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49 results for “Land Assessment”
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'
<p>Land use scenario calculation: Executable files, source code files and data files<br> This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD).</p> <p>We found a few fatal errors in the following code.<br> These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670).<br> /Step3/a01_calc_land_use.py<br> /Step3/a01_calc_land_use_std.py<br> /Step3/a01_calc_land_use_rate.py<br> /Step3/run03.bat</p>
BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment
<p>This dataset provides information on demographic variables related to population and employment as well as land use/land cover share on the basis of local administrative units (LAU). Within the BIOPLAT-EU project, this information is integrated into the webGIS sustainability assessment tool.<br> Main source of the administrative unit geometries is the spatial data set of local administrative units (LAU) (2016) provided by the European Commission (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/lau#lau19). The data set was extended by Level 2 administrative boundaries of Albania (https://data.humdata.org/dataset/albania-administrative-level-0-3-boundaries) and Level 3 administrative boundaries of Ukraine as of 2017 (https://data.humdata.org/m/dataset/ukraine-administrative-boundaries-as-of-q2-2017?force_layout=light)</p> <p>Data on demography (2016) for LAU + Albania was acquired using the Eurostat statistical database (https://ec.europa.eu/eurostat/web/main/data/database). To calculate land use share for LAU and Albania, Corine Land cover (CLC) data from 2018 was used (https://land.copernicus.eu/pan-european/corine-land-cover). CLC classes were summarized into the following classes: urban areas (UrAr), forest (Fo), permanent crops (PeCr), annual crops (AnCr), permanent meadows and pastures (PeMaPa), industrial sites (InSi), water and wetlands (We), others (Ot).</p> <p>For Ukraine data on demography provided by the State Statistics Survey of Ukraine (http://www.ukrstat.gov.ua/) . To calculate land use share per administrative unit, the land use map produced by Myroniuk et al. 2020 (https://doi.org/10.3390/rs12010187) was used. Based to this map shares of the following land use classes are calculated: urban areas (UrAr), forest (Fo), annual and permanent cropland (AnPeCr), grassland (Gra), water and wetlands (We), others (Ot).</p> <p> </p> <p><em><strong>Terms of use:</strong></em> These data are provided "as is". <em>The authors make <strong>no </strong></em><strong><em>warranty</em></strong><em>, representation, or guaranty of any type as to the completeness, accuracy, content or fitness for any particular purpose or use of any </em><strong><em>open data</em></strong><em> set made available here</em><em>, nor shall any </em><strong><em>warranties</em></strong><em> be implied with respec</em><em>t to the data provided.</em></p>
Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
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Data from: Assessing the effects of land‑use intensity on small mammal community composition and genetic variation in Myodesglareolus and Microtus arvalis across grassland and forest habitats
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Data from: Where money grows on trees: a socio-ecological assessment of land use change in an agricultural frontier
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OpenET model data for assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications
<h2>Overview</h2> <p>This dataset includes daily and monthly evapotranspiration (ET) data from the remote sensing models that comprise the [OpenET](https://openetdata.org/) ensemble as described in Melton et al., 2022 (https://doi.org/10.1111/1752-1688.12956); these data were extracted at specific locations within the contiguous United States that coincide with *in situ* measurement stations, including eddy covaraiance, Bowen-ratio, and lysimeter stations. Model ET data where extracted at each site in this dataset using flux footprints as described in Volk et al., (2023) (https://doi.org/10.1016/j.agrformet.2023.109307). These model data alongside the corresponding *in situ* ET data (https://doi.org/10.1016/j.dib.2023.109274) were subsequently used in the manuscript for the OpenET Phase II Intercomparison and Accuracy Assessment (https://doi.org/10.1038/s44221-023-00181-7). </p> <h3><br>Description of the data and file structure</h3> <p>The dataset is in a compressed (zipped) archive titled "OpenET_PhaseII_model_ET_dataset", so first it needs to be downloaded and extracted. The dataset is comprised of just three files. The first file is a Microsoft Excel file "Station_metadata.xlsx" that contains information about the *in situ* ET measurement stations where the OpenET model data was extracted. This file contains information such as site ID's, coordinates, land cover information, and site principal investigator (PI) contact information. Again, the corresponding *in situ* ET data are not included in this dataset. The other two files are tab-delimited text files containing timeseries the OpenET model data themselves, namely the daily ET [mm/day] and monthly ET [mm/month] as extracted for each model and the ensemble value as used in the OpenET Phase II Intercomparison and Accuracy Assessment. </p> <h3><br>Access information and code/software</h3> <p>OpenET data that was used here was produced using operational methods that are implemented on the Google Earth Engine platform. Monthly OpenET model data can be retrieved through Google Earth Data Catalog (e.g., https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0) or through the [online data explorer](https://openetdata.org/) or using the [OpenET API](https://openetdata.org/api-info/).</p>
Datasets for "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the datasets for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations".</p>
Data for submitted paper "Assessment of Land Reclamation Impacts on Coastal Hydrodynamics and Hydro-environment: A Case Study of Kau Yi Chau Artificial Islands in Hong Kong"
<p>Data for submitted paper "Assessment of Land Reclamation Impacts on Coastal Hydrodynamics and Hydro-environment: A Case Study of Kau Yi Chau Artificial Islands in Hong Kong"</p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
Riparian land-cover data and model code for: Multiple-region, N-mixture community models to assess associations of riparian area, fragmentation, and species richness
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Data from: Diet assessment of two land planarian species using high-throughput sequencing data
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Runoff modeling of a coastal basin to assess variations in response to shifting climate and land use: Implications for managed recharge
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Supplementary material 3 from: Vrebos D, Staes J, Broekx S, de Nocker L, Gabriels K, Hermy M, Liekens I, Marsboom C, Ottoy S, Van Der Biest K, van Orshoven J, Meire P (2020) Facilitating spatially-explicit assessments of ecosystem service delivery to support land use planning. One Ecosystem 5: e50540. https://doi.org/10.3897/oneeco.5.e50540
Calculation methods of the ecosystem services
Supplementary material 1 from: Vrebos D, Staes J, Broekx S, de Nocker L, Gabriels K, Hermy M, Liekens I, Marsboom C, Ottoy S, Van Der Biest K, van Orshoven J, Meire P (2020) Facilitating spatially-explicit assessments of ecosystem service delivery to support land use planning. One Ecosystem 5: e50540. https://doi.org/10.3897/oneeco.5.e50540
Land cover and land use classifications
Supplementary material 2 from: Vrebos D, Staes J, Broekx S, de Nocker L, Gabriels K, Hermy M, Liekens I, Marsboom C, Ottoy S, Van Der Biest K, van Orshoven J, Meire P (2020) Facilitating spatially-explicit assessments of ecosystem service delivery to support land use planning. One Ecosystem 5: e50540. https://doi.org/10.3897/oneeco.5.e50540
Overview ES specific datasets.
South Dakota Ag Land Soil Tables--Property Assessment
<p>The <a href="http://agland.sdstate.edu/Soil_Tables/">Ag land soil tables </a> displays data that is used to make baseline agricultural land assessments for property tax purposes in South Dakota. Users can hover over the points in the generated map to view the soil data. Users can also zoom and pan over the map to examine the soil data and background aerial imagery in more detail. Users can filter the points that are displayed on the map by the Land Capability Class of the soil by selecting or deselecting the boxes numbered 1-8 above or by selecting a specific soil name to map. Users can change the color bar of the map points by selecting the variable of interest. The soil data can be downloaded as a .csv or Excel file by selecting the upper tabs (i.e. Table 1, Table 2, and Additional Data). An article describing the background of the research and methods can be found in <a href="http://www.choicesmagazine.org/choices-magazine/submitted-articles/a-change-in-highest-and-best-use-policy-in-south-dakota-has-a-sizable-impact-on-agricultural-land-assessments">Choices Magazine</a> .</p>
Evaluating natural experiments in ecology: using synthetic controls in assessments of remotely-sensed land-treatments
Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks which rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large-scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational datasets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include: (1) a discrete set of treated and un-treated units, (2) a known date of treatment intervention, and (3) timeseries response data that includes both pre- and post-treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no-action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large-scale brush clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long-term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.
Dataset: First comprehensive assessment of industrial-era land heat uptake from multiple sources
<p><strong>Dataset Overview</strong></p> <p>This Zenodo repository contains a comprehensive dataset of global mean yearly land heat uptake (LHU) estimates for the historical period (all data sources) and the SSP585 scenario (exclusively for CMIP6 models). The dataset includes estimates from multiple data sources: gridded observations (OBS, 5 sources), reanalyses (REA, 7 sources), and CMIP6 simulations (CMIP6, 37 models). These LHU estimates were developed and first analyzed in <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">García-Pereira et al. (2024)</a>.</p> <p>The estimates were obtained using the one-dimensional heat conduction forward model (ConForM; <a href="https://doi.org/10.5281/zenodo.10371439" target="_blank" rel="noopener">García-Pereira et al., 2023</a>), forced with yearly global mean ground surface temperature data from each source over various time periods. The time periods include the full available range (fromINIT), as well as specific periods starting in 1950, 1960, and 1971, extending to the most recent data available. For a detailed explanation of the methods, rationale, main findings, and comparisons with previous geothermal LHU estimates, refer to <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">García-Pereira et al. (2024)</a>.</p> <p><br><strong>Dataset Contents</strong></p> <p>The dataset is provided in NetCDF format and is organized by data source type (OBS, REA, CMIP6) and time period (fromINIT, from1950, from1960, from1971). Each file follows the naming convention:</p> <blockquote> <p><em><source_type>_LHU_ym_gb_sum_<period>.nc</em></p> </blockquote> <p>where <em><source_type></em> indicates the data source (OBS, REA, CMIP6) and <em><period></em> specifies the starting time (fromINIT, from1950, from1960, from1971). For example, <em>OBS_LHU_ym_gb_sum_from1971.nc</em> contains LHU estimates derived from observational data starting in 1971. For files corresponding to from1950, from1960, and from1971, the dataset also includes mean and standard deviation calculations. Additionally, raw LHU estimates derived from CMIP6 subsurface temperature data are provided in the file <em>CMIP6/CMIP6raw_LHU_ym_gb_sum.nc</em>.</p> <p><br><strong>Citation instructions</strong></p> <p>If you use this dataset, please cite the following references:</p> <blockquote> <p>Garcia-Pereira, F. and González-Rouco, J. F. : "ConForM: a one-dimensional heat Conduction Forward Model", Zenodo, <a href="https://doi.org/10.5281/zenodo.10371439" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10371439</a>, 2023.</p> <p>García-Pereira, F., González-Rouco, J. F., Melo-Aguilar, C., Steinert, N. J., García-Bustamante, E., de Vrese, P., Jungclaus, J., Lorenz, S., Hagemann, S., Cuesta-Valero, F. J., García-García, A., and Beltrami, H.: "First comprehensive assessment of industrial-era land heat uptake from multiple sources", Earth Syst. Dynam., 15, 547–564, <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">https://doi.org/10.5194/esd-15-547-2024</a>, 2024.</p> </blockquote> <p><br><strong>Additional Resources</strong></p> <p>For further insight into the evolution of LHU, its role in terrestrial energy partitioning, and the limitations of state-of-the-art Earth System Models in representing it, we recommend exploring these additional publications:</p> <blockquote> <p>Cuesta-Valero, F. J., García-García, A., Beltrami, H., Smerdon J. E.: "First assessment of continental energy storage in CMIP5 simulations", Geophys. Res. Lett., 43, 5326–5335, <a href="https://doi.org/10.1002/2016GL068496" target="_blank" rel="noopener">https://doi.org/10.1002/2016GL068496</a>, 2016.</p> <p>Cuesta-Valero, F. J., García-García, A., Beltrami, H., González-Rouco, J. F., and García-Bustamante, E.: "Long-term global ground heat flux and continental heat storage from geothermal data", Clim. Past, 17, 451–468, <a href="https://doi.org/10.5194/cp-17-451-2021" target="_blank" rel="noopener">https://doi.org/10.5194/cp-17-451-2021</a>, 2021.</p> <p>Cuesta-Valero, F. J., Beltrami, H., García-García, A., Krinner, G., Langer, M., MacDougall, A. H., Nitzbon, J., Peng, J., von Schuckmann, K., Seneviratne, S. I., Thiery, W., Vanderkelen, I., and Wu, T.: "Continental heat storage: contributions from the ground, inland waters, and permafrost thawing", Earth Syst. Dynam., 14, 609–627, <a href="https://doi.org/10.5194/esd-14-609-2023" target="_blank" rel="noopener">https://doi.org/10.5194/esd-14-609-2023</a>, 2023.</p> <p>González-Rouco, J. F., Steinert, N. J., García-Bustamante, E., Hagemann, S., de Vrese, P., Jungclaus, J. H., Lorenz, S. J., Melo-Aguilar, C., García-Pereira, F., and Navarro, J.: "Increasing the depth of a Land Surface Model. Part I: Impacts on the soil thermal regime and energy storage", Journal of Hydrometeorology, 22(12), 3211-3230, <a href="https://doi.org/10.1175/JHM-D-21-0024.1" target="_blank" rel="noopener">https://doi.org/10.1175/JHM-D-21-0024.1</a>, 2021.</p> <p>Steinert N. J., González-Rouco, J. F., Melo Aguilar, C. A., García Pereira, F., García-Bustamante, E., de Vrese, P. Alexeev, V., Jungclaus, J. H., Lorenz, S. J., and Hagemann, S.: "Agreement of analytical and simulation-based estimates of the required land depth in climate models", Geophysical Research Letters, 48, e2021GL094273, <a href="https://doi.org/10.1029/2021GL094273" target="_blank" rel="noopener">https://doi.org/10.1029/2021GL094273</a>, 2021.</p> <p>Steinert, N. J., Cuesta-Valero, F. J., García-Pereira, F., de Vrese, P., Melo Aguilar, C. A., García-Bustamante, E., Jungclaus, J., González-Rouco, J. F.: "Underestimated land heat uptake alters the global energy distribution in CMIP6 climate models", Geophysical Research Letters, 51, e2023GL107613, <a href="https://doi.org/10.1029/2023GL107613" target="_blank" rel="noopener">https://doi.org/10.1029/2023GL107613</a>, 2024.</p> <p>von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R. P., Barker, P. M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C. M., García-García, A., Giglio, D., Gilson, J. E., Gorfer, M., Haimberger, L., Hakuba, M. Z., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F. W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A. H., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Steiner, A. K., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S. E., Wu, T., and Zemp, M.: "Heat stored in the Earth system 1960–2020: where does the energy go?", Earth Syst. Sci. Data, 15, 1675–1709, <a href="https://doi.org/10.5194/essd-15-1675-2023" target="_blank" rel="noopener">https://doi.org/10.5194/essd-15-1675-2023</a>, 2023.</p> </blockquote>
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.