Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Supplementary Material to the PhD Thesis of Luz, Zoneibe (University of Lausanne): Characterizing conodont bioapatite from the Early-Triassic: an analytical and palaeoclimatological approach

The present dataset contains the Supplements cited in the PhD Thesis of Zoneibe Augusto Silva Luz (University of Lausanne), entitled 'Characterizing conodont bioapatite from the Early-Triassic: an analytical and palaeoclimatological approach', defended the 29th of June in Lausanne. Three table of contents (TOC) are provided for each of the three thesis chapters. The main thesis is deposited at the Bibliothèque cantonale et universitaire de Lausanne, Section des thèses imprimées et des échanges,and digitally at the SERveur Académique Lausannois (Serval) (). Le présent set de données contient les Suppléments cités dans la thèse de doctorat de Zoneibe Augusto Silva Luz (Université de Lausanne), intitulée 'Characterizing conodont bioapatite from the Early-Triassic : an analytical and palaeoclimatological approach', soutenue le 29 juin à Lausanne. Trois tables des matières (TOC) sont fournies pour chacun des trois chapitres de la thèse. La thèse principale est déposée dans la Bibliothèque cantonale et universitaire de Lausanne, Section des thèses imprimées et des échanges, et électroniquement dans le SERveur Académique Lausannois (Serval) ().

opencc-by-4.0Dec 2022View details →

Dataset of image processing - High-throughput characterization of cortical microtubule arrays response to anisotropic tensile stress

The data set contains the analysis data files from the image analysis workflow developed to quantify cortical microtubules rearrangements in the case of tensile stress (<a href="https://github.com/VergerLab/MT_Angle2Ablation_Workflow">https://github.com/VergerLab/MT_Angle2Ablation_Workflow</a>), generated form a specific dataset (https://doi.org/10.5878/17te-jg54). The files include the intermediary images processed at each step of the image analysis workflow in imageJ, the log files produced by the imageJ macro describing the input and the output images and the text files containing the quantified values.

opencc-by-4.0May 2022View details →

A curated data resource of 214K metagenomes for characterization of the global resistome

Data files of the curated resource of 214K metagenomes for characterization of the global resistome. We have retrieved 214K metagenomic samples and now share the results here on Zenodo of our large-scale read mapping effort. There are five tables uploaded in three formats (TSV, HDF and MySQL dump): <ul> <li>metadata.* : contains metadata for all sequencing runs.</li> <li>ARG.* : contain read alignment counts of antimicrobial resistance genes (ARGs).</li> <li>rRNA.* : contain read alignment counts of 16S/18S rRNA genes.1</li> <li>diversity.* : contain diversity measures for ARGs and two taxonomic groups of rRNA genes (phylum, genus).</li> <li>ResFinder_anno.* : contain sequence information on the different ARGs, such as gene_lengths, resistance class, etc.</li> </ul> Note that the HDF file rRNA.h5 is split into batches of 10,000 rows. To load it, the keys are in the format of "table_{i}", where i=0,1,2,..,4736 Details on the different tables are available at https://hmmartiny.github.io/mARG/ Additionaly, we have shared the data used to create the figures in the manuscript in the ZIP file named "figure_data.zip". Any further questions or issues, please contact H.-M. Martiny at hanmar@food.dtu.dk   Update log: * 2023-01-20: Update Diversity tables due to wrong total_fragments entered for ~250 run_accessions.

Solid-state characterization of Ranolazine at different conditions

Ranolazine bulk drug and ranolazine Polymorph are prepared under different conditions. Ranolazine bulk drug dissolved in ethanol and isopropanol and store at 0 degree celsius, 40 degree celsius, 70 degree celsius and store under relative humidity. This data set includes frequencies and assignments 3367.48 stretching of the N-H, 3019.25 aromatic C-H stretching, 2929.67 asymetric aliphatic C-H stretching, 3330.84 secondary N-H stretching, 1685.67 C=O stretching of amide.

Water displacement device, engines characterization

This dataset contains 44 formulations (rows), 23 features and 1 supervisor factors (24 columns). This data have been extracted from a water displacement device that measures the volume of submerged samples. The displaced water passes through a flow sensor that produces pulses. The features in the dataset are the times between the first 20 pulses (t0 - t19), the total pulse counting (pc), the total operation time (tt) and the DC (dc) component on each experiment (formulation). The supervising value is the volume of marbles with different diameters. The work consist on tuning the water displacement device so it yields accurate volumes from pulse patterns.

opencc-by-4.0Mar 2023View details →

X-ray tomographic datasets associated with the article "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography" (DOI: 10.1016/j.conbuildmat.2024.139091)

This Zenodo repository provides two sets of 3D images, which constitute part of the dataset base for the article titled "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography", written by the same authors cited here, together with other co-authors. The article is published in the journal "Construction and Building Materials". It can be reached via the following URL: <a href="https://doi.org/10.1016/j.conbuildmat.2024.139091" target="_blank" rel="noopener">https://doi.org/10.1016/j.conbuildmat.2024.139091</a>. The core specimens were obtained in 2019 from semi-dense asphalt (SDA) pavement sections located in the Swiss Canton of Zürich. For each of three pavement sections, labelled in the following as SDA4-1yr, SD4-5yr and SDA8, 100 mm diameter cores were extracted, both inside (I) and outside (O) of the wheel path, in order to see the effect of the traffic load on the pore space characteristics. Out of the original cores for the SDA4 pavements, 5 30 mm diameter sub-cores were drilled out of their centers, both in- and out-of the wheel path, and investigated with X-ray tomography. Only 1 30 mm core was analyzed for SDA8, both in- and out- of the wheel path. The asphalt in that pavement type has lower porosity, making it less interesting from the sound absorption viewpoint. The whole dataset consists of .7z archive files. Such files have the following designations: SDA_J_K_L_Tomogram.7z or SDA_J_K_L_PoreSpaceBinTomogram.7z, where J = 1,2, K = I,O and L = 1,2,3,4,5. When referring to the specimen naming within the corresponding article, the first index, J, refers to the specimen "age": J = 1 indicates the 1-year old specimens (called SDA4-1yr within the article); J = 2 refers to the 5-year old ones (SDA4-5yr). The second index, K, refers to the location of the specimen within the pavement section course ("I" for in-wheel path and "O" for out-of-wheel path). The final index L just enumerates the distinct specimens of the same group. There are two additional groups of archive files: LNA_I_Tomogram.7z/LNA_I_PoreSpaceBinTomogram.7z refers to the single in-wheel-path, 7-year old specimen (called SDA8 within the article); LNA_O_Tomogram.7z/LNA_O_PoreSpaceBinTomogram.7z refers to the single out-of-wheel path, 7-year old specimen. The two sets/types of 3D images can be recognized by the different file naming. The first set includes the raw X-ray tomograms of the 22 specimens analyzed. Each tomogram is stored in the form of a "stack" (or series) of 16-bit unsigned integer 2D TIFF image file, being one 2D cross-section (also called "slice", in tomographic jargon) from the "tomographed" volume. Such slices are contained in a folder. The folder was then archived in a .7z archive file. The second set of 3D images is characterized by the filename pattern SDA_J_K_L_PoreSpaceBinTomogram.7z. Each zipped folder contains the slices of the binary tomogram of the whole pore space of the respective specimen, segmented according with the 3d image analysis workflow described within the article. Each slice of such tomogram was stored as a 8-bit unsigned integer 2D TIFF image file, whose pixels can have only two possible values: 255, if the pixel is inside the segmented pore space; 0 if the pixel is outside it. Almost all of the acquired tomograms have an isotropic voxel size of 0.0214 mm, meaning that each slice is separated in space from the next one by such distance. The samples SDA_2_O_1 and SDA_2_I_1 have a voxel size of 0.0220 mm, while the sample LNA_I has a voxel size of 0.0223 mm.

opencc-by-4.0Dec 2022View details →

Time and momentum resolved characterization of hybrid plasmonic heterostructure Au/WSe2

Dataset attached to paper titled "Observation of Multi-Directional Energy Transfer in a Hybrid Plasmonic-Excitonic Nanostructure" with time and momentum characterization of a 2D palsmonic heterostructure formed by Au nanoislands on bulk WSe2. It contains Angle-resolved photoemission spectroscopy (ARPES) and time-resolved ARPES data (trARPES.zip); femtosecond electron diffraction (FED) data (FED.zip); optical absorption spectroscopy data (Optical_absorbance.zip) and Transmission electron microscopy micrographs (TEM.zip). For trARPES.zip, the following table reports the grid of measurements and most important parameters: <table> <tbody> <tr> <td>Name</td> <td>Sample temperature (K)</td> <td> Pump Wavelength (nm) </td> <td>Pump Duration (fs)</td> <td>Material</td> </tr> <tr> <td>trARPES_Metis_002.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2124.mpes.nxs</td> <td>300</td> <td>800</td> <td>35</td> <td>WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2146.mpes.nxs</td> <td>70</td> <td>800</td> <td>35</td> <td>WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2197.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2198.mpes.nxs*</td> <td>70</td> <td>800</td> <td>35</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2212.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan2219.mpes.nxs*</td> <td>300</td> <td>800</td> <td>35</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan3159.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan3164.mpes.nxs**</td> <td>300</td> <td>1030</td> <td>200</td> <td>Au/WSe2</td> </tr> <tr> <td>trARPES_Phoibos_Scan3185.mpes.nxs</td> <td>300</td> <td>1030</td> <td>200</td> <td>WSe2</td> </tr> </tbody> </table> *These scans are acquired with higher angular dispersion requiring separate scans for K and Sigma valleys. **Fluence scan. FED.zip contains the following subfolders: <ul> <li>Manuscript_Figure: Experimental data and fit parameters depicted in Figure 4 of the main article.</li> <li>Analysis: Additional information for the FED data including: raw data descriptions (delay, power, filename and more), Matlab scripts with comments, masks and backgrounds for image processing. The Static_patterns subfolder contains electron diffraction patterns of pure WSe2 flakes and Au-covered WSe2 flakes.</li> </ul> Optical_absorbance.zip contains the following subfolders & subfiles: <ul> <li>without Au & with Au containing all the optical measurements of pristine and Au-covered WSe2 flakes, respectively.</li> <li>comparison_with_and_without_Au.xlsx contains the analysis of the difference curves</li> <li>manuscript_figure.txt contains the data that were used in Figure 1 of the main article.</li> </ul>

opencc-by-4.0Nov 2020View details →

Genome-wide characterization of human minisatellite VNTRs: population-specific alleles and gene expression differences

This repository consists of minisatellite VNTR genotypes for 2,800 samples (2,770 individuals). The raw VCF files were produced using <a href="https://github.com/yzhernand/VNTRseek">VNTRseek</a> on xxx data sources: <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/">30 high coverage WGS datasets</a> from the 1000 Genomes Project phase 3, <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">2,504 unrelated genomes</a> from New York Genome Center (NYGC), <a href="https://www.internationalgenome.org/data-portal/data-collection/sgdp">253 genomes from Simons Diversity Genome Project</a> (SGDP), <a href="https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/tumor-normal.html">two tumor-normal breast cancer samples</a> from Illumina Basespace, haploid genomes <a href="https://www.ncbi.nlm.nih.gov/sra/SRX652547">CHM1 </a>and <a href="https://www.ncbi.nlm.nih.gov/sra/SRX1009644">CHM13</a>, and seven genomes from the Personal Genome Project from the Genome In A Bottle Consortium (GIAB). Raw VCF files are provided for each data source separately. The raw VCF files were preprocessed (preprocess.sh) to extract genotypes and provided in VNTRseek_preprocessed_data.tar.gz (uncompressed size 10G). The R Markdown code to analyze the preprocessed data and produce figures and tables is also provided (tables_and_figures.Rmd). For more information see the ReadMe file. This work was supported in part by NSF grants IIS-1423022 and DBI-1559829.

A lack of population structure characterizes the invasive Lonicera japonica in West Virginia and across eastern North America

Figure S1. Mig-seq primers used in the current study. Dataset S1. SNP data in .vcf format for Lonicera japonica. Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.

opencc-by-4.0May 2023View details →

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv). 2. scripts.zip - scripts to generate figure 2-5 on toy data

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387). The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4). 2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b) 3. ODE_solutions.zip - self-explanatory (Figure 2c) 4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d) 5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a) 6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g The remaining datasets are published in the following links  - https://doi.org/10.5281/zenodo.7930084  - https://doi.org/10.5281/zenodo.10391802

opencc-by-4.0May 2023View details →

Dataset to: Novel aerosol diluter – Size dependent characterization down to 1 nm particle size

Dataset to: Lampimäki et al. Novel aerosol diluter – Size dependent characterization down to 1 nm particle size. Journal of Aerosol Science 172 (2023) 106180, doi: <a href="https://doi.org/10.1016/j.jaerosci.2023.106180">https://doi.org/10.1016/j.jaerosci.2023.106180</a>

Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)

This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_era5land_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_era5land_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_era5land_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from observations and climate projections to assess thermal behavior of regions in Europe (1981-2100)

This dataset provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_eobs_thresholds_Europe.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_eobs_heatwaves_Europe.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_eobs_heatwaves_Europe.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)

This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat. A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010. To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (RCP4.5) and very high (RCP8.5) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS). The database is organized in three datasets: Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>tmax: daily maximum temperature threshold.</li> <li>tmin: daily minimum temperature threshold.</li> </ul> Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are: <ul> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>start: first date of the heatwave.</li> <li>tmax: maximum temperature reached during the heatwave.</li> <li>intensity: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li>duration: duration of the heatwave.</li> </ul> Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are: <ul> <li>hazard_level: it can be a warning, an alert or an alarm.</li> <li>region: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li>experiment: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li>period: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li>decade_frequency: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li>decade_frequency_best: only applicable to the future. It determines the best projection among the models.</li> <li>decade_frequency_worst: only applicable to the future. It determines the worst projection among the models.</li> <li>year_days: average annual days.</li> <li>year_tmax_intensity: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li>year_tmin_intensity: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>

opencc-by-4.0Jul 2023View details →

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

opencc-by-4.0Jul 2023View details →

ScRAPv20230731: Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae

Saccharomyces cerevisiae Reference Assembly Panel (ScRAP) v20230731 > The haplotype-resolved and/or collapsed T2T genome assemblies for 142 S. cerevisiae strains isolated from diverse geographical and ecological niches.

openother-openAug 2023View details →

Sewage sludge batches chemical characterization

<a href="https://github.com/jmalonso55/Sewage-sludge-characteristics">https://github.com/jmalonso55/Sewage-sludge-characteristics</a> This repository contains the R script and part of the data from Alonso, J.M., Ph.D. Thesis (Alonso, J.M., 2019. Caracterização de Biossólidos para a Produção de Mudas de Espécies Arbóreas da Mata Atlântica [Thesis]. Universidade Federal Rural do Rio de Janeiro, Seropédica, Brasil).  Published as a paper with the reference: Alonso, J.M., Abreu, A.H.M., Andreoli, C.V. et al. Chemical characteristics and valuation of sewage sludge from four different wastewater treatment plants. Environ Monit Assess 196, 34 (2024). <a href="https://doi.org/10.1007/s10661-023-12211-8">https://doi.org/10.1007/s10661-023-12211-8</a> The data in this particular repository are from samples of 19 sewage sludge batches. The samples were collected in January, May, August, December 2016, February, June, September, and December 2017 from four wastewater treatment plants (WWTP). At WWTP Ilha do Governador, samples were collected in all eight seasons. Due to various reasons, it was not always possible to collect samples from the other WWTPs. The WWTP Alegria had five collections, while Barra and Sarapuí had three. The chemical characterization of the sludges was conducted based on the parameters required by Resolution nº 498/2020 of the National Council for the Environment (BRASIL, 2020). The elements P, K, Ca, Mg, Fe, Al, Na, Co, Mn, As, Ba, Cd, Cr, Cu, Ni, Se, Pb, and Zn were determined using inductively coupled plasma optical emission spectrometry (ICP-OES). The dry combustion method determined the C and N contents using a CHN-600 elemental autoanalyzer. The organic matter content was determined by multiplying the total carbon present in each sample by the “van Bemmelen” conversion factor of 1.724. The pH and electrical conductivity (EC) were determined by taking 5 g of each sample and diluting it in 50 mL of deionized water. The mixture was stirred for 30 minutes and then measured using a bench pH and conductivity meter. The valorization of the sludge batches was performed using the substitute goods method, which compares the prices of products well-established in the market with the good to be valued. The sludge was valued for its N, P, and K contents. The inputs quoted as sources of these nutrients were the mineral fertilizers urea (N), triple superphosphate (P), and potassium chloride (K). The fertilizers' megagram prices were divided by the percentage of nutrients they contain to calculate each nutrient's value. Fertilizer prices were consulted in: CONAB. Companhia Nacional de Abastecimento. Relatório de Insumos Agropecuários, Grupo Fertilizante, Sub-Grupo Químico, UF PR, Ano 2023. Brasília: CONAB, 2023. Available from: <a href="https://consultaweb.conab.gov.br/consultas/consultaInsumo.do?method=acao">https://consultaweb.conab.gov.br/consultas/consultaInsumo.do?method=acao</a> CarregarConsulta (accessed 26 July 2023).