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.
412
datasets available to search
ShareScore release 0.9.0
Dataset results
412 results for “topography”
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
Characterization and effect of biomimetic surfaces based on the topography of a self-cleaning leaf on bacterial binding
<p>Four self-cleaning leaves (Tenderheart, Cauliflower, White cabbage, and Leek) and the corresponding biomimetic surfaces were analyzed for their properties (water contact angle, surface hydrophobicity and roughness). The antifouling behavior was assessed by bacterial attachment, adhesion, and retention assays.</p>
Ellipsoidal Harmonic Forward Model derived from Earth2014 topographies up to d/o 7200: EHFM_Earth_7200
<p>The model named EHFM_Earth_7200 was derived by layer-based forward modeling technique in ellipsoidal harmonics, the maximum degree of this model reaches 7200. The relief information was provided by Earth2014 relief model. EHFM_Earth_7200 provides very detailed (~3 km) information for the Earth’s short-scale gravity field, and it is expected to be able to augment or refine existing global gravity models. To meet the existing standard, here we provide spherical harmonic coefficients, which are transformed from original ellipsoidal harmonic coefficients.</p>
Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements
<p>Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements</p> <p>DOI: 10.1021/acsami.3c19254</p> <p><span><span>I. Brotons-Alcázar, Jason. S. Terreblanche, S. Giménez-Santamarina, G. M. Gutiérrez-Finol, K. S. Ryder, A. Forment-Aliaga, E. Coronado, <em>ACS Appl. Mater. Interfaces</em> <strong>2024</strong>, <em>16</em>, 19711.</span> </span></p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Data from: Environmental variation associated with topography explains butterfly diversity along a tropical elevation gradient
<p>Few studies have evaluated the role of topography on the diversity patterns of biological communities along elevation gradients. We evaluated the influence of microclimate and vegetation structure associated with topographic variation on the richness and composition of species of different families of butterflies on a mountain located in a dry enclave (Chicamocha River Canyon) in the northern Andes, Colombia. We captured butterflies over four months at 18 elevations (300 to 1500 m a.s.l.) in two topographic positions (riverbed and hillslope) using an entomological net and traps baited with fermented fruit. In general, butterfly richness increased with elevation in both topographic positions. However, the richness-elevation relationship changed with butterfly family. The riverbed and hillslope sites host different assemblages of butterflies, and this pattern that was consistent for most families. In the riverbed, two sets of species are recognized along the elevation gradient (one below 700 m a.s.l. and the other above 1000 m a.s.l.), mainly owing to species replacement. On the hillslopes there was no clear pattern of grouping associated with elevation. Microclimate differences between the riverbed and hillslope sites along the elevation gradient were related to the vegetation structure and explained the variation in butterfly species composition. Our results highlight the role of topography not only by explaining the response of species richness and composition to environmental variation determined by elevation, but also as a factor that must be considered in the planning and management of biodiversity conservation in the mountains.</p>
Fatiando a Terra Data: Earth - Topography grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of topography (ETOPO1 ice-surface) referenced to mean sea-level (which we will consider to be the geoid).</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.7289/V5C8276M">ETOPO1</a> (grid generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-topography-10arcmin">https://github.com/fatiando-data/earth-topography-10arcmin</a></p>
Fatiando a Terra Data: Sierra Negra volcano, Ecuador - Topography
<p>This is a topography point cloud of the 2018 lava flows of the Sierra Negra volcano, located on the Galápagos islands, Ecuador. The data are generated using structure from motion (SFM) and shows nice topographic features and different roughness of the lava flows. Good to show examples of calculating slope and other terrain properties from the point cloud or gridded data.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Data were cropped to smaller region to align with previously published studies of the data and make file sizes under 10 Mb. Coordinates converted from UTM to WGS84 geographic. Export to a compressed CSV for easier loading with Pandas.</p> <p><strong>Source: </strong>Carr, B. (2020). Sierra Negra Volcano (TIR Flight 3): Galápagos, Ecuador, October 22 2018. Distributed by OpenTopography. <a href="https://doi.org/10.5069/G957196P">https://doi.org/10.5069/G957196P</a></p> <p><strong>Additional reference:</strong> Carr, B. B., Lev, E., Sawi, T., Bennett, K. A., Edwards, C. S., Soule, S. A., et al. (2021). Mapping and classification of volcanic deposits using multi-sensor unoccupied aerial systems. Remote Sensing of Environment. <a href="https://doi.org/10.1016/j.rse.2021.112581">https://doi.org/10.1016/j.rse.2021.112581</a></p> <p><strong>Source license: </strong><a href="https://doi.org/10.5069/G957196P">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/sierra-negra-topography">https://github.com/fatiando-data/sierra-negra-topography</a></p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
Fatiando a Terra Data: Southern Africa - Topography and Bathymetry
<p>This is a topography and bathymetry grid with a resolution of 1 arc-minute over Southern Africa. The grid was generated by cropping the ETOPO1 global topography grid. The heights are referenced to the mean sea level.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made:</strong> Data were cropped to a region that covers only the Southern Africa. Coordinates have been renamed to <em>longitude</em> and <em>latitude</em>. The dataset has been renamed to <em>topography</em>. The metadata of the dataset have been improved following CF-conventions. <strong> </strong></p> <p><strong>Source: </strong>ETOPO1 <a href="https://doi.org/10.7289/V5C8276M">https://doi.org/10.7289/V5C8276M</a></p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/mgg/global/dem_faq.html#sec-2.4">public domain</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/southern-africa-topography">https://github.com/fatiando-data/southern-africa-topography</a></p>
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
<p><em>Context</em></p> <p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland-dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p> <p><em>Objectives</em></p> <p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<em>Rana pipiens</em>) to: (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p> <p><em>Methods</em></p> <p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p> <p><em>Results</em></p> <p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p> <p><em>Conclusions</em></p> <p>While the <em>R. pipiens</em> populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>
Emergent Topographies of Vienna
<p>Teaser for the production<br> "Emergent Topographies of Vienna"<br> launched at Ars Electronica Festival 2021 in Vienna "Garden of knowledge / Garden of Vienna"</p> <p> </p> <p>This production was funded by the City of Vienna / Digital Humanism Call, project Algorithm Inventarium<br> and FWF, project PROVIDEDH.</p>
Data from: Effects of topography and Earth's rotation on the oblique interaction of internal solitary-like waves in the Andaman Sea
<p>This is a modeled data set from the paper:</p> <p>Shimizu, K., and K. Nakayama (2017), Effects of topography and Earth's rotation on the oblique interaction of internal solitary-like waves in the Andaman Sea, Journal of Geophysical Research - Oceans, 122, doi:10.1002/2017JC012888</p> <p>This data set includes results of MITgcm internal solitary-like wave (ISW) simulations in the Andaman Sea, and data derived from them. The model was run at two horizontal resolutions (0.005 and 0.0025 degree resolution) and three cases for the Coriolis effects (Full, Traditional, and No Coriolis). Please refer to the paper for the methods and data used to produce this data set.</p>
Spherical harmonic models of planetary topography
<p>This archive contains spherical-harmonic shape models of the Moon, Venus, and Mars that were presented in <em>Wieczorek </em>(2015).</p> <p>The individual files can be downloaded separately here:</p> <ul> <li><a href="https://zenodo.org/record/3870922">MarsTopo2600.shape.gz</a></li> <li><a href="https://zenodo.org/record/3870924">MoonTopo2600p.shape.gz</a></li> <li><a href="https://zenodo.org/record/3870926">VenusTopo719.shape.gz</a></li> </ul>
Fig. 2 in Tegumental topography and molecular characterisation of two trematodes (Platyhelminthes: Digenea) from Clarias gariepinus (Burchell, 1822) in Kenya
Fig. 2. Scanning electron micrographs of Glossidium pedatum. (A) Round oral sucker, (B) spine surrounding the oral sucker (C) location of the genital pore and a protruding cirrus, (D) structure of the cirrus sac (abbreviations: pl-papillae like lappet; os-oral sucker; pp-papillae; dp-dome papillae; vs-ventral sucker; gp-genital pore; c -cirrus; rp-rows of small papillae).
Fig. 5 in Tegumental topography and molecular characterisation of two trematodes (Platyhelminthes: Digenea) from Clarias gariepinus (Burchell, 1822) in Kenya
Fig. 5. Phylogenetic relationships of Tylodelphys mashonensis (Sudarikov, 1971) to other Diplostomidae based on cox1. Phylogram was reconstructed using Bayesian Inference (BI) with Diplostomum spathaceum (Rudolphi, 1819) as an outgroup. Nodal values <0.90 (BI) are indicated by dashes (sequences of the present study are highlighted in bold).
Fig. 3 in Tegumental topography and molecular characterisation of two trematodes (Platyhelminthes: Digenea) from Clarias gariepinus (Burchell, 1822) in Kenya
Fig. 3. Phylogenetic relationships of Glossidium pedatum Loos, 1899 to other members of Plagiorchioidea based on 28S rDNA. Phylogram was reconstructed using Bayesian Inference (BI) with Alloglossidium corti (Lamont, 1921) as an outgroup. Nodal values <0.90 (BI) are indicated by dashes (sequences of the present study are highlighted in bold).
Fig. 1 in Tegumental topography and molecular characterisation of two trematodes (Platyhelminthes: Digenea) from Clarias gariepinus (Burchell, 1822) in Kenya
Fig. 1. Geographical location of the study area: A– Kenya shaded on the African continent; B – shows position of Nyandarua County in Kenya; C – indicates the position of the Lake Ol'Bolossat and the sampling sites (S1–S3).
Fig. 4 in Tegumental topography and molecular characterisation of two trematodes (Platyhelminthes: Digenea) from Clarias gariepinus (Burchell, 1822) in Kenya
Fig. 4. Scanning electron micrographs of Tylodelphys mashonensis. (A) ventral surface ultrastructure, (B) sensory papillae surrounding the oral sucker (C) ventral sucker surrounded by small and large papillae, (D) well organized holdfast organ with spines (abbreviations: ps-pseudo suckers, os-oral sucker, ho- holdfast organ, vs-ventral sucker, pp-papillae, sp-symmetrical papillae, lp- large papillae, hf-holdfast fissure).
Subglacial Topography Training Image Database
<p>This database contains elevation data for 166 topographic training images. These training images should be cited as Yin et al., (2022). The file contains a 40,000 x166 matrix, where each column contains one TI. Each column should be reshaped to 200x200. The resolution is 500 m, and each TI is 100x100 km<sup>2</sup>. The elevation data were obtained from ArcticDEM (Porter et al., 2018) and IBCSO (Arndt et al., 2013). </p> <p>Arndt, J. E., Schenke, H. W., Jakobsson, M., Nitsche, F. O., Buys, G., Goleby, B., ... & Wigley, R. (2013). The International Bathymetric Chart of the Southern Ocean (IBCSO) Version 1.0—A new bathymetric compilation covering circum‐Antarctic waters. <em>Geophysical Research Letters</em>, <em>40</em>(12), 3111-3117.</p> <p>Porter, C., Morin, P., Howat, I., Noh, M. J., Bates, B., Peterman, K., ... & Bojesen, M. (2018). ArcticDEM. <em>Harvard Dataverse</em>, <em>1</em>, 2018-30.</p> <p>Yin, Z., Zuo, C., MacKie, E. J., & Caers, J. (2022). Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1). <em>Geoscientific Model Development</em>, <em>15</em>(4), 1477-1497.</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.