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528 results for “Land cover”
A multi-species approach to manage effects of land cover and weather on upland game birds
<p class="Text">Loss and degradation of grasslands in the Great Plains region has resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify effects of land cover and weather changes on lesser and greater prairie-chickens (<i>Tympanuchus pallidicinctus </i>and <i>T. cupido</i>, respectively), northern bobwhites (<i>Colinus virginianus</i>), and ring-necked pheasants (<i>Phasianus colchicus</i>). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie-chicken and ring-necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie-chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie-chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring-necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.</p>
Dataset for a manuscript entitled Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia
<p>Dataset for the manuscript Variation in CO<sub>2</sub> and CH<sub>4</sub> Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia authored by Sari Juutinen, Mika Aurela, Juha-Pekka Tuovinen, Viktor Ivakhov, Maiju Linkosalmi, Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Johanna Nyman, Emmi Vähä, Marina Loskutova, Alexander Makshtas, and Tuomas Laurila</p> <p>Dataset consists of CO2 and CH4 flux data measured mainly using chamber method in arctic tundra</p>
Land capability for agriculture (partial cover)
<p>The Land capability for agriculture (partial cover) spatial dataset provides information on the types of crops that may be grown in different areas dependent on environmental and soil characteristics. This map covers much of the productive agricultural land in Scotland and it can be used to determine the areas most suited to growing crops or grazing livestock. </p> <p>The Land capability for agriculture map (partial cover) was originally mapped at 1:50 000 scale by field survey and was subsequently digitised. It shows the distribution of the different land classes across virtually all of Scotland’s cultivated agricultural land and adjacent uplands. The map should be cited as: 'Soil Survey of Scotland Staff (1984-87). Land Capability for Agriculture maps of Scotland at a scale of 1:50 000. Macaulay Institute for Soil Research, Aberdeen. 10.5281/zenodo.6322760'.</p> <p>The digital dataset contains information on the 'class' of soil. Soil classes range from Class 1 (land capable of producing a wide range of crops) to Class 7 (land of very little agricultural value). Land within Class 3 is subdivided to provide further information on potential yields; Classes 4 and 5 are further divided to provide information on grasslands; Class 6 is divided on the quality of the natural vegetation for grazing. Classes 1 to 3.1 are known as prime agricultural land.</p> <p>There is an accompanying booklet that describes the classification in more detail and set out the rules and guidelines to be used. This booklet should be referenced as: Bibby, J.S., Douglas, H.A., Thomasson, A.J. and Robertson, J.S. (1991) Land capability classification for agriculture. Soil Survey of Scotland Monograph. The Macaulay Institute for Soil Research. Aberdeen. ISBN -0-7084-0508-8.</p> <p>The spatial dataset is provided under the James Hutton Institute open data licence included within the zipped dataset.</p> <p>The maintenance of this dataset is funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download">https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download </a>or viewed at https://soils.environment.gov.scot.</p> <p>THE CLASSES<br> Class 1. Land capable of producing a very wide range of crops with high yields<br> Class 2. Land capable of producing a wide range of crops with yields less high than Class 1.<br> Class 3. Land capable of producing good yields from a moderate range of crops.<br> Class 4. Land capable of producing a narrow range of crops.<br> Class 5. Land suited only to improved grassland and rough grazing.<br> Class 6. Land capable only of use as rough grazing.<br> Class 7. Land of very limited agricultural value.</p>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m)
<p>Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/.</p> <p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.</p>
Linking land-use and land-cover transitions to their ecological impact in the Amazon
<p>Authors: Cássio Alencar Nunes, Erika Berenguer, Filipe França, Joice Ferreira, Alexander C. Lees, Julio Louzada, Emma J. Sayer, Ricardo Solar, Charlotte C. Smith, Luiz E. O. C. Aragão, Danielle de Lima Braga, Plinio Camargo, Carlos Eduardo Pellegrino Cerri, Raimundo Cosme, Mariana Durigan, Nárgila Moura, Victor Hugo Fonseca Oliveira, Carla Ribas, Fernando Vaz-de-Mello, Ima Vieira, Ronald Zanetti, Jos Barlow</p> <p>Code repository for the paper: Nunes et al. Linking land-use and land-cover transitions to their ecological in the Amazon. Proceedings of the National Academy of Sciences. 2022. In this repository we included codes and data that we used to run the all the analyses presented in the paper.</p>
Quantifying the impacts of 166 years of land cover change on lowland bird communities
<p><span>Land cover change for agriculture is thought to be a major threat to global biodiversity</span><span>. </span><span>However, </span><span>its ecological impact </span><span>has rarely been quantified in the Northern Hemisphere, as broad-scale conversion to farmland mainly occurred until the 1400s-1700s in the region, limiting the availability of sufficient data.</span><span> </span><span>The Ishikari Lowland in Hokkaido, Japan offers an excellent opportunity to address this issue, as hunter–gatherer lifestyles dominated in this region until the mid-19th century and land cover maps are available for the period of land cover changes, i.e., 1850-2016.</span> <span>Using these maps and a hierarchical community model of relationships between breeding bird abundance and land cover types, we estimated that broad-scale land cover change over a 166 year period was associated with more than 70% decline in both potential species-richness and abundance of avian communities.</span> <span>We estimated that the abundance of wetland and forest species declined by >88%, whereas that of bare-ground species increased by >50%. Our results suggest that broad-scale land cover change for agriculture has led to drastic reductions in wetland and forest species and promoted changes in community composition in large parts of the Northern Hemisphere. This study provides potential baseline information that could inform future conservation policies.</span></p>
The gridded 2-m air temperature data produced by Yao et al. (2021),the depth of major lakes in Wuhan, and and the land use/land cover data in 2000, 2010, and 2020
<p>The gridded 2-m air temperature data produced by Yao et al. (2021), the depth of major lakes in Wuhan, and the land use/land cover data in 2000, 2010, and 2020 used in the manuscript </p>
Land use/land cover maps
<p>LULC data were downloaded from the Resource and Environment Data Cloud Platform of China at http://www.resdc.cn/Datalist1.aspx?FieldTyepID=1,3</p>
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p>
Global potential invasion maps of traded birds under climate and land-cover change
<p>Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).</p>
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Korean peninsula's Land use and land cover data, Landsat NDVI data, and CO2 data
<p><strong>Paper: "Greening rate in North Korea doubles South Korea"</strong></p> <p>Data in this repository include Landsat NDVI data, Land use, land cover data, and CO2 data of the Korean Peninsula.</p> <p>Korean Peninsula's <strong>NDVI data</strong> 1986-2017: <a href="https://zenodo.org/record/7221229#.Y07A2HZBxdg">https://zenodo.org/record/7221229#.Y07A2HZBxdg</a></p> <p>Korean Peninsula's <strong>Landuse Data</strong> 1986-2017: <a href="https://zenodo.org/record/7214788#.Y07BD3ZBxdg">https://zenodo.org/record/7214788#.Y07BD3ZBxdg</a></p> <p>Korean Peninsula's <strong>CO2 Data </strong>1986-2017: https://zenodo.org/record/7260091</p> <p>All three types of data are combined and displayed in this repository link. You can view individual data based on the separate link behind the data.</p>
Baltic Sea Region Land Cover Plus - Training and Validation data
<p>Training and validation data used in creating Baltic Sea Region Land Cover Plus (BSRLC+) maps: <a href="https://doi.org/10.5281/zenodo.10653871" target="_blank" rel="noopener">Dataset link</a></p> <ul> <li><strong>landcover_training_data_2006_2018.gpkg</strong>: Points data of consistent land cover from 2006 to 2018</li> <li><strong>crop_training_data_{year}.gpkg</strong>: Points data of crop types derived from <a href="https://doi.org/10.1038/s41597-023-02517-0">EuroCrop dataset </a>in particular year (2019, 2021, 2023)</li> <li><strong>landcover_validation_{year}.gpkg</strong>: Points data of validation data derived from <a href="https://doi.org/10.1038/s41597-020-00675-z">LUCAS points </a>in particular year (2009, 2012, 2015, 2018)</li> <li><strong>Metadata.pdf</strong>: Information of land cover code in each dataset</li> </ul> <p>Version notes:</p> <p>Version 2: Correcting the validation data 2018 and Metadata file</p> <p>Version 1: Original upload</p>
Supplementary File 8; The full data set derived from formal quantitative surveys of land cover types and activities of humans, livestock and wildlife (Section 2.3) that were used for the analyses described in sections 2.4, 2.5 and 2.7
Open the record for dataset details and reuse information.
Land use/land cover in Iraq and Syria, 2000-2016
<p>The land-use/land-cover (LULC) dataset covers the area between 28.35-37.84°N and 35.29-49.14°E (Iraq and Syria), and shows annual cropland extent (single- and double cropping) as well as bare soil and other (non-cropland) vegetaton in the area, for the period 2000-2016.</p> <p>The data are structured in the following folders:</p> <p><strong>Classification</strong> - yearly data on LULC. The values represent the class IDs:</p> <p>0 - Bare Soil (Includes areas with little or no vegetation, including bare soil, fallow croplands, and artificial areas (e.g. urban or built up)</p> <p>1 - Cropland (single) (Active croplands, harvested once per year (April/May), usually low intensity agriculture such as cereals.)</p> <p>2 - Cropland (double) (Active cropland with multiple harvests, usually in April/May and September/October.)</p> <p>3 - Other vegetation (All other natural vegetation cover, as well as orchards. The phenology does not show any sharp decline in greenness during harvest periods.)</p> <p><strong>Cropland_data</strong></p> <p>Showing extent of cropland (class 1 or 2) each year. 1 = cropland, 0 = not cropland.</p> <p><strong>Fallow_data_Syria</strong></p> <p>Fallow is defined based on the mode cropland extent for the period 2000-2016, and then compared with the current year. For example, if a pixel's mode value (aka most frequent class) is 1 or 2 and for a given year the value is 1 or 2 it will be classified as not fallow (0). If it's mode value is 1 or 2 and the value of a given year is 0 or 3, it is classified as fallow.</p> <p>Values:</p> <p>1 - Fallow</p> <p>0 - Not fallow, actively cropped</p> <p><strong>Summary_maps</strong></p> <p>This folder includes two maps, one with the cropland frequency (i.e. number of years actively cropped) and one with the Mode LULC value for the 2000-2016 period (class ID as in the Classification data).</p> <p><strong>Update 2024-05-21: </strong></p> <p>Added data and code used for Eklund <em>et al</em> 2024 (<em>Environ. Res. Lett.</em> <strong>19</strong> 014077) in Eklund_et_al_2024Data_Code.7zip.</p>
OpenEarthMap Land Cover Mapping Few-Shot Learning Challenge
<pre><strong>***The challenge is over, please use this verion for post-challenge research. This version contains all the data and files, <br>except the labels of the query-set of the testset that are withheld and researchers can submit the predictions at the <br>challenge leaderboard for evaluation***</strong></pre> <p><strong>Overview</strong></p> <p>This challenge is co-organized with the <a href="https://sites.google.com/view/l3divu2024/overview" target="_blank" rel="noopener">L3D-IVU 2024 CVPR</a> workshop. The challenge is an extension of the <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap </a>benchmark dataset for a generalized few-shot semantic segmentation (GFSS) task. The challenge aims to evaluate and benchmark learning methods for few-shot semantic segmentation on the OpenEarthMap dataset to promote research on geoinformatics for social good. The motivation is to enable researchers to develop few-shot learning algorithms for high-resolution RS image semantic segmentation.</p> <p><strong>Page</strong></p> <p><a href="https://cliffbb.github.io/OEM-Fewshot-Challenge/" target="_blank" rel="noopener">https://cliffbb.github.io/OEM-Fewshot-Challenge/</a></p> <p><strong>Baseline</strong></p> <p>The baseline model for the challenge is available <a href="https://github.com/cliffbb/OEM-Fewshot-Challenge" target="_blank" rel="noopener">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>The <a href="https://codalab.lisn.upsaclay.fr/competitions/19210" target="_blank" rel="noopener">challenge leaderboard </a>is opened for post-challenge research to enable researchers to evaluate the predictions on the query-set of the testset.</p> <p><strong>Description</strong></p> <p>The dataset has been designed for remote sensing few-shot learning, particularly, for GFSS tasks in land cover mapping. The dataset consists of only 408 samples from the original <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap</a> dataset for RS image semantic segmentation. It extends the original 8 semantic classes of the OpenEarthmap benchmark to 15 classes, which is split into 7:4:4 for <em>train_base_class</em>, <em>val_novel_class</em>, and <em>test_novel_class</em> disjointed sets, respectively (i.e., <em>train_base_class</em> ∩ <em>val_novel_class</em> ∩ <em>test_novel_class</em> = ∅). The 408 samples are also split into 258 as `trainset`, 50 as `valset`, and 100 as `testset`. The `trainset` is for pre-training a backbone network. It contains only the images and labels of the <em>train_base_class</em> split. Both the `valset` and the `testset` consist of a <em>support set</em> and a <em>query set</em> for a <strong>5-shot</strong> with <strong>4 novel classes</strong> and <strong>7 base classes</strong> GFSS task. The `valset` and the `testset` contain the images and labels of the <em>val_novel_class</em> and the <em>test_novel_class</em> splits, respectively.</p> <p>The challenge is in two phases: development phase and evaluation phase. The `valset` is for the development phase and the `testsets` is for the evaluation phase. Both `valset` and `testset` have 20 image-label pair examples, 5-set examples for each of the 4 novel classes in the <em>support set</em>. The `valset` and the `testset` contain an additional 30 images and 80 images, respectively, in the <em>query set</em>, which are to be predicted using the 20 labelled images in their <em>support set</em>. The labels for each image in the <em>support sets</em> do not contain any of the <em>train_base_class</em> split. Also, in each 5-set examples, the labels contain only one novel class (i.e., one novel class per 5-set examples). However, in the `valset`, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>val_novel_class</em>; and in the `testset, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>test_novel_class</em> `. Note that both the <em>support set</em> and <em>query set</em> in the `valset` are different from the ones in the `testset`.</p> <pre><br>File Structure and Content (All files are in `.tif` format): ----------------------------------------------------------- 1. **trainset.zip**: - Contains `images` and `labels` folders - `images` folder: 258 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 258 segmentation masks of the images in the `images` folder. 2. **valset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 50 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 30 ``query set`` images in the `images` folder are withheld.<br>3. **testset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 100 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 80 ``query set`` images in the `images` folder are withheld.<br> 4. **train.txt**:<br> - Contains a list of file names in the `trainset.zip`.<br> <br>3. **val.json** and **test.json**:<br> - Contains a list of file names the in the `valset.zip` and `testset.zip`, respectively. Below is<br> the structure of the `val.json` and `test.json` files.<br> - fnames = {<br> {"support_set": {8: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 9: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 10: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 11: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"]},<br> {"query_set": ["filename_1.tif", "filename_2.tif", "filename_3.tif", ...<br> ...., <br> "filename_n.tif"]} <br> }<br> Land Cover Mapping Classes Strucure: ------------------------------------<br>1. **The `trainset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> }<br><br>2. **The `valset` and `testset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> # ***Novel classes***<br> 8: '',<br> 9: '',<br> 10: '',<br> 11: ''<br> }<br><br> - The class names for the ***Novel classes*** depends on the data set.<br><br> For the `valset`, the class names can be updated as:<br> {<br> 8: 'road type 2',<br> 9: 'river',<br> 10: 'boat & ship',<br> 11: 'agric land type 2'<br> }<br><br> For the `testset`, the class names can be updated as:<br> {<br> 8: 'vehicle & cargo-trailer',<br> 9: 'parking space',<br> 10: 'sports field',<br> 11: 'building type 2'<br> }<br><br><strong>License</strong></pre> <p>See <a href="../records/7223446" target="_blank" rel="noopener">OpenEarthMap</a></p>
Mitigating urban heat island through neighboring rural land cover: Dataset
<p>A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - <strong><em>Average_LST_GEE.sh</em></strong>; Codes for calculating urban development intensity - <em><strong>UrbanDevelopmentIntensity.py</strong></em>; Regression, Machine Learning, Interpretable Machine Learning Code - <em><strong>All</strong><strong><em>R</em>egression.py, SHAP.py, ALE.py</strong></em>; a zip file containing the data used in the calculations - <em><strong>OperationalData</strong></em>.<em><strong>rar.</strong></em></p>
Corine Land Cover 2018 V2020_20u1 vector
<p>Full coverage, downlodable copy of the European Union's Copernicus Land Monitoring Service information (CLMS) Corine Land Cover dataset in vector format (geopackage).</p> <ul> <li>Doi: https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0</li> <li>Release / Major version: V2020_20u1</li> <li>Projection: EPSG:3035</li> <li>Spatial coverage: Europe</li> <li>Spatial resolution: 25 ha/100 m</li> <li>Spatial representation: Vector</li> <li>Temporal extent: 2017-2018</li> <li>Position accuracy: 100 m or better</li> <li>Thematic accuracy: ≥ 85%</li> <li>Format: Vector / Geopackage (gpkg)</li> <li>Size: 8.9 GB</li> </ul> <p><br><strong>Licence:</strong></p> <p>Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:</p> <ul> <li>When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services and shall acknowledge that the Copernicus Land Monitoring Service products and services were produced “with funding by the European Union”.</li> <li>Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.</li> <li>Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union. </li> </ul> <p>Important: the user has all intellectual property rights to the products he/she has created based on the Copernicus Land Monitoring Service products and services.</p>
Data and R code associated to the publication: "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity"
<p>This R code and dataset accompany Hanna et al's 2019 publication in Conservation Biology titled "Effects of land use, cover and protection on stream and riparian ecosystem services and biodiversity". Read the "Metadata" tab of the data file and code annotations for more information. </p>
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.