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3,583 results for “elevations”
Abundance of butterflies according to elevation, season and habitat in tropical mountain of Costa Rica
<p>The dataset is about a study of the diversity of butterflies in an elevational gradient in Costa Rica during two seasons and in two different habitats.</p>
Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets
<h1><strong>Introduction</strong></h1> <p>We provide DEMs from Pléiades stereo images and TanDEM-X acquired in March 2021 and their difference DEM (dDEM) used to estimate the elevation bias due to radar penetration on Grosser Aletschgletscher, Switzerland. Additionally, we provide the reference DEM (i.e., swissALTI3D) used for the Pléiades DEM co-registration and TanDEM-X production, stable terrain masks used for the co-registration and the glacier outline</p> <p>These are supplementary datasets associated to the publication by Bannwart et al., (2024a): “Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland”, Journal of Glaciology. DOI: <a href="https://doi.org/10.1017/jog.2024.37">https://doi.org/10.1017/jog.2024.37</a></p> <h1><strong>Datasets and Citation</strong></h1> <p>The following datasets have been used and/or produced as part of the study above. Below you find a description of the individual datasets and their generation, including a citation example for each dataset. In general, when one or more datasets are used, both the individual dataset as well as the study (Bannwart et al. 2024) need to be cited. If you refer to the entire dataset, cite the Zenodo entry as well as the study.</p> <ul> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>TanDEM-X_DEM_6m_EPSG_32632_coreg.tif</p> </li> <li> <p>Pleiades_DEM_2m_EPSG_32632_non_coreg.tif</p> </li> <li> <p>Pleiades_DEM_6m_EPSG_32632_coreg.tif </p> </li> <li> <p>swissALTI3D_2m_EPSG_32632.tif</p> </li> <li> <p>dDEM_non_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>dDEM_corrected_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_Pléiades_2m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_6m_EPSG_32632.tif</p> </li> <li> <p>stable_terrain_mask_coreg_TanDEM-X_modified_6m_EPSG_32632.tif</p> </li> <li> <p>glacier_outline_2021.shp</p> </li> </ul> <h1><strong>Data and Methods</strong></h1> <h2>TanDEM-X DEMs (non co-registered and co-registered):</h2> <p>We used an InSAR DEM generated from TanDEM-X Coregistered Single look Slant range Complex (CoSSC) data provided by the German Aerospace Center (DLR) through the project XTI_GLAC7746. The DEM originates from a single bistatic X-band (9.65 GHz) InSAR acquisition in the polarisations VV and HH from 30 March 2021 at 5:45 Central Europe Time. We used the operational processing system of the TanDEM-X mission to generate the DEM. The removal of artefacts resulted in data voids. The TanDEM-X DEM was then co-registered with the Pléiades DEM (see publication).</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>TanDEM-X © DLR 2024</p> <p>DLR-IMF: TanDEM-X Payload Ground Segment - CoSSC Generation and Interferometric Considerations, German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF) Oberpfaffenhofen, Germany, Issue 1.0, available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-PGS-TN-3129.</p> <p>DLR-EOC: TanDEM-X Ground Segment – DEM Products Specification Document, German Aerospace Center (DLR) – Earth Observation Center (EOC), EOC, DLR, Oberpfaffenhofen, Germany, 3.2 Edn., available at: https://tandemx-science.dlr.de/ (last access: 3 May 2024), doc. TD-GS-PS-0021, 2018.</p> <p><strong>Citation example:</strong></p> <p>“The TanDEM-X DEM (DLR 2024; DLR-IMF 2024; DLR-EOC 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Pléiades DEMs (non co-registered and co-registered):</h2> <p>The stereo pair was acquired on 31 March 2021 at 10:40 Central Europe Time. The images were processed with the NASA Ames Stereo Pipeline (ASP, Beyer and others, 2018) to generate the DEM with a ground-sampling distance of 2 m using the Semi-Global-Matching algorithm and the processing parameters from Deschamps-Berger and others (2020) without ground control points (GCPs). The Pléiades DEM was then co-registered with the reference DEM (see publication). To calculate the DEM differencing, the Pléiades DEM was resampled using bilinear interpolation to 6 m to match the resolution of the TanDEM-X DEM.</p> <p>Resolution: 2 m (non co-registered), 6 m (co-registered)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong></p> <p>Pléiades © CNES 2023, distribution AIRBUS DS</p> <p>Berthier, E., Lebreton, J., Fontannaz, D., Déprez, A., Michéa, D., Malet, J.-P., LEGOS-OMP / Data Terra (ForM@Ter-Theia) (2024). Pléiades Glacier Observatory Data Products. EOST. (Collection). doi: 10.25577/313a-a978</p> <p><strong>Citation example:</strong></p> <p>“The Pléiades DEM (CNES 2023; Berthier et al. 2024) from the study of Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>swissALTI3D: </h2> <p>We used the swissALTI3D DEM from swisstopo (Swiss Federal Office of Topography (2021a) as a reference DEM for (a) the co-registration of the Pléiades DEM and (b) the generation of the TanDEM-X DEM (i.e., phase unwrapping). This swissALTI3D DEM has a resolution of 2 m and was generated using aerial images acquired between July and September 2017. The downloaded version was provided in orthometric heights and swiss coordinate system CH1903+ / LV95 (EPSG:2056). Therefore, we converted to WGS84 UTM projection (EPSG:32632) using cubic interpolation and to ellipsoidal heights using the Swiss geoid model provided by swisstopo (Swiss Federal Office of Topography, 2021b). The geoid is provided in the geographic coordinate system (EPSG:4258), which was projected to UTM using the cubic method.</p> <p>Resolution: 2 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Swiss Federal Office of Topography (2021). swissALTI3D. Das hochpräzise digitale Höhenmodell der Schweiz. Bundesamt für Landestopografie swisstopo, ed. Wabern. <a href="https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html">https://www.swisstopo.admin.ch/de/geodata/height/alti3d.html</a>.</p> <p><strong>Citation example:</strong></p> <p>“The swissALTI3D (Swiss Federal Office of Topography 2021) as used in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License (Attribution).</p> <h2>DEM differencing (dDEM, non corrected and corrected):</h2> <p>We used DEM differencing to quantify the elevation difference (dh) between the TanDEM-X DEM and the Pléiades DEM. To calculate the dDEM we subtracted the Pléiades DEM from the TanDEM-X DEM (after co-registration). Values larger than ±50m in the dDEM are considered outliers and removed. Further, on the tongue of Grosser Aletschgletscher we encountered a bias in the dDEM originating from the phase unwrapping during the TanDEM-X DEM production. This resulted in unrealistic positive elevation differences at the tongue (up to 50 m). Therefore, on-glacier, we additionally removed positive differences larger than 5 m. For direct comparison of the elevation differences with the GPR, snow pit and snow core measurements one has to assume a signal propagation velocity within the snow and ice medium, which will lead to a smaller height of ambiguity in the snow volume and a reduced actual penetration depth. As a measure for the actual radar penetration depth the previously calculated dDEM due to signal penetration is scaled proportionally with (approx. 78%). The resulting file is called “dDEM_corrected”. For more information we refer to the publication.</p> <p>Resolution: 6 m</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset: </strong> </p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p><strong>Citation example:</strong></p> <p>“The dDEM (Bannwart et al. 2024b) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC-SA 4.0 International License (Attribution-NonCommercial-ShareAlike).</p> <h2>Stable terrain masks:</h2> <p>The stable terrain masks have been used to co-register the Pléiades DEM to the swissALTI3D and the TanDEM-X DEM to the Pléiades DEM. The two masks were created using the Pléiades orthophoto and a supervised classification method following the approach by Deschamps-Berger C and others (2020). The masks consist of off-glacier areas where no changes are expected between the two DEMs, and only snow-free pixels were retained. The masks are a binary file with a resolution of 2m and 6 m, respectively, in UTM32N projection. To evaluate the sensitivity of the radar penetration bias to co-registration, we tested two co-registration algorithms on two stable terrain masks used to co-register the TanDEM-X DEM. In the modified stable mask, we manually removed pixels in the sparse forest and dark shadow areas based on a visual inspection of the Pléiades orthophoto.<strong> </strong></p> <p>Resolution: 2 m (mask used to co-register Pléiades DEM to the swissALTI3D), 6 m (masks used to co-register the TanDEM-X DEM to the Pléiased DEM)</p> <p>Coordinate reference system: EPSG:32632</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899<a href="https://doi.org/10.5281/zenodo.11071899"> </a></p> <p><strong>Citation example:</strong></p> <p>“The stable terrain mask to co-register the Pléiades DEM (Bannwart et al. 2024b) in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h2>Glacier outline:</h2> <p>The outline as a shapefile of Grosser Aletschgletscher was manually adjusted to the glacier’s outline in 2021 based on the GLIMS outlines from 2015 (Paul and others, 2019) and the Pléiades orthophoto.</p> <p><strong>Citation of individual dataset:</strong></p> <p>Bannwart, J., Piermattei, L., Dussaillant, I., Krieger, L., Floricioiu, D., Berthier, E., Roeoesli, C., Machguth, H. and Zemp, M. (2024). Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland - supplementary datasets [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11071899</p> <p>Paul, F. (submitter), Azzoni, R.S., Fugazza, D., Le Bris, R., Nemec, J., Paul, F., Rabatel, A., Ramusovic, M.; Rastner, P., Schaub, Y., Schwaizer (nee Bippus), G (analysts). (2019) GLIMS Glacier Database. Boulder, Co. National Snow and Ice Data Center. http://dx.doi.org/10.7265/N5V98602.</p> <p><strong>Citation example:</strong></p> <p>“The glacier outline of Grosser Aletschgletscher in 2021 (Bannwart et al 2024b; Paul et al. 2019) as produced in Bannwart et al. (2024a)....”</p> <p><strong>Licence:</strong></p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <h1>Acknowledgements</h1> <p>We thank swisstopo for providing the swissALTI3D and CNES for their support.</p> <p> </p>
GCK variants and their annotations for "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"
<p>This file provides annoatations for GCK variants as dsecribed in "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"</p>
Pléiades Digital Elevation Models of South Lhonak Lake (Sikkim, India)
<h1>Data and methods</h1> <p>On 3 October 2023, the collapse of a frozen lateral moraine into South Lhonak Lake triggered a multi-hazard cascade in the Sikkim Himalaya, India (Sattar et al, 2025). This repository contains two DEMs derived from Pléiades satellite images that were acquired before and after the event. </p> <ul> <li>A post-event DEM computed from a pair of PHR1A stereoscopic images acquired on 2023-10-29 (20231029.tif)</li> <li>A pre-event DEM computed from a triplet of PHR1A stereoscopic images acquired on 2022-10-18 (20221018.tif)</li> </ul> <p>The DEMs (height above the WGS84 ellipsoid) were posted at 1.0 m with UTM 45N projection (EPSG:32645) and coregistered to the GLO30 Copernicus DEM. They were computed with the MicMac software in the DSM-OPT service using the following configuration: correlation window size: 3x3, regularization factor: 0.05, vertical uncertainty: 0.1, potential accuracy threshold: 0.4 (Rupnik et al. 2017).</p> <p>These DEMs allow the analysis of topographic changes in the lake area. We included examples of such analysis with the addition of the High Mountain Asia 8 m DEM (Shean 2017).</p> <h1>Acknowledgements </h1> <p>Pléiades images were acquired and processed thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure.</p> <h1>References</h1> <div> <p>Sattar et al. (2025) The Sikkim flood of October 2023: Drivers, causes and impacts of a multihazard cascade. Science, eads2659, https://doi.org/10.1126/science.ads2659</p> Rupnik, E., Daakir, M., & Pierrot Deseilligny, M. (2017). MicMac–a free, open-source solution for photogrammetry. <em>Open geospatial data, software and standards</em>, <em>2</em>, 1-9. https://doi.org/10.1186/s40965-017-0027-2</div> <div> </div> <div>Shean, D. (2017). High Mountain Asia 8-meter DEM Mosaics Derived from Optical Imagery, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/KXOVQ9L172S2. Date Accessed 07-29-2024.</div>
Spot6 and Pléiades Digital Elevation Models (DEMs) and elevation change grid to understand the South Lhonak lake (Sikkim) flood of October 2023
<p>===========================</p> <p>Introduction</p> <p> </p> <p>On 3 October 2023, a multi-hazard cascade in the Sikkim Himalaya, India, was triggered by the collapse of a frozen lateral moraine into South Lhonak Lake</p> <p>We share here the SPOT6 and Pléiades DEMs and the elevation change maps derived from these images. The collection includes four files:</p> <p>Two pre-event DEMs from SPOT6</p> <p>2018-12-01_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>2018-12-08_Lhonak_DEM_SGM_4m_on_Cop30_BiasCorr_NoData_aligned.tif</p> <p>One post-event mosaic DEM from Pléiades </p> <p>Post-Event_DEM_SGM_october2023.tif</p> <p>One grid of elevation change from 2018 to 2023</p> <p>dh_mosaic_Lhonak_SGM_median.tif</p> <p> </p> <p>===========================</p> <p>Methods</p> <p>The SPOT6 and Pléiades stereo-images were processed using the Ames Stereo Pipeline (ASP, Beyer et al., 2018), yielding a DEM in 4x4 m GSD. The processing was done using as only input the stereo-images and their orientation information, as Rational Polynomial Coefficients (RPCs). The parallel_stereo routine performs all the steps needed in the correlation of the stereo-images, yielding a pointcloud which is then interpolated using the routine point2dem. We used the semi global matching algorithm and the set of processing parameters from Deschamps-Berger et al. (2020)</p> <p>Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.</p> <p>Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.</p> <p> </p> <p>===========================</p> <p>Data Specifications:</p> <p>Cartographic projection: UTM zone 45N (EPSG:32645)</p> <p>Origin of Elevation: meters above WGS84 ellipsoid</p> <p>Raster data format: GeoTIFF</p> <p>NoData value : -9999</p> <p> </p> <p>===========================</p> <p>Acknowledgements:</p> <p>Pléiades and SPOT6 images were acquired thanks to the CIEST2 service developed and performed with the French Space Agency (CNES) by FormaTerre, Solid Earth component of the Data Terra Research Infrastructure</p> <p> </p> <p>===========================</p> <p>Dataset Attribution:</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p> </p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten ASCII
<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: Esri ASCII grid</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>
Seedling-habitat associations of Dipterocarps across elevation gradients
<b>Description: </b><p>This is a dataset produced during my MRes - Tropical Forest Ecology project and was used to for the analysis for my thesis </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/167"><b>Elevation gradients as microclimate drivers of Dipterocarp dynamics in the face of climate change</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=78">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Seedling plot data</b> (Worksheet Data)</p><p>Dimensions: 194 rows by 20 columns</p><p>Description: Seedling community composition in 2x2m subplots. NB: Only seedlings belonging to the family dipterocarpaceae were recorded.</p><p>Fields: </p><ul><li><b>Plot_no</b>: Plot location (Field type: Location)</li><li><b>Sub_Plot</b>: Subplot within Plot_no (Field type: ID)</li><li><b>Topography</b>: Local topography (Field type: Categorical)</li><li><b>Elevation</b>: Plot elevation. Values are the midpoint of 50-m wide elevational bands (i.e. 775 means the plot was between 750-800masl) (Field type: Numeric)</li><li><b>Anisoptera</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Hopea</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Shorea</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Parashorea</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Dryobalanops</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Dipterocarpus</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Cotylelobium</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Vatica</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Vateriopsis</b>: Number of seedlings within plot (Field type: Abundance)</li><li><b>Slope_N</b>: Slope measured in North compass direction (Field type: Numeric)</li><li><b>Slope_E</b>: Slope measured in East compass direction (Field type: Numeric)</li><li><b>P_A</b>: Is a Dipterocarp seedling present? (Field type: Categorical)</li><li><b>Adult_P_A</b>: Is a Dipterocarp adult tree present or absent? (Field type: Categorical)</li><li><b>Forest_type</b>: Forest classification (Field type: Categorical)</li><li><b>Soil_Type</b>: Soil classification (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2017-02-06 to 2017-06-06</p><p><b>Latitudinal extent: </b>4.7037 to 4.7773</p><p><b>Longitudinal extent: </b>116.8439 to 116.9837</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Malvales<br> -  -  -  - Dipterocarpaceae<br> -  -  -  -  - <i>Anisoptera</i><br> -  -  -  -  - <i>Cotylelobium</i><br> -  -  -  -  - <i>Dipterocarpus</i><br> -  -  -  -  - <i>Dryobalanops</i><br> -  -  -  -  - <i>Hopea</i><br> -  -  -  -  - <i>Parashorea</i><br> -  -  -  -  - <i>Shorea</i><br> -  -  -  -  - <i>Vateriopsis</i><br> -  -  -  -  - <i>Vatica</i><br></div><p></p>
Assessing the peatland hummock-hollow classification framework using high-resolution elevation models: Implications for appropriate complexity ecosystem modelling
<p>The hummock-hollow classification framework used to categorize peatland ecosystem microtopography is pervasive throughout peatland experimental designs and current peatland ecosystem modelling approaches. However, identifying what constitutes a representative hummock-hollow pair within a site and characterizing hummock-hollow variability within or between peatlands remains largely unassessed. Using structure-from-motion (SfM), high resolution digital elevation models (DEM) of hummock-hollow microtopography were used to: 1) examine how much area needs to be sampled to characterize site-level microtopographic variation; and 2) examine the potential role of microtopographic shape/structure on biogeochemical fluxes using data from 9 northern peatlands. This data set is comprised of plot DEMs, supporting data, and the script used to analyze data and produce figures presented in the manuscript submitted to Biogeosciences Discussion "ASSESSING THE PEATLAND HUMMOCK-HOLLOW CLASSIFICATION FRAMEWORK USING HIGH-RESOLUTION ELEVATION MODELS: IMPLICATIONS FOR APPROPRIATE COMPLEXITY ECOSYSTEM MODELLING".</p>
Effects of elevated temperature and CO2 concentration on floral development and sex differentiation in Morus alba L.
<p>The effects of global warming on floral development have been reported in many plants, but knowledge of floral development regarding gender and sex differentiation under elevated temperature, CO<sub>2</sub> concentration and their combination remains limited. So here we analysed flowering phase, sex ratio, floral morphology and biomass, total carbon and nitrogen data in male and female inflorescences (flowers) of <em>Morus alba </em>L. to determine whether and how they differ.</p> <p>This excel file contains the raw data for each data table and figure within a manuscript submitted to Annals of Forest Science.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Microscopy images - "An elevated rate of whole-genome duplications associated with carcinogen exposure in Black cancer patients"
<p>This repository contains microscopy data from the manuscript "An elevated rate of whole-genome duplications associated with carcinogen exposure in Black cancer patients" by Leanne M. Brown, Ryan A. Hagenson, Tilen Koklič, Iztok Urbančič, Janez Strancar, and Jason M. Sheltzer (preprint: 10.1101/2023.11.10.23298349; accepted for publication in Nature Communications).</p> <p> </p> <p>Each zip contains the set of images from individual multi-channel multi-position time-lapse experiment with different combinations of cells exposed to one nanomaterial. Files are named as: IMGxxxx_[ExperimentCode]_ROIxx_tile[TileNumber]_[Channel]_[CellType]_t[Timepoint].tif, where each of the varying elements in [..] denotes the following:</p> <ul> <li>[ExperimentCode]: tells which material the cells were exposed to - see decoding table in the file "material-codes.xlsx"</li> <li>[TileNumber]: two xy-tiles per condition</li> <li>[Channel]: cytoplasm of epi cells (LA4), membrane (MEM), cytoplasm of imu cells and tubulin (MHSTUB), nanomaterial (NANO)</li> <li>[CellType]: mono-culture of lung epithelial cells (epi), their coculture with macrophages (epiimu)</li> <li>[Timepoint]: consecutive number of the frame in the time-lapse</li> </ul> <p> </p>
Elevated CO2 Intensifies Hydrological Impacts Due to Tropical Deforestation
<p>nc files are the analyzed CESM simulation outputs and py files are the codes processing and plotting them.</p>
GBIF elevation data
GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world__s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. <p></p>https://www.gbif.org
Glacier Inventory and Elevation Change Data for the Shule River, Heihe River, and Shiyang River Basins in the Qilian Mountains (1990-2020)
<p>This dataset contains comprehensive glacier inventory data and elevation change measurements for the Qilian Mountains, specifically covering the Shule River, Heihe River, and Shiyang River basins. The dataset includes:</p> <ol> <li> <p>Glacier inventory data for the years 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The inventory provides detailed information on glacier attributes such as area, length, orientation, and other essential characteristics for each of the target years.</p> </li> <li> <p>Elevation change data for glaciers within the Qilian Mountains during the periods 2000-2010 and 2010-2020. This data tracks changes in glacier surface elevation, providing valuable insights into glacier mass balance and regional climate change impacts.</p> </li> </ol> <p>The data is critical for understanding the response of glaciers to climate change over the past three decades and will support ongoing research in glaciology, hydrology, and environmental monitoring.</p>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Bottom layer temperature and sea level elevation during the Typhoon Lekima
<p>This data is the direct observations of bottom layer temperature and sea level elevations during typhoon Lekima in August 2019.</p>
Data from: Effects of land use change and elevation on endemic shrub frogs in a biodiversity hotspot
<p>This project contains data and codes from a study investigating the effects of land use change and elevation on endemic shrub frogs in the northern Western Ghats, India. </p> <p>Species Coverage: Though the focal species in the study are <em>Pseudophilautus amboli</em> and <em>Raorchestes bombayensis</em>, the raw data also contain the following species. <em>Duttaphrynus melanostictus, Euphlyctis sp, Hoplobatrachus tigerinus, Hydrophylax bahuvistara, Indirana chiravasi, Indirana sp, Microhyla nilphamariensis, Minervarya cepfi, Minervarya gomantaki, Minervarya sp, Nyctibatrachus petraeus, Polypedates maculatus, Pseudophilautus amboli, Ramanella variegata, Raorchestes bombayensis, Rhacophorus malabaricus, Sphaerotheca dobsonii, Uperodon mormorata, Xanthophryne tigerina</em></p> <p>Geographic Coverage: Dodamarg and Sawantwadi Taluks of the Sindhudurg District, Maharashtra State, India. (15°40’–16°0’N; 73°51’–74°9’E)</p> <p>Temporal Coverage: June, July, August, September (2022).</p> <p>Field data was collected by Himanshu Lad and Ninad Gosavi.</p> <p>More details about the data can be obtained from Himanshu Lad and Rohit Naniwadekar from the Nature Conservation Foundation (www.ncf-india.org). </p>
USGS National Elevation Dataset (NED) DEM reprojected into Collection 2 Landsat analysis ready data (ARD) tiles
<p>This dataset is used to test the Classifying the raw irregular time series (CRIT) codes and model for CONUS land cover classification with a deep learning model that can directly classify Landsat irrigular time series. See the code here https://github.com/hankui/CRIT</p>
Stronger transgenerational plasticity in clonal compared to sexual offspring of Fragaria vesca: effects of drought, elevated temperature and CO2 conditions
<div>The dataset contains phenotypic data of woodland strawberry (<em>Fragaria vesca</em>) regarding its reproductive strategy, as well as climate and water availability for both parental and offspring generations.</div> <div> </div> <div><u>F1StatGPT.csv</u>: Table containing Ramet Number at Harvest, Fruit Number at Harvest, Ramet Biomass, Offspring Ramet Biomass, and Total Biomass.</div> <div><u>StomataStatGPT.csv:</u> Table containing Number of Stomata per mm² and Stomata Size.</div>
Data from: Variation in thyroid hormone levels is associated with elevated blood mercury levels among artisanal small-scale miners in Ghana
Background: Mercury can be very toxic to human health even at low dose of exposure. Artisanal small-scale miners (ASGMs) use mercury in gold production, hence are at risk of mercury-induced organ dysfunction. Aim: We determined the association between mercury exposure, thyroid function and work-related factors among artisanal small-scale gold miners in Bibiani-Ghana. Method: We conveniently recruited 137 consenting male gold miners at their work site in Bibiani-Ghana, in a comparative cross-sectional study. Occupational activities and socio-demographic data of participants were collected using a questionnaire. Blood sample was analysed for total mercury and thyroid hormones. Results: Overall, 58.4% (80/137) of the participants had blood mercury exceeding the occupational exposure threshold (blood mercury ≥5μg/L). T3(P<0.0001) and T4(P<0.0001) were significantly reduced among the exposed group compared to the non-exposed. TSH showed no significant variation between the exposed and non-exposed groups. Longer work duration (≥5years), gold amalgamation, gold smelting and sucking of excess mercury with the mouth were associated with increased odds of mercury exposure. Blood mercury showed negative correlation with T3(r = -0.29, P<0.0001), and T4(r = -0.69, P<0.0001) and positive correlation with work duration (r = 0.88, P<0.001). Even though a positive trend of association between blood mercury and TSH levels was recorded, it was not significant (r = 0.07, P = 0.4121). Conclusion: Small scale miners in Bibiani are exposed to mercury above the occupational threshold which may affect thyroid hormone levels.
Data from: Structural and defensive roles of angiosperm leaf venation network reticulation across an Andes-Amazon elevation gradient
1.The network of minor veins of angiosperm leaves may include loops (reticulation). Variation in network architecture has been hypothesized to have hydraulic and also structural and defensive functions. 2.We measured venation network trait space in eight dimensions for 136 biomass-dominant angiosperm tree species along a 3,300 m elevation gradient in southeastern Peru. We then examined the relative importance of multiple ecological, and evolutionary predictors of reticulation. 3.Variation in minor venation network reticulation was constrained to three axes. These axes described branching vs. reconnecting veins, elongated vs. compact areoles, and high vs. low density veins. Variation in the first two axes was predicted by traits related to mechanical strength and secondary compounds, and in the third axis by site temperature. 4.Synthesis. Defensive and structural factors primarily explain variation in multiple axes of reticulation, with a smaller role for climate-linked hydraulic factors. These results suggest that venation network reticulation may be determined more by species interactions than by hydraulic functions.
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.