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247 results for “matrices”
Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024
This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.
Weekly CoMix contact matrices for UKHSA COVID-19 dashboard and ONS COVID-19 infection survey age-groups
<p>Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard <strong> (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) </strong>and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey <strong>(2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+)</strong>. The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey. </p> <p>Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795).</p> <p>1. Munday, J.D., Jarvis, C.I., Gimma, A. <em>et al.</em> Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. <em>BMC Med</em> <strong>19</strong>, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0</p>
Site occupancy matrices, The River Ouse Project
<p>The <a href="http://www.sussex.ac.uk/riverouse/">River Ouse Project</a> was started by Dr Margaret Pilkington and colleagues in the Centre for Continuing Education, University of Sussex. Margaret is now retired with emeritus status and continues to run the project with a team of volunteers, in association with the University of Sussex. The team does botanical surveys of streamside grassland and steep wooded valleys (gills) in the upper reaches of the Sussex Ouse, a short flashy river arising on the southern slopes of the High Weald AONB (Area of Outstanding Natural Beauty). Survey sites are chosen on the basis of species richness, potential for restoration and contribution to flood control, and surveyed using the sampling methods outlined in Rodwell, J S (1992. British Plant Communities, Volume 3, Grasslands and Montane Communities). Survey data are transferred from the paper record taken in the field to Excel spreadsheets, and from there after validation and cleaning into two MySQL (MariaDB) databases, meadows and gills.</p> <p>The file is an extract from the meadows database. It contains binary data of the site occupancy for most of the plants encountered in meadow sites (stands, assemblies) sampled using five 2m x 2m quadrats. Details of the database are available here: <a href="https://zygodon.github.io/River-Ouse-Project-databases/">River Ouse Project databases</a>. </p> <p>For further details and access to the full database contact the author.</p>
Confusion matrices for theoretical young stellar object models
<p>This is a data table supplementing the following publication: </p> <p><em><strong>A framework for modeling the evolution of young stellar objects </strong></em>(Richardson et al. 2025, accepted to ApJ).</p> <p>It contains a set of confusion matrices comparing the evolutionary stages and classes of radiative transfer YSO models selected by proximity to protostellar evolutionary tracks. Models are included in a matrix based on their correspondence to particular modeled accretion histories, zero-age stellar masses, ages, mass accretion efficiencies, and levels of detectability (defined using flux in the ALMA Band 6 wavelength range). Details on the construction and use of the table are contained in the accompanying README file, and more information about the matrices is contained in Section 4.2 of the companion paper.</p> <p>The YSO models populating these matrices are from Richardson et al. (2024); information on them is contained in the <a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">companion work</a> and <a href="https://zenodo.org/records/10522816" target="_blank" rel="noopener">data release</a>.</p>
NTMSS Data Matrices
<p>Data matrices giving textual states of New Testament witnesses (i.e. Greek manuscripts, versions, patristic citations, lectionaries) at places of textual variation.</p>
NTMSS Distance Matrices
<p>Distance matrices relating to New Testament textual variation. Each distance matrix gives distances between various New Testament witnesses (e.g. Greek manuscripts, versions, patristic citations, lectionaries).</p>
Data for transition_matrices_plasticadrift
<p>Data belonging to the https://github.com/OceanParcels/transition_matrices_plasticadrift repository</p>
Matrices for well-conditioned biorthogonal spline wavelet bases on the interval
<p>Refinement matrices for the wavelet bases described in Chapter 2 of the thesis "Adaptive tensor product wavelet methods for solving PDEs" (T.J. Dijkema, 2009).</p> <p>Each directory of this data set contains the following matrices (in Matrix Market format):</p> <ul> <li><strong>m</strong>-<em>j</em>: two-scale matrix on level <em>j</em> (thesis p.12)</li> <li><strong>mass</strong>-<em>j</em>: mass matrix on level <em>j</em></li> <li><strong>r</strong>-<em>j</em>, <strong>tr</strong>-<em>j</em>, <strong>trinit</strong>-<em>j</em>: transformation matrix R_<em>j</em>, \tilde{R}_<em>j</em>, \tilde{R}_<em>j</em>^{init} (thesis p.24-25)</li> </ul> <p>The directory names are {d}-{td}-{ml}-{mr}-{tml}-{tmr} where:</p> <ul> <li><strong>d</strong>: Jackson estimate on primal side</li> <li><strong>td</strong>: Jackson estimate on dual side</li> <li><strong>ml</strong> / <strong>mr</strong>: number of vanishing moments on left/right boundary, primal side</li> <li><strong>tml</strong> / <strong>tmr</strong>: number of vanishing moments on left/right boundary, dual side</li> </ul>
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>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Analysis Dataset)
<p>A dataset of synchrotron X-ray diffraction (SXRD) analysis files, recording the refinement of crystallographic texture from a number of Ti-6Al-4V (Ti-64) sample matrices, containing a total of 93 hot-rolled samples, from three different orthogonal sample directions. The aim of the work was to accurately quantify bulk macro-texture for both the α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases across a range of different processing conditions.</p> <p><strong>Material </strong></p> <p>Prior to the experiment, the Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling, using a rolling mill at The University of Manchester. Rectangular specimens (6 mm x 5 mm x 2 mm) were then machined from the centre of these rolled blocks, and from the starting material. The samples were cut along different orthogonal rolling directions and are referenced according to alignment of the rolling directions (RD – rolling direction, TD – transverse direction, ND – normal direction) with the long horizontal (X) axis and short vertical (Y) axis of the rectangular specimens. Samples of the same orientation were glued together to form matrices for the synchrotron analysis. The material, rolling conditions, sample orientations and experiment reference numbers used for the synchrotron diffraction analysis are included in the data as an excel spreadsheet.</p> <p><strong>SXRD Data Collection </strong></p> <p>Data was recorded using a high energy 90 keV synchrotron X-ray beam and a 5 second exposure at the detector for each measurement point. The slits were adjusted to give a 0.5 x 0.5 mm beam area, chosen to optimally resolve both the α and β phase peaks. The SXRD data was recorded by stage-scanning the beam in sequential X-Y positions at 0.5 mm increments across the rectangular sample matrices, containing a number of samples glued together, to analyse a total of 93 samples from the different processing conditions and orientations. Post-processing of the data was then used to sort the data into a rectangular grid of measurement points from each individual sample.</p> <p><strong>Diffraction Pattern Averaging </strong></p> <p>The stage-scan diffraction pattern images from each matrix were sorted into individual samples, and the images averaged together for each specimen, using a Python notebook <a href="https://github.com/LightForm-group/sxrd-tiff-summer">sxrd-tiff-summer</a>. The averaged .tiff images each capture average diffraction peak intensities from an area of about 30 mm<sup>2</sup> (equivalent to a total volume of ~ 60 mm<sup>3</sup>), with three different sample orientations then used to calculate the bulk crystallographic texture from each rolling condition.</p> <p><strong>SXRD Data Analysis </strong></p> <p>A new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package was used to fit full diffraction pattern ring intensities, using a range of different lattice plane peaks for determining crystallographic texture in both the α and β phases. Bulk texture was calculated by combining the ring intensities from three different sample orientations.</p> <p>A .poni calibration file was created using <a href="http://www.clemensprescher.com/programs/dioptas">Dioptas</a>, through a refinement matching peak intensities from a LaB6 or CeO2 standard diffraction pattern image. Two calibrations were needed as some of the data was collected in July 2022 and some of the data was collected in August 2022. Dioptas was then used to determine peak bounds in 2θ for characterising a total of 22 α and 4 β lattice plane rings from the averaged Ti-64 diffraction pattern images, which were recorded in a .py input script. Using these two inputs, Continuous-Peak-Fit automatically converts full diffraction pattern rings into profiles of intensity versus azimuthal angle, for each 2θ section, which can also include multiple overlapping α and β peaks.</p> <p>The Continuous-Peak-Fit refinement can be launched in a notebook or from the terminal, to automatically calculate a full mathematical description, in the form of Fourier expansion terms, to match the intensity variation of each individual lattice plane ring. The results for peak position, intensity and half-width for all 22 α and 4 β lattice plane peaks were recorded at an azimuthal resolution of 1º and stored in a .fit output file. Details for setting up and running this analysis can be found in the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package. This package also includes a Python script for extracting lattice plane ring intensity distributions from the .fit files, matching the intensity values with spherical polar coordinates to parametrise the intensity distributions from each of the three different sample orientations, in the form of pole figures. The script can also be used to combine intensity distributions from different sample orientations. The final intensity variations are recorded for each of the lattice plane peaks as text files, which can be loaded into MTEX to plot and analyse both the α and β phase crystallographic texture.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated SXRD beamline metadata for each measurement. The raw data is in the form of synchrotron diffraction pattern .tiff images which were too large to upload to Zenodo and are instead stored on The University of Manchester's Research Database Storage (RDS) repository. The raw data can therefore be obtained by emailing the authors.</p> <p>The material data folder documents the machining of the samples and the sample orientations.</p> <p>The associated processing metadata for the Continuous-Peak-Fit analyses records information about the different packages used to process the data, along with details about the different files contained within this analysis dataset.</p>
Measuring Bulk Crystallographic Texture from Ti-6Al-4V Hot-Rolled Sample Matrices using Synchrotron X-ray Diffraction (Results Dataset)
<p>A dataset of crystallographic texture results for both α (hexagonal close packed, hcp) and β (body-centred cubic, bcc) phases, measured from 31 different hot-rolled Ti-6Al-4V (Ti-64) materials and 3 differently orientated samples using synchrotron X-ray diffraction (SXRD). The aim of the work was to accurately quantify bulk macro-texture for both the α and β phases across a range of different processing conditions, and to compare results with electron backscatter diffraction (EBSD) measurements. The synchrotron intensities were extracted using a new Fourier-based peak fitting method from the <a href="https://pypi.org/project/continuous-peak-fit/">Continuous-Peak-Fit</a> Python package, and then directly used to calculate the pole figures, orientation distribution functions (ODFs) and numerical values for the texture indices in <a href="https://mtex-toolbox.github.io">MTEX</a></p> <p><strong>Material </strong></p> <p>The Ti-64 materials had been hot-rolled at a range of different temperatures, and to different reductions, followed by air-cooling. Three samples of different orientation were cut from the centre of these rolled blocks, and from the starting material. The material and hot-rolling conditions are recorded in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a> as an excel spreadsheet and summarised in the table below.</p> <table align="center"> <caption>A table recording the sample number and associated hot-rolling condition.</caption> <tbody> <tr> <td> <p><em><strong>Sample Number</strong></em></p> </td> <td> <p><em><strong>Rolling Condition</strong></em></p> </td> </tr> <tr> <td>1</td> <td>825ºC, 87.5% Reduction</td> </tr> <tr> <td>2</td> <td>865ºC, 87.5% Reduction</td> </tr> <tr> <td>3</td> <td>895ºC, 87.5% Reduction</td> </tr> <tr> <td>4</td> <td>915ºC, 87.5% Reduction</td> </tr> <tr> <td>5</td> <td>935ºC, 87.5% Reduction</td> </tr> <tr> <td>6</td> <td>950ºC, 87.5% Reduction</td> </tr> <tr> <td>7</td> <td>960ºC, 87.5% Reduction</td> </tr> <tr> <td>8</td> <td>975ºC, 87.5% Reduction</td> </tr> <tr> <td>9</td> <td>1020ºC, 87.5% Reduction</td> </tr> <tr> <td>10</td> <td>β-annealed, 825ºC, 87.5% Reduction</td> </tr> <tr> <td>11</td> <td>β-annealed, 915ºC, 87.5% Reduction</td> </tr> <tr> <td>12</td> <td>β-annealed, 975ºC, 87.5% Reduction</td> </tr> <tr> <td>13</td> <td>Reduced heating from 915ºC, 87.5% Reduction</td> </tr> <tr> <td>14</td> <td>Reduced heating from 975ºC, 87.5% Reduction</td> </tr> <tr> <td>15</td> <td>825ºC, 75% Reduction</td> </tr> <tr> <td>16</td> <td>865ºC, 75% Reduction</td> </tr> <tr> <td>17</td> <td>895ºC, 75% Reduction</td> </tr> <tr> <td>18</td> <td>915ºC, 75% Reduction</td> </tr> <tr> <td>19</td> <td>935ºC, 75% Reduction</td> </tr> <tr> <td>20</td> <td>950ºC, 75% Reduction</td> </tr> <tr> <td>21</td> <td>960ºC, 75% Reduction</td> </tr> <tr> <td>22</td> <td>975ºC, 75% Reduction</td> </tr> <tr> <td>23</td> <td>1020ºC, 75% Reduction</td> </tr> <tr> <td>24</td> <td>β-annealed, 825ºC, 75% Reduction</td> </tr> <tr> <td>25</td> <td>β-annealed, 915ºC, 75% Reduction</td> </tr> <tr> <td>26</td> <td>β-annealed, 975ºC, 75% Reduction</td> </tr> <tr> <td>27</td> <td>Reduced heating from 915ºC, 75% Reduction</td> </tr> <tr> <td>28</td> <td>Reduced heating from 975ºC, 75% Reduction</td> </tr> <tr> <td>29</td> <td>As-received</td> </tr> <tr> <td>30</td> <td>As-received, β-annealed</td> </tr> <tr> <td>31</td> <td>975ºC, 50% Reduction</td> </tr> </tbody> </table> <p><strong>MTEX Data Analysis</strong></p> <p>The lattice plane intensities for 22 α and 4 β phase peaks were extracted from the Continuous-Peak-Fit analysis, also included in this <a href="https://doi.org/10.5281/zenodo.7438090">analysis dataset</a>, and saved as text files in the form of pole figures. The lattice intensity text files were analysed in MTEX using scripts from the <a href="https://github.com/LightForm-group/continuous-peak-fit-analysis">continuous-peak-fit-analysis</a> package, to plot pole figures and ODF slices, and to calculate pole figure maxima, ODF maxima, texture indices and texture component phase fractions. A kernel half-width of 10° was found to produce optimal data fitting, for highly accurate texture strength intensity values.</p> <p><strong>Metadata </strong></p> <p>An accompanying YAML text file contains associated processing metadata for the SXRD analysis, recording information about the packages used to process the data, along with details about the different files contained within this results dataset.</p>
CPT-1 whole-proteome feature matrices (EVE set)
<p><strong>Cross-protein transfer learning for variant effect prediction</strong></p> <p>This repository contains the feature matrices for CPT-1 to make variant effect prediction on 3,045 human proteins within the EVE set (<a href="https://www.nature.com/articles/s41586-021-04043-8">Frazer et al., 2021</a>), initially released with the manuscript "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects".</p> <p> </p> <p><strong>Citation</strong></p> <p>Jagota, M.*, Ye, C.*, Albors, C., Rastogi, R., Koehl, A., Ioannidis, N., and Song, Y.S.†<br> "Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects", bioRxiv (2022)</p> <p>*These authors contributed equally to this work.<br> †To whom correspondence should be addressed: <a href="mailto:yss@berkeley.edu">yss@berkeley.edu</a></p> <p>DOI: <a href="https://doi.org/10.1101/2022.11.15.516532">https://doi.org/10.1101/2022.11.15.516532</a></p>
High-quality large curated dataset of protein sequences (1.83 million) and their corresponding Position Specific Scoring Matrices
<p>As part of his master thesis at the Rostlab, which is located at the Technical University of Munich (TUM), Mr. Issar Arab developed the first language model that encodes evolutionary information of proteins explicitly. The pre-training involved the creation of a novel high-quality dataset of protein sequences (around 1.83 million proteins, or ~0.8 Billion amino acids) with their corresponding Position Specific Scoring Matrices (PSSMs). Those matrices reflect the relative frequency of each amino acid at each position in a protein and is derived from evolutionarily related proteins.</p> <p>Mr. Arab makes this work publicly available to help other researchers speed up their work to leverage AI to learn the representation of protein evolutionary information more explicitly. The set of sequences was derived by extracting all PSSMs from the <a href="https://predictprotein.org/">PredictProtein</a> (PP) cache, which were also part o the UniProt Reference Cluster with 50% sequence identity (uniref50 2019_12). The overlap between PP and uniref50 was further filtered to only include high-quality samples, e.g. only multiple sequence alignments with a certain number of aligned sequences were considered. The processing led to a training set of 1.83 Million sequences, a validation set of 879 instances, and a test set of 879 entries. The training data of proteins is reduced to 40% sequence identity, with respect to the validation/test sets, and contains sequences ranging between 18 and 9858 residues in length.</p> <p>Refer to the Jupyter notebook for a detailed description of the files' structure and a Python code snippet to correctly manipulate this data.</p> <p>To access the full original work, please visit the following link: <a href="https://mediatum.ub.tum.de/node?id=1579236">Manuscript</a> <br><br><strong>Note:</strong> The dataset was recently used to fine tune a protein sequence language model (<a href="https://github.com/issararab/PEvoLM">PEvoLM</a>). The work was presented at the CIBCB'23 conference. If you use PEvoLM or this dataset in your work, please cite the following publication:</p> <p>- Issar Arab, <strong>PEvoLM: Protein Sequence Evolutionary Information Language Model</strong>, <em>IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, Netherlands</em>, (2023), pp. 1-8, doi:<a href="https://ieeexplore.ieee.org/document/10264890">10.1109/CIBCB56990.2023.10264890</a></p>
Character coding comparisons: matrices of 115 traits for hominoids in TNT format.
<p>TNT_character_matrix_positions.csv: provides descriptions of the 115 characters and their positions in the character matrices.</p> <p>TNT_95CI_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is a 95% confidence interval for the species mean.</p> <p>TNT_DVC_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is coded using divergence coding.</p> <p>TNT_GWC_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is coded using generalized gap weighting.</p> <p> TNT_HSC_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is coded using homogeneous subset coding.</p> <p>TNT_IDO_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is coded using identification of overlap coding.</p> <p>TNT_Mean_character_matrix.tnt: TNT format character matrix with 115 traits. Each trait value is a species mean.</p> <p> </p>
System Matrices and Matlab Code for Loewner for Index-2 Systems
<p>This code accompanies the paper</p><blockquote><p><i>Implicit and explicit matching of non-proper transfer functions in the Loewner framework</i> by Ioan Victor Gosea and Jan Heiland</p></blockquote><p>that we submitted for presentation at the CDC 2024.</p>
Matrices for a thermal model of a battery pack
<p>This dataset contains matrices for a numerical thermal model of a battery pack.</p> <p>For more information see the description in the <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Battery_pack">MOR Wiki</a>.</p>
quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data
<p>This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data".</p> <p>File <strong>test_datasets.zip</strong> containes three datasets:</p> <ul> <li><em>D1.RData</em>: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li><em>D2.RData</em>: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li><em>D3.RData</em>: in silico WGS SNP data.</li> </ul> <p>File <strong>test_scripts.zip</strong> containes the code to reproduce the results.</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>
Capital use matrices
<p>This dataset is for the Capital Endogenisation - the use of capital goods by industry.</p> <p>The dataset is a straight forward update to the Zenodo record <a href="https://zenodo.org/record/3874309">https://zenodo.org/record/3874309 </a></p> <p><a href="https://zenodo.org/records/5589597">EXIOBASE v3.8.2 is available on Zenodo</a></p> <p>Matrices describe the total use of capital goods by industries in MEur, in industry (pxi) and product classification (pxp). Details on compilation procedure is described in:</p> <p>Södersten, Carl-Johan H., Richard Wood, and Edgar G. Hertwich. "Endogenizing capital in MRIO models: the implications for consumption-based accounting." <em>Environmental science & technology</em> 52.22 (2018): 13250-13259</p> <p>The capital flows stem from the consumption of capital (CFC) from EXIOBASE3, and have been benchmarked to CFC measures from the World Bank, as described in:</p> <p>Södersten, Carl-Johan, Richard Wood, and Thomas Wiedmann. "The capital load of global material footprints." <em>Resources, Conservation and Recycling</em> 158 (2020): 104811.</p> <p>More details on the employment intensity of electricity sectors in:</p> <p>Montt, Guillermo, Kirsten S. Wiebe, Marek Harsdorff, Moana Simas, Antoine Bonnet, and Richard Wood. "Does climate action destroy jobs? An assessment of the employment implications of the 2‐degree goal." <em>International Labour Review</em> 157, no. 4 (2018): 519-556.</p>
Figure 10 Optimal matrices for functions f1 (a), f2 (b), and f3 (c)-SELECTING OPERATIONS FOR ASSEMBLER ENCODING
<p>In the experiments, the following sets of operations were tested (a description of all<br> the operations specified below is presented in Appendix 1):<br> • Set 1 (all four-parameter operations used during the research reported in [15,17]):<br> CHG, CHGC0, CHGC1, CHGC2, CHGC3, CHGC4, CHGR0, CHGR1,<br> CHGR2, CHGR3, CHGR4, CHGM0, CHGM1, CHGM2, JMP;<br> • Set 2 (the most effective four-parameter operations used in the previous research):<br> CHGC0, CHGC3, CHGR0, CHGR3, CHGM0, CHGM2, JMP;<br> • Set 3: simpler variants of operations included in Set 1, the simpler operations, unlike<br> their more complex counterparts, always changed either the whole column or the<br> whole row or the whole matrix, operations from this set had maximally three<br> parameters;<br> • Set 4 (the three-parameter operations and jumps): CHG_VALUE, CHG_MEMORY,<br> JMP.</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.