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397 results for “Supplementary table”
Groundwater level data, aquifer system boundaries, and supplementary tables associated with Jasechko, S. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, doi.org/10.1038/s41586-023-06879-8 (2024).
<p>Groundwater level data, aquifer system boundaries, and Supplementary Tables associated with Jasechko, S., Seybold, H., Perrone, D., Fan, Y., Shamsudduha, M., Taylor, R.G., Fallatah, O., Kirchner, J.W. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature, https://doi.org/10.1038/s41586-023-06879-8 (2024).</p>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
Annex 2: Supplementary Tables to 'Understanding Chinese hamster translation at sub-codon resolution'
<p>Annex 2 to PhD thesis titled 'Understanding Chinese hamster ovary cell translation at sub-codon resolution'. </p>
Supplementary Tables for "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort"
<p>Supplementary Tables from "Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort".</p>
Ranta et al. (2024) Supplementary Tables
<p>This repository includes Supplementary Tables S1-S8 from the article "Magmatic Controls on Volcanic Sulfur Emissions at the Iceland Hotspot" by Ranta et al. in <em>Geochemistry, Geophysics, Geosystems (accepted and in press as of 22 April 2024).</em></p> <p><em>v2 (13 April 2024): Typos were fixed and missing MI data from Ranta (2022) were added to Table S1. These tables match the files in the published version of G-cubed.</em></p>
Supplementary file ROBINS-I Risk of Bias table in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies.
<p>ROBINS-I Risk og Bias Table</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 3: Pathological details
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines</p> <p>Supplementary Table 3: Pathological details</p>
The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; Supplementary Table 2: Metastatic sites
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines.</p> <p>Supplementary Table 2: Metastatic sites</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients' characteristics and endocrinal manifestations
<p>Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients’ characteristics and endocrinal manifestations</p>
Supplementary Table S5
<p><strong>Rywal biological evidence filtering results</strong>.</p> <p>Output of 1<sup><em>st</em></sup> filtering step by biological evidence for cv. Rywal.</p> <p>Layer _p_stRT/_I_STRT/_S_03_stCuSTr/ _A_03.1_filtering, DOI: 10.15490/fairdomhub.1.datafile.3112.1</p>
Supplementary Table S2
<p><strong>Detailed <em>de novo</em> assemblies information table</strong>.</p> <p>Primary potato transcriptome assemblies summary listing parameters used for short reads <em>de novo</em> assembly generation.</p> <p>Layer _p_stRT/_I_STRT/_S_02_denovo, DOI: 10.15490/fairdomhub.1.datafile.3091.1</p>
Supplementary Table S3
<p><strong>Désirée biological evidence filtering results</strong>.</p> <p>Output of 1<sup><em>st</em></sup> filtering step by biological evidence for cv. Désirée.</p> <p>Layer _p_stRT/_I_STRT/_S_03_stCuSTr/ _A_03.1_filtering, DOI: 10.15490/fairdomhub.1.datafile.3110.1</p>
The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities - Supplementary Tables
<p><strong>Supplementary Table 1:</strong></p> <p>A map between each of the genomes in mOTUs-db (3’747’151), the associated study and its metagenomic sample (in case of MAGs).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> STUDY → Unique mOTUs-db name of the study</code><br><code> IS_MAG → True if genome is a MAG, otherwise False </code><br><code> METAGENOMIC_SAMPLE → Unique name of the metagenomic sample or NA in case of non-MAG genome</code></p> <p>Example:</p> <p><code> GENOME STUDY IS_MAG METAGENOMIC_SAMPLE</code><br><code> ---------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_MAG_00000001 ACIN21-1 True ACIN21-1_SAMN05421555_METAG</code><br><code> RSGB23-1_GCA-006096615-V1_GENO_10000001 RSGB23-1 False NA</code></p> <p><strong>Supplementary Table 2:</strong></p> <p>A map between all non-MAG genomes (919’090) and their source (e.g. Refseq or JGI).</p> <p>Columns:</p> <p><code> GENOME → Unique mOTUs-db name of the genome</code><br><code> SOURCE_SAMPLE_LINK → Link to the original location of this genome</code></p> <p>Example:</p> <p><code> #GENOME SOURCE_SAMPLE_LINK</code><br><code> --------------------------------------------------------------------------------------------------------</code><br><code> JGIG23-1_GA0055041_GENO_10000001 https://gold.jgi.doe.gov/analysis_project?id=Ga0055041</code><br><code> RSGB23-1_GCA-006717865-V1_GENO_10000001 https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_006717865.1</code></p> <p><strong>Supplementary Table 3:</strong></p> <p>A list of all metagenomic studies processed for the mOTUs-db, their number of samples, the number of reconstructed MAGs and the associated publication.</p> <p>Columns:</p> <p><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> BIOPROJECT --> Public identifier (NCBI/JGI) of metagenomic sequencing project</code><br><code> SAMPLES --> Number of metagenomic samples</code><br><code> MAGs --> Number of reconstructed MAGs</code><br><code> PUBLICATION --> Link to publication</code></p> <p>Example:</p> <p><code> STUDY BIOPROJECT SAMPLES MAGs PUBLICATION</code><br><code> -------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1 PRJEB44456 58 1,110 https://www.nature.com/articles/s42003-021-02112-2</code></p> <p><strong>Supplementary Table 4:</strong></p> <p>Mapping between mOTUs-db sample identifier, the associated biosample and the environment.</p> <p>Columns:</p> <p><code> SAMPLE --> Unique mOTUS-db sample identifier</code><br><code> BIOSAMPLE --> Public identifier (NCBI/JGI) of metagenomic sample</code><br><code> STUDY --> Unique mOTUs-db study identifier</code><br><code> ENVIRONMENT --> Environment of metagenomic sample</code><br><code> SOURCE_SAMPLE_LINK --> Link to the original location of this sample</code></p> <p>Example:</p> <p><code> #SAMPLE BIOSAMPLE STUDY ENVIRONMENT SOURCE_SAMPLE_LINK</code><br><code> ---------------------------------------------------------------------------------------------------------------------</code><br><code> ACIN21-1_SAMN05421555_METAG SAMN05421555 ACIN21-1 marine https://www.ncbi.nlm.nih.gov/biosample/SAMN05421555/</code></p> <p><strong>Supplementary Table 5:</strong></p> <p>A list of environments covered in the mOTUs-db mapped to the respective NCBI taxonomy (if possible)</p> <p>Columns:</p> <p><code> TERM --> Unique environment name</code><br><code> NCBI TAXONOMY ID --> Link to the NCBI taxonomy</code></p> <p>Example:</p> <p><code> TERM NCBI TAXONOMY ID</code><br><code> ----------------------------------------------</code><br><code> activated sludge metagenome NCBI:txid942017</code><br><code> air metagenome NCBI:txid655179</code></p>
Supplementary table for "Deciphering the Relationship Between Circulating Metabolites and Osteoarthritis: A Comprehensive Genetic Correlation and Mendelian Randomization Studies"
Open the record for dataset details and reuse information.
Supplementary Table 1 - Raw data
<p>Supplementary Table 1, including raw data of all Dietary supplements analyzed, from the article entitled "Potential health risks surrounding ingredients of pre-workout and post-workout dietary supplements – A thorough label analysis".</p>
Supplementary tables from "From genes to communities: an integrative approach to the evolution of Varanidae"
<p>Supplementary tables for thesis submitted for the degree of Doctor of Philosophy of The Australian National University</p>
Early 3D Evolution of the SARS-CoV-2 proteome -- Supplementary Tables and Models
<p><strong>Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic</strong></p> <p><a href="https://iqb.rutgers.edu/covid-19_proteome_evolution">https://iqb.rutgers.edu/covid-19_proteome_evolution</a></p> <p> </p> <p><strong>Legends for Supplementary Figures for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Separate analysis of protein changes was performed for each study protein and complex. Description below applies to all figures.</strong></p> <p><strong>A</strong>: Observed frequencies for all USV substitutions of Native Residue (i.e., amino acid type in the reference protein sequence) changing to Substituted Residue for a given protein/complex. Red boxes enclose conservative substitutions for hydrophobic, uncharged polar, positively charged, and negatively charged amino acids, respectively in order from upper left to lower right. Cysteine, Glycine and Proline are excluded from these groupings.</p> <p><strong>B-D</strong>: Normalized Frequency histograms for ΔΔG<sup>App</sup> calculated for all USVs for a given protein/complex. These were calculated using three methods, which we refer to as hard-hard (B), soft-hard (C), and soft-soft (D), based on the scoring functions used for sidechain rotamer optimization and gradient-based energy minimization respectively (see methods). All energy values described in the text were obtained using the soft-hard method. Overlay of energy histogram with fitted bi-Gaussian curve (solid red line) and fitted single Gaussian curves for subsets of USVs with surface (green), boundary layer (yellow), or core (blue) substitutions. USVs with multiple substitutions were included in single Gaussian fitting when all substitutions mapped to the same region of the study protein. The data used for fitting includes the energies of all unique protein models produced by a given method, excluding extreme outliers with energy values greater than 3 standard deviations away from the central mean.</p> <p><strong>E-G</strong>: USV Count histograms indicate the number of USVs among the full set for a given protein in which each site included a substitution. Sites are separated by burial layer. Substitutions at sites that are absent from the available crystal structures are excluded from the histograms. In most cases, only a single protein is analyzed, and only panel E is included. In the case of complexes, a separate histogram is provided for each protein in the complex: for methyltransferase nsp10-nsp16, E is nsp10 and F is nsp16; for RDRP nsp12-nsp7-nsp8, E is nsp7, F is nsp8, and G is nsp12.</p> <p> </p> <p><strong>Legends for Supplementary Tables for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Table: USVs</strong>: All identified USVs for a protein/complex. Columns are:</p> <ul> <li>date: Date of first collection of a strain with the USV reported to GISAID</li> <li>gisaid_count: The number of sequences in the GISAID database that include the USV</li> <li>id: The GISAID strain identification for the first collected instance of the USV</li> <li>location: The country in which the first strain including the USV was collected</li> <li>substitutions: All substitutions in the USV, in the form [chain]_[sequence][site][substitution], with multiple substitutions separated by semicolons</li> <li>is_in_PDB: whether a substitution is present in the PDB model used to generate the USV structure, with multiple substitutions separated by semicolons</li> <li>multiple: whether more than one amino acid substitution is present in the USV</li> <li>conservative: whether a substitution is conservative, with multiple substitutions separated by semicolons</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure, with multiple substitutions separated by semicolons and substitutions absent from the PDB excluded</li> <li>sh_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-hard method</li> <li>sh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-hard method</li> <li>hh_rmsd: The RMSD of the USV to the reference structure when modeled using the hard-hard method</li> <li>hh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the hard-hard method</li> <li>ss_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-soft method</li> <li>ss_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-soft method</li> </ul> <p> </p> <p><strong>Table: Substitutions</strong>: All substitutions identified for a protein/complex</p> <ul> <li>chain: The chain identifier of the protein in the PDB file in which the substitution is present</li> <li>site: The residue number at which the substitution is present</li> <li>reference: The one-letter amino acid name of the residue in the reference sequence</li> <li>mutant: The one-letter amino acid name of the residue in a USV</li> <li>conservative: Indication of whether a substitution is conservative</li> <li>in_pdb: whether the substitution site is present in the PDB model used to generate the USV structure</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure</li> <li>date: date: Date of first collection of a strain with the substitution reported to GISAID</li> <li>location: The country in which the first strain including the substitution was collected</li> <li>gisaid_count: The number of sequences in the GISAID database including the substitution</li> <li>usv_count: The number of identified USVs including the substitution</li> <li>ddG: The soft-hard ΔΔG<sup>App</sup> of the USV that includes only the substitution, left empty if no single-substitution USV was identified with the substitution</li> <li>single: Indication of whether the substitution was present in a single-substitution USV</li> <li>multiple: Indication of whether the substitution was present in a USV with multiple substitutions</li> <li>associates: List of all other substitutions that were identified in a USV that included the substitution</li> <li>strains: List of all USV-representative GISAID strains that included the substitution, with the single-substitution USV strain listed first if one was available</li> </ul> <p> </p> <p><strong>Table: Gaussian Fit Statistics</strong>: Fitted models for the energies of all USVs either together (ALL) or by study protein.</p> <ul> <li>fit: The number of Gaussian curves in the fitted energy model </li> <li>protein: The protein/complex name</li> <li>method: The modeling method used to calculate energy values</li> <li>layer: The subset burial layer (surface, boundary, or core) of USVs for which the energy model was fitted, excluding all USVs with substitutions not in that layer</li> <li>μ<sub>1</sub>: Mean of the first Gaussian in the fitted model</li> <li>σ<sub>1</sub>: Variance of the first Gaussian in the fitted model</li> <li>wt<sub>1</sub>: Weight of the first Gaussian in the fitted model</li> <li>μ<sub>2</sub>: Mean of the second Gaussian in the fitted model</li> <li>σ<sub>2</sub>: Variance of the second Gaussian in the fitted model</li> <li>wt<sub>2</sub>: Weight of the second Gaussian in the fitted model</li> <li>R<sup>2</sup>: R-squared value indicating the goodness of fit</li> </ul> <p> </p> <p><strong>Description of Computed Structural Models </strong><strong>for Unique Sequence Variants for 29 </strong><strong>SARS-CoV-2 Study Proteins.</strong></p> <p><strong>USV Computed Structural Models</strong>. Computed structural models for all amino acid substituted USVs. We are providing the structural models of all study proteins modeled using the soft-hard modeling method (see Methods). Structural models are named according to the GISAID strain identification of the first strain in which the USV was identified, followed by an underscore-separated list of substitutions in the form [chain]_[sequence][site][substitution]. Atomic coordinates for each computed structural model are provided in the legacy Protein Data Bank format used by most molecular graphics software tools (see <a href="https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html">https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html</a> for detailed description).</p>
Loire & Galtier 2021 Peer Community Journal Supplementary Table S1
<p>This table shows the analyzed species and species pairs and estimated of synonymous heterozygosity (piS) and inbreeding coefficient (F)</p>
Supplementary Tables
<p><strong>Supplementary Tables for the manuscript titled: Global Comparative Transcriptomes Uncover Novel and Population-Specific Discordant Gene Expression in Esophageal Squamous Cell Carcinoma</strong></p>
ScienceDex guides
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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.