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Dataset results
161 results for “LD₅₀”
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of tobacco seedlings
<p>Mass spectrometry imaging (MSI) data set in imzML format, of complete tobacco (<em>Nicotiana tabacum</em>) seedling using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to nicotine shows accumulation in the roots and at the borders of leaves.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p>
Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging (LD-LTP MSI) of San Pedro cactus
<p>Mass spectrometry imaging (MSI) data set in imzML format, obtained from San Pedro cactus (<em>Echinopsis pachanoi</em>) cross-section using Laser Desorption Low-Temperature Plasma ionization. Mapping the ion that corresponds to mescaline shows a star-like distribution of this interesting alkaloid.</p> <p>The experiment is described in:</p> <p>Elucidating the Distribution of Plant Metabolites from Native Tissues with Laser Desorption Low-Temperature Plasma Mass Spectrometry Imaging, Abigail Moreno-Pedraza, Ignacio Rosas-Román, Nancy Shyrley Garcia-Rojas, Héctor Guillén-Alonso, Cesaré Ovando-Vázquez, David Díaz-Ramírez, Jessica Cuevas-Contreras, Fredd Vergara, Nayelli Marsch-Martínez, Jorge Molina-Torres, and Robert Winkler, Analytical Chemistry <strong>2019</strong> <em>91</em> (4), 2734-2743</p> <p>DOI: 10.1021/acs.analchem.8b04406</p> <p> </p>
LD matrices for 1000 genomes phase 1 files for EUR and YRI
<p>Pairwise LD values >=0.8 are stored for SNPs from the Phase1 1000 Genomes panel</p> <p>Format:<br> chr_snp1<br> start_snp1<br> end_snp1<br> rsid_snp1<br> chr_snp2<br> start_snp2<br> end_snp2<br> rsid_snp2<br> r^2<br> (D' provided in some files)</p> <p>EUR_hap.txt<br> from Pouya Kheradpour (Kellis Lab)<br> phase1 of 1000 genomes<br> computed by phased haplotype (preferable)<br> hg19</p> <p>EUR_geno.txt<br> from Alicia Martin (Bustamante Lab)<br> phase 1 of 1000 genomes<br> computed by genotype<br> hg19</p> <p>YRI_geno.txt<br> YRI LD from Alicia Martin (Bustamante Lab)<br> phase 1 of 1000 genomes<br> computed by genotype<br> hg19<br> </p>
Accurate and Efficient Estimation of Local Heritability using Summary Statistics and LD Matrix -- Demo datasets for the HEELS tutorials
<p>We introduced a new estimator for local heritability, "HEELS", which attains comparable statistical efficiency as the REML estimator (such as those produced by GCTA and BOLT-REML) but only requires summary-level statistics – Z-scores from marginal association tests and the empirical LD. Our method has been implemented into an open-source Python-based command line tool. </p> <p>The datasets released here can be downloaded to test the two main functions of our software package: 1) estimating local heritability; 2) computing the low-dimensional representation of the LD matrix. They are meant to accompany the HEELS tutorials we have posted onto the wiki pages of our github repository: https://github.com/huilisabrina/HEELS/wiki.</p> <p> </p>
ExoTiC-LD_data_v3.1.2
<p>Data required for the <a href="https://github.com/Exo-TiC/ExoTiC-LD">ExoTiC-LD</a> GitHub package. </p> <p>Individual stellar model grids can be downloaded and used with the ExoTiC-LD package</p> <ul> <li>kurucz.zip : 1D Kurucz stellar model grid and mu values</li> <li>stagger.zip: 3D Stagger-grid from Magic et al. (2015)</li> <li>MPS stellar model grid from Kostogryz, Nadiia, 2022, "An extended MPS-ATLAS library of stellar model atmospheres and spectra", <a href="https://doi.org/10.17617/3.NJ56TR">https://doi.org/10.17617/3.NJ56TR</a>, Edmond, V2 <ul> <li>Set 1 (this is the default for the ExoTiC-LD package)</li> <li>Set 2</li> </ul> </li> </ul> <p>You will also need to download the sensitivity.zip files which are the instrument mode throughputs required to calculate the correct limb-darkening coefficients for the correct wavelength ranges. </p>
Light and confocal micrographs on the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, the accumulation of lipid droplets, and the heterologous expression and localisation of signature LD protein homologs to tobacco pollen tubes
<p>These micrographs accompany the work "Environmental gradients reveal stress hubs predating plant terrestrialization", posted as a pre-print on bioRxiv https://doi.org/10.1101/2022.10.17.512551 </p> <p>The light and confocal micrographs show the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, especially their accumulation of lipid droplets (LDs); in confocal micrographs, LDs appeared as distinct structures upon staining with BODIPY.</p> <p>Further confocal micrographs show the heterologous expression and localisation of signature LD protein homologs detected in Mesotaenium endlicherianum SAG 12.97; heterologous expression was carried out in tobacco pollen tubes were also stained with BODIPY and proteins were tagged with mCherry.</p>
LD data 2017 L2
<p>Files with .csv are the data in csv format used for a paper entitled "New tool for the continuous in situ measurement of the pressure potential of sapwood in mature trees" by Ryogo Nakada</p> <p>"readme"s are explanation of the data.</p> <p>-----------------<br> readme.txt<br> --this file</p> <p>LDdata2017.csv<br> readme_LDdata2017.txt<br> --main data for the paper</p> <p>AMeDASdata2017.csv<br> readme_AMeDAS2017.txt<br> --hourly AMeDAS data retrieved from http://www.jma.go.jp</p> <p>radial_moe.csv<br> readme_radial_moe.txt<br> --data for Erad measurement</p> <p>2020paper.R<br> --R script for analysing the data above</p>
Drosophila simulans LD results from PLINK for Chromosome X
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and dataset</strong>s: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 3L, and 4.</p>
Drosophila simulans LD results from PLINK for Chromosome 3R
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3L, 4, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 4
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Wor</strong>k: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 3L, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 2R
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Wor</strong>k: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 3L, 3R, 4, and X.</p>
Drosophila simulans LD results from PLINK for Chromosome 3L
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2L, 2R, 3R, 4, and X</p>
Drosophila simulans LD results from PLINK for Chromosome 2L
<p><strong>Abstract</strong>: Heritable phenotypic variation in natural populations exceeds the levels predicted under mutation-selection balance where purifying selection removes variation. Balancing selection, inefficient or weak selection, polygenic adaptation, and non-equilibrium populations are all possible explanations for excess variation. Yet, available genomic data indicate an abundance of directional selection. One potential explanation is that fleeting directional selection drives beneficial mutations to high frequency in rapid waves resulting in many intermediate frequency haplotypes. This hypothesis is supported by the genomic data from a panel of 170 D. simulans genotypes established from a single stable population which show evidence for an abundance of incomplete soft sweeps. Demography, admixture, and balancing selection cannot entirely explain the patterns in these data, while transient selective sweeps can account for all the patterns of variation observed in this population. One interpretation is that constant environmental shifts rapidly change the optimal phenotype within Drosophila populations, leaving a signature of adaptive responses.</p> <p><strong>Material type</strong>: Text files of pairwise linkage disequilibrium (LD) calculations from Plink (v). </p> <p><strong>Larger Body of Work</strong>: Pervasive incomplete selective sweeps in D. simulans account for excess variation.</p> <p><strong>Related publications and datasets</strong>: Drosophila simulans VCF, LD results from chromosomes 2R, 3L, 3R, 4, and X.</p>
Madrid Retiro Weather Dataset Compliant with NGSI-LD and DCAT-AP
<h2>Description</h2> <p>Dataset that presents the NGSI-LD entity of Madrid Weather Station adapted to the NGSI-LD standard and the automatic generated metadata compliant with DCAT-AP v2 generated by the <a href="https://github.com/ging/fiware-draco/blob/master/docs/processors_catalogue/ngsi_ckan_sink.md">NGSIToCKAN</a> and <a href="https://github.com/ging/fiware-draco/blob/master/docs/processors_catalogue/update_ckan_metadata.md">UpdateCKANMetadata</a> Nifi processors and the <a href="https://github.com/YourOpenDAta/ckanext-dcatapedp">ckanext-dcatapedp CKAN extension</a></p> <h2>Paper</h2> <ul> <li>Paper: <a href="https://arxiv.org/abs/2402.06693">Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata</a></li> <li>Cite:</li> </ul> <p><code>@misc{conde2024fostering,</code><br><code> title={Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata}, </code><br><code> author={Javier Conde and Alejandro Pozo and Andrés Munoz-Arcentales and Johnny Choque and Álvaro Alonso},</code><br><code> year={2024},</code><br><code> eprint={2402.06693},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.DB}</code><br><code>}</code></p>
LD reference panel for UKB-PPP cis-pQTL
<p>NEWLOC_protein.txt includes information for 2954 cis-pQTL regions.</p> <p>Each row name in the file follows the format:</p> <p>[GeneSymbol].[GeneSymbol]_[UniProtID]_[OlinkID]_v[Version]_[PanelName]_[Number].[Chromosome]:[StartPosition]-[EndPosition];</p> <p>For example, "A1BG.A1BG_P04217_OID30771_v1_Inflammation_II_2.19:57856549-59864858"<br><br></p> <p>In the "LD.path", it includes LD files, eigenvectors, and eigen matrixes for all local regions, end by "_LDSVD.rda"</p> <p>In the "bim.path", it includes bim files for local regions, which helps to clean the summary statistics data and check if there are multiallelic or duplicated SNPs</p> <p><br><strong><br></strong></p>
LD matrices from the White British cohort in the UK Biobank in Zarr format
<p>This dataset contains the Linkage Disequilibrium (LD) matrices that were used in the analyses described in the manuscript:</p> <p><strong>Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference</strong><br> Shadi Zabad, Simon Gravel, Yue Li<br> McGill University</p> <p>LD matrices record the SNP-by-SNP correlations in a given sample of individuals from a general population. In this case, we threshold the matrices so that we only record the correlations between SNPs that are at most 3 centi Morgan apart. These matrices record the SNP correlations in a random sample of 50,000 individuals from the White British cohort in the UK Biobank dataset. There is one matrix per autosomal chromosome (chr_1, chr_2, ..., chr_22). The matrices are stored in <a href="https://zarr.readthedocs.io/en/stable/">Zarr</a> format, a chunked on-disk array storage format that allows for multi-threaded read and write access.</p> <p>To access these matrices, consult the codebase of <a href="https://github.com/shz9/magenpy"><strong>magenpy</strong></a>, our custom python package with special data structures for processing these LD matrices.</p> <p>UPDATE (03/09/2022): We updated the matrices to add the reference allele attribute (A2) and we also now have one tar archive per chromosome.<br> </p>
TAXREF-LD: Knowledge Graph of the French taxonomic registry
<p>TAXREF-LD is a Linked Data knowledge graph representing <a href="https://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref?lg=en">TAXREF</a>, the French national taxonomical register for fauna, flora and fungus, that covers mainland France and overseas territories.</p> <p>TAXREF-LD is a joint initiative of the <a href="http://www.patrinat.fr/">UMS Patrinat</a> of the <a href="http://www.mnhn.fr/">National Museum of Natural History</a>, and the <a href="http://www.i3s.unice.fr/">I3S laboratory</a>, <a href="https://univ-cotedazur.fr">University Côte d'Azur</a>, <a href="https://www.inria.fr">Inria</a>, <a href="https://www.cnrs.fr">CNRS</a>.</p> <p>Homepage: https://github.com/frmichel/taxref-ld/</p>
20th Century Press Archives JSON-LD dump for CdV 2018 Rhein-Main: persons and companies
<p>Folder metadata for all person and company folders of PM20, which have publicly accessible documents. Published for the "Coding da Vinci" Hackathon 2018.</p> <p>For a preview and further information, please see https://github.com/zbw/cdv2018-pressemappe20 (mostly in German)</p> <p> </p>
Linked collectors and determiners for: Lund Botanical Museum (LD).
Natural history specimen data linked to collectors and determiners held within, "Lund Botanical Museum (LD)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aab0cf80-0c64-11dd-84d1-b8a03c50a862">https://bionomia.net/dataset/aab0cf80-0c64-11dd-84d1-b8a03c50a862</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aab0cf80-0c64-11dd-84d1-b8a03c50a862">https://gbif.org/dataset/aab0cf80-0c64-11dd-84d1-b8a03c50a862</a>. Formatted as a Frictionless Data package.
LD reference for HDL-L
<p>Update date according to new loci definition file</p>
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