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23,670 results for “Site”
Management and plant physiology data for grassland sites in Germany
<p>Management and vegetation data for sites Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg) in Southern Germany, observed between 2012 and 2017. Weekly resolution vegetation traits are included for 2015.</p> <p>The sites are part of TERENO, a network of observatories in Germany. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Meteorology, environment and surface flux data for grassland sites in Germany
<p>Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. </p> <p>The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study
<p>Collection of data and scripts related to the paper:</p> <p>Jonas Gossen et al. "A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics" </p> <p>https://www.biorxiv.org/content/10.1101/2020.12.14.422634v2 doi: https://doi.org/10.1101/2020.12.14.422634</p> <p>ACS Pharmacology and Translational Science 2021 DOI: 10.1021/acsptsci.0c00215</p> <p> </p> <p> </p> <p><strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong></p> <p>include simulation of the binding pocket druggability, physical-chemical properties, and the binding site composition</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Protease_clean.ipynb">Protease_clean.ipynb</a> - Jupyter Notebook containing analysis of the generated data</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/allTables.zip">allTables.zip</a> - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Every10-ligand_6LU7_R3.5.zip">Every10-ligand_6LU7_R3.5.zip</a> - results of TRAPP pocket analysis on the MD frames</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/PDB-Giulia.zip">PDB-Giulia.zip</a> - TRAPP pocket analysis of 40 PDB complexes of main protease</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/TRAPP_properties_PDB.xlsx">TRAPP_properties_PDB.xlsx</a> - binding pocket properties for 40 PDB complexes of main protease summarized in a table</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/DrugPDB_3structures.xlsx">DrugPDB_3structures.xlsx</a> - binding pocket properties for 3 PDB structures </p> <p><strong>2. Docking & Screening Results</strong></p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/TRAPP_secondSelection_VS.csv">TRAPP_secondSelection_VS.csv</a> - docking/screening of selected structures from TRAPP analysis</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Fred_VS.csv">Fred_VS.csv</a> - docking of PDB structures using Fred</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Glide_VS.csv">Glide_VS.csv</a> - docking of PDB structures using Glide</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS1.xlsx">TableS1.xlsx</a> - Available structures of SARS-CoV-2 Mpro selected for binding site analyses. </p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2A.xlsx">TableS2A.xlsx</a> - SiteScore analysis of all the deposited X-ray crystal structures for the Mpro.</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2B.xlsx">TableS2B.xlsx</a> - SiteScore analysis of the MSM ensemble (4-macrostates).</p>
Road siting experiment
<p>This dataset comes from an experiment performed at the Italian National Metrology Institute (INRiM) in the framework of EMPIR project MeteoMet 2. The purpose of the experiment was to evaluate the effect of the presence of a road in near-surface temperature data. The dataset is composed by 2 files, with identical structure and datafields.</p> <p>The experiment was laid out as a series of 7 measurement points, at progressively longer distances from a local 2-laned road in the countryside around Turin, Italy. The header of the files is here described:</p> <p>"Data e ora": Date and time</p> <p>TE1101: air temperature sensor #1, distance from the road= 1 m</p> <p>TE1102: air temperature sensor #2, distance from the road= 1 m</p> <p>TE1201: air temperature sensor, distance from the road= 5 m</p> <p>TE1301: air temperature sensor, distance from the road= 10 m</p> <p>TE1401: air temperature sensor, distance from the road= 20 m</p> <p>TE1501: air temperature sensor, distance from the road= 30 m</p> <p>TE1601: air temperature sensor, distance from the road= 50 m</p> <p>TE1701: air temperature sensor, distance from the road= 100 m</p> <p> MT1302: relative humidity sensor, distance from the road= 10 m</p> <p>MT1502: relative humidity sensor, distance from the road= 30 m</p> <p>XM1505: solar radiation, distance from the road= 30 m</p> <p>ZT1503: wind direction, distance from the road= 30 m</p> <p>ST1504: wind speed, distance from the road= 30 m</p> <p> </p> <p>Road_siting_experiment_INRIM.csv is the data collected during the experiment, with measurement points located at the distances indicated in the previous list.</p> <p>Road_siting_colocation_INRiM.csv is the data collected during a second phase of the experiment, when the sensors had all been rearranged in the same spot (co-location) in order to evaluate the behaviour of the sensors while exposed to the same environmental conditions.</p>
Supplementary files for "A comparison of single and double Co sites incorporated in N-doped graphene for the oxygen reduction reaction"
<p>DFT optimised structures used for the paper "A comparison of single and double Co sites incorporated in N-doped graphene for the oxygen reduction reaction". There is a separate database for structures on the Co-N4 single site, each of the Co double sites and the molecular references. Manual and NEB paths for the splitting of OOH and O2 are included as separate databases. The structures can be retrieved using the Atomic Simulation Environment (ASE, https://wiki.fysik.dtu.dk/ase/).</p>
Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1
<p>This dataset was developed for the following article:</p> <p> Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections [version 1; peer review: awaiting peer review]. <em>F1000Research</em> 2020, <strong>9</strong>:943 (<a href="https://doi.org/10.12688/f1000research.25390.1">https://doi.org/10.12688/f1000research.25390.1</a>)</p> <p><strong>Section 1. Extended Data Tables</strong></p> <p>This archive contains the extended data tables for the research article "A proposed mechanism for molecular pathogenesis of severe RNA-viral pulmonary infections". These tables provide SRSF1, RNPS1 and hnRNP A1 binding site and information-dense cluster counts across various RNA viral genomes [including multiple SARS-CoV-2 and influenza strains] and the human transcriptome, the estimated SARS-CoV-2 doubling time necessary for viral genome SRSF1 binding site availability to exceed sites within the host transcriptome, and an analysis of influenza, dengue, and aplastic anemia patients misdiagnosed as irradiated by established radiation gene signatures.These tables are:</p> <p><strong>Section 1 - Table 1.</strong> RNPS1 and hnRNPA1 binding sites and Information-Dense Clusters for RNPS1 and<br> hnRNPA1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2A.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 1) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2B.</strong> Detailed Analysis of Information-Dense Clusters for SRSF1 (Replicate 2) in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 2C.</strong> Detailed Analysis of Information-Dense Clusters for RNPS1 in RNA Virus Genomes<br> <strong>Section 1 - Table 2D.</strong> Detailed Analysis of Information-Dense Clusters for hnRNP A1 in RNA Virus<br> Genomes<br> <strong>Section 1 - Table 3.</strong> Binding Site Analysis of Multiple Coronavirus Strains (Both Strands)<br> <strong>Section 1 - Table 4A.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Negative Strand Only)<br> <strong>Section 1 - Table 4B.</strong> Binding Site Analysis of Multiple Influenza A (H3N2) Strains (Both Strands)<br> <strong>Section 1 - Table 5.</strong> SRSF1, RNPS1 and hnRNPA1 Binding Sites and Information-Dense Clusters by Gene<br> <strong>Section 1 - Table 6A.</strong> Transcriptome-Wide Information Dense Clusters Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6B. </strong>Exome-Wide Information Dense Clusters within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 6C.</strong> Transcriptome-Wide Scan of Strong Binding Sites Intersecting DRIP- and DRIPc-seq<br> Intervals<br> <strong>Section 1 - Table 6D. </strong>Exome-Wide Scan of Strong Binding Sites within DRIP- and DRIPc-seq Intervals<br> <strong>Section 1 - Table 7.</strong> Rate of False Positives for Influenza, Dengue Virus and Aplastic Anemia Using<br> Radiation Signatures<br> <strong>Section 1 - Table 8.</strong> Radiation Model Genes Contributing to False Positives for Patients with Influenza A,<br> Dengue Virus, and Aplastic Anemia<br> <strong>Section 1 - Table 9A.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Positive-Strand Sites Only)<br> <strong>Section 1 - Table 9B.</strong> Doubling Time of SARS-CoV-2 Needed to Exceed Host Transcriptome SRSF1 Binding<br> Sites (Both Strands Considered)</p> <p><strong>Section 2. All SRSF1, hnRNPA1 and RNPS1 binding site tracks for human and viral genomes</strong></p> <p>We provide bedgraph tracks which provide the location and strength of binding sites (and binding site clusters) for SRSF1, RNPS1 and hnRNPA1 across the human transcriptome (GRCh37), the human exome (including +/-300nt surrounding the exon; non-intergenic only), and for all viral genome investigated in this study (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [two strains]). Note that if no clusters were found for a particular viral genome, a file for said genome will not be present in the Zenodo archive.</p> <p>Folder “Cluster-to-DRIPseq-Intersection-Tracks” contain tracks which indicate where binding site clusters have been identified, intersected with DRIP-seq and DRIPc-seq intervals which indicate where there is evidence of R-Loop formation in the human genome. The DRIP-seq dataset (GSE68845) is not strand specific. DRIPc-seq (GSE70189) is strand specific, and has been taken into account in the intersection (e.g. tracks only list positive strand clusters found in positive-strand DRIPc-seq intervals).</p> <p>Due to sheer size, the human transcriptome and exome tracks which indicate the location of individual binding sites are split into two separate files (separated by strand). While the custom tracks containing human binding site information are designed to be uploaded to the UCSC Genome Browser, files containing transcriptome-wide binding site information may be too large to be uploaded and may require further filtering (i.e. by chromosome).</p> <p>To be classified as a cluster, binding sites on the same strand must have <em>Ri</em> values which sum to >50 bits, each binding site must have a neighboring site within 25nt, and all binding sites in the cluster must have <em>R<sub>i</sub></em> greater than a minimum bit threshold. For human transcriptomes and exomes, this bit minimum was set to <em>R<sub>sequence</sub></em>. The bit minimum for viral binding sites was set to 0.1 * <em>R<sub>sequence</sub></em>. The information density-based clustering algorithm utilized in this work is described in Lu and Rogan 2018 (<a href="https://f1000research.com/articles/7-1933/v2">https://f1000research.com/articles/7-1933/v2</a>) and archived source code is available through Zenodo (<a href="https://dx.doi.org/10.5281/zenodo.1892051">https://dx.doi.org/10.5281/zenodo.1892051</a>).</p> <p><strong>Section 3. Binding site clusters - lollipop plots</strong></p> <p>Lollipop plots present the genomic coordinates and information densities of clusters across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). The height of the "lollipop" corresponds to the information density of a cluster. Labels above "lollipops" present the start and end genomic coordinate (GRCh37) of the cluster followed by the number of sites in the cluster enclosed in brackets. Lollipop plots associated with human transcriptomes/exomes each contain a single gene. Influenza has 8 segments and each segment requires its own plot, other viral genomes examined are presented in a single plot.</p> <p>File naming convention for human plots:</p> <ul> <li>RBP_Gene.png</li> <li>e.g. RNPS1_ADK.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>Virus[.InfluenzaSegment].RiThreshold.Strand.RBP.png</li> <li>e.g. Wuhan-Hu-1.complete-genome.4.2-bits.PosStrand.hnRNPA1.png</li> </ul> <p>The specified Ri threshold indicates all binding sites which comprise a cluster have <em>R<sub>i</sub></em> greater-than or equal to the threshold.</p> <p><strong>Section 4. Ri(b,l) matrices for all binding sites scanned</strong></p> <p>The information theory-based position weight matrices for the following RNA binding proteins (RBP) used in this study: SRSF1, hnRNPA1 and RNPS1. We investigated binding using two different RNPS1 binding models. While similar, these two models contained binding site information on opposing sides of the binding site motif which is why we found it prudent to scan with both models.</p> <p>Structure of each file:</p> <p>Line #1: Start position, End position and<em> R<sub>sequence</sub></em> [average strength of sequences used to generate the model]</p> <p>Subsequent lines describe the information on each position of the binding site:</p> <ul> <li>First four columns: <em>R<sub>i</sub></em> contribution of nucleotide at this position of the matrix [A, C, G, T]</li> <li>Row #5: Position of the matrix</li> <li>Last four columns: Number of binding sites used to generate model with a particular nucleotide at this position of the matrix [A, C, G, T]</li> </ul> <p>Example:</p> <p>-2.965775 1.282153 0.034225 -4.906891 0 1 19 8 0</p> <p>At zero position of the matrix (first nucleotide), a ‘C’ would have a positive contribution to binding site strength, a ‘G’ would be relatively neutral, and an ‘A’ or ‘T’ would negatively contribute to binding site strength.</p> <p>Generation of R<sub>i</sub>(b,l) matrices and computation of <em>R<sub>i</sub></em> values and can be accomplished by utilizing the Delila package (<a href="https://alum.mit.edu/www/toms/delila/delilaprograms.html">https://alum.mit.edu/www/toms/delila/delilaprograms.html</a>).</p> <p><strong>Section 5. Ri and intersite distance - histograms</strong></p> <p>Two sets of histograms present <em>R<sub>i</sub></em> distribution and intersite distance distribution across the human transcriptome, human exome, and viral genomes (Coronavirus, Dengue, HIV-1 [two strains] and Influenza [one strain]). </p> <p>File naming convention for human plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Human-[DRIPc]-AllChrs-RBP[-RiThreshold].png</li> <li>e.g. IntersiteDistances500-Human-AllChrs-hnRNPA1-4.6-bits.png</li> </ul> <p>File naming convention for viral plots (elements in square brackets do not always appear):</p> <ul> <li>[IntersiteDistancesThreshold-]Strand-RBP-Virus[.InfluenzaSegment][-RiThreshold].png</li> <li>e.g. IntersideDistances1000-PosStrandOnly-SRSF1-top50000sitesReplicate1-HIV-1-Strain-B.png</li> </ul> <p>Intersite distance thresholds of 500 or 1000 were assigned for all intersite distance histograms. Any distances above the corresponding threshold were excluded from the plot. Plots presenting <em>R<sub>i</sub></em> distributions contain a dashed line indicating <em>R<sub>sequence</sub></em> if it is visible within the scope of the plot.</p> <p><strong>Section 6. Perl Scripts and Descriptions</strong></p> <p>This archive contains all Perl scripts discussed in this archive's associated manuscript and a document file which describes them ("Perl-Script-Descriptions-Page.docx"). The programs and their general functions are as follows:</p> <p>“ClusterToDRIPseqAnalysisProgram.pl” – reports which information-dense clusters are located within DRIPc- and/or DRIP-seq intervals (individually and by gene)</p> <p>“ClusterToDRIPseqAnalysisProgram.GeneDensityFinder.pl” – uses the output from script “ClusterToDRIPseqAnalysisProgram.pl” to determine the number and the density of information-dense clusters within a gene (total clusters within the gene and those within DRIPc-seq intervals)</p> <p>“calculateIntersiteDistance.pl” – determines the distance between all binding sites in the same gene from a list of genomic coordinates</p> <p>“removeOutliersHigherThanN.pl” – discards intersite distances computed by script “calculateIntersiteDistance.pl” that are greater than a specified threshold</p> <p>“getStatisticsOnCol.pl” – calculates the count, geometric mean, median, arithmetic mean, and standard deviation of values from the output of script “removeOutliersHigherThanN.pl”</p> <p>“ScanDataSummaryProgram.pl” – determines the number of binding sites (above a specified <em>R<sub>i</sub></em> threshold) found within known genes (the program also reports the total expression of those genes using external A549 and pneumocyte expression datasets) from binding site coordinate data</p> <p>“TotalBindingSitePerCellCalculator.pl” – estimates the number of binding sites expressed in a single A549 or pneumocyte cell at any given time.</p>
Supplementary Dataset for "Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites"
<p>These datasets are supplementary to the paper "<strong>Representativeness of Eddy-Covariance Flux Footprints for Areas Surrounding AmeriFlux Sites</strong>" by Chu et al. </p> <ul> <li>Dataset S1. Summary of site-specific footprint metrics <ul> <li>filename: All_site_fpt_summary.csv</li> <li>readme: All_site_fpt_summary-README.csv</li> </ul> </li> <li>Dataset S2. All monthly footprint climatology weight maps <ul> <li>filename: monthly_footprint_climatology_weight_map.zip <ul> <li>the zip folder contains individual files of all monthly footprint weight maps</li> <li>filename: <Site-ID>_<Year>_<Month>_<DAY/NIGHT>_fpt_weight.tif</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S3. All site-year footprint climatology overlapped with true-color satellite images. <ul> <li>filename: site-year_footprint_climatology_realcolor_map.zip <ul> <li>the zip folder contains individual files of footprint climatologies from all site-years</li> <li>filename: <Site-ID>_<Year>_<Spatial_Extent>_shrink_footprint_climatology.png</li> </ul> </li> <li>readme: README.txt </li> </ul> </li> <li>Dataset S4. Site-specific results and representativeness index based on the land cover type analysis. <ul> <li>filename: All_site_land_cover_dominant_summary2.csv</li> <li>readme:All_site_land_cover_dominant_summary2-README.csv</li> </ul> </li> <li>Dataset S5. Site-specific results and representativeness index based on the EVI analysis. <ul> <li>filename: All_site_Landsat_EVI_fpt_comparison2.csv</li> <li>readme: All_site_Landsat_EVI_fpt_comparison2-README.csv</li> </ul> </li> <li>Dataset S6. All available site-month EVI and time-explicit representativeness. <ul> <li>filename: All_site_Landsat_EVI_all_cutout2.csv</li> <li>readme: All_site_Landsat_EVI_all_cutout2-README.csv</li> </ul> </li> </ul>
Github data for static site generators (SSG) popularity
<p>Number of Github stars, forks, open issues, create and last modified dates for 30 open source static site generators (SSG), including Hugo, Jekyll and Gatsby.</p>
Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site
<p>Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site"</p> <p>Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani </p> <p>Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication.</p> <p>Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load)</p> <p>Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis</p>
GUV total ozone column and effective cloud transmittance from three Norwegian sites 1995-2019
<p>Total ozone column (TOC) and effective cloud transmittance (eCLT) from GUV-511 in Oslo (Norway), GUV-541 from Andøya/Tromsø (Norway), and GUV-541 from Ny-Ålesund (Svalbard, Norway).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research<br> Collaborative institute: Norwegian Radiation and Nuclear Safety Authority, DSA</p> <p>Method described in: Dahlback, A. (1996), Measurements of biologically effective UV doses, total ozone abundances, and cloud effects with multichannel, moderate bandwidth filter instruments, Appl. Opt. 35, 6514–6521</p> <p>1h average noon-time values, based on data with 1-minute time resolution.</p> <p>TOC retrievals from 305/320 nm channel ratio<br> eCLT retrievals from 340 nm channel </p> <p>Calibrations based on the FARIN2005-campaign and annual site visits with a travelling reference GUV instrument from DSA (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD009731)</p> <p>Funding: NILU - Norwegian Institute for Air Research, Norwegian Environment Agency, Norwegian Ministry of health and Care Services</p>
Public dataset for Antibiotic Prophylaxis for Surgical Site Infections as a Risk Factor for Infection with Clostridium difficile
<p>This is the minimal publicly available dataset for the manuscript titled "Antibiotic Prophylaxis for Surgical Site Infections as a Risk Factor for Infection with Clostridium difficile". We have also included the data dictionary. </p>
Udayagiri, Madhya Pradesh. General plan of the site.
<p>Udayagiri, Madhya Pradesh. General plan of the site, showing configuration of the hill, the location of principal caves, temples and main tanks.</p>
Environmental proxy data from the Chap archaeological site (Kyrgyzstan)
<p>Sediment particle size, magnetic susceptibility, loss-on-ignition (LOI) and pollen data from the Chap archaeological site (described in Motuzaite Matuzeviciute et al. 2019, 2021, 2022).</p> <p>Particle size analysis using Malvern Mastersizer 2000 after treatment with HCl and H2O2.</p> <p>Particle size end members (EM 1 & 2) modelled using Analysize package (Paterson & Heslop 2015) for Matlab.</p> <p>Dimensionless magnetic susceptibility (Κ) readings were taken using a Bartington MS2B at low (0.46 kHz) and high (4.6 kHz) frequencies. Mass magnetic susceptibility (χ) was calculated by dividing Κ by sample bulk density. The percentage of frequency dependent components (χ­­<sub>fd%</sub>) was calculated as 100[(χ<sub>lf </sub>– χ<sub>hf</sub>)/χ<sub>lf</sub>].</p> <p>For LOI calculations, samples were weighed, fired at 550 degrees C for 4 hours, re-weighed then fired at 950 degrees C for 4 hours. </p> <p>Pollen was extracted following heavy liquid methods described in Leipe et al. (2019).</p> <p>Units in dataset:</p> <p>Centimetre (cm)</p> <p>Relative abundance (%)</p> <p>Absolute abundance (#)</p> <p>Sorting (σg) – after Folk & Ward (1957)</p> <p>Mass magnetic susceptibility (SI/g)</p>
Ocean Drilling Program Site 959 Datasets
<p><strong>-Version V4 </strong>now includes the raw data associated with the paper "Evidence for limited atmospheric pCO2 rise at the onset of the Miocene Climatic Optimum", by Wubben et al.: item 'Miocene CO2; raw data' (md5:972e685a012a1f1915b4cfb6a3c5292b). An early version of the manuscript is published in the PhD thesis of Evi Wubben, entitled "Long-term and orbital-scale climate and carbon cycle change across the Miocene Climatic Optimum", ISBN 978-90-6266-692-8, openly available at the Utrecht University Repository. doi: 10.33540/2518. </p> <p> </p> <p><strong>-Version 1.1.0</strong></p> <p>Datasets updated with new Eocene Site 959 data added as supplement to:</p> <ul> <li>"Global warming and equatorial Atlantic paleoceanographic changes during early Eocene carbon cycle perturbation V" by Kegel et al.</li> </ul> <p> </p> <p><strong>-Version 1.0.0: </strong></p> <p>Data supplement to:</p> <ul> <li> <p>"Polar amplification of orbital-scale climate variability in the early Eocene greenhouse world" by Fokkema et al. (2024). </p> </li> <li> <p>"Tropical Warming and Intensification of the West African Monsoon during the Miocene Climatic Optimum" by Wubben et al. (2024).</p> </li> <li>"Early to Middle Miocene Orbitally-Paced Climate Dynamics in the Eastern Equatorial Atlantic" by Spiering et al. (2024). </li> </ul> <p>Updated age model and datasets of bulk magnetic susceptibility, bulk carbonate oxygen and carbon isotopes, bulk organic carbon isotopes, ICP-OES, palynology and GDGTs from Ocean Drilling Program (ODP) Leg 159 Site 959.</p> <p>This upload contains datasets by Kegel et al. (2024); Fokkema et al. (2024); Wubben et al. (2024); Spiering et al. (2024); Cramwinckel et al. (2018); Frieling et al. (2018; 2019) and Van der Weijst et al. (2022).</p>
Data associated to "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis"
<p>Data for replication of main results in "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis". The folder "data_estim" contains all necessary data to replicate all estimations in the article (see the R code "codes_cbs-cost") with three .csv files: dvf_estim.csv, dvfbasol_estim.csv and cell200_simulation.csv. The variable names in these files are as follow:</p><p> </p><p>Identifier Variables:</p><p>- IDMUTATION: identifier for each transacted property</p><p>- comm_code: identifier for each commune defined in 2021</p><p>- admin_code: identifier for urban areas defined in 2021</p><p>- iris2014_code: identifier for each neighborhood defined in 2014</p><p>- cell200_code: identifier for each 200-meters gredded cells</p><p>- dvf_x: longitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- dvf_y: latitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- basol_code: identifier for each CBS (only reported in dvfbasol_estim.csv)</p><p>- anneemut: year of transaction for each property</p><p> </p><p>Dependent Variable:</p><p>- pm2: price in euro per square meter of transacted properties</p><p> </p><p>Interest Variables:</p><p>- areaha_basol250: area in hectare of CBS between 0 and 250 meters from transacted property</p><p>- areaha_basol500: area in hectare of CBS between 250 and 500 meters from transacted property</p><p>- areaha_basol1000: area in hectare of CBS between 500 and 1000 meters from transacted property</p><p>- areaha_basol2000: area in hectare of CBS between 1000 and 2000 meters from transacted property</p><p>- areaha_basol3000: area in hectare of CBS between 2000 and 3000 meters from transacted property</p><p>- area250_indpro: area in hectare of CBS with industrial manufacturing activities between 0 and 250 meters from transacted property</p><p>- area500_indpro: area in hectare of CBS with industrial manufacturing activities between 250 and 500 meters from transacted property</p><p>- area1000_indpro: area in hectare of CBS with industrial manufacturing activities between 500 and 1000 meters from transacted property</p><p>- area2000_indpro: area in hectare of CBS with industrial manufacturing activities between 1000 and 2000 meters from transacted property</p><p>- area3000_indpro: area in hectare of CBS with industrial manufacturing activities between 2000 and 3000 meters from transacted property</p><p>- area250_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 0 and 250 meters from transacted property</p><p>- area500_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 250 and 500 meters from transacted property</p><p>- area1000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 500 and 1000 meters from transacted property</p><p>- area2000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 1000 and 2000 meters from transacted property</p><p>- area3000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 2000 and 3000 meters from transacted property</p><p>- area250_othact: area in hectare of CBS with other or unknown activities between 0 and 250 meters from transacted property</p><p>- area500_othact: area in hectare of CBS with other or unknown activities between 250 and 500 meters from transacted property</p><p>- area1000_othact: area in hectare of CBS with other or unknown activities between 500 and 1000 meters from transacted property</p><p>- area2000_othact: area in hectare of CBS with other or unknown activities between 1000 and 2000 meters from transacted property</p><p>- area3000_othact: area in hectare of CBS with other or unknown activities between 2000 and 3000 meters from transacted property</p><p>- areaha_specific250: area in hectare of CBS specific to a unique CBS between 0 and 250 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific500: area in hectare of CBS specific to a unique CBS between 250 and 500 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific1000: area in hectare of CBS specific to a unique CBS between 500 and 1000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific2000: area in hectare of CBS specific to a unique CBS between 1000 and 2000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p> </p><p>Robustness Variables:</p><p>- pm2mean_iris: average transaction price per square meter of neighborhood IRIS</p><p>- shpoorhouse: share in percentage of poor households </p><p>- dvfschool_nb250: number of schools within 250 meters of property</p><p>- dvfschool_nb500: number of schools within 500 meters of property</p><p>- dvfschool_nb1000: number of schools within 1000 meters of property</p><p>- dvfschool_nb2000: number of schools within 2000 meters of property</p><p>- dvfschool_nb3000: number of schools within 3000 meters of property</p><p>- dvfroad_nb250: number of road connections within 250 meters of property</p><p>- dvfroad_nb500: number of road connections within 500 meters of property</p><p>- dvfroad_nb1000: number of road connections within 1000 meters of property</p><p>- dvfroad_nb2000: number of road connections within 2000 meters of property</p><p>- dvfroad_nb3000: number of road connections within 30000 meters of property</p><p>- dvfrail_nb250: number of railway stations within 250 meters of property</p><p>- dvfrail_nb500: number of railway stations within 500 meters of property</p><p>- dvfrail_nb1000: number of railway stations within 1000 meters of property</p><p>- dvfrail_nb2000: number of railway stations within 2000 meters of property</p><p>- dvfrail_nb3000: number of railway stations within 3000 meters of property</p><p> </p><p>Control Variables:</p><p>- center_dist: distance in kilometers of transacted property from urban area center</p><p>- sterr: surface area in square meter of parcel of each property</p><p>- sbati: surface area in square meter of building surfaces</p><p>- vente_cla: transaction through a classical process (binary variable)</p><p>- vente_adj: transaction through adjudicated process (binary variable)</p><p>- vente_ech: transaction through special exchange process (binary variable)</p><p>- vente_exp: transaction through expropriation process (binary variable)</p><p>- vente_efa: transaction before completion (binary variable)</p><p>- nblocmai: number of houses in each transaction</p><p>- nblocapt: number of apartments in each transaction</p><p>- nblocdep: number of building dependencies in each transaction</p><p>- nblocact: number of properties for commercial purpose in each transaction</p><p>- nbapt1pp: number of apartment with 1 room in each transaction</p><p>- nbapt2pp: number of apartment with 2 rooms in each transaction</p><p>- nbapt3pp: number of apartment with 3 rooms in each transaction</p><p>- nbapt4pp: number of apartment with 4 rooms in each transaction</p><p>- nbapt5pp: number of apartment with 5 and more rooms in each transaction</p><p>- nbmai1pp: number of house with 1 room in each transaction</p><p>- nbmai2pp: number of house with 2 rooms in each transaction</p><p>- nbmai3pp: number of house with 3 rooms in each transaction</p><p>- nbmai4pp: number of house with 4 rooms in each transaction</p><p>- nbmai5pp: number of house with 5 and more rooms in each transaction</p><p>- pm2mean_comm: average transaction price in euro per square meter of commune</p><p>- dvfmonument_nb500: number of historical monuments between 0 and 500 meters from transacted property</p><p>- dvfmonument_nb1000: number of historical monuments between 500 and 1000 meters from transacted property</p><p>- dvfmonument_nb2000: number of historical monuments between 1000 and 2000 meters from transacted property</p><p>- dvfindus_nb500: number of active industrial sites between 0 and 500 meters from transacted property</p><p>- dvfindus_nb1000: number of active industrial sites between 500 and 1000 meters from transacted property</p><p>- dvfindus_nb2000: number of active industrial sites between 1000 and 2000 meters from transacted property</p><p>- sh_apt: share of apartments in neighborhood IRIS</p><p>- sh_1945: share in percentage of properties with a building age before 1945</p><p>- sh_1970: share in percentage of properties with a building age before 1970</p><p>- sh_1990: share in percentage of properties with a building age before 1990</p><p>- sh_ap90: share in percentage of properties with a building age between 1990 and 2015</p><p>- sh_2015: share in percentage of properties with a building age after 2015</p><p>- clc1000_urbanhousing: share in percentage of land within 1000 meters of transacted properties with housing</p><p>- clc1000_urbanpark: share in percentage of land within 1000 meters of transacted properties with urban parks</p><p>- clc1000_recreation: share in percentage of land within 1000 meters of transacted properties with recreative activities</p><p>- clc1000_industrial: share in percentage of land within 1000 meters of transacted properties with industrial activities</p><p>- clc1000_transport: share in percentage of land within 1000 meters of transacted properties with transport infrastructures</p><p>- clc1000_nature: share in percentage of land within 1000 meters of transacted properties with natural land use</p><p>- clc1000_agr: share in percentage of land within 1000 meters of transacted properties with agricultural land use</p><p>- clc1000_forest: share in percentage of land within 1000 meters of transacted properties with forest</p><p>- clc1000_water: share in percentage of land within 1000 meters of transacted properties with water</p><p> </p><p> </p>
Water chemistry of LTER-Europe research site Lake Paione Inferiore LTER_EU_IT_088 (1984-2013)
<p>This dataset provides information about water chemical parameters for Lake Paione Inferiore LTER_EU_IT_088: pH, Total alkalinity, conductivity, total nitrogen, major cations (calcium, magnesium, sodium, potassium), major anions (sulphate, nitrate, chloride) and silica for the period 1984-2013.</p> <p>Lake Paione Inferiore (LPI) is a high altitude Alpine lake, located at 2002 m a.s.l. in the Bognanco Valley, Province of Verbania, Piedmont Region, Italy. It has a surface area of 0.86 ha and a maximum depth of 13.5 m. The Lake, together with Lake Paione Superiore (LPS), is included in the monitoring sites of the UN-ECE Program ICP WATERS (International Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes) for which the CNR Water Research Institute is the National Focal Centre for Italy.</p> <p>This dataset includes the following files: Metadata LTER_EU_IT_088.xls and per each parameter one xls file with data records. Dataset for water chemistry of LPI for the period 2014-2020 is available at <a href="https://doi.org/10.5281/zenodo.10519349">https://doi.org/10.5281/zenodo.10519349</a></p> <p>Detailed description of the site LPI is available at https://deims.org/c128d2f9-beb0-45ba-89bb-df9e12f95b0f</p>
JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets
<h2>JR100 Expedition 379T Site J1002 beryllium isotope, XRF element count and carbon isotope data sets (Finalised 8th of April 2024)</h2> <h3>How to cite these data:</h3> <p>The full data were published in Sproson <em>et al.</em>, 2024.</p> <p>Sproson AD, Yokoyama Y, Miyairi Y, Aze T, Clementi VJ, Riechelson H, Bova SC, Rosenthal Y, Childress LB & Expedition 379T Scientists. Near-synchronous Northern Hemisphere and Patagonian ice sheet variation over the last glacial cycle. <em>Nature Geoscience</em> <a href="https://doi.org/10.1038/s41561-024-01436-y">https://doi.org/10.1038/s41561-024-01436-y</a> (2024).</p> <h3>Files:</h3> <p><strong>Supplementary Table 1: </strong>Multiple linear regression results between 10Be/9Be ratios and sedimentation rate, K/Ca, Fe/Ca, Al/Ti (this study), Green/Blue (Li <em>et al.</em>, 2022), and Global Mean Sea Level (Lambeck <em>et al.</em>, 2014). The multiple linear regression was calculated using the MATLAB(R) function “regress”.</p> <p><strong>Supplementary Table 2: </strong> Age-depth model and beryllium isotope measurements for Site J1002. The age-depth model was calculated from radiocarbon dates and oxygen isotope stratigraphy (Li <em>et al.</em>, 2022) using the BIGMACS modelling routine (Lee <em>et al.</em>, 2022). Beryllium-9 and beryllium-10 were measured by Adam D. Sproson by HR-ICP-MS and AMS at the Atmosphere and Ocean Research Institute (Sproson <em>et al.</em>, 2021) and University of Tokyo (Matsuzaki et al., 2007), respectively. 10Be/9Be* ratios were corrected for 10Be paleo-production following von Blanckenburg <em>et al.</em> (2015). </p> <p><strong>Supplementary Table 3:</strong> X-ray Fluorescence Ti, K, Fe, Ca, and Al element counts per second for Site J1002 measured at the Lamont-Doherty Earth Observatory by Vincent J. Clementi.</p> <p><strong>Supplementary Table 4: </strong>Carbon isotope measurements for the benthic foraminifera, U. peregrina, measured at Rutgers University by Vincent J. Clementi.</p> <h3>Format:</h3> <p>Depth (m CCSF-A) = core composite depth below seafloor.</p> <p>Calendar age (kyr BP) = age in thousand years before present.</p> <p>[10Be]reac, [9Be]reac = the concentration of 10Be and 9Be in the reactive phase of marine sediments. </p> <p>Sample ID = expedition sample designation specifying hole (e.g., A), core number (e.g., 1), type (i.e., H), section number (e.g., 1), and then section half (i.e., W).</p> <p>σ = standard deviation.</p> <h3>References:</h3> <p>Lambeck K, Rouby H, Purcell A, Sun Y, Sambridge M. Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. <em>Proceedings of the National Academy of Sciences.</em> 2014;111(43):15296-15303. </p> <p>Lee T, Rand D, Lisiecki LE, Gebbie G, Lawrence CE. Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment. <em>EGUsphere.</em> 2022;2022:1-29.</p> <p>Li C, Clementi VJ, Bova SC, <em>et al.</em> The sediment green‐blue color ratio as a proxy for biogenic silica productivity along the Chilean Margin. <em>Geochemistry, Geophysics, Geosystems</em>. 2022:e2022GC010350. </p> <p>Matsuzaki H, Nakano C, Tsuchiya Y, <em>et al.</em> Multi-nuclide AMS performances at MALT. <em>Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms.</em> 2007;259(1):36-40. </p> <p>Sproson AD, Aze T, Behrens B, Yokoyama Y. Initial measurement of beryllium‐9 using high‐resolution inductively coupled plasma mass spectrometry allows for more precise applications of the beryllium isotope system within the Earth Sciences. <em>Rapid Communications in Mass Spectrometry.</em> 2021;35(8):e9059. </p> <p>Von Blanckenburg F, Bouchez J, Ibarra DE, Maher K. Stable runoff and weathering fluxes into the oceans over Quaternary climate cycles. <em>Nature Geoscience. </em>2015;8(7):538-542. </p>
volatile organic compounds that were measred in various sites in Israel, available by the Israel Ministry of Environmental Protection
<p>The dataset comprises measurements of volatile organic compounds sampled at multiple sites across Israel from 2010 to 2024, encompassing urban, rural, and suburban locations.</p>
Salinity, Turbidity, Wind from the S1-GB pylon at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the S1-GB dynamic pylon, in the Northern Adriatic Sea (around 7 miles offshore within the Po Delta on a bottom depth of 22.5 m), Italy. Specifically, it encompasses measurements on atmospheric parameters above the water surface and measurements at a defined depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The S1-GB pylon is situated at 44,74° N; 12,45° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records offering an invaluable insight into the dynamic characteristics of the marine environment over nearly a decade. The S1-GB pylon is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. The site contributes also to the DANUBIUS and JERICO Research Infrastructures.</p>
Salinity, Turbidity, Wind from the E1 buoy at the LTER site Delta del Po and Costa Romagnola (2012-2021)
<p>The present database comprises observations spanning from 2012 to 2021, focusing on abiotic parameters collected from the E1 meteo-oceanographic buoy in the Northern Adriatic Sea (north of Rimini city on a bottom depth of 10.5 m), Italy. Specifically, it encompasses measurements taken atmospheric parameters above the water surface and measurements at a defined nominal depth (https://vocab.nerc.ac.uk/collection/P01/current/ADEPZZ01/) of salinity (URI: https://vocab.nerc.ac.uk/collection/OD1/current/SAL/) in PSU (Practical Salinity Units), turbidity (URI: http://vocab.nerc.ac.uk/collection/P25/current/TURB/) in NTU (Nephelometric Turbidity Units; http://vocab.nerc.ac.uk/collection/P06/current/USTU/), wind speed (URI: http://vocab.nerc.ac.uk/collection/P25/current/WINDS/) in m/s (meters per second; http://vocab.nerc.ac.uk/collection/P06/current/PMPS/), and wind from direction (URI: http://vocab.nerc.ac.uk/standard_name/wind_from_direction/) in degrees (angular degrees, 0 represents the true north; http://vocab.nerc.ac.uk/collection/P06/current/UAAA/). The buoy is located at 44,14° N; 12,57° E (WGS-84 coordinate system) and is managed by the Institute of Marine Science of the National Research Council (ISMAR-CNR) in Bologna. The dataset relies on a Comma Separated Values (CSV) file and it is composed by 82391 records, offering an invaluable insight into the dynamic characteristics of the marine environment in this area over nearly a decade. The E1 buoy is part of the site “Delta del Po and Costa Romagnola”, which belongs to the Long Term Ecological Research national and international networks (LTER-Italy, LTER-Europe and ILTER) and eLTER-RI. </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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