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43 results for “Desis”
Data on the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII)
<p>This dataset contains the quantitative measurement of the Digital Economy and Society Index (DESI), the ASEAN Digital Integration Index (ADII), and the Digital Intelligence Index (DII) in 2019.</p>
DESIS-derived water quality parameters for Lake Hume (Australia) (2020/02/26)
<p>This dataset contains DESIS-derived water quality (WQ) products of Lake Hume (Australia) for the 26 February 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). DESIS data courtesy of the German Aerospace Center (DLR, 2020).</p>
DESIS-derived water quality parameters for Lake Mulargia (Sardinia, Italy) (2020/08/17)
<p>This dataset contains DESIS-derived water quality (WQ) products of Lake Mulargia (Sardinia, Italy) for the 17 August 2020. Available parameters are: True-color image (RGB), Colored Dissolved Organic Matter (CDOM), Chlorophyll-a (CHL), and Suspended Particulate Matter (SPM). WQ parameters have been calculated using CNR’s bio-optical model BOMBER parameterized with the inherent optical properties specific of the case study. The data are available as GeoTiff files in WGS 84 / UTM zone 32N (EPSG: 32632). DESIS data courtesy of the German Aerospace Center (DLR, 2020).</p>
The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra
<p>Data files to reproduce the published figures from Alexander et al. (2022), AJ, in press (<a href="https://ui.adsabs.harvard.edu/link_gateway/2022arXiv220808517A/arxiv:2208.08517">arXiv:2208.08517</a>), titled "The DESI Survey Validation: Results from Visual Inspection of the Quasar Survey Spectra". The figure number is encoded in the file name to make it easy to relate the published figures to the corresponding data file. Please see the published paper for detailed information on what is plotted for each figure.</p>
Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys
<p>This repository contains the data released in the paper "Galaxy Zoo DESI: Detailed Morphology Classifications for 8.7M Galaxies in the DESI Legacy Imaging Surveys" <em>(DOI to follow on publication).</em></p> <p>We release detailed morphology measurements for bright (<em>r </em>< 19) galaxies in the DESI Legacy Imaging Surveys footprint. These measurements estimate the presence of bars, spirals arms, ongoing mergers, and more.</p> <p>---</p> <p><strong>GZ DESI Detailed Morphology Catalogs</strong></p> <p>These catalogs are created by training deep learning models on Galaxy Zoo volunteer responses, to predict what volunteers might say for new galaxies. The models are available at [www.github.com/mwalmsley/zoobot](www.github.com/mwalmsley/zoobot). Our measurements are predicted vote fractions i.e. the fraction of volunteers expected to select a given answer for a given question.</p> <p>We share two catalog versions containing the same morphology measurements but presented in different ways.</p> <p>gz_desi_deep_learning_catalog_friendly.parquet contains the morphology measurements</p> <p>gz_desi_deep_learning_catalog_advanced.parquet contains the same measurements, and additional information:</p> <p>- _friendly includes only relevant vote fractions, defined as vote fractions to answers of questions that a majority of volunteers would have been asked. This removes predicted vote fractions for e.g. the fraction of volunteers answering "2 spiral arms" to a galaxy with no spiral arms. _advanced includes all vote fractions and instead reports the (column "proportion_asked"). The user must select which vote fractions they consider relevant (we suggest proportion_asked > 0.5, which recovers the _friendly fractions).</p> <p>- _advanced includes columns with estimated credible intervals (error bars) around each vote fraction. These are calculated from the vote fraction posterior predicted by our models.</p> <p>Finally, we separately present volunteer votes collected for 96k galaxies during the GZD-8 campaign, i.e. after the release of GZ DECaLS but before this (GZ DESI) release. These are split into the _core and _extended catalogs, where _extended includes galaxies which received five or more votes for "artifact". The models above were trained on these votes as well as votes from GZ DECaLS.</p> <p>---</p> <p><strong>External Catalog</strong></p> <p>For convenience, we also include an additional catalog of non-morphology measurements created by other authors (external_catalog.parquet) cross-matched to our morphology catalogs. Please credit those authors if you use this catalog (references are in the GZ DESI paper).</p> <p>A particularly important external measurement is redshift. Morphology is increasingly hard to resolve at higher redshift and so <strong>distant galaxies appear less featured</strong>. external_catalog.parquet includes the column "redshift", which is the SDSS spectroscopic redshift where available and a photometric redshift estimate otherwise (again, see the GZ DESI paper for references and credit). You may want to select only galaxies at lower redshifts.</p> <p>---</p> <p><strong>Data Notes</strong></p> <p>Parquet is a fast csv-like format which can be read with pd.read_parquet(loc, columns=[some columns]). Parquet files are read column-by-column (rather than row-by-row) and so you can chose which columns to load. You can easily check which columns are available using columns=['foo'] and reading the error message. We suggest loading only the columns you need when working with the larger catalogs. This will require much less memory than loading every column.</p> <p>We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.</p> <p>Please cite the paper (DOI to follow on publication) when using the data in this repository.</p> <p>---</p> <p><strong>History</strong></p> <p>v0.0.1 - closed pre-release for internal review</p> <p>v1.0.0 - draft public release. Removed low-z pre-filtered catalogs.</p> <p>v1.0.1 - first public release. Added .csv version of _friendly catalog. Tweaked catalog formatting for clarity and consistency.</p>
DESI 2022 indicator detailed data
<p>EUROSTAT detailed data for the DESI 2022 indicator (from 2013, if available):</p><ul><li>At least basic digital skills</li><li>ICT specialists</li><li>Female ICT specialists</li><li>Fixed very high capacity network coverage</li><li>5G coverage</li><li>SMEs with a basic level of digital intensity</li><li>AI</li><li>Cloud</li><li>Big data</li><li>Digital public services for citizens</li><li>Digital public services for businesses</li></ul><p>Source: https://ec.europa.eu/eurostat</p>
Candidate cluster-scale gravitationally lensed quasars from CluMPR and DESI Legacy Survey
<p>Candidate cluster-scale strongly-lensed quasars from DESI Legacy Survey (DESI QSO targets) and CluMPR DESI Legacy Survey galaxy cluster catalog. </p> <p>Paper decribing the CluMPR galaxy cluster catalogs and candidate lensed quasar catalogs: The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster catalogue (M. J. Yantovski-Barth et al.)</p> <p>Description: </p> <p>To search for lensed quasars, we use two Einstein radii: one corresponds to wide angle lensing by the entire cluster (𝑀 = 10^15 𝑀⊙ ), and the other corresponds to lensing by the core of the cluster (𝑀 = 0.25 ∗ 10^15 𝑀⊙ ).</p> <p>We use the colors in bands g-r, g-z, and r-W1 to evaluate candidate lensed quasars. If at least two quasars are within 1 magnitude of each other in all 3 colors, we rate the candidate at Grade C. If at least three quasars are within 1 magnitude of each other in all 3 colors, we rate the the candidate at Grade B. If either 4 quasars or two combinations of 3 quasars are within 1 magnitude of each other in all 3 colors, we rate the candidate as Grade A.</p>
Supplementary data to "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE"
<p>Supplementary material to DESI's publication "GD-1 STELLAR STREAM AND COCOON IN THE DESI EARLY DATA RELEASE" to comply with the data management plan. Data associated with revised version of paper submitted to The Astrophysical Journal. Directory contains data points for all figures with file names indicating the figure to which the data corresponds as well as a FITS file for Table 4. Data for 13 figures and one table (46 FITS files) included in .zip directory</p>
Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue
<p>Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following<em> in vivo</em> exposure to house dust mite extract.</p> <p>Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<span><a href="https://github.com/targeted-lipidomics/quantMSImageR"><span>https://github.com/targeted-lipidomics/quantMSImageR</span></a></span><span>).</span></p>
DESI EDR qsofit systematic redshift
<p>The dataset includes improved systematic redshift for quasars in DESI EDR computed from the spectral decompositions.</p>
DESI Complete Calibration of the Color-Redshift Relation (DC3R2): Results from early DESI data
<p>The data and python code used to reproduce the figures in the paper of the same name, J. McCullough et al.</p>
The CluMPR Galaxy Cluster Catalogue for DESI Legacy Survey DR9
<p>Galaxy cluster catalog and cluster member galaxy catalogs compiled using the CluMPR cluster-finding algorithm. </p> <p>Paper decribing the CluMPR algorithms and cluster catalogs: The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster catalogue (M. J. Yantovski-Barth et al.)</p> <p>File description: </p> <p>DESI_clusters_2024_simple contains the official cluster catalog,</p> <p>DESI_clusters_2024_extended is the official cluster catalog + some clusters which were flagged and removed,</p> <p>north_members_reweighted contains the member galaxies for clusters in the north region of DESI Legacy Survey,</p> <p>south_members_reweighted contains the member galaxies for clusters in the south region of DESI Legacy Survey.</p>
Catalog-level blinding on the bispectrum for DESI-like galaxy surveys
<p>Points used in the Figures in the paper "Catalog-level blinding on the bispectrum for DESI-like galaxy surveys".</p>
DESI 2017_2022 ODDEA Project Complete Database
<p>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em><span>Project ID: HORIZON MSCA-SE 101086381)</span></em>. The file contains data for 27 EU countries in the years 2017 - 2022. The file contains 4 data sheets processed in the arrangement from general data (DESI Total) to detailed data (dimensions, sub-dimensions and indicators). The file also contains raw data collected in five sheets by topic and a sheet with a list of all indicators with their description.</p> <p>Dataset consist of two files. Excel file presents data and Word file presents DESI 2022 dimensions and indicators list. </p> <p> </p>
Data for the reproduction of figures from the DESI MWS value added catalogue paper
<p>The data to reproduce the figures from the DESI EDR MWS value added catalogue paper (Koposov et al 2024)</p>
X-ray Holographic Data Set of 15 μm Spheres obtained at Petra III, P10, GINIX at DESY, Hamburg
<p>This is the data set presented in the article "<strong><em>Phase Retrieval for Near-field X-Ray Imaging beyond Linearisation or Compact Support</em></strong>".</p> <p>It is a matlab file containing 3 fields:</p> <p>geometry contains the following fields: all distances z_01 (focus-sample) z_02(sample-detector) and derived quantites M (magnification), z_eff (effective propagation distance from Fresnel scaling), F (Fresnel number) and dxeff (effective pixelsize)</p> <p>images is a cell array of the 4 flat field corrected (not aligned nor magnified) measurements.</p> <p>p is a parameter structure. p.measurements contains the fully corrected data used for the reconstruction. p.F contains the rescaled Fresnel numbers - use these for reconstruction, corresponding to the order of measurements. </p>
Production of Alternate Realizations of DESI Fiber Assignment for Unbiased Clustering Measurement in Data and Simulations
<p>A critical requirement of spectroscopic large scale structure analyses is correcting for selection of which galaxies to observe from an isotropic target list. This selection is often limited by the hardware used to perform the survey which will impose angular constraints of simultaneously observable targets, requiring multiple passes to observe all of them. In SDSS this manifested solely as the collision of physical fibers and plugs placed in plates. In DESI, there is the additional constraint of the robotic positioner which controls each fiber being limited to a finite patrol radius. A number of approximate methods have previously been proposed to correct the galaxy clustering statistics for these effects, but these generally fail on small scales. To accurately correct the clustering we need to upweight pairs of galaxies based on the inverse probability that those pairs would be observed (Bianchi & Percival 2017). This paper details an implementation of that method to correct the Dark Energy Spectroscopic Instrument (DESI) survey for incompleteness. To calculate the required probabilities, we need a set of alternate realizations of DESI where we vary the relative priority of otherwise identical targets. These realizations take the form of alternate Merged Target Ledgers (AMTL), the files that link DESI observations and targets. We present the method used to generate these alternate realizations and how they are tracked forward in time using the real observational record and hardware status, propagating the survey as though the alternate orderings had been adopted. We detail the first applications of this method to the DESI One-Percent Survey (SV3) and the DESI year 1 data. We include evaluations of the pipeline outputs, estimation of survey completeness from this and other methods, and validation of the method using mock galaxy catalogs. </p>
DESI 2023 details for AI indicator
<p>The following data has been selected from EUROSTAT isoc_eb_ai database:</p> <ul> <li>Enterprises don't use any AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use one AI system (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use two AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use three AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use four AI systems (of E_CHTB, E_BDAML, E_BDANL, E_RBTS)</li> <li>Enterprises use AI technologies performing analysis of written language (text mining)</li> <li>Enterprises use AI technologies converting spoken language into machine-readable format (speech recognition)</li> <li>Enterprises use AI technologies generating written or spoken language (natural language generation)</li> <li>Enterprises use AI technologies identifying objects or persons based on images (image recognition, image processing)</li> <li>Enterprises use machine learning (e.g. deep learning) for data analysis</li> <li>Enterprises use AI technologies automating different workflows or assisting in decision making (AI based software robotic process automation)</li> <li>Enterprises use AI technologies enabling physical movement of machines via autonomous decisions based on observation of surroundings (autonomous robots, self-driving vehicles, autonomous drones)</li> <li>Enterprises use at least one of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises don't use any of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use at least two of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use at least three of the AI technologies: AI_TTM, AI_TSR, AI_TNLG, AI_TIR, AI_TML, AI_TPA, AI_TAR</li> <li>Enterprises use AI technologies for marketing or sales</li> <li>Enterprises use AI technologies for production processes</li> <li>Enterprises use AI technologies for organisation of business administration processes</li> <li>Enterprises use AI technologies for management of enterprises</li> <li>Enterprises use AI technologies for logistics</li> <li>Enterprises use AI technologies for ICT security</li> <li>Enterprises use AI technologies for human resources management or recruiting</li> <li>Enterprises use AI technologies for at least one of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> <li>Enterprises use AI technologies for at least two of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> <li>Enterprises use AI technologies for at least three of the purposes: AI_PMS, AI_PPP, AI_PBA, AI_PME, AI_PLOG, AI_PITS, AI_PHR</li> </ul>
Observations of M31 by DESI survey
<p>This is collection of data associated with the paper "DESI Observations of the Andromeda Galaxy: Revealing the Immigration History of our Nearest Neighbor” by Dey, Najita, Koposov, et al. 2022</p> <p>The collection consists of 4 files: </p> <ul> <li>DESI_M31_MEASUREMENT_CATALOG.fits which contains measurements by DESI of the M31 objects (as described in the paper)</li> <li>FigureFiles.zip contains information necessary to reproduce the figures in the paper</li> <li>simulation.zip contains the snapshots of the numerical N-body simulation of a disrupting satellite presented in the paper</li> <li>TargetFiles.zip contains information on original target lists of objects of different types prepared for DESI observations.</li> </ul>
Data for "The DESI One-Percent Survey: Evidence for Assembly Bias from Low-Redshift Counts-in-Cylinders Measurements"
<p>Summary statistics, covariance matrices, and MCMC results for each of our HOD samples. For links to the original data catalogs and instructions to reproduce the analysis, see the README at: <a href="https://github.com/AlanPearl/galtab/tree/main/galtab/paper2/">https://github.com/AlanPearl/galtab/tree/main/galtab/paper2/</a></p> <p>In brief, the desi_observations/desi_obs_*.npz files contain information about each threshold/redshift sample. Data is loaded via `obs_data = np.load(filename, allow_pickle=True)`, and all available fields can be shown via `obs_data.keys()`. Most importantly, our target data and its corresponding covariance matrix can be accessed with the "mean" and "cov" keys, respectively. These arrays can be sliced into our three observables using the slice objects accessed with the "slice_n", "slice_wp", and "slice_cic" keys.<br><br>The emcee MCMC chains for each sample can be found under desi_results/results_*/emcee_backend.h5. To access the chain data (i.e., to construct corner plots of our HOD parameters), you can either follow our paper plot notebooks linked in the README above, or see the <a href="https://emcee.readthedocs.io/en/stable/">emcee documentation</a>.</p>
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