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30,948 results for “Profile”
QAH potential profile measurements
<p>Dataset and analysis code accompanying the manuscript "Measured potential profile in a quantum anomalous Hall system suggests bulk-dominated current flow," available at <a href="https://arxiv.org/abs/2112.13123">https://arxiv.org/abs/2112.13123</a>.</p> <p>Simulation code available at <a href="https://github.com/itrosen/hall-solver">https://github.com/itrosen/hall-solver</a>.</p>
Profile of microbial changes after the application of different phosphate forms
<p>This data set includes the results of the application of different P forms that may be present at different rates in REFLOW fertilizers on soil microbial activities, composition and biomass.</p> <p> </p> <p>The dataset is under embargo until the corresponding publication becomes available.</p>
CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles
<p>This archive contains data used for the paper:</p> <p>CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles. Detection of 12 new DIBs in the YJ band and the introduction of a combined ISM sight line and stellar analysis approach</p> <p>Paper-DOI: 10.1051/0004-6361/202142990</p> <p>It contains reduced oCRIRES spectra. For more details on the reduction see the paper.</p>
Identifying and profiling structural similarities between Spike of SARS-CoV-2 and other viral or host proteins with Machaon - Pre-computed features for replication
<p>Machaon's computed features that were used in the structural comparisons with Spike protein.</p> <p>DATA_PDBS_vir_whole_1-3.zip files are parts of a single folder.</p> <p> </p>
Root symbionts alter the volatile profile of herbivore-infested tomato plants and aid the attraction of a predator
<p>Beneficial root microbes are among the most frequently used biocontrol agents in cropping systems, since they have been shown to promote plant growth and crop yield. Moreover, they are able to enhance protection against pathogens and insect herbivores by activating plant resistance mechanisms. Plant defense responses against herbivorous insects include the induction of metabolic pathways involved in the synthesis of defense-related metabolites. These metabolites include volatile organic compounds (VOCs), which attract natural enemies of the herbivores as a form of indirect resistance. Considering that beneficial root microbes may affect direct herbivore resistance, we hypothesized that also indirect resistance may be affected. We tested this hypothesis in a study system composed of tomato, the arbuscular mycorrhizal fungus <em>Rhizophagus irregularis</em>, the growth-promoting fungus <em>Trichoderma harzianum</em>, the generalist chewing herbivore <em>Spodoptera exigua </em>and the omnivorous predator<em> Macrolophus pygmaeus</em>. Using a Y-tube olfactometer we found that <em>M. pygmaeus</em> preferred plants with <em>S. exigua </em>herbivory, but microbe-inoculated plants more than non-inoculated ones. We used a targeted GC-MS approach to assess the impact of beneficial microbes on the emission of volatiles twenty-four hours after herbivory to explain the choice of <em>M. pygmaeus</em>. We observed that the volatile composition of the herbivore-infested plants differed from that of the non-infested plants, which was driven by the higher emission of green leaf volatile compounds, methyl salicylate, and several monoterpenes and sesquiterpenes. Inoculation with microbes had only a marginal effect on the emission of some terpenoids in our experiment. Gene expression analysis showed that the marker genes involved in the jasmonic and salicylic acid pathways were differentially expressed in the microbe-inoculated plants after herbivory. Our results pinpoint the role of root symbionts in determining plant-microbe-insect interactions up to the third trophic level, and elucidates their potential to be used in plant protection.</p>
Players' Profiles and Satisfaction for Game Elements across Levels: Dataset
<p>Dataset produced in a study to measure the impact of levels' generation and adaptation to the players' preferences.</p> <p>The study was conducted in the context of a Master's thesis on Game Adaptivity.</p>
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
Water and chlorine in the Martian subsurface along the traverse of NASA's Curiosity rover: DAN measurement profiles along the traverse
<p>This dataset contains a water map and a table of water and chlorine content in shallow Martian subsurface derived from the DAN instrument data from a landing site up to MSL sol 3333. DAN is a neutron spectrometer onboard the NASA’s Curiosity rover (see Mitrofanov, I. G., et al., (2012). Dynamic Albedo of Neutrons (DAN) experiment onboard NASA’s Mars Science Laboratory. Space Science Reviews, 170(1–4), 559–582. <a href="https://doi.org/10.1007/s11214-012-9924-y">https://doi.org/10.1007/s11214-012-9924-y</a>).</p> <p>The DAN instrument consists of two separate units: the DAN DE is the detector and electronics block, the DAN PNG is pulsed neutron generator. The DAN DE contains two proportional counters filled with <sup>3</sup>He gas for recording thermal and epithermal neutrons up to energy of 100 eV (CTN detector) and epithermal neutrons from 0.4 eV up to 100 eV (CETN detector). DAN provides two types of measurements: active and passive. When DAN DE operates in the passive mode, its detectors record the local neutron background. In the active mode the DAN PNG unit generates short pulses of 14 MeV neutrons, and DAN DE record additional counts of post-pulse emission of moderated neutrons after their interactions with nuclei of shallow subsurface.</p> <p>The results of active and passive DAN measurements along the traverse are assigned to two independent types of pixels: Pixel with Active Data (PAD) and Pixels of Passive Data (PPD). The content of water reported as Water Equivalent Hydrogen (WEH) for both kinds of pixel. The content of absorption equivalent chlorine (AEC) reported only for PAD. Statistical errors are given in each pixel. Method of active data analysis is described in Lisov, D. I., et al., (2018). Data Processing Results for the Active Neutron Measurements by the DAN Instrument on the Curiosity Mars Rover. Astronomy Letters, 44(7), 482–489. <a href="https://doi.org/10.1134/S1063773718070034">https://doi.org/10.1134/S1063773718070034</a>. The "Method of Referencing by Active Data" (MRAD) to analyze passive data was described in Nikiforov, S. Y., et al., (2020). Assessment of water content in Martian subsurface along the traverse of the Curiosity rover based on passive measurements of the DAN instrument. Icarus, 346, 113818. <a href="https://doi.org/10.1016/j.icarus.2020.113818">https://doi.org/10.1016/j.icarus.2020.113818</a>.</p> <p>PPD pixels are presented as squares in the water map, PAD are presented as circles. Table of water and chlorine contains seven values for each pixel: (1) is the successive number of pixel, (2) is a mark of its type, either PAD or PPD, (3) is longitude and (4) is latitude coordinates of the center of the pixel, (5) is the associated member of the MSL stratigraphic column, and (6) is estimated WEH values (wt.%) in PAD or PPD and (7) is estimated AEC value in PAD (wt.%).</p>
Processing and Data for "Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats"
<p><strong>Description: </strong></p> <p>These files contain processed BGC-Argo float data, figure data, the radiocarbon productivity subset, bootstrapping results, and the associated Python/Matlab code to calculate net primary productivity from daily cycles of optical backscatter and dissolved oxygen.</p> <p>The raw float data used in this study are available from the Argo Global Data Assembly Centers in Brest, France (ftp://ftp.ifremer.fr/ifremer/argo/dac/coriolis) and Monterey, California (ftp://usgodae.org/pub/outgoing/argo/dac/coriolis). The raw MODIS satellite-based productivity data is available from the Oregon State University Ocean Productivity site (<a href="http://orca.science.oregonstate.edu/npp_products.php">http://orca.science.oregonstate.edu/npp_products.php</a>). The raw MODIS satellite-based euphotic depth estimates are available from the NASA L3 browser (<a href="https://oceancolor.gsfc.nasa.gov/l3/">https://oceancolor.gsfc.nasa.gov/l3/</a>). The original ship-based estimates of net primary productivity are available from the Pangaea (<a href="https://doi.pangaea.de/10.1594/PANGAEA.932417">https://doi.pangaea.de/10.1594/PANGAEA.932417</a>) and the British Oceanography Data Centre (<a href="https://www.bco-dmo.org/dataset/814803">https://www.bco-dmo.org/dataset/814803</a>).</p> <p><strong>Please cite as: </strong></p> <p>Stoer, A., and Fennel, K. 2022. Processing and Data for Estimating ocean net primary productivity from daily cycles of carbon biomass measured by profiling floats. Zenodo. doi: 10.5281/zenodo.6977161.</p> <p><strong>Python/MATLAB Software Description: </strong></p> <p>dielFit_GOPeqCR.m: This code is from Johnson and Bif (2021). We have added outputs for standard errors for linear and PvE models and sunrise/sunset times. To run this code with the associated Python software a MATLAB engine needs to be installed. Please see: <a href="https://www.mathworks.com/help/matlab/matlab-engine-for-python.html">https://www.mathworks.com/help/matlab/matlab-engine-for-python.html</a></p> <p>argo_so_processing_20220815.py: This code is the first of two pieces of software for estimating net primary productivity from floats in the Southern Ocean. The program below obtains the data from the BGC Argo database (Argo, 2021) and processes it. Simple data quality control, interpolation, biogeochemical calculations, and data binning occur. The processed float data is located in the folder 'Processed Argo Transects'.</p> <p>argo_daily_npp_20220815.py: This code using processed Argo float data that contains oxygen and particle backscatter measurements to infer net primary production. The code combines the float that meet the criteria of sampling at all local hours of the day throughout its lifetime. Then, it constructs diel cycles from this data by finding the median value of each hour and uses the code from Johnson and Bif (2021), which is a modified version from Barone et al. (2019). The algorithm used to convert particle backscatter to particulate organic carbon is from Graff et al. (2015). We assume that dissolved primary productivity accounts for 30% of total primary productivity (Moran et al., 2022).</p> <p>argo_daily_npp_bootstrap_20220815.py: This code using processed Argo float data that contains co-located oxygen and particle backscatter measurements to infer net primary production. This code is very similar to argo_daily_npp_20220815.py but randomly samples a subset of the co-located profiles at different sample sizes before calculating net primary productivity. Productivity is calculated at each sample size 1000 times. The results of this analysis is located in the folder 'Bootstrapped Results'. </p> <p>More details can be found in the code itself. </p> <p><strong>Data Descriptions: </strong></p> Data from 'Processed Argo Transects' Folder | Description for each variable <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>depth</td> <td>Average depth of depth bin</td> <td>m</td> </tr> <tr> <td>mid_depth</td> <td>Center of depth bin</td> <td>m</td> </tr> <tr> <td>pressure</td> <td>Average pressure in depth bin</td> <td>dbar</td> </tr> <tr> <td>profile_index</td> <td>Profile number or index</td> <td> </td> </tr> <tr> <td>profile_longitude</td> <td>Average longitude of profile</td> <td>degE</td> </tr> <tr> <td>profile_latitude</td> <td>Average latitude of profile</td> <td>degN</td> </tr> <tr> <td>profile_time</td> <td>Average UTC time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_time</td> <td>Average local time of profile</td> <td>yyyy-mm-dd hh:mm:ss</td> </tr> <tr> <td>profile_local_hour</td> <td>The hour of the local timestamp</td> <td> </td> </tr> <tr> <td>salinity</td> <td>Seawater salinity</td> <td>PSU</td> </tr> <tr> <td>temperature </td> <td>Seawater temperature</td> <td>degC</td> </tr> <tr> <td>oxygen</td> <td>Dissolved oxygen concentration</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_saturation</td> <td>Saturated dissolved oxygen concentration calculated from the Garcia and Gordon (1992) equation.</td> <td>umol kg-1</td> </tr> <tr> <td>oxygen_anom</td> <td>The difference between observed dissolved oxygen concentration and saturated oxygen </td> <td>umol kg-1</td> </tr> <tr> <td>bbp470</td> <td>Optical backscatter coefficient at 470 nm. Particulate organic carbon is calculated in argo_daily_npp_20220815.py</td> <td>m-1</td> </tr> </tbody> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>wmo</td> <td>WMO number of float</td> <td> </td> </tr> <tr> <td>profile_index</td> <td>Profile index or profile number taken by float</td> <td> </td> </tr> <tr> <td>profile_latitude</td> <td>Average profile latitude</td> <td>degN</td> </tr> <tr> <td>profile_longitude</td> <td>Average profile longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>fod</td> <td>Fraction of day</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Sinusoidal curve fit to oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc</td> <td>Sinusoidal curve fit to particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>oxy_med</td> <td>Hourly median oxygen</td> <td>mol m-3</td> </tr> <tr> <td>oxy_sem</td> <td>Hourly standard error of oxygen</td> <td>mol m-3</td> </tr> <tr> <td>poc_med</td> <td>Hourly median particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>poc_sem</td> <td>Hourly standard error of particulate organic carbon</td> <td>mol m-3</td> </tr> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N, co-located)</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>region</td> <td>Name of data subset (e.g., 30-40 deg N) </td> <td> </td> </tr> <tr> <td>depth</td> <td>Depth of profile</td> <td>m</td> </tr> <tr> <td>zeu</td> <td>1% euphotic depth from Lee et al. (2013) algorithm from NASA (2022) L3 satellite products. </td> <td>m</td> </tr> <tr> <td>n_profiles_bpp</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>n_profiles_oxy</td> <td>Number of oxygen profiles</td> <td> </td> </tr> <tr> <td>n_floats_bbp</td> <td>Number of floats with backscatter measurements</td> <td> </td> </tr> <tr> <td>n_floats_oxy</td> <td>Number of floats with oxygen measurements</td> <td> </td> </tr> <tr> <td>gop_do</td> <td>Gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_serr</td> <td>Standard error of gross oxygen productivity estimated from dissolved oxygen</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly oxygen data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly oxygen data</td> <td> </td> </tr> <tr> <td>oxy_sr</td> <td>The calculated sunrise time as a fraction of the day</td> <td> </td> </tr> <tr> <td>oxy_ss</td> <td>The calculated sunset time as a fraction of the day</td> <td> </td> </tr> <tr> <td>gpp_bbp</td> <td>Gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gpp_bbp_serr</td> <td>Standard error of gross carbon productivity estimated from optical backscatter</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_do_p</td> <td>p-value of curve fit to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_do_r2</td> <td>r-squared value of curve to hourly particulate organic carbon data</td> <td> </td> </tr> <tr> <td>gop_bbp</td> <td>Gross oxygen productivity calculated from gross carbon productivity (gpp_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>gop_bbp_serr</td> <td>Standard error of gross oxygen productivity calculated from gross carbon productivity (gpp_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp</td> <td>Net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_bbp_serr</td> <td>Standard error of net primary productivity calculated from backscatter-based gross oxygen productivity (gop_bbp_serr)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do</td> <td>Net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do)</td> <td>mol m-3 yr-1</td> </tr> <tr> <td>npp_do_serr</td> <td>Standard error of net primary productivity calculated from oxygen-based gross oxygen productivity (gop_do_serr)</td> <td>mol m-3 yr-1</td> </tr> </tbody> </table> <table> </table> Data for Fig. S1 | Description for number_of_bbp_profiles_in_each_year.csv and number_of_oxy_profiles_in_each_year.csv <table><tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>year</td> <td>Year</td> <td> </td> </tr> <tr> <td>bbp470</td> <td>Number of backscatter profiles</td> <td> </td> </tr> <tr> <td>oxygen_anom</td> <td>Number of oxygen profiles</td> <td> </td> </tr> </tbody> </table> <table> </table> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>mid_depth</td> <td>Depth of NPP profile</td> <td>m</td> </tr> <tr> <td>mean</td> <td>Mean volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>median</td> <td>Median volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>min</td> <td>Minimum volumetric 14C-NPP at depth</td> <td>mmol m-3 yr-1</td> </tr> <tr> <td>maximum</td> <td>Maximum volumetric 14C-NPP</td> <td>mmol m-3 yr-1</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th><strong>Variable</strong></th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>subset</td> <td>Number of profiles randomly sampled from the co-located dataset</td> <td> </td> </tr> <tr> <td>int_npp_do</td> <td>Euphotic-depth-integrated net primary productivity calculated from oxygen-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>int_npp_bbp</td> <td>Euphotic-depth-integrated net primary productivity calculated from backscatter-based gross oxygen productivity</td> <td>mol m-2 y-1</td> </tr> <tr> <td>gop_do_r2</td> <td>R-squared of the sinusoidal curve to the diel cycle of oxygen anomaly</td> <td> </td> </tr> <tr> <td>gpp_bbp_r2</td> <td>R-squared of sinusoidal curve to the diel cycle of particulate organic carbon</td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>ROSE</td> <td>Topographic (negative values are below sea level)</td> <td>m</td> </tr> <tr> <td>ETOPO05_Y</td> <td>Latitude</td> <td>degN</td> </tr> <tr> <td>ETOPO05_X</td> <td>Longitude</td> <td>degE</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <table> <tbody><tr> <th>Variable</th> <th>Description</th> <th>Units</th> </tr> </tbody><tbody> <tr> <td>database</td> <td>Database the data was extracted from</td> <td> </td> </tr> <tr> <td>Month</td> <td>Month of NPP measurement</td> <td>month of year</td> </tr> <tr> <td>npp_14c</td> <td>Net primary productivity estimated from the radiocarbon method</td> <td>mmol m-3 y-1</td> </tr> <tr> <td>depth</td> <td>depth of 14C-NPP measurement</td> <td>m</td> </tr> </tbody> </table> <table> </table>
Data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'
<p>This is the data supplement for 'Global Dataset of Thermohaline Staircases obtained from Argo Floats and Ice-Tethered Profilers'. Both algorithm and dataset described in this publication can be found in this folder.</p> <p>Please cite 'Global dataset of thermohaline staircases obtained from Argo floats and Ice-Tethered Profilers' when using this data set (doi: 10.5194/essd-2020-197).</p> <p>The newest/most updated version of the code can be found on GitHub: https://github.com/cvanderboog/Staircase-detection-algorithm.</p> <p> </p> <p> </p> <p> </p>
Data files: Single-cell RNA profiling of Plasmodium vivax-infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets
<p>Scripts, preprocessed count matrices, and single-cell data objects generated in <strong>“Single-cell RNA profiling of <em>Plasmodium vivax</em><em>-</em>infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets” </strong></p>
Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome
<p>This is a comprehensive collection of diverse epigenomic profiling data in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements (ENCODE, <a href="http://www.encodeproject.org">http://www.encodeproject.org</a>) consortium. A total of 6,305 alignment profiles from various high-throughput sequencing assays available on the ENCODE database were preprocessed and filtered according to ENCODE’s data standard</p>
Profile data of the ADMIRE project use case applications
<p>HPC application traces from the use cases in the ADMIRE project. They were collected with TAU monitoring tools.</p> <p>Applications traced are described at: https://www.admire-eurohpc.eu/UseCases/</p> <p>- <em>Application 1: <a href="http://meteo.uniparthenope.it/">Monitoring and Modelling Marine, weather and Air quality</a> </em></p> <p>- <em>Application 2: Car-Parrinello molecular dynamic simulation of large molecules and small proteins </em></p> <p><em>- Application 3: Simulation of large scale turbulent flow.</em></p> <p><em>- Application 4: Continental-scale land cover mapping with scalable and automatic deep learning frameworks.</em></p> <p><em>- Application 5: Super-resolution imaging using Opera microscopy and SRRF/ImageJ software. </em></p> <p><em>- Application 6: Software Heritage Management & Indexing.</em></p> <p> </p> <p> </p> <p> </p>
LC impacts -24h profiles - current mix and future scenarios, marginal demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - marginal demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Random and systematic uncertainties for OMPS-LP ozone profiles
<p>This data set contains random and systematic uncertainties for ozone profiles retrieved at the University of Bremen from OMPS-LP observations. The uncertainties are expressed in relative values and are reported as vertical profiles (every 5 km) and as a function of latitude (5 bands, i.e., SH polar, SH mid-latitude, tropics, NH mid-latitudes, NH polar) and season. The user can associate to any OMPS-LP ozone profile the uncertainty corresponding to the appropriate latitude-season bin. The description of the uncertainties is provided in the AMT paper "Assessment of the error budget for stratospheric ozone profiles retrieved from OMPS limb scatter measurements", Arosio et al. 2022.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 3,076 Campylobacter jejuni isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 2,794-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/4/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 3,076 <em>Campylobacter jejuni </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of Sequence Type [ST]). In total, 476 different STs are represented in this dataset, with ST21, ST50, ST48, ST45 and ST257 being the most represented ones and, together, corresponding to 29.1% of the dataset.</p> <p>File “Cj_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Cj_profiles_wgMLST.tsv” corresponds to a tab separated file with the 2,794-loci wgMLST profiles of each solate presented in the metadata file. The files “profiles/Cj_profiles_cgMLST_95.tsv”, “profiles/Cj_profiles_cgMLST_98.tsv” and “profiles/Cj_profiles_cgMLST_100.tsv” correspond to a 1,012-loci, 987-loci and 29-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>C. jejuni</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://pubmlst.org/organisms/campylobacter-jejunicoli">PubMLST</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 3,539 samples. The majority of them are associated with the INNUENDO project (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>). The remaining ones are associated with five BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB31119">PRJEB31119</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB38253">PRJEB38253</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB40238">PRJEB40238</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB4165">PRJEB4165</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA350537">PRJNA350537</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 3,076 isolates passed this curation step and were included in the final dataset. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 2,794-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/4">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 2,794-loci wgMLST profiles of the 3,076 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 1,012-loci, 987-loci and 29-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,999 Escherichia coli isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 7,601-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/5/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,999 <em>Escherichia coli </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 411 different serotypes are represented in this dataset, with O157:H7 being the most represented one, corresponding to 37.1% of the dataset.</p> <p>File “Ec_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Ec_profiles_wgMLST.tsv” corresponds to a tab separated file with the 7,601-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Ec_profiles_cgMLST_95.tsv”, “profiles/Ec_profiles_cgMLST_98.tsv” and “profiles/Ec_profiles_cgMLST_100.tsv” correspond to a 2,826-loci, 2,704-loci and 465-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>E. coli </em>genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/ecoli">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 2,688 samples associated with three BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA230969">PRJNA230969</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB27020">PRJEB27020</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA248042">PRJNA248042</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,999 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with <a href="https://github.com/B-UMMI/seq_typing">seq_typing</a> v2.2. wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 7,601-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/5">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31st, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 7,601-loci wgMLST profiles of the 1,999 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 2,826-loci, 2,704-loci and 465-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Genome assemblies and respective wg/cgMLST profiles of a diverse dataset comprising 1,434 Salmonella enterica isolates
<p><strong>Dataset</strong></p> <p>This dataset comprises the genome assemblies and respective 8,558-loci whole-genome (wg) Multiple Locus Sequence Type (MLST) profiles [INNUENDO schema (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>) available in <a href="https://chewbbaca.online/species/8/schemas/1">chewie-NS</a> (<a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>)] of a final set of 1,434 <em>Salmonella enterica </em>samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the <a href="https://www.ncbi.nlm.nih.gov/">National Center for Biotechnology Information</a> (NCBI) Sequence Read Archive (SRA) at the beginning of the analysis (November 2021). This set of samples was carefully selected to cover a wide genetic diversity (assessed in terms of serotype). In total, 125 different serotypes are represented in this dataset, with Typhimurium (including monophasic), Enteritidis and Infantis being the most represented ones and, together, corresponding to 56.2% of the dataset.</p> <p>File “Se_metadata.xlsx” contains metadata information for each isolate, including ENA/SRA accession number, BioProject and in-silico MLST ST and serotype.</p> <p>The directory “assemblies/” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. </p> <p>The file “profiles/Se_profiles_wgMLST.tsv” corresponds to a tab separated file with the 8,558-loci wgMLST profiles of each isolate presented in the metadata file. The files “profiles/Se_profiles_cgMLST_95.tsv”, “profiles/Se_profiles_cgMLST_98.tsv” and “profiles/Se_profiles_cgMLST_100.tsv” correspond to a 3,261-loci, 3,179-loci and 874-loci cgMLST profiles of each isolate presented in the metadata file, respectively. These profiles were determined as explained below.</p> <p> </p> <p><strong>Dataset selection and curation</strong></p> <p>With the objective of creating a diverse dataset of <em>S. enterica</em> genome assemblies, we collected information about the genetic diversity (serotype) of the isolates available at <a href="https://enterobase.warwick.ac.uk/species/index/senterica">Enterobase</a> database in the beginning of this analysis (November 2021) and in other previous works. Based on this information, we selected an initial dataset comprising 1,779 samples associated with four BioProjects (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB16326">PRJEB16326</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB20997">PRJEB20997</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB30335">PRJEB30335</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB39988">PRJEB39988</a>). Their WGS data was downloaded from ENA/SRA with <a href="https://github.com/rpetit3/fastq-dl">fastq-dl</a> v1.0.6. Read quality control, trimming and assembly were performed with the Aquamis v1.3.9 (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145556/">Deneke et al. 2021</a>) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was >98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,434 isolates passed this curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31540993/">Zhang et al. 2019</a>). wgMLST profiles of each of these isolates were determined with chewBBACA v2.8.5 (<a href="https://pubmed.ncbi.nlm.nih.gov/29543149/">Silva et al. 2018</a>), using the 8,558-loci INNUENDO schema available in <a href="https://chewbbaca.online/species/8">chewie-NS</a> (<a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2018.EN-1498">Llarena et al. 2018</a>; <a href="https://academic.oup.com/nar/article/49/D1/D660/5929238">Mamede et al. 2022</a>) and downloaded on May 31<sup>st</sup>, 2022. Three cgMLST schemas were obtained with <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> v1.0.0 (<a href="https://www.researchsquare.com/article/rs-1404655/v1">Mixão et al. 2022</a>) using the 8,558-loci wgMLST profiles of the 1,434 isolates as input and setting distinct “--site-inclusion” thresholds: 0.95, 0.98 and 1.0 (i.e., keep schema loci called in at least 95%, 98% and 100% of the samples, resulting in a 3,261-loci, 3,179-loci and 874-loci allelic matrices, respectively).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.</p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
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