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162 results for “interpolation”
Supplement_Stochastic_Reconstruction_and_Interpolation_of_Precipitation_Fields_Using_Combinded_Information_CML_and_RG
<p>This file includes the synthetic dataset used in the study "Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges", including the full synthetic reference fields (VR_precip_h_30052013_02062013.nc) as well as the used synthetic rain gauge (precip_VRObs_dry+wet_30052013_02062013_Pgr1mm.csv) and commercial microwave links (VROBS_MWL_selection_Pgr1mm.csv) input data.</p> <p>The simulated ensembles of possible realizations of the precipitation fields are stored for the synthetic (simulation_VR) and real (simulation_realworld) world dataset. A corresponding description of the grid can be found in gridinfo.txt.</p>
Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation"
<p>Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". And This database contains GNSS velocity and strain rate field data for mainland China obtained by the spherical spline method. Also, we include the sub-graph data for each graph of the manuscript "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". We have detailed the meaning and format of each data in the "readme.txt", and a separate "readme.txt" is included in each zip.</p>
Reduced variable ARM interpolated sonde data April 2017- March 2022
<p>Tar bundle of reduced ARM interpolated sonde data from the SGP C1 site for Bath post-graduate Maths4DL projects.</p><p>Data presented as daily files in annual tar files.</p>
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Circular variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Interpolated Quaternary and Jurassic surfaces below a 4.5 m dextral railroad offset near Summerville, SC
<p>Oblique view of the detrended morphology of the base of the Quaternary encountered in shallow auger holes in a ±30 km region near Summerville South Carolina (Weems and Lewis, 2002, Weems <em>et al</em>., 2014) and smoothed morphology of the prominent Jurassic reflector from reflection profiles from Chapman and Beales (2011). The smoothed Jurassic reflector is encountered at 560-850 m depth (Vert.= Hor. Scale x50); The Quaternary horizon lies at 3-9 m depth and its vertical scale is exagerrated x 5000.</p> <p>The vertical Summerville/ Gants fault is depicted with two colors. A dislocation model fit to a 4.5 m, 1.5 km scale dextral offset to the railroad (black line crossing the fault) shows that it slipped below ≈450 m, and this part of the fault is shaded red. Also shown with a broken dashed line are the eastern and southern edges of the Pneholoway Marine Terrace.</p> <p>The movie rotates about a point near Summerville that was raised by ≈1 m in the earthquake, and illustrates the coincidence of the upwarped base of the Quaternary and the Summerville/Gants fault, suggesting that the eastern edge of the Penholoway Terrace has been raised a few meters by earthquakes prior to the 1886 Charleston earthquake.</p>
Data from: A generalized distribution interpolated between the exponential and power law distributions and applied to pill bug (Armadillidium vulgare) walking data
<p>The walking pattern of an organism is typically designated as either a Lévy walk or a Brownian walk based on whether the frequency distribution of its linear step lengths follows a power law distribution or an exponential distribution. However, there are many cases where actual data cannot be classified into either of these categories. In this paper, we propose a general distribution that includes the power law and exponential distributions as special cases. This distribution has two parameters: one parameter represents the exponent, similar to the power law and exponential distributions, and the other is a shape parameter representing the shape of the distribution. By introducing this distribution, an intermediate distribution model can be interpolated between the power law and exponential distributions. In this study, the proposed distribution was fitted to the frequency distribution of the step length calculated from the walking data of pill bugs. The autocorrelation coefficients were also calculated from the time-series data of the step length, and the relationship between the shape parameter and time dependency was investigated. The results indicate that individuals whose step length frequency distributions are closer to the power law distribution have stronger time dependence.</p> <p>C++ program for parameter estimation of generalized distributions and source code for statistical analysis using R.</p>
Data for: Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
<p>This is the dataset for the article "Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials" by Juno Nam and Rafael Gomez-Bombarelli.</p> <ul> <li>Preprint: <a href="https://arxiv.org/abs/2404.10746">https://arxiv.org/abs/2404.10746</a></li> <li>Code: <a href="https://github.com/learningmatter-mit/alchemical-mlip">https://github.com/learningmatter-mit/alchemical-mlip</a></li> </ul>
ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. Results of tropical cyclone tracking algorithm for TC Irma, Florence, Teddy and Ida simulations with ECMWF IFS.
<p>ECMWF IFS potential salinity and temperature data interpolated to ALAMO float positions. The data is from forecasts of tropical cyclone Irma, Florence, Teddy and Ida performed at horizontal atmosphere resolutions of TCo1279, TCo2559, TCo3999, TCo7999 and ocean resolutions of eORCA025 and eORCA025.</p> <p> </p> <p>Data also contains the results of tracking these tropical cyclones in the ECMWF IFS simulations. </p>
Research data for: "Band Structure Interpolation using Optimized Local Orbitals from Linear-Scaling Density-Functional Theory"
<p>This file was created by Laura E. Ratcliff on 23rd March 2018. It contains the input files employed for the ONETEP and CASTEP calculations in the above publication.</p> <p>Directory Listing:</p> <p>castep/</p> <p>Contains the input files used to generate the CASTEP reference data.</p> <p>onetep/</p> <p>Contains the input files used to generate all of the ONETEP data, where filenames are labelled with the number of repeat CNT units, val (cond) indicates a valence (conduction) calculation and the suffixes follow a similar labelling convention to that employed in the manuscript.</p>
Fig. 5 Landscape interpolations for the Plateau a and Piedmont b in Pliocene origins, Pleistocene refugia, and postglacial range expansions in southern devil scorpions (Vaejovidae: Vaejovis carolinianus)
Fig. 5 Landscape interpolations for the Plateau a and Piedmont b clades based on genetic differentiation among COI haplotypes. Peaks (reddish colors) represent areas of high genetic diversity and valleys (blue colors) indicate areas of low diversity
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning"
<p>Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2019.10.011" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asr.2019.10.011</a>). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (<a href="https://doi.org/10.3390/ijgi1010032">https://doi.org/10.3390/ijgi1010032</a>).</p> <p>These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'.</p>
What Can Generative Modelling Do for Interpolation of Extremely Sparse Wind Farm Seismic Data
<p>2024 Global energy transition abstract about diffusion model data interpolation. </p>
Pre-processed WRF output, interpolated from gridded data to 74 station locations, in the Midwest U.S.
<p>The dataset includes pre-processed WRF simulation data for the historical period of 1980 to 2022 and future projections from 2058 to 2100 under two climate change scenarios: RCP 4.5 and RCP 8.5. The data has been interpolated from gridded data to 74 station locations in the Midwest United States. Each file contains data for a single station and a single month with a 3-hourly time step. This dataset can be used to generate intensity-duration-frequency (IDF) curves using the procedure published at https://doi.org/10.5281/zenodo.13685451.</p>
Adaptive Rational Interpolation and Higher-order SVD for Low-rank Tensor Approximation in Structural Dynamics Simulations
<h1>Vibroacoustic Data Compression and Interpolation</h1> <p>This is the code that accompanies the paper</p> <blockquote> <p>Adaptive Rational Interpolation and Higher-order SVD for Low-rank Tensor Approximation in Structural Dynamics Simulations</p> </blockquote> <p>which we (Jan Heiland, Victor Gosea, Ulrich Römer, Davide Pradovera, Harikrishnan Sreekumar, Sabine Langer) submitted for presentation at the ECC-2025.</p> <p>See the <code>README.md</code> for information and instruction to reproduce the results of the paper.</p>
Radar Precipitation Estimates (Radolan RW product) interpolated onto LfULG Stations Saxony
<p>Radar Precipitation Estimates interpolated onto LfULG Air Quality Measurement / Monitoring Stations</p> <p><strong>Processing Steps</strong></p> <ul> <li>Radolan RW data were retrieved from DWD Climate Data Center: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/historical/bin/</li> <li>Radolan RW data were interpolated onto a subset of LfULG Stations using the Nearest Neighbor method</li> <li>statistics (average, maximum & standard deviation) are provided for a 5 x 5 km**2 cutout</li> </ul> <p><strong>Data Description</strong></p> <ul> <li>data are packed into TAR Archives for each station</li> <li>individual data are stored as ASCII tables for each month and station (CSV with space as separator)</li> <li>variable meaning is described in the header section of each file</li> </ul> <p><strong>Example for Data Input with Python</strong></p> <pre><code class="language-python">import numpy as np import xarray as xr import datetime def read_rado_dat( filename ): ''' Reads Radolan RW time series from ASCII files and returns `xarray` Dataset. Parameters ---------- filename : str input filename Returns ------- rr : xr.Dataset time series data (rain rates in mm/h) ''' print(f'.. open {filename}') dat = np.genfromtxt( filename ) ndat = len(dat) print(ndat) time = [] for i in range( ndat ): d = dat[i] t = datetime.datetime(int( d[0] ), int( d[1] ), int( d[2] ), int( d[3] ), int( d[4] )) time += [t,] rr = xr.Dataset() rr['time'] = time rr['rr'] = xr.DataArray( data = dat[:, 7], dims = 'time', coords = {'time':time}) rr['rr_mean'] = xr.DataArray( data = dat[:, 8], dims = 'time', coords = {'time':time}) rr['rr_max'] = xr.DataArray( data = dat[:, 9], dims = 'time', coords = {'time':time}) rr['rr_std'] = xr.DataArray( data = dat[:, 10], dims = 'time', coords = {'time':time}) m = (rr != -999) return rr.where( m )</code></pre> <p> </p> <p> </p>
Maricopa County, AZ interpolated daily precipitation rasters
<p>This dataset contains daily layers of precipitation data from weather stations in Maricopa County, Arizona, USA, from October 2012 - September 2017. The layers contain raw measurements as well as spatial interpolation between weather stations done by kriging. Elevation was used as a covariate for the kriging algorithm.</p> <p>The data are presented in a zip file. When unzipped, the data layers will be larger than 9 GB.</p>
Interpolation
<p>Interpolation with CDO</p>
Data set of ""Reconciling high-resolution strain rate of continental China from GNSS data with the spherical spline interpolation""
<p>Date data of ”Reconciling high-resolution strain rate of continental China from GNSS data with the spherical spline interpolation“</p> <p>"readme.txt" is the description of those zips. And Each zip also contains a "readme.txt", which is a description of the respective zip.</p>
Synthetic Histology: Videos of Class Interpolation
<p>Supplementary information for the manuscript titled "Deep Learning Generates Synthetic Cancer Histology for Explainability and Education". These are videos of synthetic histology generated by a conditional generative adversarial network (cGAN), trained on thyroid cancer images and conditioned on whether the image is from a tumor with BRAF-like or RAS-like gene expression. The videos show class interpolation for a given seed, starting with a synthetic histology image of a BRAF-like tumor and transitioning to an image of a RAS-like tumor. These videos illustrate the histologic manifestation of BRAF-RAS gene expression in thyroid tumors using synthetic histology.</p>
Best Learned Models: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes
<p>Best learned model learned with the dataset available <a href="http://https://doi.org/10.5281/zenodo.8033058">here</a> for the mTAN-GP, mTAN-MLP, mTAN-LTAE, and raw-LTAE.</p> <p>For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_mtan_gp_irregular_sits">open source repository</a>.</p>
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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.