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77 results for “landform”
Physical soil characteristics, microbial community composition, extracellular enzymatic activity, biologically based phosphorus (BBP) pools, and available phosphorus from two soil depths, four microhabitats, and four landforms at the Jornada Experimental Range, 2021.
This dataset contains physical soil characteristics, PLFA based microbial community composition, extracellular enzymatic activity, nitrate and ammonium activity, and phosphorus availability in various phosphorus pools (Biologically Based Phosphorus, potassium sulfate, Olsen-P). Soils were collected from two depths (0-2cm, 2-30 cm), four microhabitats (grass, shrub, biocrust, interspace), and four landforms (alluvial flat, alluvial fan remnant, erosional scarplet, fan piedmont – see coordinates) within the Jornada Experimental Range in July 2021 to answer questions about how these variables change across these spatial scales in drylands. This project was a collaboration between researchers at New Mexico State University and The University of Texas at El Paso as part of the Drylands Critical Zone Thematic Cluster within the Critical Zone Network. This dataset is complete.
Regional landform and landscape digital maps for the Eastern Guiana Shield
<p>Archive containing digital <strong>maps of 'landform types' and 'landscape units' for French Guiana and the State of Amapa (Brazil).</strong> These maps accompany the paper 'Using textural analysis for regional landform and landscape mapping, Eastern Guiana Shield', <em>Geomorphology</em> (doi:10.1016/j.geomorph.2 018.03.017) and have been produced according to the methods presented therein.</p> <p><br> </p>
Global landform and lithology class at 250 m based on the USGS global ecosystem map
<p>Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the <a href="https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/">USGS Global Ecosystem Map</a>, i.e. the EcoTapestry map. Water bodies masked out. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models / relief and soil,</li> <li>lithology = variable: lithological class,</li> <li>usgs.ecotapestry = determination method: USGS Global Ecosystem Map,</li> <li>p = probability 0-100%,</li> <li>250m = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014 = time reference: year 2014,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple-Like Aeolian Landforms on Mars
<p>The dataset includes wind regime data for Golmud, Sebei, and the west bank of the Narin Gol River in the Qaidam Basin, as well as sediment grain size and morphological parameters of the megaripples. In addition, we provide R language source code for data processing and visualization.</p><p>The primary directory contains the data and the source code in the R language. The data includes sediment grain size, morphological parameters and wind regime analysis data of megaripples. Modifying the working path and installation package is necessary to call the R source code for data loading.</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Crater Lake GeoTIFF
<p>An elevation model of Crater Lake, Oregon, USA</p> <p>Landform features: caldera, cinder cone, lava flow</p> <p>Resolution: 3.33 meter, 5,200 x 5,200 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Great Sand Dunes GeoTIFF
<p>An elevation model of Great Sand Dunes, Colorado, USA</p> <p>Landform features: active dune field, sand sheet, sabkha</p> <p>Resolution: 3.3 meter, 5,300 x 5,300 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 2.0.0 replaced the previous erroneous elevation model of another geographic area.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kočevje Rog GeoTIFF
<p>An elevation model of Kočevje Rog, Slovenia</p> <p>Landform features: karstified plateau, karst</p> <p>Resolution: 2 meter, 4,500 x 4,500 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon GeoTIFF
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: GeoTIFF</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Bryce Canyon ASCII
<p>An elevation model of Bryce Canyon, USA</p> <p>Landform features: narrow rock formations known as hoodoos</p> <p>Resolution: 1 meter, 4,000 x 3,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms
<p>This is a set of elevation models of archetypal landforms: </p> <ul> <li>volcanic caldera (Crater Lake, Oregon, USA),</li> <li>active sand dunes (Great Sand Dunes, Colorado, USA),</li> <li>a braided riverbed (Jackson Hole, Wyoming, USA),</li> <li>folded ridges (Massanutten Mountain, Virginia, USA),</li> <li>stabilized sand dunes (Sandhills, Nebraska, USA),</li> <li>crater of a shield volcano (Kilauea, Hawaii, USA),</li> <li>karst plateau (Kočevje Rog, Slovenia),</li> <li>narrow rock formations, aka hoodoos (Bryce Canyon, USA)</li> </ul> <p>All elevation models were derived from NED LiDAR sources with cell sizes ranging from 1 to 10 meters. The size of the models varies between approximately 4,000 × 4,000 and 5,500 × 5,500 height samples. The elevation models are available in georeferenced GeoTIFF and Esri ASCII file formats.</p> <p>Version 2 adds models of Kilauea, Hawaii, USA, Kočevje Rog, Slovenia, and Bryce Canyon, USA.</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea GeoTIFF
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: GeoTIFFI</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Kilauea ASCII
<p>An elevation model of Kīlauea, Hawaii, USA</p> <p>Landform features: shield volcano crater</p> <p>Resolution: 1 meter, 7,200 x 6,800 height samples</p> <p>File format: Esri ASCII</p> <p>This is one model of a set of elevation models: <a href="http://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery
<p><span>This repository contains data and scripts used in the study titled 'AI-Based Tracking of Fast-Moving Alpine Landforms Using High Frequency Monoscopic Time-Lapse Imagery' published as a <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2570/" target="_blank" rel="noopener">preprint </a>in Earth Surface Dynamcis (EGU) . Please check the README.docx for </span><span>folder structure with descriptions of each folder and file.</span></p>
periglacial landforms
<p>Periglacial landforms in UP produced by ARCMAP</p>
SGS-LTER GIS layer with detailed information on Landforms on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
Data from: Two new species of Parnassia (Celastraceae) from karst cave and Danxia landform in Southwest China
Two new species of Parnassia found in mountainous areas of Chongqing City, Southwest China, are described and illustrated: P. zhengyuanum M.X. Ren & J. Zhang and P. simianshanensis M.X. Ren, J. Zhang & Z.Y. Liu respectively. Parnassia zhengyuanum is found in a limestone cave near Mt. Jinfo, Nanchuan District, Chongqing City. This tiny plant has special spoon-shaped leaves and undivided staminodes with green apices. Parnassia simianshanensis was discovered in Mt. Simian, Jiangjin County, Chongqing City. This species grows along streams on red sandstones, i.e. in Danxia landform. It can be easily distinguished by its unique rhombic leaves and the five- or four-branched staminodes. The conservation status of P. zhengyuanum and P. simianshanensis are assessed respectively as 'Critically Endangered' (CR) and 'VU' (Vulnerable) according to IUCN criteria. The roles of karst and Danxia landforms in the speciation process are discussed.
Dataset used in "Catchment landforms predict groundwater-dependent wetland sensitivity to recharge changes".
<p>Dataset in the form of text files, all 60 presented catchments are identified by the HydroATLAS database ID in each file. Catchment topography figures are available following their HydroATLAS ID. For the seepage distribution, one text file is presented for each catchment named with the corresponding catchment ID. Code to generate desaturation parameters (lambda and n) and ML models are saved in a common directory (catchment_desaturation_estimator), code is commented to facilitate reproducibility. </p>
Regolith-landform map of the Tanami Region, Australia
<p>Images used for mapping and modelling the regolith-landform of the Tanami Region, Australia.</p> <p>Zip file: Temporally mergered Landsat TM; RGB, DS754 and Gozzard ratios (ECW)</p> <p><span><span>Zip file: Relief imagess (PNG, PGW); </span></span><span>Slope images (PNG, PGW); </span><span>TWI images (PNG, PGW); </span><span>Flow accumulation images (PNG, PGW)</span></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.