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5,023 results for “landing”
Coding frame and dataset for the study: Land-use governance: The interplay of social, market, and policy drivers – A global systematic review
<p>This file entails the coding frame and dataset used to conduct the systematic literature review "Land-use governance: The interplay of social, market, and policy drivers – A global systematic review".</p> <p>This study was first published as Chapter 2 of the PhD Dissertation "From soil to society - Rethinking governance for multifunctional land use and management" (Elsa L. Dingkuhn, 2025), and in a modified form in the journal Earth System Governance (Dingkuhn et al. 2025).</p> <p>The file consists of three sheets:<br>- Coding frame: Includes coding instructions and definitions used to extract and categorize the data.<br>- Variables: A list of dataset variables with explanations.<br>- List of included studies: The 81 studies from which the data was sourced.<br>- Data_List of observations: The dataset itself, consisting of 718 observations extracted from the included studies.</p>
Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.
<p>Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the <strong>land surface temperature (heatmaps)</strong> of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets.</p> <p>There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health. </p> <p>The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows.</p> <p>- 2013-2017<br>- 2014-2018<br>- 2015-2019<br>- 2016-2020<br>- 2017-2021<br>- 2018-2022<br>- 2019-2023</p> <p>The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>.</p> <p>The format of this dataset is organized in two ZIP format files:</p> <p>- LANDSAT_8_L2SP_000000-milan_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-logrono_LST_peak.zip</p> <p>- LANDSAT_8_L2SP_000000-athens_LST_peak.zip</p> <p>Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST.</p> <p>The values of the images are in degree Celsius and nodata value is -9999.</p> <p> </p>
Taiwan land cover data LUH2 SSPs
<p>This new version updates the LU map of Taiwan, downscaled from the LUH2 SSPs dataset, for the years 2016 to 2099 at a spatail resolution of 500 m by 500 m. Taiwan is defined within the LUH2 dataset boundaries, ranging from 120° to 122°E and 21.5° to 25.5°N. The dataset includes five scenarios: SSP1 with low challenges from adaptation and mitigation; SSP2 with intermediate challenges; SSP3 with high challenges; SSP4 where adaptation challenges dominate; and SSP5 where mitigation challenges dominate.</p> <p>The future land-use share was directly extracted from the LUH2 dataset at a spatial resolution of 25 km x 25 km. To downscale this information for Taiwan, a long-term land-use change/transition probability map is required as a reference to allocate future land types from coarse to fine spatial resolution. In this study, we applied the long-term land-use change/transition probability map derived from the (potential land-use change, PLC) method. The spatial allocation algorithm integrates PLC maps from historical LULCC reconstructions with gross change information from future land-use maps based on the LUH2 dataset.</p> <p>For a detailed description of the downscaling approach, please refer to the study "Navigating Land-Use Trends: Bridging Present Realities and Future Projections in Taiwan" by Chen et al. (in submission).</p> <p>If you have any questions, please contact Dr. Yi-Ying Chen at Academia Sinica, Taiwan, via email: yiyingchen@gate.sinica.edu.tw.</p>
New Zealand Vertical land movement and sea rise projections
<p><strong>UPDATE from version v3: This update corrects a missing scenario (SSP3-7.0) with no VLM from the SLR projections tables. It also includes updated 0 entries for 2005 for both the VLM and noVLM projections.<br></strong></p> <p> </p> <p>This dataset provides the estimated vertical land movement around the New Zealand coast and associated sea rise projections described by Naish et al., 2024: </p> <p>Naish, T. et al. (2024) The significance of vertical land movements at convergent plate boundaries in probabilistic sea-level projections for AR6 scenarios: the New Zealand case. <em>Earth's Future</em></p> <p><em>README:</em></p> <p><strong>NZ SeaRise Data Description</strong></p> <p> </p> <p>This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0). Users can download two data sets for selected sites: (1) Site details and (2) Sea level projections. A brief description of these data is included below.</p> <p>1. Site Details</p> <p>Provides location data and estimates of vertical land movement for each site.</p> <p>For the VLM file:</p> <p>id = site location</p> <p>Lon = longitude</p> <p>Lat = latitude</p> <p>Vertical Rate = average annual vertical velocity in mm/yr</p> <p>Vertical Rate BOP corrected (mm/yr) = average annual vertical velocity in mm/yr with a correction for a movement (see Hamling et al., 2016, Hamling et al., 2022) for details. <strong>NOTE: These are the vertical rates used for the sea level projections.</strong></p> <p>1-sigma uncertainty = Error estimate for vertical velocity in mm/yr</p> <p>Number of obs = Number of individual scatterers and/or GNSS used to estimate the vertical rate.</p> <p>QF = Quality factor for vertical velocity estimates of the land surface derived from InSAR data averaged for 2 km-spaced sites (1=good, 5=poor). This factor considers the number of observations available for each coastal location, the radial distance used to bin the observations and the distance to the nearest GNSS station. After selecting the optimal search radius, a distance weighted mean is calculated for of all the points with additional weight given to any available GNSS observations.</p> <p>Average distance between coastal point and observations =Average distance (in km) of all the points used to estimate the vertical rate from the coastal site indicated by the ID an lon, lat values.</p> <p>2. Sea level projections tables</p> <p>Provides sea level projections data for each site. Table ‘NZSeaRise_proj_novlm.csv’ provides projections without estimates of local VLM and table ‘NZSeaRise_proj_vlm.csv’ provides projections that include estimates of local VLM.</p> <p>Example download table:</p> <p> </p> <p>Confidence = identifies low or medium confidence projection</p> <p>siteId = site location</p> <p>year = Projection year (Common Era)</p> <p>0.17 = 17th percentile value</p> <p>0.50 = 50th percentile (mean) value</p> <p>0.83 = 83rd percentile value</p> <p>SSP ǀ scenario = Shared Socio-economic Pathway and relevant change in forcing at 2100 in W m<sup>2</sup></p> <p> </p> <p> </p>
National Checklists: Territory of the French Southern and Antarctic Lands Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
LAND USE AND LAND COVER OF THE UPPER AND MIDDLE BASIN OF THE LUJÁN RIVER
<p>The general objective of this work was to update the zoning and characterization of the land use and land cover of the upper and middle basin of the Luján River from high-resolution satellite images. Eight categories of land cover and land use were determined: Agricultural (A), Agricultural-livestock (AGa), Livestock (G), Horticultural (H), Forest (M), Water bodies (Ag), Low density city (Cb) and High density city (Ca). For the classification, high-resolution satellite images provided by Google Earth by the constellation of satellites of the company Maxar Technologies in the years 2021 and 2022. he scale used for the digitalization of the land use and land cover on the screen with digital GIS tools was 1:2,000. For the visual interpretation, different advanced vector digitization tools of the QGIS software were used on the aforementioned satellite images. All generated layers were georeferenced according to the WGS84 system with POSGAR 2007 - FAJA 5 projection.</p>
Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration
<p>Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527</p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
Supplementary dataset for "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"
<p>Supplementary dataset with measured data for publication "Phenylacetic acid metabolism in land plants: novel pathways and metabolites"</p>
Are terrestrial biosphere models fit for simulating the global land carbon sink?
<p>This repository contains the data and code required for reproducing the results presented in the paper "Are terrestrial biosphere models fit for simulating the global land carbon sink?" by Seiler et al., 2021. The study evaluates an ensemble of terrestrial biosphere models (<a href="https://sites.exeter.ac.uk/trendy/">TRENDY</a>; v9; S3 simulations) against a wide range of reference data using the Automated Model Benchmarking R package (AMBER; version 1.1.1). The only requirement for reproducing our results is access to a Linux machine with <a href="https://docs.conda.io">conda</a>, an open-source package management system and environment management system, installed. Follow the steps described in the <em>readme</em> file to install AMBER and run the analysis. The repository also contains all output produced by our analysis. </p>
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
Fig. 1 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 1. − Study area including the Pama reserve and neighbouring PAs of the western WAPO complex. The Pama, Tindangou and Madjoari areas are enclaves where agriculture is allowed. The small country map in the lower right shows the position of the study area within Burkina Faso.
Fig. 3 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 3. − Maps of mean maximum plant size (calculated as average of maximum plant size of all species predicted as present within a grid cell). A. Grasses (Poaceae) (30-360 cm); B. Woody species (3-25 m). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
Fig. 2 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 2. − Maps of species richness. A. All plant species (2-211 spp.); B. Graminoids (0-50 spp.); C. Forbs (0-86 spp.); D. Woody species (0-52 spp.); E. Weedy species (0-48 spp.); F. Non-weedy species (0-140 spp.). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types
<p>Dataset accompanying manuscript <em>"A comparison among three ways to assemble wall-to-wall land-cover maps from distribution models of vegetation types". </em>Datasets contain a wall-to-wall map of vegetation types covering the study area of terrestrial Norway, produced using three methods for assembling individual predictions from Distribution models (<em>probability-based method</em>, <em>performance-based method</em> and <em>prevalence-based method</em>). </p>
Fig. 3 in Oncholaimus langhovdensis sp. nov. (Nematoda: Enoplea: Oncholaimida), a New Species of Free-living Marine Nematode from Langhovde, Dronning Maud Land, East Antarctica
Fig. 3. Oncholaimus langhovdensis sp. nov., SEM images. A female ICHUM 5322 (A, B, D, F) and a male ICHUM 5321 (C, E). A, head, anterior view; B, head, lateral view; C, male tail, lateral view; D, female tail, lateral view; E, male cloacal region, ventral view; F, vulva, ventrolateral view. Scale bars: A, B, E, F, 10 µm; C, D, 50 µm.
Fig. 2 in Oncholaimus langhovdensis sp. nov. (Nematoda: Enoplea: Oncholaimida), a New Species of Free-living Marine Nematode from Langhovde, Dronning Maud Land, East Antarctica
Fig. 2. Oncholaimus langhovdensis sp. nov. Male ICHUM 5311 (holotype; A, B), females ICHUM 5316 (C, D) and 5317 (E). A, testes; B, left spicule, lateral view; C, female posterior region; D, uvette and osmosium, right lateral view; E, uvette and osmosium, ventral view. Abbreviations: a.t., anterior testis; d.e., ductus entericus; d.u., ductus uterinus; e., egg; i., intestine; os., osmosium; ov., ovary; p.t., posterior testis; ut., uterus; uv., uvette; v., vulva; v.d., vas deferens. Scale bars: A, C, 500 µm; B, 10 µm; D, E, 50 µm.
Dataset from "The Land-to-Ocean Loops of the Global Carbon Cycle" article
<p>Dataset of Tables S1, S2 and S3 from the paper entitled "The Land-to-Ocean Loops of the Global Carbon Cycle".</p>
Fig. 6 in Shell Morphology, Growth Pattern And Population Dynamics Of The Land Snail Xerolenta Obvia (Menke, 1828) In Two Areas Of Different Climatic Conditions Within A Temperate Climate Region
Fig. 6. Xerolenta obvia growth model under natural conditions. Size ranges of sexually ma- ture snails are shown in dark grey (4.5–5.4 whorls) and light grey (4.25–4.4 whorls); solid lines = SW population, dashed lines = NE population; 1 = first model variant, 2 = second model variant (details in text)
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
<p>Regrowing secondary forests dominate tropical regions today, and a mechanistic understanding of their recovery dynamics provides important insights for conservation. In particular, land-use legacy effects on the fauna have rarely been investigated. One of the most ecologically dominant and functionally important animal groups in tropical forests are ants. Here, we investigated the recovery of ant communities in a forest – agricultural habitat mosaic in the Ecuadorian Chocó region. We used a replicated chronosequence of previously used cacao plantations and pastures with 1 – 34 years of regeneration time to study the recovery dynamics of species communities and functional diversity across the two land use legacies. We compared two independent components of responses on these community properties: resistance, which is measured as the proportion of an initial property that remains following the disturbance; and resilience, which is the rate of recovery relative to its loss. We found that compositional and trait structure similarity to old-growth forest communities increased with regeneration age, whereas ant species richness remained always at a high level along the chronosequence. Land-use legacies influenced species composition, with former cacao plantations showing higher resemblance to old-growth forests than former pastures along the chronosequence. While resistance was low for species composition and high for species richness and traits, all community properties had similarly high resilience. In essence, our results show that ant communities of the Chocó recovery rapidly, with former cacao reaching predicted old-growth forest community levels after 21 years and pastures after 29 years. Recovery in this community was faster than reported from other ecosystems and was likely facilitated by the low-intensity farming in agricultural sites and their proximity to old-growth forest remnants. Our study indicates the great recovery potential for this otherwise highly threatened biodiversity hotspot.</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.