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5,155 results for “Data Base”
Data for "Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments"
<p>Flow Cytometry and plate reader data from "High Throughput Experimentation to replicate Yeast Gates Experiment," accompanied with jupyter noteboooks to replicate the analyses. Sequencing data is <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA784977">available separately</a>.</p> <p>Files named <code>flow_cytometrya<em>x</em></code> should be concatenated: they are individual slices of a gzipped tar file. Concatenate them and then extract with <code>tar xzf <em>filename</em></code>.</p> <p>The paper is available on <a href="https://biorxiv.org/cgi/content/short/2022.05.31.493627">Biorxiv</a>.</p> <p>The Jupyter notebooks used to analyze this data for the paper are <a href="https://github.com/rpgoldman/replication-paper-data-analysis">available on GitHub</a>.</p>
Data associated with the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data".
<p>This dataset refers to the publication "Interannual variability in the Australian carbon cycle over 2015-2019, based on assimilation of OCO-2 satellite data". https://doi.org/10.5194/acp-2022-15.</p> <p> </p>
"Chronomodel" Bayesian chronological models for East Borneo, based on data from the Liang Abu and Kimanis sites
<p>Bayesian chronological models generated using the <a href="https://chronomodel.com"><em>ChronoModel</em></a> software East Borneo (Indonesia), based on data from the Liang Abu and Kimanis (Arifin, 2017) archaeological sites.</p> <p>Two models were generated:</p> <ul> <li> a “<strong>conservative</strong>” model, observing the Bayesian approach and the distinction between<br> prior and posterior information;</li> <li>a “<strong>restricted</strong>” model: excluding possible outliers and without application of a “Fresh-<br> water reservoir effect” correction.</li> </ul> <p>Four files are provided:</p> <ul> <li>abu-kimanis-conservative-model.chr: model specification for the “conservative” model</li> <li>abu-kimanis-conservative-model_synthetic-stats-table.csv: results for the “conservative” model</li> <li>abu-kimanis-restricted-model.chr: model specification for the “restricted” model</li> <li>abu-kimanis_restricted-model_synthetic-stats-table.csv: results for the “restricted” model</li> </ul> <p>The .chr files can be open and edited using the <em>ChronoModel</em> software.</p>
An updated map of GRCh38 linkage disequilibrium blocks based on European ancestry data
<p>A map of approximately independent linkage disequilibrium (LD) blocks has many uses in statistical genetics. Current publicly available LD block maps are based on sparse recombination maps and are only available for GRCh37 (hg19) and prior genome assemblies. We generated LD blocks in GRCh38 for European (EUR) ancestry populations using a recent recombination map based on more than 115,000 individuals. This new map consists of 1,361 independent LD blocks across the 22 autosomal chromosomes and can be accessed at https://github.com/jmacdon/LDblocks_GRCh38</p>
Data for the "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" manuscript
<p>This repository contains the post-processed data files for plotting and interpreting the results of "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" study.</p> <p>The files are all netCDF4 except for the analysis files that show global mean values in a .txt format.</p> <p>The Version 3 & Version 4 tar files are smaller than Version 2 as we performed some code clean-up for some post-processing scripts that excluded redundant data files that were very large and that were not used for plotting or the analysis for the manuscript.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
GSDM-WBT: Global station-based daily maximum wet-bulb temperature data for 1981-2020
<p>The wet-bulb temperature integrates the temperature and humidity to comprehensively describe the thermal environment and the energy regulation of human bodies. Daily maximum wet-bulb temperature is an important indicator to be used for research on extreme humid heat. GSDM-WBT is a new dataset of global station-based daily maximum wet-bulb temperature, which was produced through calculating wet-bulb temperature, data quality control, infilling missing values and homogenisation based on the HadISD station-based observations and the NCEP-DOE reanalysis data. GSDM-WBT covers the complete daily series of 1834 stations around the world from 1981 to 2020. We provide the NetCDF files of GSDM-WBT for each station and one compressed file containing all data.</p>
Data for conductance-based simulations of "Cortical oscillations support sampling-based computations in spiking neural networks"
<p>This repository contains the full data generated by the conductance-based simulations described in: <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009753">Cortical oscillations support sampling-based computations in spiking neural networks</a>. The code is accessible via <a href="https://doi.org/10.5281/zenodo.5512526.">this repository</a>.</p>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Data archive accompanying "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, doi:10.1002/2017SW001785]
<p>This archive contains the data used to create the plots presented in "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, SWx, doi:10.1002/2017SW001785].</p> <p>Format: MATLAB save file</p> <p>Contents:</p> <p>1. CHAMP and GRACE-A accelerometer-derived densities and ephemeris;</p> <p>2. TIE-GCM GPI model output sampled on both satellites;</p> <p>3. IRIDEA prior and posterior model output sampled on both satellites;</p> <p>4. Short description and units for all variables</p>
Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"
<p>Data set for: Le Merre P, Esmaeili V, Charrière E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1. '2018_LeMerre_Neuron.pdf' - this is a pdf version of the online publication.<br> 2. 'Chronic_LFP_data.mat' - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3. 'Silicon_Probe_data.mat' - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4. 'Opto_Inactivation_data.mat' - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5. 'Mus_Inactivation_data.mat' - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6. 'Learning_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7. 'Exposed_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8. 'p_value_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig2B_Amplitude_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m'; 'plot_fig4A_SEP_H_vs_M.m’.<br> 9. 'p_value_colormap2.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m’; ’plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 10. 'scatterplot_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m'.<br> 11. 'SEP_colormtrx.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig1B_Sensory_Evoked_Potentials.m'; 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m'.<br> 12. 'zscore_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 13. 'Chronic_LFP_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Chronic_LFP_dataViewer.m'.<br> 14. 'Chronic_LFP_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Chronic_LFP_data.mat'.<br> 15. 'Silicon_Probe_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Silicon_Probe_dataViewer.m'.<br> 16. 'Silicon_Probe_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Silicon_Probe_data.mat'.<br> 17. 'plot_fig1B_Sensory_Evoked_Potentials.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18. 'plot_fig1C_Silicon_Probe_Hit_trials.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19. 'plot_fig2A_SEP_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20. 'plot_fig2B_Amplitude_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21. 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22. 'plot_fig3A_SEP_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23. 'plot_fig3B_Amplitude_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24. 'plot_fig3C_ROC_Trained_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25. 'plot_fig3C_ROC_Randomization.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26. 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27. 'plot_fig4A_SEP_H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28. 'plot_fig4B_Amplitude_ H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29. 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30. 'plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31. 'plot_fig4D_Photoinhibitions.m' - this is a Matlab code, which analyses the data in 'Opto_Inactivation_data.mat', and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32. 'plot_figS2D_Performance_DetectionTask_NeutralExposition.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33. 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34. 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35. 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36. 'plot_figS4B_Pharmacological_Inactivations.m' - this is a Matlab code, which analyses the data in 'Mus_Inactivation_data.mat', and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37. 'Load_LFP_Multisite_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Chronic_LFP_data.mat'.<br> 38. 'Load_Silicon_Probe_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Silicon_Probe_data.mat'.<br> 39. 'Load_Optogenetic_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Opto_Inactivation_data.mat'.<br> 40. 'Load_Pharmacological_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Mus_Inactivation_data.mat'.<br> 41. 'bonf_holm.m' - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code 'plot_figS4B_Pharmacological_Inactivations.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42. 'boundedline.m' - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m’; 'plot_fig3C_ROC_Trained_vs_Exposed.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4A_SEP_H_vs_M.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43. 'inpaint_nans.m' - this is a Matlab code, which is called in the Matlab code 'boundedline.m'.<br> 44. 'PSTH_Simple.m' - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.</p>
XML Data for "Four Early Chan Texts from Dunhuang - A TEI-based Edition"
<p>This zip archive contains the XML data, schema, stylesheets, and associated material that are the basis for a print edition of 48 Dunhuang manuscript witnesses of four early Chan Buddhist texts. The print edition is:</p> <p>Marcus Bingenheimer 馬德偉, Chang Po-Yung 張伯雍 (eds.): <em>Four Early Chan Texts from Dunhuang – A TEI-based Edition</em> 早期禪宗文獻四部 —— 以TEI標記重訂敦煌寫卷:楞伽師資記,傳法寶紀,修心要論,觀心論. Taipei: Shin Wen Feng 新文豐, 2018.<br> (Vol. 1: Facsimiles and Diplomatic Transcription 摹寫版 (ISBN: 978-957-17-2274-0), Vol. 2: Parallel, Punctuated and Annotated Edition 對照與點注版 (ISBN: 978-957-17-2275-7), Vol. 3: Calligraphy Practice 抄經版 (ISBN: 978-957-17-2276-4).)</p> <p>See the included README.txt for further information.</p>
Measurement data of a three-phase grid-side converter with a grid-forming synchronverter-based control method with current limitation
<p>The data set was recorded for a publication currently undergoing the submission process. The published data correspond to the data presented in the figures. The first column represents the time vector. All other columns are linked to the corresponding scenario by an identifier in the column name. The column name also contains the name of the recorded signal and the associated unit. The naming convention is <identifier_to_figure>_<recorded_signal>_<unit>.</p>
Data and code from: Climate-based prediction of carbon fluxes from deadwood in Australia
This repository contains the code for the publication 'Climate-based prediction of carbon fluxes from deadwood in Australia'.
Supplementary material: Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
<p>The ridge plots attached here accompany the supplement to the manuscript <em>Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium</em> (Dzinalija et al., 2024)<em>. </em>These are full results of Figures 1, 2C, and 3C of the manuscript and Figures S3, S5, S7 and S9 of the supplement depicting whole-brain Bayesian multilevel models run using the Regional Bayesian Analysis toolbox (RBA; Chen et al., 2019). Whole-brain analyses were parcelated into the Schaefer-Yeo 7-network 200-parcel cortical atlas (Schaefer et al., 2018) and the Melbourne 32-region subcortical atlas (Tian et al., 2020). Results are presented according to contrast of interests: [Negative > Neutral], [OCD > Neutral], [Threat > Neutral], and [OCD > Threat], and effects of interest: [Diagnosis = OCD or HC], [MED = medication], [AO = age of onset], [YBOCS = OCD severity], and [Intercept = task effect]. </p> <p>P+ values denote the probability that there is increased brain activation in a given region of the Schaefer 200-parcel 7-network cortical atlas and Melbourne 32-region subcortical atlas. We used the guidelines proposed by Chen et al. (2019) to infer credibility of evidence, namely taking a positive posterior probability (P+) of <0.10 or >0.90 as indication of moderate evidence and, <0.05 or >0.95 or <0.025 or >0.975 as strong or very strong evidence, respectively. To interpret the pairwise comparisons presented as Group1-vs-Group2, posterior distributions to the right of the green no-effect line represent regions in which individuals in Group 1 show credible evidence for higher activation than individuals in Group 2. Regions with posterior distributions to the left of this line show credible evidence for higher activity in Group 2 than in Group 1. (Darker) red color represents regions in which individuals in Group 1 show moderate-to-very-strong evidence for higher activation than Group 2. (Darker) blue color represents regions in which Group 2 show moderate-to-very-strong evidence for higher activation than Group 1. Grey color represents regions in which there is no strong evidence of a difference between Group 1 and Group 2. Schaefer-Yeo 200-parcel atlas name abbreviations can be retrieved <a href="https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations">via this link.</a></p>
TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set
<p>TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set </p> <p>Sandip S. Dhomse </p> <p>School of Earth and Enviro, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2 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). 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. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 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.</p> <p>Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</p> <p>zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km)</p> <p>zmcof2_TCOM_plev_T2Dz_2020_2023.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. Xarrays “resample” can be used to get monthly means.</p>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
MPS Data set with images of medieval charters for handwriting-style based dating of manuscripts
<pre>The MPS benchmark data set for handwritten manuscript dating ____________________________________________________________ This data set is collected for the Dutch NWO project: Medieval Paleographical Scale (MPS) by Petros Samara Project website: http://application02.target.rug.nl/monk/Projects/MPS/ Copyright (c) Huygensinstituut, Den Haag, 2016 University of Groningen, 2016. All rights reserved. Organisation of the data: Each .tar.gz file contains a number of NetPBM images. The format is chosen because of its simplicity. Also, there is no doubt about lossy compression in the processing chain. The file names are of the format 'MPS<year>_<seqnr>.ppm', for example, 'MPS1300_0056.ppm'. Note: the files are not in a separate directory, they will be extracted in place. However, due to the unique naming, there is no problem extracting them in one single current (destination) directory. The actual type of the image can be gray scale (.pgm) or color (.ppm), in '8-bit DirectClass' according to ImageMagick's 'identify' tool. The images were cropped out of larger photographs because of irrelevant elements such as a Kodak color calibrator and non-text content such as supporting surface (table) backgrounds, seals (emblems), ribbons, etc. No effort has been made to obtain a balanced set of samples over years: the given frequencies of occurrence in archives are used. There is evidently less data in years before 1375 A.D. while some periods provides us with ample data for historical reasons (e.g, 1450 A.D.). It would have been a pity if the scarce years had determined and limited the size of this data set. Selection criteria for data reduction, whether random or systematic, would have been arbitrary. In any case, these images were used in our publications, such that the performance results of future attempts on manuscript dating can be compared with earlier results. The performances that have been reached using our algorithms are in the order of an MAE (mean average error) of 10 years. If you have any questions, please contact us: Sheng He (heshengxgd@gmail.com) Petros Samara (petros.samara@huygens.knaw.nl) Jan Burgers (jan.burgers@huygens.knaw.nl) Lambert Schomaker (L.Schomaker@ai.rug.nl) Please cite our papers if you use this data set: [1] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Image-based historical manuscript dating using contour and stroke fragments. Pattern Recognition(PR), Vol. 59, pp. 159-171, 2016 [2] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Towards style-based dating of historical documents. International Conference on Frontiers in Handwriting Recognition(ICFHR), Crete, Greece, 2014 [3] Sheng He, Petros Samara, Jan Burgers, Lambert Schomaker. Multiple-Label Guided Clustering Algorithm for Historical Document Dating and Localization IEEE Trans. on Image Processing, Vol. 25(11), Nov. 2016. http://ieeexplore.ieee.org/document/7551181/</pre> <p>Data are collected thanks to Dutch NWO grant project 380-50-006</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
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