Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,583
datasets available to search
ShareScore release 0.9.0
Dataset results
3,583 results for “elevations”
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: GeoTIFF</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>
Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"
<p>This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see:</p> <p>Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101</p> <p>These hearths were identified using this data:</p> <p>Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977</p> <p>The above is derived from:</p> <p>Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441</p> <p> </p>
Elevation modulates the phenotypic responses to light of four co-occurring Pyrenean forest tree species
<p>Data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient. The dataset contains three files:</p> <ol> <li><strong>Biomass.txt: </strong>Data on plant biomass per fraction (leaf, stem and roots) 4 years after plantation. Included variables:<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant in that plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (factor): code to identify uniquely each plant<br> - GLI (num): Global Light Index, the amount of irradiance that receives each seedling<br> - Code (factor): code to identify uniquely each plant<br> - PLB (numeric): total plant biomass (g)<br> - LFB (numeric): leaf biomass (g)<br> - STB (numeric): stem biomass (g)<br> - RTB (numeric): root biomass (g)<br> - LMF (numeric): leaf mass fraction (LFB/PLB)<br> - SMF (numeric): stem mass fraction (STB/PLB)<br> - RMF (numeric): root mass fraction (RTB/PLB)<br> - SLA (numeric): specific leaf area<br> - H (numeric): plant height (mm)<br> - D (numeric): plant diameter at root collar (mm)<br> - PB2 (numeric): total plant biomass without considering leaves (g)<br> - SF2 (numeric): stem mass fraction without considering leaves (STB/PB2)<br> - RF2 (numeric): root mass fraction without considering leaves (RTB/PB2)</li> <li><strong>init_biomass.txt:</strong> for biomass at the moment of plantation<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - N (numeric): number of plant<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Planta (factor): code to identify uniquely each plant<br> - File (numeric): code to identify uniquely each plant<br> - PB (numeric): total plant biomass (g)<br> - LB (numeric): leaf biomass (g)<br> - SB (numeric): stem biomass (g)<br> - RB (numeric): root biomass (g)</li> <li><strong>WaterPot.txt</strong>: data on plant water potential for seedlings of four Pyrenean tree species planted along an elevation gradient during a period of intense drought<br> - Piso (factor): elevational stage at which the seedling was planted. Two levels: montane (M) or subalpine (S)<br> - Luz (factor): whether the seedling was plantes at a gap or in the understory. Two levels: gap (O) or understroy (T)<br> - N (numeric): number of plant <br> - Parcela (factor): identifier ofthe plot<br> - Sp (factor): species. Four levels: BEPE (Betula pendula) / PISY (Pinus sylvestris) / PIUN (Pinus uncinata) / ABAL (Abies alba)<br> - Estacion (factor): the moment for the measurement. One level: September<br> - GLI (numeric): global light index, the ration of total irradiance received by the plant at the moment of plantation<br> - WPt (numeric): water potential (bars)</li> </ol>
LeConte Bay Seafloor Elevation Data
<p>Multibeam data of LeConte Bay seafloor from the inner fjord.</p> <p>These are processed multibeam sonar data from LeConte Bay, southeast Alaska. The data come from five cruises grouped into five files:<br> 1) Aug 1999 and Sep 2000 (20-m resolution) - 1 file;<br> 2) August 2016 (20-m and 4-m resolution) - 2 files;<br> 3) May 2017 (2-m resolution) - 1 file;<br> 4) Sep 2018 (2-m resolution) - 1 file.</p> <p>The datasets collected in 2016-2018 originally included ice-face data, as reported in Sutherland et al., 2019 (DOI 10.1126/science.aax3528). The data presented here include only the seafloor data, exclusive of the ice face. They are referenced to WGS84, UTM zone 8 (meters easting/northing), and mean sea level.</p> <p>Reference and more details: Eidam, E.F., Sutherland, D.A., Duncan, D., Kienholz, C., Amundson, J.M., Motyka, R.J. Morainal bank evolution and fjord infilling during a tidewater glacier stillstand, on seasonal to decadal timescales. In prep for JGR-Earth Surface, September 2019.</p>
Microclimate predicts frost-hardiness of alpine Arabidopsis thaliana populations better than elevation
<p>In mountain regions, topological differences on the micro-scale can strongly affect microclimate and may counteract the average effects of elevation, such as decreasing temperatures. While these interactions are well understood, their effect on plant adaptation is understudied.</p> <p> </p> <p>We investigated winter frost hardiness of Arabidopsis thaliana accessions originating from 13 sites along altitudinal gradients in the Southern Alps during three winters on an experimental field station on the Swabian Jura and compared levels of frost damage with the observed number of frost days and the lowest temperature in eight collection sites.</p> <p> </p> <p>We found that frost-hardiness increased with elevation in a log-linear fashion. This is consistent with adaptation to a higher frequency of frost conditions, but also indicates a decreasing rate of change in frost hardiness with increasing elevation. Moreover, the number of frost days measured with temperature loggers at the collection sites correlated much better with frost-hardiness than the elevation of collection sites, suggesting that populations were adapted to their local microclimate. Notably, the variance in frost days across sites increased exponentially with elevation. Together, our results suggest that strong microclimate heterogeneity of high alpine environments can preserve functional genetic diversity among small populations.</p> <p> </p> <p>Synthesis. Here we tested how plant populations differed in their adaptation to frost exposure along an elevation gradient and whether microsite temperatures improve the prediction of frost hardiness. We found that local temperatures, particularly the number of frost days, is a better predictor of the frost hardiness of plants than elevation. This reflects a substantial variance in frost frequency between sites at similar high elevations. We conclude that high mountain regions harbor microsites that differ in their local microclimate and thereby can preserve a high functional genetic diversity among them. Therefore, high mountain regions have the potential to function as a refugium in times of global change.</p>
The Structure of Sub-nm Platinum Clusters at Elevated Temperatures (Supplementary Information)
<p><strong><em>This dataset consists of raw data and denoised scanning transmission electron microscopy videos of sub-nm sized clusters of Pt on a carbon substrate. The data is used in the article "The Structure of Sub-nm Platinum Clusters at Elevated Temperatures" published in Angewandte Chemie International Edition, 2019, DOI:10.1002/anie.201911068</em></strong><strong><em> </em></strong></p> <p><strong>Video S1.</strong> A typical sub-nm amorphous cluster at room temperature. 0.5 nm scale bar.</p> <p><strong>Video S2.</strong> Two typical crystalline sub-nm clusters at 350°C. 0.5 nm scale bar.</p> <p><strong>Video S3. </strong>In this high-speed recording at 147 fps, the high beam current required for this fast imaging has suppressed the crystallinity of the cluster, despite the temperature of 350°C. 0.5 nm scale bar.</p> <p><strong>Video S4. </strong>The unusually stable 13-atom cluster at the bottom forms an fcc cuboid, and can be seen rotating at three orientations, as shown by the inset model and in Fig. 3a-c. 0.5 nm scale bar.</p> <p><strong>Video S5. </strong>This 15-atom cluster initially forms an fcc cube, then transforms into multiple hcp structures. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p> <p><strong>Video S6. </strong>The cluster in this movie is a 22-atom truncated rectangular cuboid. 0.5 nm scale bar.</p> <p><strong>Video S7. </strong>In the center and bottom, two 6-atom octagons are rotating (shown in Fig. S4) as they add onto their larger neighboring clusters. The 13-atom cluster in the top forms an unusually stable fcc cuboctahedron from frame 219. 0.5 nm scale bar.</p> <p><strong>Video S8. </strong>The cluster on the bottom left forms a fleeting icosahedron-like structure. 0.5 nm scale bar.</p> <p><strong>Video S9. </strong>This cluster shows fcc structures, despite being recorded at 200°C, but with a very low beam dose. (Recorded at 2 fps, but animated at 5x real time at 10fps). 0.5 nm scale bar.</p>
NMNH Botany Elevation Data: Dinoflagellates
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
NMNH Botany Elevation Data: Mins
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
NMNH Botany Elevation Data: Single Points
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
NMNH Botany Elevation Data: Maxs
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
NMNH Botany Elevation Data: Median
Elevation data from specimens in the collections of the Smithsonian National Museum of Natural History Botany Department.<p></p>
Gummern - Digital elevation model 0.5 m, created from Pléiades Neo tri-stereo satellite imagery
<h2>Abstract</h2> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Gummern_PleiadesNeo_DSM05m & Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>surface model, Pleiades Neo, tri-stereo</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation & GNSS</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>1.10.2023 (27.10.2023 – GNSS)</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>1.12.2023 (6.11.2023 – GNSS)</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster / Vector</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF / CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.30m (0.01m – GNSS)</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Supplementary data for "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study"
<p>This is the base data for the retrieval of water vapor, temperature and surface temperature based on forward simulated IASI measurements in the spectral bands between 1190-1400 and 645-800 cm-1, as well as 5 channels in the atmospheric window between 901.5 and 1115.75 cm-1.</p> <p>The data includes 1599 atmospheric states over tropical ocean regions, which is a subset of the ECMWF IFS diverse profile database with focus on a broad sampling of humidity states, published by Eresmaa et al. (2014). The full dataset is also available as part of the ARTS (Atmospheric Radiative Transfer Simulator) XML database (https://radiativetransfer.org/tools/). The data also includes the forward modelled spectra in units of brightness temperatures and the associated spectral frequency grid. ARTS is used as the forward model (https://radiativetransfer.org).</p> <p>This dataset is supplementary to the article "Are elevated moist layers a blind spot for hyperspectral infrared sounders? - A model study" that has been submitted to Atmospheric Measurement Techniques (AMT).</p>
DATASET: A low elevation imaging radar using a non-uniform coplanar receiver array for E~region observations
<p>Ionospheric Continuous-wave E region Bistatic Experimental Auroral Radar 3-Dimensional (ICEBEAR-3D) dataset for validation of the receiver antenna array reconfiguration, Suppressed-Spherical Wave Harmonic Transform (Suppressed-SWHT), and proper geometry for vertical interferometry using the geocentral angle.</p>
Supplementary material for "High turn-over rates at the upper range limit and elevational source-sink dynamics in a widespread songbird"
<p><strong>Abstract</strong></p> <p>The formation of an upper distributional range limit for species breeding along mountain slopes is often based on environmental gradients resulting in changing demographic rates towards high elevations. However, we still lack an empirical understanding of how the interplay of demographic parameters forms the upper range limit in highly mobile species. Here, we study apparent survival and within-study area dispersal over a 700 m elevational gradient in barn swallows (<em>Hirundo rustica</em>) by using 15 years of capture-mark-recapture data. Annual apparent survival of adult breeding birds decreased while breeding dispersal probability of adult females, but not males increased towards the upper range limit. Individuals at high elevations dispersed to farms situated at elevations lower than would be expected by random dispersal. These results suggest higher turn-over rates of breeding individuals at high elevations, an elevational increase in immigration and thus, within-population source-sink dynamics between low and high elevations. The formation of the upper range limit therefore is based on preference for low-elevation breeding sites and immigration to high elevations. Thus, shifts of the upper range limit are not only affected by changes in the quality of high-elevation habitats but also by factors affecting the number of immigrants produced at low elevations.</p>
Model output from Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier
<p>This model output dataset accompanies the draft paper 'Inverting ice surface elevation and velocity for bed topography and slipperiness beneath Thwaites Glacier'.</p>
Model Output and Figure Scripts for: "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records"
<p>New climate model output and figure scripts for the paper "Uncertainty in reconstructing paleo-elevation of the Antarctic Ice Sheet from temperature-sensitive ice core records".</p>
Post-remediation evaluation of contaminated site using geophysical methods: Digital Elevation Model Olkusz (Poland) 20220629
<p>The Digital Elevation Model is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal<br> length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is a DEM with a 51.1 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326). </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Data from: Nest orientation and proximity to snow patches are important for nest site selection of a cavity breeder at high elevation
<p><strong>Abstract</strong></p> <p>Reproductive timing and location are central to breeding success across taxa. Many species have evolved specific strategies to cope with environmental variability including shifts in timing of reproduction tracking resource availability or selecting favourable nest location. In mountain ecosystems, complex topography and pronounced seasonality result in particularly high spatiotemporal variability of environmental conditions, and the risk of climate-induced resource mismatches is particularly acute given that temperature is increasing more rapidly than in the lowlands.<br>We investigated how a high-elevation passerine, the white-winged snowfinch <em>Montifringilla nivalis</em>, selects its nest site in relation to nest cavity characteristics, habitat composition and snow condition. We used a combination of field habitat mapping and satellite remote sensing to compare occupied nest sites with randomly selected pseudo-absence sites. In the first half of the breeding season, snowfinches preferred nest cavities oriented towards the morning sun while they used cavities proportional to their availability later on. This preference might relate to the nest microclimate offering eco-physiological advantages, namely thermoregulatory benefits for incubating adult and nestlings under the harsh conditions typically encountered in the alpine environment. Nest sites were consistently located in areas with greater-than-average snow cover at hatching date, likely mirroring the foraging preferences for tipulid larvae developing in meltwater along snowfields. Due to the particularly rapid climate shifts typical of mountain ecosystems, spatiotemporal mismatches between foraging grounds and nest sites are expected in the future, which may negatively influence demographic trajectories of the species concerned. The installation of well-designed nest boxes in optimal habitat configurations could to some extent help mitigate this risk.</p> <p> </p>
Historical digital elevation models (DEMs) and orthoimage mosaics for North American Glacier Aerial Photography (NAGAP) program, version 1.0
<p>This data archive contains digital elevation models (DEMs) and orthoimages generated from scanned historical aerial photographs from the North American Glacier Aerial Photography program available from the NSF Arctic Data Center (ADC, arcticdata.io). </p> <p>The scanned images were preprocessed using the <a href="https://github.com/friedrichknuth/hipp">Historical Image Pre-Processing</a> v0.1 software. Photogrammetric processing was performed with the <a href="https://github.com/friedrichknuth/hsfm">Historical Structure from Motion</a> v0.1 software. </p> <p>All DEM and orthoimage products are provided in the UTM Zone 10N (EPSG:32610) projected coordinate system. Elevation values are in meters above the WGS84 ellipsoid. </p> <p>See <a href="https://www.sciencedirect.com/science/article/pii/S0034425722004850">manuscript</a> and <a href="https://ars.els-cdn.com/content/image/1-s2.0-S0034425722004850-mmc1.pdf">supplement</a> for processing details and further dataset description.</p> <p>This release contains data products for two study sites in Washington state, USA:</p> <p><strong>Mount Baker</strong><br> 1970-09-09<br> 1970-09-29<br> 1974-08-10<br> 1977-09-27<br> 1979-10-06<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-09-15<br> 1992-09-18</p> <p><strong>South Cascade</strong><br> 1967-09-21<br> 1970-09-29<br> 1974-08-10<br> 1977-10-03<br> 1979-08-20<br> 1979-10-06<br> 1984-08-14<br> 1986-09-05<br> 1987-08-21<br> 1990-09-05<br> 1991-09-09<br> 1992-07-28<br> 1992-09-15<br> 1992-09-18<br> 1992-10-06<br> 1994-09-06<br> 1996-09-10<br> 1997-09-23</p> <p>The 00_thumbnails.jpg provides a quicklook overview at both sites.</p> <p><strong>The DEM and ortho file names are structured as follows:</strong><br> hsfm_NAGAP_[site-name]_[date]_[type].tif</p> <p><strong>For example:</strong><br> hsfm_NAGAP_south-cascade_19670921_ortho.tif</p> <p><strong>Where:</strong><br> [site-name] = Either mount-baker or south-cascade<br> [date] = Image acquisition date in YYYYMMDD format<br> [type] = File type</p> <p><strong>For each DEM and ortho pair, we provide the following:</strong><br> _1m_dem.tif = Digital elevation model posted at 1 m resolution <br> _ortho.tif = Orthoimage mosaic posted at the median image ground sample distance, rounded up to the nearest second decimal place.<br> _metadata.tar.gz = Metadata tarball containing:<br> _ortho_footprints.geojson = Orthoimage mosaic footprint polygons provided in GeoJSON format (EPSG:4326)<br> _dem_footprints.geojson = DEM footprint polygons provided in GeoJSON format (EPSG:4326)<br> _cameras.csv = Image file names, positions, and orientations</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.