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1,773 results for “Supplementary data,”
Supplementary data to: Global sinusoidal seasonality in precipitation isotopes
<p>Supplementary Materials to:</p> <p>Allen, S. T., Jasechko, S., Berghuijs, W. R., Welker, J. M., Goldsmith, G. R., and Kirchner, J. W.: Global sinusoidal seasonality in precipitation isotopes, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-61, in review, 2019.</p> <p>Data S1 List of fitted δ<sup>18</sup>O and δ<sup>2</sup>H sine parameter values (robust fitted, and amount-weighted fitted) and site characteristics</p> <p>Data S2 Gridded δ<sup>18</sup>O amplitude TIFF file</p> <p>Data S3 Gridded δ<sup>18</sup>O phase TIFF file</p> <p>Data S4 Gridded δ<sup>18</sup>O offset TIFF file</p> <p>Data S5 Gridded δ<sup>2</sup>H amplitude TIFF file</p> <p>Data S6 Gridded δ<sup>2</sup>H phase TIFF file</p> <p>Data S7 Gridded δ<sup>2</sup>H offset TIFF file</p> <p>Data S8 Gridded precipitation amount amplitude TIFF file</p>
EasyQuant Files, Supplementary Data, and Sequences
<p>This folder contains the raw data used for quantifications (and the quantifications in an Excel sheet).<span> A Word document with the nascent chain sequences is also included. </span></p>
LLAMAFUZZ real-world programs supplementary experiment data
<div> <p>This is the raw experimental data of LLAMAFUZZ and AFL++ on a set of real-world programs. We choose FuzzBench as running framework to ensure the fairness.</p> </div>
Supplementary Data: The evolutionary and palaeobiogeographic origin of the dinosaurs
<p>Supplementary data and R code for the manuscript: Accounting for sampling heterogeneity suggests a low palaeolatitude origin for dinosaurs.</p>
Supplementary data repository for publication "Machine hammer peening as a post-processing treatment for powderbed manufactured maraging steel X3NiCoMoTi18-9-5 (1.2709)"
<p>Supplemantary data repository for investigation on powderbed 3D printed tesnile specimens in raw and postprocessed conditions.</p> <p>This readme contains a brief description of the chosen materials, parts and testing devices as well as the perfomed testing methods, including tensile testing, hardness testing and microstructural analysis, with references to the respective supplementary data in the repository. An explanation of the data aquisition and processing methods, together with the used nomenclature is given as well.</p> <p>The dataset is compressed as `Dataset.zip` to preserve the folder structure.</p> <p>See README.MD or README.PDF for detailed description of the dataset.</p> <p> </p>
Supplementary data, Global strategy for the conservation and use of pea genetic resources
<p>Supplementary data, Global strategy for the conservation and use of pea genetic resources</p>
Tomography manuscript submission - supplementary data
<p>Tomography manuscript submission - supplementary data</p>
Supplementary Data for "Detection of peptide segments protruding from the MHC-I binding groove"
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Supplementary data
<p>Supplementary</p>
Processed data supplementary to "Systematic benchmarking of single-cell ATAC sequencing protocols"
<p>Fragments.tsv.gz for all 15 samples + merged, each for the full sequencing data and library downsampled data (42k reads/cell for each sample). Also contains expression matrices for the multiome (full_10x_Multiome_?_RNA_gex.zip) and 10x scRNA-seq data (10x_scRNA_?_gex.zip).</p>
Supplementary data: "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys"
<p>These supplementary data contain input and output files of EMTO density-functional theory (DFT) calculations and IPython/Jupyter notebooks that were used in analyzing the data for the paper "Challenges in automated high-throughput ab initio thermodynamics of magnetic high-entropy alloys".</p> <p>The data are organized as follows:</p> <ul> <li><em>alloy_discovery</em> folder contains the analysis notebooks and the processed EMTO DFT output data in a h5 database. <ul> <li>An installation of Python and Jupyter notebooks is required to run the notebooks. A recommended way of installing them is the Anaconda Python distribution: https://www.anaconda.com/download/</li> <li>It is recommended to first run the Requirements notebook, which checks what Python dependencies are needed to be downloaded.</li> <li>DFT output data is stored as an HDF5 database is located in the <em>results</em> subfolder.</li> <li>Figures generated with the notebooks are located in the <em>figures </em>subfolder.</li> </ul> </li> <li><em>output_files </em>folder contains raw output files of EMTO DFT calculations.</li> <li><em>input_files </em>folder contains raw input files that were used in the EMTO DFT calculations.</li> </ul>
Supplementary data
<p>Supplementary</p>
Data for Spreadsheet Guardian (supplementary video tutorials)
<p>Video tutorials used in our spreadsheet studies. Supplements "Data for Spreadsheet Guardian".</p>
Supplementary material data: GO analysis of IPF
<p>Figure S1: Hierarchical clustering using differentially expressed genes across all samples from GSE2052, Figure S2: Hierarchical clustering using differentially expressed genes across all samples from GSE44723, Figure S3: Hierarchical clustering using differentially expressed genes across all samples from GSE24206, Figure S4: The relative expression of the key genes was analyzed by RT-PCR, Table S1: Sequences of primers for candidate genes.</p>
Supplementary molecular docking and dynamics data of poxin inhibitors of mpox
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Supplementary Data for "Internal gonopod reconstruction in an amber-preserved millipede from the Cretaceous: Laeviglyphiulus patrickmuelleri n. gen., n. sp. (Diplopoda, Spirostreptida, Cambalopsidae)"
<p>This dataset contains the Supplementary Data for</p> <p>Rühr, P.T. & Wesener, T. (<strong>xxx</strong>): Internal gonopod reconstruction in an amber-preserved millipede from the Cretaceous: <em>Laeviglyphiulus patrickmuelleri</em> n. gen., n. sp. (Diplopoda, Spirostreptida, Cambalopsidae). <em>XXX</em> <strong>XXX</strong>(xxx): xxx-xxx. doi: xxx.</p> <ul> <li>μCT-Scanner: SKYSCAN 1272 (Brucker microCT, Kontich, Belgium) with Bruker Control Software 1.1.7</li> <li>Reconstruction: Bruker NRecon v.1.7.3.1</li> <li>Rotation and Cropping: Bruker DataViewer v.1.5.6.2</li> <li>Resolution: 1.3 µm/pixel</li> <li>The enhancement of local contrast (CLAHE) was done using Stefan Fischer's ImageJ macro: <ul> <li>http://stefischer.de/2019/06/12/apply-clahe-filter-on-a-stack-in-fiji/</li> </ul> </li> </ul>
Supplementary Data and Models of Melt-based Thermo-barometer for paper "'No Free Lunch' in Tabular Geochemical Data: An example of Shallow versus Deep Machine Learning Algorithms for Geothermobarometry"
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Supplementary processed data for Chandler et al. Single-cell transcriptomics identifies aberrant glomerular angiogenic signalling in the early stages of a murine model of WT1 kidney disease
<p><strong><em>Summary datafiles for Chandler et al.</em></strong></p> <ul> <li>Processed single-cell RNA sequencing data, derived from murine glomeruli (isolated using the dynabead technique) from <em>n = 2</em> wild-type <em>Wt1<sup>+/+</sup></em> (Ctrl) and <em>n = 2</em> mutant <em>Wt1<sup>R394W/+</sup></em> (Mut) littermates of WT1 glomerulopathy, followed by 10x Genomics Chromium v3 platform.</li> <li>Please refer to associated scripts for analysis of the data: <a href="https://github.com/daniyal-jafree1995/collaborations/blob/main/Chandleretal_2022_WT1glomerulopathyscRNAseq.R">https://github.com/daniyal-jafree1995/collaborations/blob/main/Chandleretal_2022_WT1glomerulopathyscRNAseq.R</a> </li> <li>File descriptions as below: <ul> <li>barcodes.tsv.gz - barcodes file required for input to Read10X function in Seurat</li> <li>features.tsv.gz - features file required for input to Read10X function in Seurat</li> <li>matrix.mtx.gz - matrix file required for input to Read10X function in Seurat</li> <li>WT1_scRNAseq.rds - RDS file containing processed and annotated Seurat object for downstream analysis</li> </ul> </li> </ul>
Floating litter with epibionts Central and South America - Supplementary Data
<p>Supplementary data: Surveys of litter with interactions on beaches from Mexico to Chile. Data validated for the positive buoyancy and the presence of epibionts by type of litter. Main groups of organisms identified growing on litter. </p>
Supplementary material for the article: Analysis of transcriptomics data from COVID-19 patients: pilot re-search
<p><strong>Supplementary table 1:</strong> <strong>Genes affected by COVID-19 infection.</strong> Differentially expressed genes analysis by DESeq2 statistical test – whole result table. Comparison of gene expressions between 72 samples from COVID-19 positive patients and 24 samples from healthy donors. Significance was judged by adjusted p-vlaue, with threshold set on 0.1. </p> <p><strong>Supplementary table 2:</strong> <strong>Genes affected by severity of COVID-19.</strong> Differentially expressed genes analysis by DESeq2 statistical test – whole result table. Comparison of gene expressions between 23 samples from COVID-19 positive patients with severe symptoms and 25 with mild symptoms. Significance was judged by adjusted p-vlaue, with threshold set on 0.1. </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.