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162 results for “interpolation”
WorldSeasons: a seasonal classification system interpolating biomes within the year for improved temporal aggregation
<p>We present a seasonal classification system to improve the temporal framing of comparative scientific analysis. Research often uses yearly aggregates to understand inherently seasonal phenomena like harvests, monsoons, and droughts. This obscures important trends across time and differences through space by including redundant data. Our classification system allows for a more targeted approach. We split global land into four principal climate zones: desert, arctic and high montane, tropical, and temperate. A cluster analysis with zone-specific variables and weighting splits each month of the year into discrete seasons based on the monthly climate. We expect the data will be able to answer global comparative analysis questions like: are global winters less icy than before? Are wildfires more frequent now in the dry season? How severe are monsoon season flooding events? This is a natural extension of the historical concept of biomes, made possible by recent advances in climate data availability and artificial intelligence.</p>
Interpolated N2O fluxes on the GLBRC Scaleup Sites at the Kellogg Biological Station, Hickory Corners, MI (2010 to 2014)
Dataset AbstractTo create a complete time series of N2O fluxes from the GLBRC Scaleup fields the fluxes were interpolated from 2010 to 2014.original data source http://lter.kbs.msu.edu/datasets/168
ERA-5 reanalysis results interpolated onto the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>ERA-5 fields at 1-hour temporal and grid size of 0.25° x 0.25° (0.5° x 0.5° for wave variables) have been downloaded from <a href="https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels">https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels</a>.</p> <p>The data are interpolated using two methods:</p> <p>'nearest': the value of the nearest ERA-5 grid cell is use;</p> <p>'linear': the values from the nearest grid cells in space and time are linearly interpolated to the [date_time, latitude, longitude] coordinate of the ship</p> <p>providing a number of atmospheric, land and oceanic climate variables interpolated along the five-minute cruise track.</p> <p>The data repository can be checked out at: <a href="https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track">https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track</a></p> <p><strong>Dataset contents</strong></p> <ul> <li>era5-on-cruise-track-5min-legs0-4-linear.csv, data file, comma-separated values</li> <li>era5-on-cruise-track-5min-legs0-4-nearest.csv, data file, comma-separated values</li> <li>interpolate-to-shiptrack.py, processing script, text/x-python</li> <li>download-ecmwf.ipynb, processing script, application/x-ipynb+json</li> <li>ecwmf_interpolate.zip, processing scripts, zip file</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This interpolation of the ERA-5 reanalysis output to the five-minute averaged cruise track and velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
WS22 database: combining Wigner Sampling and geometry interpolation towards configurationally diverse molecular datasets
<p>The WS22 database provides a collection of molecular datasets that explores a broad configurational space of flexible organic molecules with varying sizes and complexity. It includes several chemical properties calculated with a quantum chemical (QM) method. Complementary to the structured datasets, this repository also provides the molecular geometries for the equilibrium structures together with the corresponding output of the QM frequency calculations. Details about the methodology, content, and structure of the WS22 datasets are provided in the README file included in this repository.</p>
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
Open the record for dataset details and reuse information.
CLaMS mean age of air tracers for 15/01/2011 interpolated on simulated CAIRT retrieval grid
<p>The dataset contains simulation results from the Chemical Lagrangian Model of the Stratosphere (CLaMS) for January 15, 2011. These results are interpolated onto the simulated retrieval grid of the Changing-Atmosphere Infrared Tomography Explorer (CAIRT). The data includes six trace gases (SF₆, N₂O, CFC-11 (F11), CFC-12 (F12), HCFC-22 (F22), and CH₄) and the "exact" model mean age of air (BA). The file is provided in NetCDF format.</p>
profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean
<p>The dataset includes profiles of chlorophyll and photosynthetically available radiation (PAR) from Bio-Argo float measurements for 2013-2020 interpolated on regular 2 m grid for the World Ocean </p> <p>Data was collected from open archive (<em>Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)) </em><a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a></p> <p>Global array of Bio-Argo floats equipped with Chl (mg m−3) and PAR(μmol photons m-2 s-1) sensors at -60°S..60°N was used in this study. Data for 2013-2020 was downloaded from the IFREMER data archive (ftp://ftp.ifremer.fr/, <a href="https://doi.org/10.17882/42182">https://doi.org/10.17882/42182</a>). It includes 464 floats measuring Chl (~ 70000 profiles), and 167 floats measuring both PAR (~26000 profiles) and Chl. Before the analysis, the measurements of each Bio-Argo buoy were visually checked to filter the outliers in Chl or PAR data. After visual analysis about 1600 profiles of PAR and 2800 profiles of Chl were excluded from the dataset.</p> <p>Chl (mg m−3) was retrieved from a Chl fluorometer (excitation at 470 nm; emission at 695 nm) sensors of three types (FLBB, ECO-Triplet, or MCOMS). We use the raw fluorescence-based estimates of Chl (product “non-adjusted Chl”) derived directly from the measurements of fluorescence with factory calibration coefficients without the corrections on non-photochemical quenching, CDOM fluorescence, and other effects (see (<a href="http://www.argodatamgt.org/Documentation">http://www.argodatamgt.org/Documentation</a>)).</p> <p>A multispectral ocean color radiometer (OCR-504, SATLANTIC Inc.) was used to measure PAR. Only instantaneous PAR measurements made within ± 1.5 hours from noon (10:30-13:30 hours) were used.</p> <p>Then the data from all buoys were interpolated on regular 2-m grid and included in one dataset.</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Interpolated Clay Fraction and Resistivity model of the Aare Valley, Switzerland
<p>The dataset is an underground model of the Upper Aare Valley in Switzerland. It has been made in the framework of the Phenix project at the University of Neuchâtel. It has been produced by applying the CF prediction method (doi : 10.5194/hess-18-4349-2014) to an EM dataset (doi : 10.5194/essd-13-2743-2021).</p> <p>The Model was then interpolated using Multiple Point statistics, with robust uncertainty quantification (doi : In review).</p> <p>The two files contain the same data, as pointset or gridVTK files. The data contained are :</p> <ul> <li>Log10(Resistivity)</li> <li>Log10(Resistivity) Uncertainty (STD)</li> <li>ClayFraction</li> <li>ClayFraction Uncertainty (STD)</li> </ul> <p>The X,Y,Z positions are provided in UTM32N (epsg : 32632).</p>
VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic grids for interpolation
<p>VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic interpolation grids in ASCII format. The generation of these grids is described in http://doi.org/10.1007/s00190-015-0871-8</p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
Interpolation of the median grain size of the first 2 cm sediment layer in the former saltworks of Salin de Giraud in 2017
<p>Sediment samples were collected in the summer of 2017 over the entire study area at 500 m spacing and in the channels. Grain size analysis of the collected sediment samples was conducted using a Malvern Mastersizer 2000© laser beam grain sizer. The median grain size (d50 in µm) at each sample location was then interpolated over the entire study area. Interpolation was made with the SAGA-GIS software (version 7.9.0). According to the cross-validation error, the best method for the D50mm interpolation was the Modified Quadratic Shepard. The 10-fold validation provided an R² of 0.93, an NMRSE of 24.5, an RMSE of 83.9, an MRE of 7039 with the fit set to “node”, the quadratic neighbours and weighting neighbours set to 50 and the spatial resolution was set to 10 m. The resultant interpolation map was then categorized following the nomenclature of Blott and Pye (2001) provided in the file style_sediment_map.qml.</p>
Maps of interpolated paleotemperatures in Western Europe from MIS 14 to MIS 11
<p>To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11 and BIO12) were computed from the Oscillayers dataset, for 11 subdivisions of the Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review).<br> Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019).<br> BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986).<br> The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.</p>
Maps of interpolated paleotemperatures in Western Europe from MIS 14 to MIS 11
<p>To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11) were computed from the Oscillayers dataset, for 11 subdivisions of the Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review).<br> Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019).<br> BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986).<br> The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.</p>
CORDEX GCM source.grids for interpolating CORDEX data for ATLAS
<p>This is the list of all source.grid files used for the conservative interpolation of all the outputs from regional climate models from the CORDEX experiment used in ATLAS (https://www.ipcc.ch/report/ar5/wg1/atlas-of-global-and-regional-climate-projections/)</p>
A geometry preserving, conservative, mesh-to-mesh isogeometric interpolation algorithm for spatial adaptivity of the multigroup, second-order even-parity form of the neutron transport equation
<p>In this paper a method is presented for the application of energy-dependent spatial meshes applied to the multigroup, second-order, even-parity form of the neutron transport equation using Isogeometric Analysis (IGA). The computation of the inter-group regenerative source terms is based on conservative interpolation by Galerkin projection. The use of Non-Uniform Rational B-splines (NURBS) from the original computer-aided design (CAD) model allows for efficient implementation and calculation of the spatial projection operations while avoiding the complications of matching different geometric approximations faced by traditional finite element methods (FEM). The rate-of-convergence was verified using the method of manufactured solutions (MMS) and found to preserve the theoretical rates when interpolating between spatial meshes of different refinements. The scheme’s numerical efficiency was then studied using a series of two-energy group pincell test cases where a significant saving in the number of degrees-of-freedom can be found if the energy group with a complex variation in the solution is refined more than an energy group with a simpler solution function. Finally, the method was applied to a heterogeneous, seven-group reactor pincell where the spatial meshes for each energy group were adaptively selected for refinement. It was observed that by refining selected energy groups a reduction in the total number of degrees-of-freedom for the same total L2 error can be obtained.</p>
Interpolated data on bioavailable strontium in the southern Trans-Urals, 2020-2022 version 3.1 (current)
<p><strong>Description</strong></p> <p><strong>The Interpolated Strontium Values dataset Ver. 3.1 </strong>presents the interpolated data of strontium isotopes for the southern Trans-Urals, based on the data gathered in 2020-2022. The current dataset consists of five sets of files for five various interpolations: based on grass, mollusks, soil, and water samples, as well as the average of three (excluding the mollusk dataset). Each of the five sets consists of a CSV file and a KML file where the interpolated values are presented to use with a GIS software (ordinary kriging, 5000 m x 5000 m grid). In addition, two GeoTIFF files are provided for each set for a visual reference. </p> <p><a href="../records/10253264/files/Averaged%205000%20m%20interpolated%20points.kml?download=1">Average 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Averaged%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain averaged values of all three sample types.</p> <p><a href="../records/10253264/files/Grass%205000%20m%20interpolated%20points.kml?download=1">Grass 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Grass%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the grass sample dataset.</p> <p><a href="../records/10253264/files/Mollusks%205000%20m%20interpolation%20raster.tif?download=1">Mollusks 5000 m interpolated points.kml</a> / <a href="../records/10253264/files/Mollusks%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the mollusk sample dataset.</p> <p><a href="../records/10253264/files/Soil%205000%20m%20interpolated%20points.kml?download=1">Soil 5000 m interpolated points.kml </a>/ <a href="../records/10253264/files/Soil%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the soil sample dataset.</p> <p><a href="../records/10253264/files/Water%205000%20m%20interpolated%20points.kml?download=1">Water 5000 m interpolated points.km</a>l / <a href="../records/10253264/files/Water%205000%20m%20interpolation.csv?download=1">csv</a>: these files contain data interpolated from the water sample dataset.</p> <p>The current version is also supplemented with GeoTiff raster files where the same interpolated values are color-coded. These files can be added to Google Earth or any GIS software together with KML files for better interpretation and comparison.</p> <p><a href="../records/10253264/files/Averaged%205000%20m%20interpolation%20raster.tif?download=1">Averaged 5000 m interpolation raster.tif</a>: this file contains a raster representing the averaged values of all three sample types.</p> <p><a href="../records/10253264/files/Grass%205000%20m%20interpolation%20raster.tif?download=1">Grass 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the grass sample dataset.</p> <p><a href="../records/10253264/files/Mollusks%205000%20m%20interpolation%20raster.tif?download=1">Mollusks 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the mollusk sample dataset.</p> <p><a href="../records/10253264/files/Soil%205000%20m%20interpolation%20raster.tif?download=1">Soil 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the soil sample dataset.</p> <p><a href="../records/10253264/files/Water%205000%20m%20interpolation%20raster.tif?download=1">Water 5000 m interpolation raster.tif</a>: this file contains a raster representing the data interpolated from the water sample dataset</p> <p>In addition, the cross-validation rasters created during the interpolation process are also provided. They can be used as a visual reference of the interpolation reliability. The grey areas on the raster represent the areas where expected values do not differ from interpolated values for more than 0.001. The red areas represent the areas where the error exceeded 0.001 and, thus, the interpolation is not reliable. </p> <p> </p> <p><strong>How to use it?</strong></p> <p>The data provided can be used to access interpolated background values of bioavailable strontium in the area of interest. Note that a single value is not a good enough predictor and should never be used as a proxy. Always calculate a mean of 4-6 (or more) nearby values to achieve the best guess possible. Never calculate averages from a single dataset, always rely on cross-validation by comparing data from all five datasets. Check the cross-validation rasters to make sure that the interpolation is reliable for the area of interest. </p> <p> </p> <p><strong>References</strong></p> <p>The interpolated datasets are based upon the actual measured values published as follows:</p> <p>Epimakhov, Andrey; Kisileva, Daria; Chechushkov, Igor; Ankushev, Maksim; Ankusheva, Polina (2022): Strontium isotope ratios (87Sr/86Sr) analysis from various sources the southern Trans-Urals. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.950380</p> <p> </p> <p><strong>Description of the original dataset of measured strontium isotopic values</strong></p> <p>The present dataset contains measurements of bioavailable strontium isotopes (87Sr/86Sr) gathered in the southern Trans-Urals. There are four sample types, such as wormwood (n = 103), leached soil (n = 103), water (n = 101), and freshwater mollusks (n = 80), collected to measure bioavailable strontium isotopes. The analysis of Sr isotopic composition was carried out in the cleanrooms (6 and 7 ISO classes) of the Geoanalitik shared research facilities of the Institute of Geology and Geochemistry, the Ural Branch of the Russian Academy of Sciences (Ekaterinburg). Mollusk shell samples preliminarily cleaned with acetic acid, as well as vegetation samples rinsed with deionized water and ashed, were dissolved by open digestion in concentrated HNO 3 with the addition of H 2 O 2 on a hotplate at 150°C. Water samples were acidified with concentrated nitric acid and filtered. To obtain aqueous leachates, pre-ground soil samples weighing 1 g were taken into polypropylene containers, 10 ml of ultrapure water was added and shaken in for 1 hour, after which they were filtered through membrane cellulose acetate filters with a pore diameter of 0.2 μm. In all samples, the strontium content was determined by ICP-MS (NexION 300S). Then the sample volume corresponding to the Sr content of 600 ng was evaporated on a hotplate at 120°C, and the precipitate was dissolved in 7M HNO 3. Sample solutions were centrifuged at 6000 rpm, and strontium was chromatographically isolated using SR resin (Triskem). The strontium isotopic composition was measured on a Neptune Plus multicollector mass spectrometer with inductively coupled plasma (MC-ICP-MS). To correct mass bias, a combination of bracketing and internal normalization according to the exponential law 88 Sr/ 86 Sr = 8.375209 was used. The results were additionally bracketed using the NIST SRM 987 strontium carbonate reference material using an average deviation from the reference value of 0.710245 for every two samples bracketed between NIST SRM 987 measurements. The long-term reproducibility of the strontium isotopic analysis was evaluated using repeated measurements of NIST SRM 987 during 2020-2022 and yielded 87 Sr/ 86 Sr = 0.71025, 2SD = 0.00012 (104 measurements in two replicates). The within-laboratory standard uncertainty (2σ) obtained for SRM-987 was ± 0.003 %. </p>
Optimally interpolated dissolved oxygen based on the World Ocean Database 2018 and CMIP6 models
<p>Optimal interpolation of observed and modeled dissolved oxygen data from WOD18 and CMIP6. Technical details are provided in the publication (Ito et al., 2023). </p><p>Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of global O2 loss in optimally interpolated historical ocean observations, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-72, in review, 2023.</p>
What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica (Interpolated Data)
<p>This dataset accompanies the paper 'What can radar-based measures of subglacial hydrology tell us about basal shear stress? A case study at Thwaites Glacier, West Antarctica' in Journal of Glaciology, and can be used alongside the code found on Github (https://github.com/rohaizharis/inversion_radar2022) to reproduce the figures. The dataset consists of ice-penetrating radar data (specularity and relative reflectivity) and basal shear stress inversions that have been linearly interpolated onto radar flight tracks.</p>
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50 in Growth, age at sexual maturity, longevity and natural mortality of Alpheus brasileiro (Caridea: Alpheidae) from the south-eastern coast of Brazil
Figure 5. Alpheus brasileiro Anker, 2012. Logistic curve interpolation where 50% of females reach functional sexual maturity (CL50).
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.