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12,753 results for “Corrections”

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edi60/100

Correction Factors for Dissolved Organic Carbon Extracted from Soil in New England 2012-2013

Oxidizable dissolved organic carbon (DOC) is regularly measured in environmental samples using a colorimetric method with Mn(III)-pyrophosphate as the oxidizing agent. It is simpler to use and has a much higher throughput than the commonly used dichromate oxidation and combustion methods. Here, we demonstrate that the method often leads to an underestimation or overestimation of the concentration of common organic compounds in solutions. To our knowledge, no published study has taken this fact into account when analyzing DOC data. Hence, we compared Mn(III)-pyrophosphate-based results with measurements performed with a total organic carbon combustion analyzer for samples of organic and mineral soil horizons of two temperate deciduous forests (Harvard Forest, Hubbard Brook), of organic soil horizon of a primary growth hemlock stand (Harvard Forest), and of a peatland (Caribou Bog) located in New England, USA. The Mn(III)-pyrophosphate method consistently underestimated DOC concentration in soil extracts. We present correction factors for the different types of soil studied. By employing correction factors, we find the method can be an inexpensive, accurate, and high throughput tool to measure DOC in environmental samples.

openCC0Dec 2023View details →
zenodo52/100

A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction

<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a &ldquo;reanalysis&rdquo;).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA&rsquo;s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA&rsquo;s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until&nbsp; 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de M&eacute;t&eacute;orologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p>&nbsp;Variables and attributes<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; lon:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72) = FLOAT(lon)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: longitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_east<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; lat:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(36) = FLOAT(lat)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: latitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_north<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp; sigma:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(25) = FLOAT(sigma)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: sigma<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: sigma_level = p/ps<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp; soil:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(18) = FLOAT(soil)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: none<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4&nbsp; time:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: model time<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: days since 00:00:00 (the beginning of the file)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5&nbsp; controle:&nbsp;&nbsp;&nbsp; FLOAT(100) = FLOAT(lentable)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: Table of run parameters<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; description:&nbsp; MGCM run&nbsp;&nbsp;&nbsp; 5.000<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6&nbsp; Ls:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: deg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7&nbsp; tsurf:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Surface temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8&nbsp; ps:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: surface pressure<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: Pa<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9&nbsp; co2ice:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: co2 ice thickness (column mass density)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 10&nbsp; fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 11&nbsp; fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 12&nbsp; temp:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 13&nbsp; u:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Zonal (east-west) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 14&nbsp; v:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Meridional (north-south) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 15&nbsp; rho:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: density<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-3<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 16&nbsp; udrag:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Drag velocity<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17&nbsp; udragt:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Threshold velocity for dust lifting<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 18&nbsp; aerosol:&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust opacity considering layer thickness<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI (opacity/m)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19&nbsp; taudustvis:&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Dust optical depth<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20&nbsp; q01:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: mix. ratio<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg/kg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 21&nbsp; dqsdevtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust devil lift rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 22&nbsp; dqsstrtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: near surface wind stress dust lifting rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 23&nbsp; dqssedtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust sedimentation rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2021View details →
zenodo48/100

One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.

<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship&#39;s super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship&#39;s location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>

opencc-by-4.0May 2020View details →
zenodo48/100

Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis

<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce &nbsp;analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p>&nbsp;</p> <p>&nbsp;</p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ &amp; VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an &quot;explore unknown residuals&quot; analysis as described in the detailed methods in the supplemental of the paper.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix &quot;BeforeMOCCASIN&quot; or &quot;AfterMOCCASIN&quot; and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment.&nbsp;</p>

opencc-by-4.0Nov 2020View details →
zenodo48/100

ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset

<p>MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases.&nbsp;</p> <p>Each file contain monthly mean zonal mean ozone profiles. There are 6 data files.</p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a>&nbsp;contains ozone profiles on&nbsp;geometric height levels (1 to 60 km) &nbsp;in&nbsp;mixing ratio units, whereas&nbsp;&nbsp;<a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a>&nbsp;contains ozone profile in number density units.</p> <p>Similarly,&nbsp;</p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a>&nbsp;contains ozone profiles on 43 MLS pressure levels&nbsp;&nbsp;(1000 to 0.1&nbsp;hPa) &nbsp;in&nbsp;mixing ratio units, whereas&nbsp;&nbsp;<a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a>&nbsp;contains ozone profile in number density units.</p> <p>Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution.</p> <p>There are two straospheric column files</p> <p>ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and</p> <p>ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc</p> <p>Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries.</p> <p>A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).</p>

opencc-by-4.0Jun 2021View details →
zenodo48/100

Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines

<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample&nbsp; with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF:&nbsp; Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T&gt;G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T&gt;G) in NDD_05 proband</p>

opencc-by-4.0Dec 2023View details →
zenodo48/100

Dataset to: Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (CATENA) - Version 2 (Corrected)

<p><strong>Version update: Coordinates were not correct in previsous version and have been corrected now in version 2</strong></p> <p>&nbsp;</p> <p>Dataset to the manuscript: Schiedung et al. (2022, Catena) Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada (&nbsp;<a href="https://doi.org/10.1016/j.catena.2022.106194">https://doi.org/10.1016/j.catena.2022.106194</a> )</p> <p>Data files, variables and parameter are described in <em>Var_names_dd_all.csv</em> for all data on each sample and <em>Var_names_dd_composites.csv </em>for all data on composited samples per site and depth. DRIFT data and corresponding explenation are in <em>Schiedung_CATENA_DRIFT_v1.1.zip.</em></p> <p>&nbsp;</p> <p><strong>&nbsp;</strong></p>

opencc-by-4.0Feb 2024View details →
zenodo48/100

Coefficients for the SMACPy atmospheric correction algorithm

<p>This dataset stores coefficients used by the updated Simplified Method for Atmospheric Correction - Python (SMACPy). The coefficients are used, together with meteorological data, to remove the effects of atmospheric gases and aerosols from a satellite image.</p> <p>Currently, this is a beta version and more updates to these coefficients may be expected in the future.</p> <p>&nbsp;</p> <p>Coefficients exist for various aerosol types, all defined from the equivalent 6S (<a href="https://doi.org/10.1109/36.581987">10.1109/36.581987</a>) aerosol types:</p> <p>BIOMA: Biomass burning plumes.</p> <p>CONTI: Continental aerosol.</p> <p>DESER: Desert dust aerosol.</p> <p>MARIT: Maritime aerosol, primarily sea salt.</p> <p>STRATO: Stratospheric aerosol.</p> <p>URBAN: Urban pollution aerosol.</p> <p>NOAER: A profile with no aerosol effects, just atmospheric gases and Rayleigh scattering.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>In this version the following satellites / sensors are supported:</p> <p>NASA Aqua / MODIS</p> <p>NASA Terra / MODIS</p> <p>Fengyun-4A / AGRI</p> <p>GEO-KOMPSAT-2A / AMI</p> <p>GOES-16 / ABI</p> <p>GOES-17 / ABI</p> <p>Himawari-8 / AHI</p> <p>Landsat-8 / OLI</p> <p>Meteosat-8 / SEVIRI (no HRV channel)</p> <p>Meteosat-9 / SEVIRI (no HRV channel)</p> <p>Meteosat-10 / SEVIRI (no HRV channel)</p> <p>Meteosat-11 / SEVIRI (no HRV channel)</p> <p>NOAA-20 / VIIRS (M and I bands)</p> <p>Suomi-NPP / VIIRS (M and I bands)</p> <p>Sentinel-2A / MSI</p> <p>Sentinel-2B / MSI</p> <p>Sentinel-3A / OLCI</p> <p>Sentinel-3B / OLCI</p> <p>Sentinel-3A / SLSTR</p> <p>Sentinel-3B / SLSTR</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2021View details →
zenodo48/100

RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0 operated at Heidelberg University

<p>The data set contains bias-corrected column averaged dry air mole fractions (XCO2) retrieved with the RemoTeCv2.4.0 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 2019-06-30. 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>&nbsp;</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>To cite the data in publications:</p> <p>Andr&eacute; Butz (2019), RemoTeC full-physics retrieval GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 version 2.4.0, Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany, Accessed: [Date], 10.5281/zenodo.5886662</p> <p>&nbsp;</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 2.4.0</p> <p>DOI: 10.5281/zenodo.5886662</p> <p>Version: 2.4.0</p> <p>Format: netCDF</p> <p>Spatial Coverage: -180.0,-90.0,180.0,90.0</p> <p>Temporal Coverage: 2009-04-18 to 2019-06-30</p>

opencc-by-4.0Feb 2022View details →
zenodo48/100

RoCliB - Bias corrected CORDEX RCM dataset over Romania

<p>This dataset contains a set of four climate variables from 10 General Circulation Models (GCMs), dynamically downscaled in the EURO-CORDEX initiative by several Regional Climate Models (RCMs) and adjusted (bias-corrected) over Romania for the period 1971&ndash;2100. The climate models data were obtained from the&nbsp;<a href="https://cordex.org/data-access/">EURO-CORDEX archive</a>. Two climate change scenarios were selected, namely the moderate (RCP4.5) and business-as-usual scenario (RCP8.5).&nbsp;The multivariate bias correction by the N-dimensional probability density method (MBCn) was used&nbsp;to&nbsp;bias correct the RCMs outputs [1], using as reference the ROCADA gridded dataset [2].</p> <p>Characteristic:</p> <ul> <li><strong>Climate variables</strong>: air temperature (tasAdjust - Celsius degree), maximum air temperature (tasmaxAdjust - Celsius degree), minimum air temperature (tasminAdjust - Celsius degree) and precipitation (prAdjust - mm)</li> <li><strong>Bias-correction method:</strong>&nbsp;multivariate bias correction (N-pdft)</li> <li><strong>The reference period used for bias correction: </strong>1971-2005</li> <li><strong>The observational dataset used as a reference for bias correction:&nbsp;</strong>ROCADAv1</li> <li><strong>Temporal resolution:</strong> daily</li> <li><strong>Temporal extent</strong>:&nbsp; <ul> <li>Historical: 1971-2005;</li> <li>RCP4.5 and RCP8.5: 2006-2100.</li> </ul> </li> <li><strong>Spatial resolution:</strong>&nbsp;0.1&nbsp;degrees (~10km)</li> <li><strong>Spatial extent:</strong> from 20.1&nbsp;to &nbsp;29.8&deg;E and 43.5&nbsp;to 48.4&deg;N</li> <li><strong>File format: n</strong>etCDF,&nbsp;&nbsp;CF-1.4-compliant format using netCDF4 compression</li> <li><strong>Coordinate system:&nbsp;</strong>WGS 1984 (EPSG:4326)</li> <li><strong>Naming conventions:&nbsp;</strong><em>variablename</em>_ROU-11_<em>cmip5experiment</em>_<em>globalmodel</em>_<em>run</em>_r<em>egionalmodel</em>_<em>rcmversionid</em>_<em>timefrequency</em>_<em>starttime-endtime</em><em>.</em>nc</li> <li><strong>RMCs</strong> (Institution or working group, RCM&nbsp;Model, GCM&nbsp;Institute, GCM&nbsp; Driving):&nbsp; <ul> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 CNRM-CERFACSCNRM-CM5</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E CNRM-CERFACS CNRM-CM5</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4CNRM-CERFACS CNRM-CM5</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 ICHECEC-EARTH</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4I CHECEC-EARTH</li> <li>Royal Netherlands Meteorological Institute (KNMI) RACMO22E ICHECEC-EARTH</li> <li>Danish Meteorological Institute (DMI) HIRHAM5 ICHECEC-EARTH</li> <li>Climate Limited-area Modelling Community (CLMcom) CCLM4-8-17 MPI-MMPI-ESM-LR</li> <li>Swedish Meteorological and Hydrological Institute (SMHI) RCA4 MPI-MMPI-ESM-LR</li> <li>Climate Service Center Germany (GERICS) REMO2015 NCC NorESM1-M</li> </ul> </li> </ul> <p><strong>The terms of use</strong> for RoCliB&nbsp;datasets are the same as those from the original EURO-CORDEX simulations obtained from ESGF servers:&nbsp;<a href="https://is-enes-data.github.io/cordex_terms_of_use.pdf">https://is-enes-data.github.io/cordex_terms_of_use.pdf</a>.</p> <p><strong>To access and visualize</strong> relevant facts and statistics about climate change based on the&nbsp;RoCliB&nbsp;datasets use&nbsp;<a href="http://suscap.meteoromania.ro/en/roclib">http://suscap.meteoromania.ro/en/roclib</a>.</p> <p><strong>Acknowledgement</strong><br> This work was supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CCCDI-UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III. We also acknowledge the World Climate Research Programme&#39;s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5.</p>

opencc-by-4.0Apr 2021View details →
zenodo48/100

A Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013) from DMSP-OLS data

<p>DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA&#39;s National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect.</p> <p>We used a&nbsp; series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL).</p> <p>The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc&nbsp;resolution and are made available in GeoTIFF format. Pixel Unit: &#39;DN&#39;(Digital Number).</p> <p>Each GeoTIFF filename has 4 filename fields that are separated by an underscore &quot;_&quot;. A filename extension follows these fields. The fields are described below using this example filename:</p> <p>CCNL_DMSP_1992_V1</p> <p>Field 1: CCNL(Consistent and Corrected Nighttime Light dataset)</p> <p>Field 2: Platform&nbsp;&quot;DMSP&quot;</p> <p>Field 3: Year&nbsp;&ldquo;1992&rdquo;</p> <p>Field 4: version &ldquo;V1&rdquo;</p>

opencc-by-4.0Sep 2020View details →
zenodo48/100

Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'

<p><strong>Note:&nbsp;An updated dataset covering the majority of Greenland&#39;s marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at&nbsp;<a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>.&nbsp;</strong></p> <p>Data supporting the paper:</p> <blockquote> <p>Chudley, T. R., Howat, I. M., Yadav, B. N., &amp; Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers.&nbsp;<em>The Cryosphere.&nbsp;</em>16, 2629&ndash;2642, https://doi.org/10.5194/tc-16-2629-2022</p> </blockquote> <p>Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbr&aelig;, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley&nbsp;<em>et al.</em>&nbsp;(2022).&nbsp;&nbsp;</p> <p>NetCDF files are created by, and tested to be&nbsp;readable by, Python&#39;s xarray package.</p> <p>The dimensions of the netCDF file are as follows:</p> <ul> <li><strong>X</strong> - <em>x&nbsp;</em>coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>Y</strong> -&nbsp;<em>y</em>&nbsp;coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li><strong>time</strong> - temporal midpoint of velocity field.</li> </ul> <p>The variables of the netCDF file are as follows:</p> <ul> <li><strong>dmag</strong> - the absolute magnitude of the velocity, in metres per day.</li> <li><strong>dx</strong> - the velocity in the&nbsp;<em>x</em>&nbsp;direction, in metres per day.</li> <li><strong>dy</strong> - the velocity in the&nbsp;<em>y</em>&nbsp;direction, in metres per day.</li> <li><strong>date1</strong> - the date and time of the first scene acquisition.</li> <li><strong>date2</strong> - the date and time of the second scene acquisition.</li> <li><strong>baseline</strong> - the temporal baseline, in days, between scene acquisitions.</li> <li><strong>orbit_pair</strong> - the combination of orbital pathways in the string format &#39;RXXX_RYYY&#39;, where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li><strong>mag_rmse</strong> - the root mean square error of the absolute velocity of the off-ice area.&nbsp;</li> <li><strong>dx_mean</strong> - the mean velocity of the off-ice area in the&nbsp;<em>x</em>&nbsp;direction.</li> <li><strong>dx_sd</strong> - the standard deviation of the velocity of the off-ice area in the&nbsp;<em>x</em>&nbsp;direction.</li> <li><strong>dy_mean</strong> -&nbsp;the mean velocity of the off-ice area in the&nbsp;<em>x</em>&nbsp;direction.</li> <li><strong>dy_sd</strong> -&nbsp;the standard deviation of the velocity of the off-ice area in the <em>y</em>&nbsp;direction.</li> </ul>

opencc-by-4.0Jun 2022View details →
zenodo48/100

RESCUER: Cosmological K-corrections for star clusters

<p>**RESCUER: Cosmological K-corrections for star clusters**<br>Authors: Marta Reina-Campos and William E. Harris<br>Date: May 2024</p> <p>Manuscript arXiV ID: arXiv:2310.02307 --&nbsp;Accepted by MNRAS on May 2024</p> <p>* These tables contain the K-corrections and their uncertainties calculated for star clusters using the E-MILES stellar library.<br>* The authors assumed that star clusters are well represented by single-age and metallicity simple stellar populations (SSPs) described by the BaSTi stellar isochrones and the Chabrier 2003 initial mass function.<br>* Each table corresponds to the K-corrections and their uncertainties for a given combination of filters. The authors considered eleven broad-band filters from the HST/ACS and the JWST/NIRCam cameras.&nbsp;<br>* The uncertainties are estimated using all models within 0.3 dex and 20% in metallicity and age space, respectively, of the target model, and they correspond to the distances to the 10-90th percentiles of the K-corrections of these models.<br>* The tables labeled "csv_homo_filter_X_" correspond to homochromatic K-corrections within the wavelength range of the filter X, whereas those labeled "csv_hetero_filter_X_filter_Y_" contain the K-correction from the observed filter X to the rest-frame filter Y.</p> <p>Within every table:<br>* The K-corrections are given in AB mags<br>* The first column represents the redshift at which the K-correction has been calculated<br>* All of the subsequent columns correspond to the redshift evolution of the K-correction (_target) and their asymmetric uncertainties (_lower and _upper) for a given stellar population, as indicated at the top<br>* The stellar populations are labeled as in the E-MILES stellar library: e.g. "Ech1.30Zm2.27T01.0000", corresponds to a SSP of [M/H] = -2.27 and 1 Gyr old<br>* Dummy values of -100 are placed in the redshifts larger than than the one allowed by the Planck 2018 cosmology.<br>* When an uncertainty equals zero indicates that the target K-correction was smaller/larger than the 10th/90th percentile of the distribution of K-corrections. This typically occurs in models at the edge of the grid of models (i.e. at a corner).</p>

opencc-by-4.0Sep 2023View details →
zenodo48/100

Mars Odyssey Neutron Spectrometer Time Series of Corrected Cateogry 1 Counting Rates, 2002-2017

<p>Ubinned time series of derived neutron data from the Mars Odyssey Neutron Spectrometer (MONS) Category 1 data, from 200 - 2017.</p>

opencc-by-4.0Jul 2019View details →
zenodo48/100

Dataset for Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean

<p>Dataset from Clock drift corrections for large aperture ocean bottom seismometer arrays: application to the UPFLOW array in the mid-Atlantic Ocean DOI: 10.1093/gji/ggae354.</p> <p>This dataset includes the clock drift polynoms refered to jthe deployment date (jul day from 2022) and consecutive days up to the recovery date. Two types of formats.</p> <ol> <li>Txt files</li> <li>Python Pickle files with a Dictionary containing the NumPY polynom1D and additional information.</li> </ol>

opencc-by-4.0Oct 2024View details →
zenodo48/100

Correction by focus: Sound files and transcriptions

<p>This data was collected to examine the prosodic reflexes of corrective focus in canonical and cleft clauses of Chinese, English, French, German. Experimental factors: FOCUS: subject|object, CONSTRUCTION: canonical|cleft; ITEMS: 4, SPEAKERS: 16 (per language). See text file ALI.txt for further details.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2021View details →
zenodo48/100

Kinetochore life histories reveal an Aurora B dependent error correction mechanism in anaphase

<p>Dataset of kinetochore tracks in human RPE1 cells showing chromosome dynamics and segregation from prometaphase through to anaphase as described in detail in Sen, Harrison, Burroughs and McAinsh, 2021, https://doi.org/10.1101/2021.03.30.436326&nbsp;Tracks correspond to 3D time-lapse&nbsp;movies&nbsp;of Ndc80-eGFP and were acquired in the 488nm channel using 1\% laser power, 50 ms exposure time/z-plane, 93 z-planes, 307 nm z-step, which results in 4.7 s/z-stack time frame. Cells are subject to nocodazole arrest-and-release or equivalent treatment with DMSO as indicated in the folder names, and some cells are subject to additional treatment with ZM to inhibit Aurora B (also indicated in folder names). Tracks were produced using kinetochore tracking software, KiT v2.3 (see Armond et al., 2016, Bioinformatics), available from&nbsp;https://github.com/cmcb-warwick/KiT/&nbsp;</p>

opencc-by-4.0Oct 2021View details →
edi48/100

Corrected LIDAR-derived digital elevation model of the Duplin River salt marshes

LIDAR (light detection and ranging) data were acquired on March 9-10, 2009 by the National Center for Airborne Laser Mapping (NCALM) for the Duplin River (35 km2). A 1 m spatial resolution gridded digital elevation models (DEM) was produced from these data. The accuracy of the DEM was assessed using real time kinematic (RTK) GPS ground reference data and elevations were corrected following the method of Hladik and Alber (2012).

openCustomJan 2020View details →
zenodo44/100

Water vapor database for atmospheric correction of Landsat imagery

<p>Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.</p> <p>We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (<a href="https://doi.org/10.1029/2002JD003023">https://doi.org/10.1029/2002JD003023</a>;&nbsp;MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA:&nbsp;<a href="https://doi.pangaea.de/10.5067/MODIS/MOD05_L2.006">doi:10.5067/MODIS/MOD05_L2.006</a>,&nbsp;<a href="https://doi.pangaea.de/10.5067/MODIS/MYD05_L2.006">doi:10.5067/MODIS/MYD05_L2.006</a>). The dataset is comprised of daily global water vapor data for February 2000 to December&nbsp;2020&nbsp;for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.</p> <p>The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, <a href="https://github.com/davidfrantz/force">https://github.com/davidfrantz/force</a>, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a>), which is freely available software under the terms of the GNU General Public License v. &gt;= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016,&nbsp;<a href="https://doi.pangaea.de/10.1109/TGRS.2016.2530856">doi:10.1109/TGRS.2016.2530856</a>) to perform atmospheric correction of Landsat imagery. This dataset is an update of&nbsp;<a href="https://doi.pangaea.de/10.1594/PANGAEA.893109">https://doi.pangaea.de/10.1594/PANGAEA.893109</a> and should be used from now on.</p>

opencc-by-4.0Dec 2017View details →
zenodo44/100

Bias-corrected monthly precipitation data over South Siberia for 1979-2019

<p>Bias-<strong>C</strong>orrected <strong>P</strong>recipitation data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CPSS 1.2</strong>) contains monthly precipitation data for the area within the coordinates 50&ndash;65 N, 60&ndash;120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change&hellip;, 2017) and precipitation data records from ground weather stations (Il&rsquo;in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25&deg; in the latitude and 0.25&deg; in the longitude.&nbsp; CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results.&nbsp;Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at&nbsp;<a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p>Il&rsquo;yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. <em>Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges</em>. Available at&nbsp;&nbsp;<a href="http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov"><em>http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov</em></a></p>

opencc-by-4.0Jan 2021View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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