forest" (value 3), and "other land use" (value 0). For full years (2016-2019), change detection runs from January 1 to December 16, with B1 and B2 corresponding to land use just before January 1 (as the first change is detected on this date) and after December 16, respectively. For the first year (2015) the period runs from May 1 to December 16, and for the last year (2020) from January 1 to April 30. Band 3 (B3) assigns a biweekly cutting date and band 4 (B4) assigns a biweekly planting date (value 1-24). </p>
Replication data for "The uncertain future of protected lands and waters" - protected area base layer for Amazonia
<p>We created a database of terrestrial and coastal protected areas (PAs) for all nine Amazonian countries following the IUCN definition for PAs and including only state-designated and state-managed PAs. We used the best available sources of archival data, including original legal documents, to confirm information about PAs. We included PAs that currently exist, as well as those that existed previously but have been degazetted. We note that this database differs from the World Database of Protected Areas (WDPA) for several reasons:</p> <p>• we focus on nationally-designated PAs and omit international or local designations</p> <p>• we include previously protected areas</p> <p>• we exclude other area-based conservation interventions other than state-designated and state-managed PAs (such as indigenous lands, privately protected areas, recreational sites, and community based natural resource management areas) which are included in the WDPA in certain countries</p> <p>• We use the establishment date as provided in each PA’s gazettement legal document, rather than the Status Year field in the WDPA, which lists the year that the PA’s current designation was established (46)</p> <p>• We use the spatial extent as provided in each PA’s gazettement legal document, rather than the spatial extent provided in the WDPA. The spatial extent in the WDPA (Rep_Area) is reported by nations and may represent the area as measured in GIS or paper maps, rather than the legally gazetted area.</p> <p>See Table S16 for detailed information by country describing the sources of PA data used for the nine Amazonian countries. </p> <p>Citation of original paper: Golden Kroner, R. E., Qin, S., Cook, C. N., Krithivasan, R., Pack, S. M., Bonilla, O. D., Cort-Kansinally, K. A., Coutinho, B., Feng, M., Martínez Garcia, M. I., He, Y., Kennedy, C. J., Lebreton, C., Ledezma, J. C., Lovejoy, T. E., Luther, D. A., Parmanand, Y., Ruíz-Agudelo, C. A., Yerena, E., … Mascia, M. B. (2019). The uncertain future of protected lands and waters. <em>Science</em>, <em>364</em>(6443), 881–886. <a href="https://doi.org/10.1126/science.aau5525">https://doi.org/10.1126/science.aau5525</a></p>
Virtual RHI lidar scans retrieved in high-fidelity wake vortex simulations of landing aircraft under turbulent crosswind conditions - LES Lidar Simulator (LLS)
<p>This dataset contains two types of virtual measurements of multiple pulsed lidar integrated into high-fidelity hybrid RANS-LES wake vortex simulations of a landing Airbus A340 aircraft. Simulations have been performed for four different atmospheric conditions, varying in crosswind and therefore turbulence in the atmosphere.</p> <p>The two lidar simulator types are:</p> <ul> <li>LLS: LES Lidar Simulator (no noise, Signal to Noise Ratio >> 1). The analysis is based on the Range Gate Weighting function (RWF) [based on formulations in for example [1]]. Given the assumption of no instrument noise, no spectral analysis in the frequency domain is required.</li> <li>LLSn: LES Lidar Simulator with noise (realisitic Signal to Noise Ratio). Based on formulations in [2], where non-linear low-pass spatial filters accurately model real field measurements. Both background noise and aerosol noise contained in real lidar measurements are modeled. </li> </ul> <p>Provided are the raw lidar scans in RHI format, with the position and strength of the wake vortices within each lidar scan given by a pressure-vorticity tracking algorithm from the wake vortex simulation (simulation truth, ST). Misidentifications have been removed from the provided dataset. In addition to the labels for each wake vortex lidar scan, also wind background scans are provided for the crosswind simulation cases. These background scans give a better understanding of the prevailing atmospheric condition within which the aircraft lands. </p> <p>Furthermore the evaluation of the LLS scans using the Method of Radial Velocities (RV method) [3], a state-of-the-art wake vortex characterization method for lidar scans is provided for a subset of the LLS dataset. For analysing the impact of the RWF for LLS scans, selected lidar scans are also given in a 'naive' fashion (lidar scans assuming point measurements are possible).</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>Overview</strong></h3> <p><strong>In total each dataset (LLS and LLSn) contains 8 Aircraft landing simulation with associated </strong><strong>virtual lidar RHI scans:</strong></p> <ul> <li>2x no wind</li> <li>6x crosswind (specified at height b_0)</li> <li>0.5w_0 from port direction</li> <li>0.5w_0 from starboard direction</li> <li>1.0w_0 from port direction</li> <li>1.0w_0 from starboard direction</li> <li>2.0w_0 from port direction</li> <li>2.0w_0 from starboard direction</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <h3><strong>The dataset has 13 folders:</strong></h3> <div> <p><strong>POS1_POS2_POS8_scans = Individual scans of various simulations as well as background wind scans (no wake vortices). </strong></p> <p>POS1_POS2: 0_0, 0_5, 1_0, 2_0 corresponds to the wind, same as POS1_POS2 below</p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> </div> <div> </div> <div>Scan naming convention: POS1_POS2_POS3_POS4_POS5_POS6_POS7.csv</div> <div> </div> <div>Example: 0_5_D_248_8_161.2201878198302_168.4201878198237.csv</div> <div> </div> <div> <ul> <li>POS1_POS2: Together they form a factor which is multiplied with the initial descend speed of the wake vortex pair, w_0. The definition for w_0 can be found in [4]. It is common for the crosswind speed to be set according to the multiples of w_0. Due to the landing of the A340 aircraft, and the logarithmic nature of the wind simulation, we set the crosswind at the b_0 altitude of the simulation. For a definition of b_0, also see [4]. For the above example, 0.5w_0 is the crosswind speed. Note that every 26 lidar positions (POS4), the direction of the crosswind changes, if there is a crosswind.</li> </ul> </div> <div> - Crosswind approaches from port side: POS4: 0-25, 52-77, 104-129, 156-181, 208-233, 260-285</div> <div> - Crosswind approaches from starboard side: POS4: 26-51, 78-103, 130-155, 182-207, 234-259, 286-311</div> <div> <ul> <li>POS3: Label indicating which part of the numerical simulation this scan belongs to (a full landing simulation consists of A-D). A: Hybrid RANS-LES, B,C,D: temporal LES (in order). For the above example, D indicates that the lidar scan was recorded during the last part of the simulation. W: Prior to the wake vortex simulations with crosswind, a background wind scan for each lidar position is recorded.</li> </ul> </div> <div> <ul> <li>POS4: Specifies the number of the lidar, implications are the longitudinal position along the glide path of the aircraft. In the packs of lidar positions described in the description of POS1_POS2, the last lidar position is the closest to the touchdown point of the aircraft, smaller lidar positions within this pack are further away (in order) - also see the lid_plane_info directory. Note that the lidar position also adjusts the spectrum of the elevation angles used (and thus the size of the lidar scan). For that see the associated virtual lidar scan raw data. In the above example we have lidar position 248, thus crosswind approaching from starboard and rather mid-way of the longitudinal glide path direction.</li> </ul> </div> <div> <ul> <li>POS5: Specifies the number of the scan for this lidar position (POS4) and simulation (POS1__POS2). In the above example this is scan number 8.</li> </ul> </div> <div> <ul> <li>POS6_POS7: Specifies the simulation time within which the scan was measured during the aircraft landing simulation (POS1__POS2). In the above example this is 161.2201878198302 s to 168.4201878198237 s.</li> </ul> </div> <div>The scans purely with wind, an no wake vortices are stored in an additional directory within conv_scans. The case '0_0' (no wind) does not require wind scans. </div> <div> </div> <div>Within the scan files, we have a four (LLS) or five (LLSn) columns:</div> <div> <ul> <li>t: Simulation time</li> <li>ELE: Elevation angle [deg] of the lidar beam</li> <li>R: Range from lidar [m]</li> <li>v_r: LOS velocity (radial velocity) along lidar beam</li> <li>snr: Signal to Noise Ratio </li> </ul> <p> </p> <p><strong>wind_POS8_scans = Simulated lidar scans of the background wind for LLS and LLSn.</strong></p> <p>POS8: LLS or LLSn, corresponding to the type of lidar simulator (without or with instrument noise, respectively)</p> <p>Scans simulated here are in the same format as <em>POS1_POS2_POS8_scans</em>. Wind scans for point measurement scans are found in <em>naive_scan_subset </em>- the scans with POS4 = 0 should be used if more are available.</p> <p> </p> <p><strong>naive_scan_subset = Simulated lidar scans simulating velocity point measurements.</strong></p> </div> <div> <p>The format is the same as for <em>POS1_POS2_POS8_scans</em>, with the difference of being sorted first by wind subdirectories and then lidar number (LID).</p> <p> </p> <p><strong>labels = Labels of wake vortices for the above wake vortex scans. </strong></p> </div> <div>We have 4 main files, where each file represents the targets for one wind strength 0.0w_0, 0.5w_0, 1.0w_0, 2.0w_0.</div> <div>Within the labels files, we have a multitude of columns with different data:</div> <div> <ul> <li>#Time: Simulation (not scan) time.</li> <li>y_uw : lateral position in simulation domain of upwind (port) vortex.</li> <li>z_uw : height position in simulation domain of upwind (port) vortex.</li> <li>y_dw : lateral position in simulation domain of downwind (starboard) vortex.</li> <li>z_dw : height position in simulation domain of downwind (starboard) vortex.</li> <li>G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex [m^2/s].</li> <li>G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex [m^2/s].</li> <li>cr_uw: Core radius of upwind (port) vortex (in meters).</li> <li>cr_dw: Core radius of downwind (starboard) vortex (in meters).</li> <li>uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar.</li> <li>dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar.</li> <li>uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m).</li> <li>vortex age uw [s]: Age of upwind (port) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>vortex age dw [s]: Age of downwind (starboard) vortex (after first generated by aircraft at respective measurement plane of lidar).</li> <li>scan: Associated scan.</li> <li>LID: Associated lidar number.</li> </ul> <p>On top of the summarizing files for each simulation, in the subdirectory <em>individual</em>, the label for each scan is given in a separate file.</p> <p>The above gives information on the simulation truth, furthermore the file <em>labels_with_rv.csv</em> can be found here, where the following extra label columns are given for a subset of scans: [in the following Conv and Naive refer to LLS scans and point measurement scans, respectively]</p> <ul> <li>wind: Strength of crosswind, same as POS1_POS2 in <em>POS1_POS2_scans. </em>The sign corresponds to the wind direction. Negative is a crosswind from the port side of the aircraft, positive is a crosswind from the starboard side of the aircraft.</li> <li>Conv RV: 1 indicates the RV method has been evaluated for LLS scans, 0 if not.</li> <li>Naive RV: 1 indicates the RV method has been evaluated for point measurement scans, 0 if not.</li> <li>conv rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on LLS scans [m^2/s].</li> <li>conv rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on LLS scans.</li> <li>conv rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>conv rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on LLS scans.</li> <li>naive rv G_515_uw: Gamma 515 Circulation strength (see additional notes) of upwind (port) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv G_515_dw: Gamma 515 Circulation strength (see additional notes) of downwind (starboard) vortex computed using the RV method on point measurement scans [m^2/s].</li> <li>naive rv uw distance from lidar [m]: Horizontal distance of upwind (port) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv dw distance from lidar [m]: Horizontal distance of downwind (starboard) vortex from respective lidar using the RV method on point measurement scans.</li> <li>naive rv uw height [m]: Vertical distance of upwind (port) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>naive rv dw height [m]: Vertical distance of downwind (starboard) vortex from respective lidar (floor, as lidars are place at an altitude of 0 m) using the RV method on point measurement scans.</li> <li>uw phi [deg]: Elevation angle to the center of the upwind (port) vortex from the simulation truth.</li> <li>uw range [m]: Range from the lidar to the center of the upwind (port) vortex from the simulation truth.</li> <li>dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex from the simulation truth.</li> <li>conv rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on LLS scans.</li> <li>conv rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>conv rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on LLS scans.</li> <li>naive rv uw phi [deg]: Elevation angle to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv uw range [m]: Range from the lidar to the center of the upwind (port) vortex using the RV method on point measurement scans.</li> <li>naive rv dw phi [deg]: Elevation angle to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>naive rv dw range [m]: Range from the lidar to the center of the downwind (starboard) vortex using the RV method on point measurement scans.</li> <li>y err conv uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>y err conv dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>z err conv uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>z err conv dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>y err naive uw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>y err naive dw [m]: Cartesian localization error (RV minus ST) in y-direction (lateral) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>z err naive uw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>z err naive dw [m]: Cartesian localization error (RV minus ST) in z-direction (vertical) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>D err conv uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on LLS scans.</li> <li>D err conv dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on LLS scans.</li> <li>D err naive uw [m]: Euclidean distance error for the Cartesian coordinates to the vortex center between simulation truth and RV method of upwind (port) vortex center on point measurement scans.</li> <li>D err naive dw [m]: Euclidean distance for the Cartesian coordinates to the vortex center between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans.</li> <li>plus_minus_phi: Indicates whether a RHI lidar scan features a positive lidar scanning rate (plus), or a negative one (negative).</li> <li>conv err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on LLS scans [m^2/s]. </li> <li>conv err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on LLS scans [m^2/s]. </li> <li>naive err G_515_uw: Circulation error (RV minus ST) between simulation truth and RV method of upwind (port) on point measurement scans [m^2/s]. </li> <li>naive err G_515_dw: Circulation error (RV minus ST) between simulation truth and RV method of downwind (starboard) on point measurement scans [m^2/s]. </li> <li>conv err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>conv err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [deg].</li> <li>naive err uw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [deg].</li> <li>naive err dw phi: Elevation angle error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [deg].</li> <li>conv err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on LLS scans [m].</li> <li>conv err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on LLS scans [m].</li> <li>naive err uw range: Range from lidar error (RV minus ST) between simulation truth and RV method of upwind (port) vortex center on point measurement scans [m].</li> <li>naive err dw range: Range from lidar error (RV minus ST) between simulation truth and RV method of downwind (starboard) vortex center on point measurement scans [m].</li> </ul> <p><em>0_0_movement.csv</em> gives insight on the vertical movement on selected vortices in selected lidar scans of the 0_0 wind case. </p> <p>The following data columns exist within the file (note csv file with ; separator):</p> <ul> <li>Str_dw: Vertical movement of the downwind (starboard) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>Prt_uw: Vertical movement of the upwind (port) vortex, 0 is neutral, -1 is downward, and 1 is upward.</li> <li>scan_code: Identfies associated lidar scan with POS3_POS4_POS5 as in <em>POS1_POS2_scans.</em></li> </ul> <p> </p> <p><strong>lid_plane_info = Additional guidance on the simulated lidars.</strong></p> <p>Each simulation case has a separate file due to minimal ground speed changes of the aircraft. </p> <p>The following data columns exist within each file:</p> <ul> <li>Index: Gives the lidar number corresponding to POS4 and LID.</li> <li>Plane Pos [x]: Gives the longitudinal position of the lidar with respect to the glide path of the aircraft in meters.</li> <li>time plane passed [s]: Gives the simulation time when the aircraft first passes the measurement plane (related to the previous column.</li> <li>height plane passed [m]: Aircraft altitude at the respective lidar positon.</li> <li>lateral position lidar from GP [m]: Lateral position of the lidar from the glide path of the aircraft.</li> </ul> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> </div> <h3>Additional Notes: </h3> <div> <ul> <li>Gamma 515 circulation [4]: The Gamma 515 circulation is the averaged circulation of a vortex evaluated at radii 5-15m from the vortex center. </li> </ul> </div> <div> <div> <ul> <li>Lidar scans may contain 2 or 1 vortex. In the case of 1 vortex, the other has G_515_?? set to nan. Note that the position may still available in the labels data sets, but only vortices with available G_515_?? values should be used.</li> <li>The upwind and downwind definitions should purely be taken as names, rather than physical meaning. The names are derived from the simulation, due to mirroring and other post-processing these can be misleading however.</li> </ul> </div> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> <p><strong>Funding</strong>: This dataset was generated within the mFUND <a href="https://bmdv.bund.de/SharedDocs/DE/Artikel/DG/mfund-projekte/kiwi.html">KIWI project</a> funded by the Federal Ministry for Digital and Transportation Germany and the DLR undertaking "Wetter und Disruptive Ereignisse".</p> <p><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></p> <p><strong>Acknowledgements:</strong></p> <p>The necessary RANS simulations were performed as part of the EU-funded AWIATOR by DLR's Institute of Aerodynamics and Flow Technology.</p> <p>We acknowledge Airbus for the allowance to use the RANS data.</p> <p>The LES was performed with the incompressible Navier-Stokes code MGLET [5] and kindly provided by the Technical University of Munich, Hydromechanics.</p> <p>The wake vortex simulations were computed on the high performance computer SuperMUC-NG by Leibniz-Rechenzentrum (LRZ).</p> </div> <div><strong>--------------------------------------------------------------------------------------------------------------------------------------------</strong></div> <div> </div> <div><strong>References</strong>: </div> <div> </div> <div>[1] Robey, Rachel, and Julie K. Lundquist. "Behavior and mechanisms of Doppler wind lidar error in varying stability regimes." <em>Atmospheric Measurement Techniques</em> 15.15 (2022): 4585-4622.</div> <div> </div> <div>[2] Stephan, Anton, Norman Wildmann, and Igor Smalikho. "Effectiveness of the MFAS Method for Determining the Wind Velocity Vector from Windcube 200s Lidar Measurements." <em>Atmospheric and Oceanic Optics</em> 32.5 (2019): 555-563.</div> <div> </div> <div>[3] Smalikho, Igor., et al. "Method of radial velocities for the estimation of aircraft wake vortex parameters from data measured by coherent Doppler lidar." <em>Optics Express</em> 23.19 (2015): A1194-A1207.</div> <div> </div> <div>[4] Gerz, Thomas, Frank Holzäpfel, and Denis Darracq. "Commercial aircraft wake vortices." <em>Progress in Aerospace Sciences</em> 38.3 (2002): 181-208. </div> <div> </div> <div>[5] Manhart, Michael. "A zonal grid algorithm for DNS of turbulent boundary layers." <em>Computers & fluids</em> 33.3 (2004): 435-461.</div>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
Land based IMTA culture of abalone (Haliotis tuberculata) juvenile, anemone (Anemona sulcata) and Ulvella lens
<p>France Haliotis implemented a co-culture trial in abalone (Haliotis tuberculata) nursery tanks in a flow through system in order to improve growth and survival of abalone juveniles, reduce amphipods and copepods populations while recycling nutrients</p>
Underlying data - Digital Twin for Rainbow Trout (Oncorhynchus mykiss) land-based aquaculture
<p>Datasets for replicating Figures 5, 6, 7 and 8 of the article "Digital twins for land-based aquaculture: a case study for rainbow trout (<em>Oncorhynchus mykiss</em>)", by Adriano C. Lima, Edouard Royer, Matteo Bolzonella, and Roberto Pastres.</p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
Europe and China Refined Land cover (ECRLC) (10m)
<p>Europe and China Refined Land cover (ECRLC) V2 is a 10 m Sentinel 1 & 2-based land cover database for Paris Region (Europe), Aarhus (Europe), Velika Gorica (Europe), Beijing (China), Shanghai (China), and Ningbo (China) for 2020.</p>
Multiple Land-use / Land-cover Dataset (MLULC)
<p>This dataset covers the French metropolitan territory (500,000km²). It includes</p> <ul> <li> <p>Six open access land-cover maps from various providers (<a href="https://land.copernicus.eu/pan-european/corine-land-cover">CLC</a>, <a href="https://land.copernicus.eu/global/products/lc">CGLS-LC100</a>, <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?collection=OSO">OSO</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE cover</a>, <a href="https://geoservices.ign.fr/ocsge">OCS-GE use</a>, <a href="https://www.data.gou v.fr/en/datasets/mode-doccupation-du-sol-mos-en-11-postes-en-2017/">MOS</a>).</p> </li> <li> <p> A May 2019 Sentinel-2 L3A mosaic ( cloudless image using all maps available during a month) including RVB and NIR. Provided by <a href="https://theia.cnes.fr/atdistrib/rocket/#/search?page=1&collection=SENTINEL2&processingLevel=LEVEL3A">Theia</a>.</p> </li> <li> <p>A Manually built ground truth of 2300 random points annotated with their labels in each map nomenclature.</p> </li> <li> <p>A consolidated ground truth with the original 2300 and 400 non-random points focusing on rare classes.</p> </li> <li> <p>A suggested train/val/test split (60%,5%,35%). Note that all ground truth points belong to patches of the suggested <strong>test</strong> set.</p> </li> </ul> <p>Since this dataset is intended to be used with a deep learning algorithm, the data is split into tiles of 6x6km² following a grid given with the dataset.</p> <p>More information is provided in README.</p> <p>Note that exception made of the ground truth, all the data (Land covers and Sentinel-Images) aren't our property and are only shared as authorized by their respective original license.</p>
Figure 3: Scheme of the procedure adopted for implementing the Sand Dune Acts of 1903/1908, to reclaim the lands affected by sand drifting
<p>Figure 3 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
Discrete Land-Water Tiles Dataset
<p>A quad-tree based dataset of land and water tiles with world-wide coverage in WGS84 coordinate system (EPSG:4326) and a resolution of up to quad-level 18 (~150m height and width at the equator). This data separates the world into land and water tiles with any 4-tuple of tiles of the same classification value being recursively aggregated into their respective larger parent tiles. E.g. the quadkeys "3120", "3121", "3122", "3123" are aggregated into "312" if (and only if) they all share the same classification and would again be aggregated into "31" if "310", "311" and "313" also share that classification.</p> <p>The dataset is provided as a comma separated value (CSV) file and a spatialite database file with roughly 133 million entries and consists of the following fields:</p> <p>- classification - classification of tiles into land (0) or water (5)<br> - quadkey - the unique identifying quadkey of a tile<br> - size_tile_area_aea - the tile size in meter in Albers Equal Area (AEA) projection<br> - size_land_area_aea - the size of the land parts of a tile in AEA projection (only in coastal tiles)<br> - size_water_area_aea - the size of the water parts of a tile in AEA projection (only in coastal tiles)<br> - geometry - the tile geometry in EPSG:4326<br> - land_area - the land polygon geometry in EPS:4326 (only in coastal tiles)<br> - water_area - the water polygon geometry in EPSG:4326 (only in coastal tiles)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.