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7,370 results for “supplement”
Supplemental Data for "Self-similarity of spectral response functions for fractional quantum Hall states"
<p>Scripts and data to supplement the paper "Self-similarity of spectral response functions for fractional quantum Hall states".</p>
June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)
<p><strong>June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)</strong></p> <p><strong>Description</strong></p> <p>Supplementary dataset to:</p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other</p> <p>These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.</p> <p>Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only</p> <p><strong>File descriptions</strong></p> <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> <p><strong>References</strong></p> <p>Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647</p> <p>*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a></p> <p>**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p> </p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
Artifacts supplementing the ACM DTRAP 2020 article "Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version)"
<p>These research artifacts supplement the following two publications:</p> <ul> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT [ACSAC 2019], DOI 10.1145/3359789.3359800, more details at https://crocs.fi.muni.cz/public/papers/acsac2019</li> <li>Will You Trust This TLS Certificate? Perceptions of People Working in IT (extended version) [ACM DTRAP 2020], DOI 10.1145/3419472, more details at https://crocs.fi.muni.cz/public/papers/dtrap2020</li> </ul> <p>The artifacts contain the full experimental setup (as described in Section 2.1 of the paper) and the complete anonymized dataset underlying the evaluation presented in Sections 3 and 4.</p> <p>The experimental setup contains the documents accompanying the task: the informed consent, pre-task questionnaire, task description, trust scales, and the list of questions posed during the post-task interview (all in PDFs). We further include the custom website with certificate validation documentation for the “redesigned” condition (static HTML). While working on the task, participants in the “redesigned” condition could access this website via a link that was in the redesigned error messages. Furthermore, we provide the software with which the participants interacted. It contains the displayed error messages and validated certificates. These things are available both individually and incorporated in a snapshot of a virtual machine used at the experiment (importable directly into VirtualBox).</p> <p>The collected data is presented in a single dataset (SPSS format; you can use PSPP as a free alternative). It includes the analysis syntax files to obtain the numerical results presented in the paper. For each participant, the dataset contains: 1) pre-task questionnaire answers, 2) reported trust ratings, 3) sub-task timing, 4) information on whether they browsed the Internet and 5) the interview codes assigned. Note that we do not publish the interview transcripts to preserve participant privacy.</p>
Artifacts supplementing the RSA-CT 2018 paper "Why Johnny the Developer Can't Work with Public Key Certificates"
<p>Supplemental materials for the paper "Why Johnny the Developer Can't Work with Public Key Certificates" (DOI 10.1007/978-3-319-76953-0_3, more details at https://crocs.fi.muni.cz/public/papers/rsa2018) contain the following:</p> <ul> <li>Informed consent participants had to sign (experiment design approved by Research Ethics Committee of Masaryk University)</li> <li>General questionnaire & System usability scale questionnaire</li> <li>User tasks & certificates to validate</li> </ul>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
E-Supplement to the Relocation of the seismicity of the Caucasus region
<p>The e-Supplement contains the Ground Truth event list in the Caucasus region, the input and output files for the Bayesloc multiple event location algorithm, as well as the event catalog and event bulletin in ISF2.1 format constructed from the Bayesloc results.</p> <p>This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-855583</p>
Supplemental Data for "Energy minimization of paired composite fermion wave functions in the spherical geometry"
<p>Includes extra data for "Energy minimization of paired composite fermion wave functions in the spherical geometry".</p>
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms (supplemental material: brain wave loops movies)
<p>This is a collection of videos supplementing the paper "Universal theory of brain waves: from linear loops to nonlinear synchronized spiking and collective brain rhythms"</p> <p>Examples of wave trajectories and emergent persistent loop patterns for the spherical shell cortex model with<br> varying amounts of tensor anisotropy and inhomogeneous shell layer thickness.</p> <p><br> Examples of brain wave trajectories and emergent persistent loop patterns for cortical fold geometry with different<br> approaches used for estimation of inhomogeneity and anisotropy. Among those examples are several simple cases with variable inhomogeneity and fixed anisotropy (similar to the above spherical shell cortex model) as well as with more complex estimates of anisotropy based on multiple diffusion gradients MRI (dMRI) acquisitions.</p>
Data_supplemental figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration
<p>Data of supplemental figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF format (10.1194_jlr.M092908_Fig. S2). Corresponding raw data obtained from cellomics HTC array scan analysis provided as seven files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_11-12_1-4) All further experiment related information protocols as meta-data-files (31003A-179400_date_examiner_17BHSD12_8_11-12_M_1) as TXT format.</p>
Data_supplemental Figure 3_11β-Hydroxysteroid dehydrogenases control access of 7β,27-dihydroxycholesterol to retinoid-related orphan receptor γ
<p>Data of supplemental figure 3 from 11β-Hydroxysteroid dehydrogenases control access of 7β,27-dihydroxycholesterol to retinoid-related orphan receptor γ</p> <p>Dataset (doi:10.1194/jlr.M092908) contains the original figure as PNG-format (10.1194_jlr.M092908_Fig. S3). Corresponding raw data obtained from liquid scintillation analysis provided as three files in CSV format (31003A-179400_DATE_SK_KB_27Oxysterol_11_6_1-3). All further experiment related information and subsequent data analysis provided as meta-data-file (31003A-179400_DATE_SK_KB_27Oxysterol_11_6_M) as TXT format</p>
Supplement of manuscript "microorganisms-746193"
<p>This folder contains analysis data from the study submitted as manuscript (Manuscript ID microorganisms-746193):</p> <p>TITLE:</p> <p>Functional genomics differentiate inherent and environmentally influenced traits in dinoflagellate and diatom communities</p> <p>AUTHORS:</p> <p>Stephanie Elferink, <a href="mailto:Stephanie.westphal@awi.de"> Stephanie.westphal@awi.de</a>, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> </p> <p>Uwe John, <a href="mailto:Uwe.John@awi.de"> Uwe.John@awi.de</a>, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity</p> <p>Stefan Neuhaus, <a href="mailto:Stephan.neuhaus@awi.de">Stephan.neuhaus@awi.de</a>, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> </p> <p>Sylke Wohlrab, <a href="mailto:Sylke.wohlrab@awi.de"> Sylke.wohlrab@awi.de</a>, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity</p> <p>JOURNAL:</p> <p>MDPI - microorganisms</p> <p>MS-ID:</p> <p>microorganisms-746193</p> <p>HOWTO:</p> <p>Sequences identified as Alveloates or Stramenopiles (description in manuscript) had been were classified more accurately by PhyloAssigner version 6.166 (https://github.com/jungbluth/phyloassigner, Vergin et al., 2013, DOI:10.1038/ismej.2013.32) with a phylogenetic placement onto reference trees based on 18S/28S concatenated alignments, according to Elferink et al. 2017 (DOI: 10.1016/j.dsr.2016.11.002).</p> <p>CONTENT:</p> <p>reference databases:</p> <p>- Alveolata_SSU-LSU-concat_310715_636.phyloassignerdb</p> <p>- Stramenopiles_SSU_LSU_concat_030815_1777.phyloassignerdb</p> <p>query sequence files:</p> <p>- Alveolata_seqtab_SIGN_dada2.fasta</p> <p>- Alveolata_seqtab_SIGN_dada2.fasta</p> <p>created output folder including the taxonomic annotation:</p> <p>- Alveolata_seqtab_SIGN_dada2.place.out</p> <p>- Stramenopiles_seqtab_SIGN_dada2.place.out</p> <p>text file containing the used commands:</p> <p>- commands</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
Online Supplemental Materials for: "Total Error and Variability Measures for the Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in OnTheMap"
<p>This archive contains supplementary materials for the published manuscript.</p> <p>We report results from the first comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total flow-employment, beginning-of-quarter employment, full-quarter employment, average monthly earnings of full-quarter employees, and total quarterly payroll. Beginning-of-quarter employment is also the main tabulation variable in the LEHD Origin-Destination Employment Statistics (LODES) workplace reports as displayed in OnTheMap (OTM), including OnTheMap for Emergency Management. We account for errors due to coverage; record-level non-response; edit and imputation of item missing data; and statistical disclosure limitation. The analysis reveals that the five publication variables under study are estimated very accurately for tabulations involving at least 10 jobs. Tabulations involving three to nine jobs are a transition zone, where cells may be fit for use with caution. Tabulations involving one or two jobs, which are generally suppressed on fitness-for-use criteria in the QWI and synthesized in LODES, have substantial total variability but can still be used to estimate statistics for untabulated aggregates as long as the job count in the aggregate is more than 10.</p>
Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data
<p><strong>Evaluating Registrations of Serial Sections With Distortions of the Ground Truths</strong></p> <p>This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows:</p> <ul> <li>*_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.</li> <li>*_local-only.7z: only local distortions applied.</li> <li>*_local-DIST.7z: the distortion maps for local distortions.</li> <li>*_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.</li> <li>_*vis.7z: visualizations of distortions.</li> <li>_rigid_ground.7z: the real rigid transformations used in the global phase.</li> <li>*_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.</li> </ul> <p>There are three main modalities and one further, as a reference:</p> <ul> <li>CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)</li> <li>EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)</li> <li>LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)</li> <li>REAL_*: a region from real serial sections from a rabbit lung, 2 images.</li> </ul> <p>We also supply elastix parameter files.</p> <p>A preprint has been uploaded to <a href="https://arxiv.org/abs/2011.11060">arXiv</a>. The definite version is available from <a href="https://ieeexplore.ieee.org/abstract/document/9594850/media#media">IEEE</a>. The source code of the distorter is available from <a href="https://github.com/olegl/distort">GitHub</a>.</p>
Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences - supplemental material & data
<p>Table and data repository for the manuscript "Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences"</p> <p><strong>Supplemental Tables:</strong></p> <ul> <li>table1_ESIT Project Table</li> <li>table2_TIN-ACT Project Table</li> <li>table3_ITN_tinnitus</li> <li>table4_ITN_other</li> <li>table5_Individual PhDs</li> </ul> <p><strong>Individual-level and de-identified survey data (raw data):</strong></p> <ul> <li>raw_data_ITN_tinnitus (survey results from PhDs as part of an ITN with a focus on tinnitus)</li> <li>raw_data_ITN_other (survey results from PhDs associated to ITNs with another focus)</li> <li>raw_data_Individual_Phds (survey results from PhDs not part of an ITN)</li> </ul>
Supplemental Material: What we talk about when we talk about software test flakiness
<p>This supplemental material details the definitions of the concepts that have been found by conducting the scoping review of both the white and grey literature introduced in Section 2 of the manuscript titled: <strong>"What we talk about when we talk about software test flakiness</strong>".</p> <p> </p>
Supplemental Data for Gullikson et. al 2016b
<p>This dataset contains the reduced spectra that were used in Gullikson, Kraus, & Dodson-Robinson (2016). The spectra are telluric-corrected with the TelFit code, and each echelle order is present in its own fits extension in the form of a binary table.</p> <p>They can be read in with the "kglib" python library, using "kglib.utils.HelperFunctions.ReadExtensionFits(filename)"</p>
Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements (Supplemental Material)
<p>Supplemental material to <a href="https://openaccess.leidenuniv.nl/handle/1887/40902"><em>Mechanical Response of Foams: Elasticity, Plasticity, and Rearrangements</em>; hdl:1887/40902</a>. The supplemental material consists of 9 videos:</p> <p><strong>S1, S2</strong><br /> Two examples of foam under shear, φ = 0.85 (S1) and φ = 1.25 (S2). The foam is sheared from s<sub>CD</sub> = − 0.2 to s<sub>CD</sub> = + 0.2 at γ̇=3 × 10<sup>−5</sup> /s. Time and a scale bar are indicated in the top right; the video is sped up 250× .<br /> <br /> <strong>S3,S4</strong><br /> Difference imaging for direct (top) and affine-corrected (bottom) images, for the same systems as in S1 and S2. Both the real space (left) and difference images (right) are shown. The direct difference images are dominated by the affine deformation, while the affine-corrected difference images highlight the nonaffine motion in the system.</p> <p><strong>S5,S6</strong><br /> Tracked particle trajectories for the same systems as in S1 and S2. Particle trajectories are indicated using white curves. Left: direct tracking data, right: affine-corrected tracking data.</p> <p><strong>S7</strong><br /> Compression of a foam from φ = 0.77to φ = 1.41 under ε̇ = − 3 × 10<sup>−5</sup> /s. (left) Real space image; (right) from top to bottom: flame graph and log<sub>10</sub> A and β from the power law fit Eq. (4.21). Time is indicated on the top left, φ is indicated at the bottom left. With increasing confinement, we observe a transition from fully smooth to fully intermittent behavior.</p> <p><br /> <strong>S8,S9</strong><br /> Two examples of foam under shear, φ = 0.9 (S8) and φ = 1.5 (S9). The foam is sheared from s<sub>CD</sub> = −0.2 to s<sub>CD</sub> = +0.2 at γ̇=3 × 10<sup>−5</sup> /s. At low density, A ≈ 10<sup>−6</sup> and β ≈ 1.6 are fairly constant, while we can clearly distinguish the quiet and active periods for the high density foam.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.