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12,753 results for “Corrections”
Demonstrating real-time and low-latency quantum error correction with superconducting qubits
<p>Data associated with results presented in "Demonstrating real-time and low-latency quantum error correction with superconducting qubits".</p> <p>HDF5 files include raw data collected during experiments. Datasets for experiments performed with different number of measurement rounds are saved in separate groups. The group attributes contain information including the total number of measurement rounds. Groups also contain the stim circuits associated with each experiment, which are used for software decoding, and qubit_mappings, which maps each stim coordinate to the corresponding qubit ID on the Ankaa-2 device. Each group has a hard_measurements and soft_measurements group containing the hard and soft measurement results. Measurement results are grouped in datasets per qubit, storing results in the order of measurement execution during the experiment, and with each row representing a separate repetition of the experiment.</p> <p>When decoding with the FPGA decoder we also store the decoder register outcomes in decoder_shot_results. In particular, the first column indicates the logical correction computed by the FPGA decoder – values 0 and 2 correspond to no logical error detected and 1 corresponds to logical error being detected by the decoder.</p> <p>The HDF5 file with data for the fast-feedback experiment ("fast_feedback_raw_data.h5") includes the reference_data group storing reference data. It contains the "delays" group (used to measure T1 in FigS4(d)), "measurement_fidelity" group (used to calculate measurement confusion matrix in Fig S4e, and "double_measurement" group (used to compute post-measurement state distribution in Fig S4f).</p> <p>Also included are files containing the logical error probabilities (LEPs), and CSV files containing timings, both containing data used to plot figures.</p>
Nonlinearity corrections and bad pixel masks for the WINTER sensors
<p>Nonlinearity corrections and bad pixel masks for the WINTER sensors to be used with https://github.com/winter-telescope/winternlc. </p>
Corrected IODP Gamma Ray Attenuation (GRA) densities and calculated porosities derived from the LILY Database
<div>The dataset <strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> is derived from an analysis of data from the LILY Database (<a href="https://doi.org/10.5281/zenodo.8408296">https://doi.org/10.5281/zenodo.8408296</a>) as described in Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). The file contains over 3.7 million corrected gamma ray attenuation (GRA) bulk density data derived from the LILY database file GRA_DataLITH.csv. It also contains over 3.7 million porosity estimates that are computed from the corrected GRA bulk density using grain densities computed for each lithology from Moisture and Density (MAD) grain densities (derived from LILY file MAD_DataLITH.csv).</div> <div> </div> <div><strong>Citation: </strong>Please cite Childress et al. (2024) when using these data:</div> <div>Childress, L.B., Acton, G.D., Percuoco, V.P., Hastedt, M., 2024. The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data, <em>Geochemistry, Geophysics, Geosystems, 25</em>, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>.</div> <div> </div> <div><strong>GRA_Densities_Corrected_and_Porosities_2023-12-26.csv</strong> file size uncompressed is 950 Mb.</div> <div> </div> <div><strong>Data File format:</strong></div> <ul> <li>Exp: expedition number</li> <li>Site: site number</li> <li>Hole: hole number</li> <li>Core: core number</li> <li>Type: Type indicates the coring tool used to recover the core (typical types are F, H, R, X; see Table S3 in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Sect: section number</li> <li>Offset (cm): position of the observation, measured relative to the top of a section.</li> <li>Depth CSF-A (m): location of the observation expressed relative to the top of a hole.</li> <li>Bulk density (GRA): bulk GRA density measured on whole core sections in g/cm^3.</li> <li>Timestamp (UTC): date and time the observation was made.</li> <li>Instrument: abbreviation or mnemonic for the GRA sensing device used to make this observation (GRA1 or GRA2).</li> <li>Instrument group: abbreviation or mnemonic for the data collection device (logger) used to acquire this observation (WRMSL).</li> <li>Text ID: automatically generated unique database identifier for a sample, visible on printed labels.</li> <li>Prefix: Prefix of the lithology</li> <li>Principal: Principal lithology</li> <li>Suffix: Suffix of the lithology</li> <li>Full Lithology: full lithologic name = Prefix + Principal + Suffix</li> <li>Simplified Lithology: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>)</li> <li>Lithology Type: Sedimentary, Igneous, or Metamorphic</li> <li>Degree of Consolidation: consolidation state of the lithology.</li> <li>Lithology Subtype: categorization of lithologies (see Supporting Information in Childress et al., 2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>).</li> <li>Expanded Core Type: the actual coring type used, because some coring types were incorrectly grouped in the "Type" column (see Childress et al., 2024 for an explanation)</li> <li>Latitude (DD): Latitude in decimal degrees</li> <li>Longitude (DD): Longitude in decimal degrees</li> <li>Water Depth (mbsl): water depth in meters below sea level</li> <li>Grain Density: grain density associated with the Principal lithology, computed from MAD data</li> <li>Mean MAD Bulk Density: mean MAD bulk density associated with the Principal lithology.</li> <li>Std MAD Bulk Density: standard deviation in the MAD bulk densities for each Principal lithology.</li> <li>Correction Basis: the GRA bulk densities are corrected based on coring tool used. If the RCB was used, then the lithology cored by the RCB is used in determining the size of the correction.</li> <li>Median Difference: The correction that will be applied based on the median difference between the raw GRA bulk density and the colocated MAD bulk density for a specific Correction Basis.</li> <li>GRA Bulk Density Corrected: The corrected GRA bulk density in g/cm^3.</li> <li>Porosity: porosity computed from the corrected GRA bulk densities and grain density.</li> <li>Deviation: difference between "GRA Bulk Density Corrected" and "Mean MAD Bulk Density", which is the deviation the corrected density has from that expected for its Principal lithology.</li> <li>N Deviations: The number of standard deviations by which the observation differs from the expected value (= Deviation/(Std MAD Bulk Density)), which is useful for identifying outliers.</li> </ul> <h3>GitHub Repository:</h3> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a title="IODP LILY GitHub Repository" href="https://github.com/IODP?tab=repositories">IODP LILY GitHub Repository</a> </li> </ul>
Coefficients for a Proxy Long-Wavelength Correction for the SWOT 1-Day Repeat Mission
<p>This archive contains NetCDF-formatted files containing the weighting coefficients which define the proxy long-wavelength correction (LWC) described in a manuscript submitted to AGU Earth and Space Science, "The Significance of the Long-Wavelength Correction for Studies of Baroclinic Tides with SWOT".</p>
Collider Bias Correction for Multiple Covariates in GWAS Using Robust Multivariable Mendelian Randomization
<p>This repository contains the data underlying the figures in paper "Collider Bias Correction for Multiple Covariates in GWAS<br>Using Robust Multivariable Mendelian Randomization".</p> <p> </p> <p> </p> <p>The file names and sheet names in the xlsx file indicate the corresponding figures of data. </p> <p><br>The underlying data of manhattan plots and QQ plots are in text file. For other figures, the underlying data are in the spreadsheet.</p> <p>In each file, column names indicate the MVMR method used to obtain the result. </p> <p>For example: </p> <p>In text files:</p> <p>The abbreviation "mPC" refers to metabolomic principle components.</p> <p>beta_no_correction: the SNP effect estimate without bias correction.</p> <p>beta_cml or beta_MVMR_cml: the standard error of SNP effect estimate after the bias correction of MVMR-cML.</p> <p>SE_UVMR_cml: the standard error of SNP effect estimate after the bias correction of UVMR-cML.</p> <p>p_value_Egger or p_value_MVMR_Egger: the p-value of SNP effect estimate after the bias correction of MVMR-Egger regression.</p> <p><br>In the spreadsheet, column names follow the same style. </p> <p>The GWAS data is also available. The column names follows the plink output file. The detailed explanations are available at https://www.cog-genomics.org/plink/2.0/formats#glm_linear</p>
Senator Beck Basin Corrected Radiation (Water Years 2005-2023)
<p>This is qa/qc'd hourly data from two snow energy balance instrumentation towers in Senator Beck Basin Study Area, San Juan Mountains, CO. Data processed by McKenzie Skiles (Snow HydRO Lab, University of Utah). </p> <p>All data originates from the Center for Snow and Avalanche Studies, if used they should be recognized, please see their data policy: <a href="https://snowstudies.org/csas-archival-data/">https://snowstudies.org/csas-archival-data</a><a href="https://snowstudies.org/csas-archival-data/">/</a> </p> <p>Correction of radiation data is described in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR011985">Painter et al., 2012.</a></p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Code for producing a consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data
<p>The DMSP-OLS NTL product suffers from three main problems, i.e.inter-annual inconsistency, saturation, and blooming effect which will affect the accuracy of urban extraction and the estimation of the social-economic indexes. To address these problems, we adopted three correction methods to rectify inter-annual inconsistency, saturation, and blooming effects.<br> The code is written based on the Javascript API provided by the Google Earth Engine platform(https://earthengine.google.com/)</p>
Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies
<p>Bias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for:</p> <p>-period 1981-2099/2100,</p> <p>-6 RCM,</p> <p>-3 scenarios (RCPs 2.6, 4.5 and 8.5),</p> <p>-7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and</p> <p>-18 domains and 23 locations within these domains.</p> <p>Bias correction and further downscaling to 0.1° was done using <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging.</p> <p><strong>Organization of the data</strong></p> <p>The name of the files are <em>domain</em>-<em>type</em>.zip, where <em>type</em> is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: <em>experiment</em>/<em>modelNumber</em>/<em>variable</em>.nc for gridded and <em>experiment</em>/<em>modelNumber</em>/<em>variable-pilotFieldNumber</em>.txt for point data, where <em>experiment </em>is rcp26, rcp45 or rcp85.</p> <p><em>domain and pilotFieldNumber</em></p> <table> <tbody> <tr> <td> <p><strong>domain</strong></p> </td> <td> <p><strong>domain </strong><strong>location (min and max. Longitude, min and max latitude</strong><strong>)</strong></p> </td> <td> <p><strong>pilotFieldNumber</strong></p> </td> <td> <p><strong>pilot field </strong><strong>location (longitude, latitude)</strong></p> </td> <td> <p><strong>case study</strong><strong> number</strong></p> </td> <td> <p><strong>country</strong></p> </td> <td> <p><strong>Name (OPTAIN case study)</strong></p> </td> </tr> <tr> <td> <p>01</p> </td> <td> <p>50.95 51.45 14.55 15.05</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>1</p> </td> <td> <p>DEU</p> </td> <td> <p>Schoeps</p> </td> </tr> <tr> <td> <p>02</p> </td> <td> <p>46.35 47.05 6.55 7.15</p> </td> <td> <p>2</p> </td> <td> <p>46.816667 6.95</p> </td> <td> <p>2</p> </td> <td> <p>CHE</p> </td> <td> <p>Petite Glane</p> </td> </tr> <tr> <td> <p>02_1</p> </td> <td> <p>46.75 47.25 7.25 7.75</p> </td> <td> <p>1</p> </td> <td> <p>46.983333 7.466667</p> <p> </p> </td> </tr> <tr> <td> <p>02_34</p> </td> <td> <p>47.35 47.85 8.35</p> </td> <td> <p>3</p> <p>4</p> </td> <td> <p>47.433333 8.516667</p> <p>47.683333 8.616667</p> </td> </tr> <tr> <td> <p>02_5</p> </td> <td> <p>46.15 46.65 5.95 6.45</p> </td> <td> <p>5</p> </td> <td> <p>46.4 6.233333</p> </td> </tr> <tr> <td> <p>03a</p> </td> <td> <p>46.65 47.15 17.45 17.95</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> </td> <td> <p>46.92649 17.68246</p> <p>46.9166 17.68976</p> <p>46.91283 17.69754</p> <p>46.91283 17.69723</p> </td> <td> <p>3a</p> </td> <td> <p>HUN</p> </td> <td> <p>Csorsza</p> </td> </tr> <tr> <td> <p>03b</p> </td> <td> <p>46.45 46.95 16.65 17.15</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>3b</p> </td> <td> <p>HUN</p> </td> <td> <p>Felso Valicka</p> </td> </tr> <tr> <td> <p>04</p> </td> <td> <p>52.35 52.85 18.45 18.95</p> </td> <td> <p>1</p> </td> <td> <p>52.597469 18.728617</p> </td> <td> <p>4</p> </td> <td> <p>POL</p> </td> <td> <p>Upper Zglowiaczka</p> </td> </tr> <tr> <td> <p>05</p> </td> <td> <p>46.35 46.85 15.35 15.85</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>5</p> </td> <td> <p>SVN</p> </td> <td> <p>Pesnica</p> </td> </tr> <tr> <td> <p>06</p> </td> <td> <p>46.45 46.95 16.15 16.65</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>6</p> </td> <td> <p>HUN/SVN</p> </td> <td> <p>Kebele/Kobiljski</p> </td> </tr> <tr> <td> <p>07</p> </td> <td> <p>49.85 50.35 4.75 5.25</p> </td> <td> </td> <td> <p> </p> </td> <td> <p>7</p> </td> <td> <p>BEL</p> </td> <td> <p>La Wimbe</p> </td> </tr> <tr> <td> <p>08</p> </td> <td> <p>55.15 55.75 23.55 24.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>55.522057 23.799235</p> <p>55.42233194 23.82580339</p> </td> <td> <p>8</p> </td> <td> <p>LTU</p> </td> <td> <p>Dotnuvele</p> </td> </tr> <tr> <td> <p>09</p> </td> <td> <p>45.45 45.95 9.65 10.15</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>9</p> </td> <td> <p>ITA</p> </td> <td> <p>Cherio</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>59.45 59.95 10.75 11.25</p> </td> <td> <p>1</p> <p>2</p> <p>3</p> <p>4</p> <p>5</p> <p>6</p> <p>7</p> <p>8</p> </td> <td> <p>59.71949 10.83576</p> <p>59.6833306 10.8833298</p> <p>59.6833306 10.8833298</p> <p>59.665 10.9475</p> <p>59.665 10.9475</p> <p>59.841012 10.903597</p> <p>59.757631 11.072031</p> <p>59.539623 10.856447</p> </td> <td> <p>10</p> </td> <td> <p>NOR</p> </td> <td> <p>Krogstad</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>46.45 46.95 17.55 18.05</p> </td> <td> <p>1</p> <p>2</p> </td> <td> <p>46.658333 17.75583</p> <p>46.656944 17.75833</p> </td> <td> <p>11</p> </td> <td> <p>HUN</p> </td> <td> <p>Tetves</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>49.35 49.85 14.75 15.25</p> </td> <td> <p>1</p> </td> <td> <p>49.616837 15.078266</p> </td> <td> <p>12</p> </td> <td> <p>CZE</p> </td> <td> <p>Cechticky</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>55.85 56.35 25.85 26.45</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>13</p> </td> <td> <p>LVA</p> </td> <td> <p>Dviete</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>59.75 60.25 17.55 18.05</p> </td> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>14</p> </td> <td> <p>SWE</p> </td> <td> <p>Ingvastaan Lehstaan</p> </td> </tr> </tbody> </table> <p> </p> <p><em>modelNumber</em></p> <table> <tbody> <tr> <td> <p><strong>modelNumber</strong></p> </td> <td> <p><strong>Driving Model (GCM)</strong></p> </td> <td> <p><strong>Ensemble</strong></p> </td> <td> <p><strong>RCM </strong></p> </td> <td> <p><strong>End date</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r12i1p1</p> </td> <td> <p>CCLM4-8-17</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>EC-EARTH</p> </td> <td> <p>r3i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>31.12.2100</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>HIRHAM5</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RACMO22E</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>HadGEM2-ES</p> </td> <td> <p>r1i1p1</p> </td> <td> <p>RCA4</p> </td> <td> <p>30.12.2099</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>MPI-ESM-LR</p> </td> <td> <p>r2i1p1</p> </td> <td> <p>REMO2009</p> </td> <td> <p>31.12.2100</p> </td> </tr> </tbody> </table> <p> </p> <p><em>variable</em></p> <table> <tbody> <tr> <td> <p><strong>variable</strong></p> </td> <td> <p><strong>description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>Tmean</p> </td> <td> <p>Mean temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmin</p> </td> <td> <p>Min temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>Tmax</p> </td> <td> <p>Max temperature</p> </td> <td> <p>°C</p> </td> </tr> <tr> <td> <p>prec</p> </td> <td> <p>Precipitation</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>solarRad</p> </td> <td> <p>Solar radiation</p> </td> <td> <p>MJ/m2</p> </td> </tr> <tr> <td> <p>windSpeed</p> </td> <td> <p>Wind speed at 2m</p> </td> <td> <p>m/s</p> </td> </tr> <tr> <td> <p>relHum</p> </td> <td> <p>Relative humidity</p> </td> <td> <p>%</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Methodolody</strong></p> <p>Bias correction was done using non-parametric empirical quantile mapping with modified method from R package <a href="https://cran.r-project.org/web/packages/qmap/index.html">qmap</a>. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010.</p> <p>The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages <a href="https://cran.r-project.org/web/packages/automap/index.html">automap</a> and <a href="https://cran.r-project.org/web/packages/gstat/index.html">gstat</a> with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model <a href="https://webmap.ornl.gov/wcsdown/dataset.jsp?dg_id=10008_1">Shuttle Radar Topography Mission</a> was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862756.</p>
Variability in the global ocean carbon sink from 1959-2020 by correcting models with observations (LDEO-HPD)
<p><strong>* The latest versions of this dataset are maintained and available here: <a href="https://zenodo.org/record/7901433">https://zenodo.org/record/7901433</a> *</strong></p> <p>The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO2 and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO2 data to global coverage by using GOBMs as priors, applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO2 product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO2 growth and volcanic eruptions.</p> <p>This data is the final reconstruction of air-sea CO2 fluxes for 1959-2020 using the mean pCO2 from the corrected models. Both annual flux time series and spatially explicit fluxes are included. RIVERINE CARBON EFFLUX ADJUSTMENTS ARE NOT INCLUDED WITHIN THESE FILES. File metadata provides units.</p>
Raw datasets for work on aberration-corrected transmission electron microscopy with Zernike phase plates
<p>Raw data (electron microscopy images and spectra) obtained for work on aberration-corrected transmission electron microscopy with Zernike phase plates as published in Ultramicroscopy.</p>
Datasets for Background and Shading Correction of Optical Microscopy Images by BaSiC -- Downsampled Version
<p>This repository holds downsampled example data for publication: "<strong>A BaSiC tool for background and shading correction of optical microscopy images, Nature Communications (2017)</strong>" DOI: <a href="https://doi.org/10.1038/ncomms14836">https://doi.org/10.1038/ncomms14836</a>. For full-resolution testing data, please refer to Zenodo repository at DOI: <a href="https://zenodo.org/record/6334810#.YvD6zHZBxD8">10.5281/zenodo.6334810</a>.</p>
Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging – dataset
<p>Raw volumetric data used in the work "Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging" (<a href="http://doi.org/10.1364/BOE.466403">doi.org/10.1364/BOE.466403</a>). The data is packaged using the FIJI BigStitcher into HDF5 file. The file is split into 89 parts in ZIP format. Additionally we provide XML file needed for opening the data with BigStitcher and the TXT file with the nominal locations of the volumes based on the readings from the X-Y translation stage. The volumes inside the HDF5 file are already registered for stitching using the BigStitcher pairwise registration and global optimization procedure. Using the BigStitcher option "Resave to TIFF" one can access the raw data that we processed in the work. The processing code which operates on TIFF files is available here: <a href="https://github.com/biopto/QPI-stitching-2D-3D">https://github.com/biopto/QPI-stitching-2D-3D</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Syndromes Dataset
<p>Simulated sydromes measurement of quantum surface code error correction.<br>Used for the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>".</p> <p>File names: <code>d-<surface_code_distance>_pfr-<physical_fault_rate>_nb-<number_of_samples></code></p> <p>Each file is formatted as csv with the following columns:</p> <ul> <li>label: binary label (0: no error, 1: error)</li> <li>syndromes: syndrome measurement sequence (tuples of the form (round, syndromes))</li> <li>quantity: number of samples for this label + syndrome sequence</li> </ul> <p>Only distance 3 is currently available with 10M samples for each physical fault rate.</p> <p>The data generation relies on <a href="https://github.com/quantumlib/Stim" target="_blank" rel="noopener">Stim</a>.</p>
A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data
<p>Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"</p>
Data for "Optimization of decoder priors for accurate quantum error correction"
<p>Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.</p>
RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 2009-2023, version 2.4.1 operated at Heidelberg University
<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.1 full-physics algorithm (Butz et al. 2011, Guerlet et al. 2013) applied on GOSAT TANSO-FTS Level 1B (L1B) data from 2009-04-18 to 2023-08-29. The GOSAT TANSO-FTS L1B data product is produced by JAXA/NOIES/MOE and provided by ESA. The XCO2 data together with related variables are aggregated as daily files, only good quality retrievals are included.</p> <p>If the data is used for publications, please contact andre.butz@uni-heidelberg.de to discuss potential co-authorship and technical details.</p> <p> </p> <p>Summary:</p> <p>Shortname: REMOTEC_L2_CO2_GOSAT</p> <p>Longname: RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.1</p> <p>DOI: 10.5281/zenodo.12773070</p> <p>Version: 2.4.1</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2023-08-29</p>
Datasets for "BB correction" algorithm evaluation and analysis
<p>The datasets contain the raw and processed data relevant to the "BB (black body) correction" method, an algorithm to correct for scattering and systematic bias in quantitative neutron imaging from experimental measurements. Five CT datasets imaging various hydrogenous and non hydrogenous materials are uploaded. The raw data folders for each dataset contains the sample projections over the tomographic range as well as the image used for referencing: the dark current, the open beam files and the additional "BB images", obtained with an interposed grid of neutron absorbers (BBs) from which scattering and bias components are estimated. The processed folders contain the CT reconstruction with and without the BB correction. All processing was done with the MuhRec open source software (https://zenodo.org/record/1311698#.W30exxixVtg)</p>
Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)
<p>This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany).<br>The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase.<br>For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder.<br>For further measurement details, please refer to the mentioned original article.</p>
Data for "Quantum error correction below the surface code threshold"
<p>Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor.</p> <p>See README at the root of each zip archive for detailed description of each dataset.</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.