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Dataset results
15 results for “undersampling”
Reconstruction Algorithms in Undersampled AFM Imaging - results
<p>This data set contains numerical simulation results from experiments for the paper "Review of compressed sensing reconstruction algorithms in AFM cell imaging", submitted to IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of an HDF5 file containing the simulation results as well as MD5 and SHA checksums of the HDF5 database for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://doi.org/10.5281/zenodo.18745.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
Reconstruction Algorithms in Undersampled AFM Imaging - final results
<p>This data set contains numerical simulation results from experiments for the paper "Reconstruction Algorithms in Undersampled AFM<br /> Imaging", published in IEEE Journal of Selected Topics in Signal Processing.</p> <p>The data set consists of a set of HDF5 files containing the simulation results as well as MD5 and SHA checksums of the HDF5 databases for validating the integrity of the data after download.</p> <p>The data set is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/).</p> <p>Python scripts used for producing these results as well as Python scripts for extracting images and data used in the accompanying paper from the database can be found in the accompanying deposition http://dx.doi.org/10.5281/zenodo.32959.</p> <p>The data set contains images, and reconstructed versions of these, originally published in the data set available at http://dx.doi.org/10.5281/zenodo.17573.</p>
Algorithms for Reconstruction of Undersampled Atomic Force Microscopy Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using a variety of interpolation and reconstruction methods.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (reconstruction_goblet_ID_0_of_1.hdf5).</li> <li>The Python script which was used to create the database (reconstruction_goblet.py).</li> <li>Auxillary Python scripts needed to run the simulations (optim_reconstructions.py, it_reconstruction.py, interp_reconstructions.py, gamp_reconstructions.py, and utils.py).</li> <li>MD5 and SHA256 checksums of the database and Python script files (reconstruction_goblet.MD5SUMS, reconstruction_goblet.SHA256SUMS).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D
<p>This dataset contains experimental data related: "Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D", T.Craig, A. Kadu, K.J. Batenburg and S. Bals, <em><strong>Nanoscale</strong></em>, 2023,<strong>15</strong>, 5391-5402 (2023).</p>
Figure 2 in Spiders (Arachnida: Araneae) of Saba Island, Lesser Antilles: Unusually high species richness indicates the Caribbean Biodiversity Hotspot is woefully undersampled
Figure 2. Spider species richness by island area showing dramatic undersampling of most islands and lack of expected positive species-area relationship. Islands are ordered by size from smaller to larger as follows: Saba, Nevis, St. Kitts, Antigua, Grenada, Turks and Caicos, Barbados. See text for data sources.
Figure 1 in Spiders (Arachnida: Araneae) of Saba Island, Lesser Antilles: Unusually high species richness indicates the Caribbean Biodiversity Hotspot is woefully undersampled
Figure 1. Saba Island (17o38'N, 63o14'W), Lesser Antilles. Thirty-three collection sites mapped using Google Earth. Numbers correspond to sites listed in Table 1.
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into ten parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> <p>These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>$ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>$ tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 114 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into four parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> <p>These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 70 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset
<p>This repository contains the raw data and code nessecary to reproduce the results from the paper "Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms"</p> <p>The main file is the python-notebook 'reproducibility.ipynb', which details the full process for reproduction of the results shown in the paper. The two additional .py files are included for computation which takes longer and can be parallelized.</p> <p>The folder 'irace_conf_static_modcma.zip' contains the verification runs: 200 independent runs of each configuration. Indexes are according to 'Irace_confs_static_modcma_v2.csv'</p> <p>The folder 'logs_baseline_cs.zip' contains the raw irace files on which the analysis is based. This data is taken from the following repository:<br> de Nobel, Jacob, Vermetten, Diederick, Wang, Hao, Doerr, Carola, & Bäck, Thomas. (2021). Data and Code from: Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4524959</p>
Data from: Explaining high-diversity death assemblages: undersampling of the living community, out-of-habitat transport, time-averaging of rare taxa, and local extinction
Molluscan benthic assemblages provide unique opportunities for understanding both spatial and temporal patterns of biodiversity. Species richness in the shell remains found at a site (i.e. the death assemblage) is typically several times higher than in the counterpart living assemblage, reflecting a complex history of settlement, dissemination and decomposition post-mortem. We used high-density temporal and spatial sampling (>37'000 individuals representing 196 taxa) of a shallow (5-8 m) nearshore sandy habitat off the coast of south-east Sardinia (Italy, Mediterranean Sea) to study the factors responsible for differences in the relative diversity of living and death assemblages. We found that death assemblages at all sites were considerably more diverse than living communities (1.5-3.5x more dead than living taxa after sample-size standardization), with 78% of all taxa solely recovered as empty shells, resulting in low live-dead agreement. By carefully filtering the raw data and combining them with habitat information extracted from the literature, we disentangled the major causes of this discordance and quantified their individual effects. Increased dead diversities could not be attributed to undersampling of the living community, but instead resulted from three phenomena of decreasing importance: the post-mortem, out-of-habitat transport of non-indigenous taxa (57% of dead-only taxa were allochthonous), the time-averaged presence of rare indigenous taxa (40% of dead-only taxa), and the likely local extirpation of a small number of species (3% of dead-only taxa). Our approach demonstrates how ecological inferences based on death assemblages can be improved by restricting analyses to demonstrably indigenous taxa, and highlights how mollusc shell remains can be used to provide information over both ecological and evolutionary timescales.
Population of White Dwarfs - undersampled x1000
<p>As generated by StellarMortis.</p>
Distribution. Sierra Leone and Guinea E to Cameroon (and Bioko I) and across Congo Basin to Uganda, Rwanda, and W Kenya; there are no records over much of interior of Congo Basin, which may be undersampled. in Molossidae
Distribution. Sierra Leone and Guinea E to Cameroon (and Bioko I) and across Congo Basin to Uganda, Rwanda, and W Kenya; there are no records over much of interior of Congo Basin, which may be undersampled.
Data from: Explaining high-diversity death assemblages: undersampling of the living community, out-of-habitat transport, time-averaging of rare taxa, and local extinction
Open the record for dataset details and reuse information.
Undersampling correction methods to control γ-dependence for comparing β-diversity between regions
Open the record for dataset details and reuse information.
Establishment of Radiomics Database by Clinical Application of Multiparametric MRI Based on Incoherent Undersampling
ClinicalTrials.gov study NCT02944006. IPD Sharing: NO. Countries: 1. Publications: 0.
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