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159 results for “O3”
OMPS-NPP L2 LP USask Ozone (O3) Vertical Profile swath daily V1.1
<p>The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km.</p> <p>Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4.</p>
PM2.5, PM10, NO2, O3 from Copernicus Air Quality Forecast March-June 2019, 2020 and 2021
<p>PM2.5, PM10, NO2, O3 Copernicus Air Quality Forecasts March-June 2019, 2020 and 2021 retrieved from the ADAM platform data cube (http://reliance.adamplatform.eu). Datasets are monthly averaged.</p> <p>The resulting extracted datasets are stored in netCDF format and cover Europe.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
TCOM-O3: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric ozone profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated Ozone (O3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time ozone profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used (hence starting date is 01 January 2000). PDF file shows comparison between v1.0 and v1.1 as well as TOMCAT data.</p> <p>Dataset also includes two files containing daily mean zonal mean hydrogen fluoride profiles on height (10-60 km) and pressure (300-0.1 hPa) levels:</p> <p>zmo3_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 60 km)</p> <p>zmo3_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request.</p>
Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3
<p>We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})</p>
Total O3 columns at polar regions: TOMCAT/SLIMCAT passive and active tracers and merged SAOZ-MSR2 dataset
<p>Passive and active total ozone columns simulated by the chemical transport model TOMCAT/SLIMCAT (Chipperfield, 1999) and the merged dataset of total ozone from Système d'Analyse par Observation Zénithale (SAOZ, Pommereau and Goutail, 1988) ground-based instruments and Multi-Sensor Reanalysis (MSR2, van der A et al., 2010, 2015) for the polar stations described in the Table here below.</p> <p><strong>Table. Arctic and Antarctic stations included in the study: station name and ID, latitude, longitude and measurement periods of SAOZ and MSR2 datasets.</strong></p> <table> <tbody> <tr> <td> <p><strong>Station (ID)</strong></p> </td> <td> <p><strong>Lat, Lon</strong></p> </td> <td> <p><strong>SAOZ dataset</strong></p> </td> <td> <p><strong>MSR2 dataset </strong></p> </td> </tr> <tr> <td> <p>Eureka, Nunavut (EU)</p> </td> <td> <p>80.1°N, 86.4°W</p> </td> <td> <p>2005-2020</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Ny-Alesund, Svalbard (NY)</p> </td> <td> <p>78.9°N, 11.9° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Thule, Greenland (TH)</p> </td> <td> <p>76.5°N, 68.8°W</p> </td> <td> <p>1999-2003, 2005-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Scoresbysund, Greenland (SC)</p> </td> <td> <p>70.5°N, 22.0°W</p> </td> <td> <p>1991-2017, 2019-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sodankyla, Finland (SK)</p> </td> <td> <p>67.4°N, 26.6° E</p> </td> <td> <p>1991-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Sondre Stromfjord, Greenland (SS)</p> </td> <td> <p>67.0°N, 50.6°W</p> </td> <td> <p>2018-2022</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Zhigansk, Russia (ZH)</p> </td> <td> <p>66.8°N, 123.4° E</p> </td> <td> <p>1992-2013</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Salekhard, Russia (SA)</p> </td> <td> <p>66.5°N, 66.7°E</p> </td> <td> <p>2002-2016</p> </td> <td> <p>1990-2022</p> </td> </tr> <tr> <td> <p>Marambio, Antarctica (MB)</p> </td> <td> <p>64.2°S, 56.7°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Dumont d’Urville, Antarctica (DD)</p> </td> <td> <p>66.7°S, 140.0°E</p> </td> <td> <p>1989-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Rothera, Antarctica (RO)</p> </td> <td> <p>67.6°S, 68.1°W</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Syowa, Antarctica (SW)</p> </td> <td> <p>69.0°S, 39.6°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Neumayer, Antarctica (NM)</p> </td> <td> <p>70.7°S, 8.3°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Terra Nova, Antarctica (TN)</p> </td> <td> <p>74.8°S, 164.5°E</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Concordia, Antarctica (DO)</p> </td> <td> <p>75.1°S, 123.4°E</p> </td> <td> <p>2007-2021</p> </td> <td> <p>1989-2021</p> </td> </tr> <tr> <td> <p>Halley, Antarctica (HB)</p> </td> <td> <p>75.6°S, 26.8°W</p> </td> <td> <p>-</p> </td> <td> <p>1989-2021</p> </td> </tr> </tbody> </table> <p> </p> <p>Each file corresponds to the whole winter time data set of the station identified by its ID: O3_PassActSLIMCAT_CompositeMSR2SAOZ_ID.txt</p> <p>1<sup>st</sup> column: Year</p> <p>2<sup>nd</sup> column: Day of Year (DoY)</p> <p>3<sup>rd</sup> column: Passive ozone column (without chemistry) modelled by TOMCAT/SLIMCAT (O3pasS).</p> <p>4<sup>th</sup> column: Active ozone column (with full chemistry) modelled by TOMCAT/SLIMCAT (O3actS).</p> <p>5<sup>th</sup> column: merged data from SAOZ observations and MSR2 (O3comp)</p> <p>Passive and active tracers of SLIMCAT were normalized to O3comp data in the beginning of the winter.</p> <p>NaN is used when there is no observation either from SAOZ instrument or MSR2 dataset or from the model.</p> <p> </p> <p><strong>References</strong></p> <p>Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. Roy. Meteor. Soc., 132, 1179–1203, <a href="https://doi.org/10.1256/QJ.05.51">https://doi.org/10.1256/QJ.05.51</a>, 2006.</p> <p>Pommereau, J.-P., and Goutail, F.: O<sub>3</sub> and NO<sub>2</sub> ground-based measurements by visible spectrometry during arctic winter and spring 1988, Geophys. Res. Lett., 15, 891–894, <a href="https://doi.org/10.1029/GL015i008p00891">https://doi.org/10.1029/GL015i008p00891</a>,1988.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Multi sensor reanalysis of total ozone, Atmos. Chem. Phys., 10, 11277–11294, <a href="https://doi.org/10.5194/acp-10-11277-2010">https://doi.org/10.5194/acp-10-11277-2010</a>, 2010.</p> <p>van der A, R. J., Allaart, M. A. F., and Eskes, H. J.: Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012, Atmos. Meas. Tech., 8, 3021–3035, <a href="https://doi.org/10.5194/amt-8-3021-2015">https://doi.org/10.5194/amt-8-3021-2015</a>, 2015.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O3 search sensitivity estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>Observing Run 3 (O3) Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' third observing run (O3).</p> <p>Details of the individual files can be found in</p> <ul> <li> o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the injected distributions and the HDF file format adopted.</p> <p>Separate files are provided for the two parts of the run, O3a and O3b, specified by the GPS start times and durations in the filenames, and for the entire O3 run (filename with no times specified).</p> <p>Separate files are also provided for subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. The subpopulations are combined into a single file (mixture) containing a mixture model that spans the union of all subpopulation.</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5546675 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — O1+O2+O3 Search Sensitivity Estimates
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the papers (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a> and <a href="http://dcc.ligo.org/LIGO-P2100239/public">dcc.ligo.org/LIGO-P2100239/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><strong>O1 + O2 + O3 Search Sensitivity Estimates</strong></p> <p>This document contains HDF injection summary files for search sensitivity estimates spanning the LIGO–Virgo–KAGRA (LVK) Collaborations' first (O1), second (O2), and third (O3) observing runs.</p> <p>Details of individual files can be found in</p> <ul> <li> o1+o2+o3-sensitivity-estimates.md</li> </ul> <p>including descriptions of the HDF file format adopted.</p> <p>Separate files are provided for individual subpopulations that span the Binary Neutron Star (bns), Neutron Star–Black Hole (nsbh), Binary Black Hole (bbh), and Intermediate Mass Black Hole (imbh) mass ranges. Additionally, a single file spanning the union of those mass ranges (mixture) is provided.</p> <p>Sensitivity estimates for O1 and O2 are available via semi-analytic methods (estimates of the optimal network signal-to-noise ratio). Sensitivity estimates for O3 are available from real search results. Analysts should specify detection thresholds separately for each type of sensitivity estimate (e.g., a signal-to-noise cut for O1+O2 and a false alarm rate cut for O3).</p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5636815 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave search analyses, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas .</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p> <p>Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022</p>
Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.
<p>This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors.</p> <p>Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.</p>
ESA SEOM-IAS – Measurement and ACS database O3 UV region
<p>The database contains measurements and absorption cross sections generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded at the German Aersopace Center (DLR) to provide a new absorption cross section database for ozone according to the needs of the TROPOMI instrument aboard the Sentinel 5-P satellite. The data are compiled in 2 zip files, one for the measurements (O3_UV_region_measurement_database_13112018.zip), one for the absorption cross sections (O3_UV_region_absorption_cross_section_database_13112018.zip) and a readme file (ESA_SEOM_IAS_spectra_O3UVRegion_readme.docx).</p> <p>The file “ACS_pTpoly_2nd+1st_order_BGcoefffit_constant_offset_V2.asc” in version II replaces the older version which contained an error in the wavelength axis. The readme file was updated, too.</p>
ESA SEOM-IAS – Measurement and line parameter database O3 MIR region
<p>The database contains measurements and line parameters generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded and analysed at the German Aerospace Center (DLR) and the University of Reims (URCA) to provide a new line position and intensity database for ozone fundamentals in the mid infrared region. The data are compiled in five zip files, three for the measurements (O3_MIR_region_DLR_measurement_database_20112018.zip, O3_MIR_region_DLR_measurement_N2O2broad_database_07012021.zip, O3_MIR_region_URCA_measurement_database_20112018.zip), two for the line parameter databases (O3_MIR_region_DLR_parameter_database_08012021.zip, O3_MIR_region_URCA_parameter_database_20112018.zip) and a readme file (ESA_SEOM_IAS_O3MIRRegion_readme_V3.docx).</p> <p>Note: The DLR parameter database is replaced by a newer version.</p>
Binary black hole merger rate constraints using GWTC-3 and full-O3 stochastic background constraints
<h1>README</h1> <p>This dataset contains posterior measurements of the redshift-dependent merger rate, mass distribution, and spin distribution of binary black holes following the O3b observing run of the LIGO-Virgo-KAGRA network, including both direct compact binary detections and constraints on the astrophysical gravitational-wave background.</p> <p>In particular, the goal of this work is to constrain a more complex model for the black hole merger rate, with the comoving rate density evolving as</p> <p>$$<br>R(z) \propto \frac{(1+z)^\alpha}{1 + \left(\frac{1+z}{1+z_p}\right)^{\alpha + \beta}}.<br>$$</p> <p>At redshifts \(z < z_p\), the merger rate grows approximately as \(R(z) \propto (1+z)^\alpha\), whereas at \(z>z_p\) it falls as \(R(z) \propto (1+z)^{-\beta}\).</p> <p>The analysis was performed as described in <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab9743">Callister <em>et al</em> (2020)</a> and <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021)</a>, now using binary black hole detections from the GWTC-3 catalog (<a href="https://link.aps.org/doi/10.1103/PhysRevX.13.041039">Abbott <em>et al</em> 2023a</a>, <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">2023b</a>).</p> <ul> <li>The parameter estimation samples used are those provided by the LIGO-Virgo-KAGRA collaboration at https://zenodo.org/records/8177023</li> <li>Selection effects are calculated and mitigated using the suite of pipeline injections available at https://zenodo.org/records/7890398</li> <li>Cross-correlation measurements of the stochastic gravitational-wave background are available at https://dcc.ligo.org/LIGO-G2001287, and correspond to results presented in <a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> (2021).</a></li> </ul> <p>As discussed in <a href="https://link.aps.org/doi/10.1103/PhysRevX.13.011048">Abbott <em>et al</em> (2023b)</a>, the results of this combined BBH + stochastic analysis are categorically unchanged relative to results previously obtained using GWTC-2 events (<a href="https://link.aps.org/doi/10.1103/PhysRevD.104.022004">Abbott <em>et al</em> 2021</a>); sensitivities are not yet sufficient to resolve the redshift at which the black hole merger rate peaks and turns over.</p> <h1>Contents</h1> <ul> <li><code><strong>processed_emcee_samples_together_r00r01.npy</strong></code>: File containing posterior samples when jointly analyzing BBH detections and stochastic background upper limits.</li> <li><code><strong>processed_emcee_samples_noStochastic_r00r01.npy</strong></code>: File containing posterior samples analyzing only direct BBH detections.</li> <li><code><strong>run_emcee_plPeak.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_together_r00r01.npy</code></li> <li><code><strong>run_emcee_plPeak_noStochastic.py</strong></code>: Script performing joint hierarchical inference using BBH detections and stochastic background limits; used to generate posterior samples in <code>processed_emcee_samples_noStochastic_r00r01.npy</code></li> </ul> <h1>Accessing posterior samples</h1> <p>Posterior samples are contained in the files <code>processed_emcee_samples_together_r00r01.npy</code> and <code>processed_emcee_samples_noStochastic_r00r01.npy</code>. This is loaded via python as, e.g.</p> <blockquote> <p>>>> import numpy as np</p> <p>>>> dataset = np.load('processed_emcee_samples_together_r00r01.npy')</p> </blockquote> <p>Contained in this file is a single <code>numpy</code> array of size <code>(# of posterior samples, # of hyperparameters)</code>:</p> <blockquote> <p>>>> dataset.shape</p> <p>(1152, 13)</p> </blockquote> <p> </p> <p>The 13 hyperparameters are defined as follows:</p> <table> <tbody> <tr> <td>Column</td> <td>Name</td> <td>Definition</td> </tr> <tr> <td><code>dataset[:, 0]</code></td> <td><code>xeff_mu</code></td> <td>Mean effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 1]</code></td> <td><code>xeff_sig</code></td> <td>Standard deviation of effective inspiral spin</td> </tr> <tr> <td><code>dataset[:, 2]</code></td> <td><code>R0</code></td> <td>Total BBH merger rate at redshift \(z=0\)</td> </tr> <tr> <td><code>dataset[:, 3]</code></td> <td><code>mMin</code></td> <td>Minimum black hole mass</td> </tr> <tr> <td><code>dataset[:, 4]</code></td> <td><code>mMax</code></td> <td>Maximum black hole mass</td> </tr> <tr> <td><code>dataset[:, 5]</code></td> <td><code>lmbda</code></td> <td>Power-law index on primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 6]</code></td> <td><code>mu_peak</code></td> <td>Mean of Gaussian peak in primary mass distribution</td> </tr> <tr> <td><code>dataset[:, 7]</code></td> <td><code>sig_peak</code></td> <td>Standard deviation of Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 8]</code></td> <td><code>frac_peak</code></td> <td>Fraction of events occupying Gaussian peak</td> </tr> <tr> <td><code>dataset[:, 9]</code></td> <td><code>bq</code></td> <td>Power-law index on mass ratio distribution \(p(q\|m_1)\)</td> </tr> <tr> <td><code>dataset[:, 10]</code></td> <td><code>alpha</code></td> <td>Slope of \(R(z) \propto (1+z)^\alpha \) at low redshifts</td> </tr> <tr> <td><code>dataset[:, 11]</code></td> <td><code>beta</code></td> <td>Slope of \(R(z) \propto (1+z)^{-\beta}\) at high redshifts</td> </tr> <tr> <td><code>dataset[:, 12]</code></td> <td><code>zpeak</code></td> <td>Redshift at which \(R(z)\) peaks and turns over</td> </tr> </tbody> </table> <p> </p> <p>The exact usage of the above parameters can be seen in the included scripts <code>run_emcee_plPeak.py</code> and <code>run_emcee_plPeak_noStochastic.py</code>, with which the inference was performed. </p>
TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017
<p>Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017.</p> <p>Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006).</p> <p>Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021). </p> <p>Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP. </p>
Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem". in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem".
Dataset of execution times of programs compiled under different GCC optimizaion levels (-O0, -O1, -O2, -O3) on the Xilinx Zynq UltraScale+ MPSoC ZCU104 (Cortex A53)
<p>This dataset contains execution time measurements for a set of programs from the TACLe benchmark suite, including <em>lift</em>, <em>powerwindow</em>, <em>bsort</em>, <em>matrix1</em>, <em>epic</em>, <em>dijkstra</em>, mpeg2 and <em>quicksort</em>.</p> <p>The data was collected using RT-Bench, an open-source framework designed to execute these benchmarks periodically in a real-time context.</p> <p>Each program was compiled using five different GCC optimization levels (-O0, -O1, -O2, -O3, and -Ofast) and executed periodically 1000 times.</p> <p>The experiments were conducted on the Zynq ZCU104 platform, which features a quad-core Arm Cortex A53 processor, running the Ubuntu 22.04 LTS arm64 OS with the 5.15.0-1015-xilinx-zynqmp kernel.</p> <p>This dataset is published at <a href="https://2024.ieee-etfa.org/" target="_blank" rel="noopener">ETFA</a> 2024 as part of a work-in-progress paper called "<a href="https://hal.science/hal-04667091/" target="_blank" rel="noopener">Impact of compilation optimization levels on execution time variability</a>"</p>
Supplementary data for the manuscript "Comparison of computational and experimental saturation vapor pressures of α-pinene + O3 oxidation products"
<p>COSMO-files of potential ozonolysis products of α-pinene for the manuscript:<br> Hyttinen, N., Pullinen, I., Nissinen, A., Schobesberger, S., Virtanen, A., and Yli-Juuti, T.: Comparison of computational and experimental saturation vapor pressures of α-pinene + O<sub>3</sub> oxidation products, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-775, in review, 2021.</p>
LESO-US-O3-HOURLY
<p>Hourly surface ozone (O3) concentration in the United States on a 0.1 deg scale. The date range is from 2012 to 2021. This is the product of the LESO framework. The algorithm publications are 'Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China' and 'Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation'.</p> <p><span>The unit for LESO US is in ppm, consistent with EPA standards. To convert it to µg/m³, multiply by 1000 × 1.97.</span></p>
LESO-EU-O3-HOURLY
<p>Hourly surface ozone (O3) concentration in Europe on a 0.1 deg scale. The date range is from 2012 to 2021. This is the product of the LESO framework. The algorithm publications are 'Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China' and 'Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation'.</p>
LESO-CN-O3-HOURLY
<p>Hourly surface ozone (O3) concentration in China (mainland) on a 0.1 deg scale. The date range is from 2012 to 2021. This is the product of the LESO framework. The algorithm publications are 'Learning Surface Ozone From Satellite Columns (LESO): A Regional Daily Estimation Framework for Surface Ozone Monitoring in China' and 'Satellite-derived estimates of surface ozone by LESO: Extended application and performance evaluation'.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.