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18,657 results for “Impact”
Climatic and societal impacts of a "forgotten" cluster of volcanic eruptions in 1108-1110 CE
<p>This repository contains all the tree-ring and historical archives used by Guillet et al. (2020) to assess the climatic impacts of the 1108-1110 CE volcanic eruptions</p> <p>For more information, we refer the user to the readme file entitled "Guillet_et_al_SciReports2020_Readme.txt"</p> <p>We note that investigations of European historical archives are still carried ongoing. The file entitled "Guillet_et_al_SciReports2020_Supp_Info_Table_S1_S2_Historical_Sources.xlsx" will be updated as new material is discovered.</p> <p>We welcome every addition or contribution that may help to extend the number of historical sources available and better document the climatic and societal response to the 1108-1110 CE cluster of eruptions. Thank you ;-)!</p>
Undinarchaeota illuminate DPANN phylogeny and the impact of gene transfer on archaeal evolution
<p><strong>General Description </strong></p> <p>Repository with all analyses described our paper: <a href="https://www.nature.com/articles/s41467-020-17408-w">Undinarchaeota illuminate DPANN phylogeny and the impact of gene transfer on archaeal evolution</a>.</p> <p>If you find this work useful for your own analyses, please cite this work.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The evolution and diversification of Archaea is central to the history of life on Earth. Cultivation-independent approaches have revealed the existence of the DPANN archaea: a radiation of organisms with small cell and genome sizes. Currently, the placement of the various DPANN lineages and in turn the early evolution of metabolism and symbiosis are debated. Here, we reconstructed genomes of a thus far uncharacterized archaeal phylum-level lineage UAP2 (<em>Candidatus</em> Undinarchaeota). Comparative genomics revealed that members of the Undinarchaeota have small estimated genome sizes and, while potentially being able to conserve energy through fermentation, likely depend on partner organisms for the acquisition of vitamins, amino acids and other metabolites. In contrast to previous indications, our phylogenomic analyses robustly placed the Undinarchaeota as independent lineage between two major and highly supported clans of ‘DPANN’. Furthermore, our work suggests that DPANN archaea have exchanged core genes with their hosts by horizontal gene transfer, adding to the difficulty of placing DPANN in the tree of life (ToL). In several cases, this pattern is sufficiently dominant that known symbiont-host clades can be identified by inferring routes of HGT across the ToL. Together, our findings provide crucial insights into the origins and evolution of DPANN archaea and their hosts.</p> <p><strong>The annotation workflow for archaeal/bacterial genomes that was used for this paper is also available on github (<a href="https://github.com/ndombrowski/Genome_annotations">here</a>) and an updated version that includes the COG search is available on: </strong><a href="https://github.com/ndombrowski/Annotation_worfklow">https://github.com/ndombrowski/Annotation_workflow</a></p> <p> </p> <p><strong>Repository Contents</strong></p> <p><strong>1_Genome_files.tar.gz</strong> includes all Undinarchaeota (original name UAP2) metagenome-assembled genomes (MAGs). This includes: </p> <ol> <li>The original contigs for each UAP2 MAG (fna files)</li> <li>The prokka output for each UAP2 MAG (faa files)</li> <li>A concatenated file of all proteins from each UAP2 MAG and all archaeal reference genomes (364 genomes in total). This folder also includes a list of archaeal genomes investigated.</li> </ol> <p><strong>2_Phylogenies.tar.gz</strong> includes all files for the phylogenetic analyses. This includes the following folders:</p> <p>1. Files for the concatenated species trees for different taxa sets. These files are related to the following parts of the manuscript: Supplementary Table 6; Figure 1 and Supplementary Figures S8-S58. The folder includes the following:</p> <ul> <li>Folder '1_unaligned_sequences' includes individual protein sequences extract from the different taxa sets.</li> <li>Folder '2_alignments' includes the alignment files generated by MAFFT.</li> <li>Folder '3_alignments_trimmed' includes the alignments trimmed with BMGE.</li> <li>Folder '4_phylogenies' includes the IQ-TREE output for all phylogenies as well as color-annotation file for figtree. Additionally files rooted with minimal ancestor deviation (MAD) rooting (*.rooted) are provided. Note, that for the final figures the *treefile_renamed (i.e. the iqtree file with the full taxa string) were artificially rooted using the DPANN archaea. The numbering corresponds to Supplementary Table S6 of the main manuscript.</li> <li>Folder ' 5_pdfs' includes the PDFs for each tree</li> </ul> <p>2. Files for single gene trees that includes:</p> <ul> <li>The folder '1_arcogs' includes the unaligned proteins, alignments, trimmed alignments, trees and pdfs for the single gene trees based on the arCOG identifiers. The arCOGs were extract from 12 UAP2 MAGs + 352 archaeal + 3020 bacterial + 100 eukaryotic genomes. ArCOGs were only considered if they occurred in at least 3 UAP2 genomes. Notice, these files were used to investigate UAP2 for HGT events and correspond to the following parts of the manuscript: Figure 4 and Supplementary Tables 4, 5, 20-22. Additionally, the folder 0_parsing includes some information on how to generate count tables for each marker gene.</li> <li>The folder '151_markers' including the proteins, alignments, trimmed alignments, trees and pdfs for evaluating the 151 marker set used for the concatenated species tree. Files were provided for the 127 and 364 taxa set. These files were used as a basis for the concatenated species trees that were used to generated Supplementary Figures S8-S58. Additionally, the trees were used for ranking marker proteins and generating Supplementary Tables 4-5. For the 364 taxa set, the folder also included a subfolder 0_parsing that provides scripts to investigate some statistics for each marker protein, including the average protein length, average alignment length and average bootstrap support.</li> <li>The folder '3_other_individual_trees' includes the proteins, alignments and phylogenies for the 16S_23S, RubisCO and primase analyses. The data was used to generate the following parts of the manuscript: Supplementary Table 11, Supplementary Figures 3-5, 57 and 59.</li> </ul> <p><strong>3_Scripts.tar.gz</strong> includes all files for the phylogenetic analyses. This includes the following folders:</p> <p>1. The files for the main workflow for the annotations and phylogenies.</p> <ul> <li>This folder includes the workflow to generate annotations for archaeal genomes as well as an example script that was used to generate phylogenies. These analyses were typically run on a in-house bioinformatics cluster with 4x Xeon Gold 6140 2.3 GHz processors using bash, python and perl. The used system runs a Linux operating system, Red Hat Enterprise 7.5.</li> </ul> <p>2. A folder providing any required dependencies that include:</p> <ul> <li>any python or perl scripts that were used during this study and/or that are mentioned in the methods section</li> <li>Databases used for the annotations, esp. if these were slightly modified. Notice, changes typically include parsing of the mapping files or modifications of the sequence headers for easier parsing.</li> <li>mapping files needed to link the genome accession ids to the taxonomy string as well as lists of protein IDs used for different phylogenies (i.e. 14 + 48 arCOGs used for protein phylogenies)</li> </ul> <p>3. R scripts (including all needed input files) used to: </p> <ul> <li>generate tables and figures for the annotations, i.e. Figure 2 and 3 and Supplementary Tables 7, 8, 9, 12, 13-15 and Supplementary Figures 60, 62-64 . The input folder includes the raw output from the annotation workflow and includes annotations for the 12 UAP2 MAGs as well as 352 archaeal reference genomes.</li> <li>generate tables and figures for the HGT analyses, i.e. Figure 4 and Supplementary Tables S20-22 Here, proteins based on arCOGs were extracted from 364 archaeal, 3020 bacterial and 98 eukaryotic genomes and used to generate single protein phylogenies. The resulting trees were used to investigate horizontal gene transfer events and the necessary scripts are provided in this folder.</li> <li>generate tables and figures for the amino acid identify (AAI) comparisons, i.e. Supplementary Table S3 and Supplementary Figure S2. </li> <li>rank the marker genes for concatenated species trees for the 127 and 364 taxa set. These were used to generate Supplementary Tables S4 and S5.</li> </ul> <p><strong>General comment:</strong></p> <p>In contrast to the previous version, this datasets includes some small additional scripts generated during the revision process of the corresponding manuscript.</p> <p> </p>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
<p>Data Release and facsimile of the survey, presented in the submission 3238 to the CSCW 2021 conference.</p> <p> </p>
Impact of delayed response on Wearable Cognitive Assistance
<p>This dataset contains the data associated with our research project titled Impact of delayed response on Wearable Cognitive Assistance. A preprint of the associated paper can be found at https://arxiv.org/abs/2011.02555. See the README.txt file for dataset details.</p>
JCR Journals, sorted by Impact Factor 2011 with the JCR edition indication
Description of the spreadsheet: “Journals in JCR sorted by IF’11” lists the journals from Thomson Reuters JCR website; it’s sorted by edition (science and social science) and Impact Factor 2011 descending (but not difunded). Fields: Abbreviated Journal title, ISSN, JCR ed. Methodology: 1. We copy and paste from the web pages the list in a unique spreadsheet. 2. We agregate the JCR edition: SCI=1 and SSCI=2. 3. We sort by Edition and Impact Factor and delete this column values. 4. We upload the excel file to data banks
Data from: Investigating the impact of street lighting changes on garden moth communities
<p>This data package accompanies:<br><em>Plummer et al (2016). Investigating the impact of street lighting changes on garden moth communities. Journal of Urban Ecology. DOI 10.1093/jue/juw004</em></p> <p>It contains a copy of the two derived datasets used to complete the analyses presented in the paper. File details:</p> <p><strong>1. ReadMe.txt: </strong>Includes a description of the variables included in each dataset.</p> <p><strong>2. Plummer_JUrbanEcol_2016_BACI_dataset.csv: </strong>A .csv file including two years (2011 & 2013) of macro-moth community data (abundance, richness, diversity) for 18 garden locations in Birmingham, UK. Data are summarised per garden and year, together with data for proximity to street lamp replacement.</p> <p><strong>3. Plummer_JUrbanEcol_2016_light_composition_dataset.csv:</strong> A .csv file including one year (2013) of garden moth community data (abundance, richness, diversity; including macro- and micro-moths) for 18 garden locations in Birmingham, UK. Data are summarised per trapping event, together with associated street lighting and habitat characteristics for each garden. </p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Background data: Untangling the effects of multiple human stressors and their impacts on fish assemblages in European running waters
<p>This dataset presents some backkground data from the EFI+ database. Related work addresses human stressors and their impacts on fish assemblages at pan-European scale by analysing single and multiple stressors and their interactions. Based on an extensive dataset with 3105 fish sampling sites, patterns of stressors, their combination and nature of interactions, i.e. synergistic, antagonistic and additive were investigated. </p> <p>Data were derived within the EU-project "Improvement and Spatial extension of the European Fish Index (EFI+)". EFI+, an EU FP6 research project from 2007-2009 was designed to gain new knowledge and to further develop and improve new biological assessment methods to meet needs of the Water Framework Directive (WFD). </p> <p>Background data are available for boxplots and barplots shown in the related research article in STOTEN.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Hydrodynamic field data near Galveston, Texas wetland edges to help assess storm impacts and erosion
<p>Water free surface elevation measurements via submerged pressure transducers along transects near Galveston Bay wetland edges</p>
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Dynamic Traces from CEA Curie
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the dynamic trace utilizing <a href="https://www.cs.huji.ac.il/labs/parallel/workload/l_cea_curie/index.html">Curie's workload</a> carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per target fair share value that the user that we track its usage. Within, we have a file with the name indicating if the workflow is wrapped or not, the type, vertical or horizontal, and the instant of submission. This file is in <a href="https://www.cs.huji.ac.il/labs/parallel/workload/swf.html">SWF</a> format. Under the results folder, we have the same organizaion: each .trace is the raw file produced by the simulator. </p> <p>The workload log from the CEA Curie system was graciously provided by Joseph Emeras (<a href="mailto:Joseph.Emeras@imag.fr">Joseph.Emeras@imag.fr</a>).</p>
Data for: Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy
<p><em><strong>Version v2:</strong> <br></em>Added tiff-image stacks</p> <p><em><strong>Version v1:</strong><br></em>The data is supplementary to the publication "Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy", DOI: <a href="https://doi.org/10.1016/j.compscitech.2022.109362">10.1016/j.compscitech.2022.109362</a> as well as to the dissertation: "Morphology and Fracture of Block Copolymer and Core-Shell Rubber Particle Modified Epoxies and their Carbon Fibre Reinforced Composites", urn: <a href="https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-63437">urn:nbn:de:hbz:386-kluedo-63437</a></p> <p>Key words: Polymer-matrix composites (PMCs), Impact behaviour, Low velocity impact, Barely visible impact damage, Damage tolerance, X-ray computed tomography, Fractography, Carbon fibre reinforced composite (CFRP)</p> <p>The data set is a collection of TXRM data of several low energy impact damages in CFRP specimens. The data was acquired via XCT (X-Ray Computed Tomography).</p> <p>Material details:</p> <ul> <li>Carbon fibre reinforced composite</li> <li>Thickness: ~ 1.65mm</li> <li>Matrix polymer: Epoxy-based (DGEBA): Sika CR144 + Anhydride curing agent (Huntsman Aradur917) + 1-Methylimidazole</li> <li>Carfon-fibre fabric: ECC Carbon fabric Style 763, based on Toho Tenax HTA40 E13, 140g/m²</li> <li>Layup: 13 layers, stacking sequence (45/-45/45/-45/90/0/90)s, (15% 0°/23% 90°/62% ± 45°) </li> <li>the average carbon fibre volume content was 52.5 ± 1.8 vol.-%</li> <li>cured ply-thickness: 126.1 μm</li> <li>Impact energies: 1J, 3J, 7J, 9J, 13J</li> <li>manufactured via autoclaving</li> </ul> <p>The reasearch received funding from the German Academic Exchange Service (DAAD) within the funding program “Kurzstipendien fuer Doktoranden” (grant number: 57438025).</p>
History, Adoption and Key impacts of precision agriculture
<p>Precision agriculture technologies have revolutionized modern farming practices, offering innovative solutions to optimize crop production, minimize resource use, and enhance environmental sustainability. This research paper explores the historical evolution, adoption trends, and importance of precision agriculture technologies in contemporary agriculture.</p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Inter-Chemical Correlation results for the study: HHEARx2018-2120 (The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children)
Title: The impact of tobacco smoke exposure and environmental exposures on the pulmonary microbiome and outcomes of critically ill children <br>Species: Homo sapiens <br>Number of samples: 1090 <br>Number of named analytes: 12 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=42 <br>
Climate Solutions Explorer - hazard, impacts and exposure data
<p><a name="_GoBack"></a>The Climate Solutions Explorer website maps and presents information about mitigation pathways, avoided climate impacts, vulnerabilities and risks arising from development and climate change. <a href="https://www.climate-solutions-explorer.eu"><strong>www.climate-solutions-explorer.eu</strong></a></p> <p>Using the latest data, state-of-the-art models were used to assess the future trends of indicators of development- and climate-induced challenges.</p> <p>Updated gridded global climate and impact model data are based on CMIP6 and CMIP5 projections, using a subset of models from the ISIMIP project that have been consistently downscaled and bias-corrected. The data includes various indicators (~42) relating to extremes of precipitation and temperature (e.g. from Expert Team on Climate Change Detection and Indices), hydrological variables including runoff and discharge, heat stress (from wet bulb temperature) events (multiple statistics and durations), and cooling degree days, as well as further indicators relating to air pollution (PM2.5 from the GAINs model), and crop yields and natural habitat land-use change (biodiversity pressure) from the GLOBIOM model.</p> <p>Indicators were calculated at a spatial resolution of 0.5° (approximately 50km at the equator), and subsequently spatially aggregated to the country level – from which population and land area exposure to the impacts were calculated. This has enabled the country-by-country comparison of national climate impacts and avoided exposure. Impacts were calculated at global mean temperature intervals, i.e. 1.2, 1.5, 2, 2.5, 3, and 3.5 °C, compared to a pre-industrial climate.<br><br></p> <p><strong>The dataset includes: </strong></p> <ul> <li>Global gridded projections (in netCDF format) of all the climate impact indicators at 0.5° spatial resolution, at global warming levels of 1.2, 1.5, 2, 2.5, 3, and 3.5 °C<br><br>For each GWL, maps for the absolute indicator values, the relative difference, and the scores are provided. The naming format is: cse_[short_indicator_name]_[ssp]_[gwl]_[metric].nc4. Please note that the Greenland ice sheet and the desert areas have been masked out for the hydrology indicators for these datasets.<br><br></li> <li>Intermediate output data, including gridded maps of absolute values, relative differences, and scores for all ensemble members, as well as gridded maps of the multi-model ensemble statistics for the global warming levels and the reference period <br><br>For the ensemble member data, the naming format is [gcm]_[ssp/rcp]_[gwl]_[short_indicator_name]_global_[start_year]_[end_year].nc4 or [ghm]_[gcm]_[ssp/rcp]_[gwl]_[soc]_[short_indicator_name]_global_[start_year]_[end_year]_[metric].nc4 for the hydrology indicators. <br><br></li> <li>Tabular data (.csv) aggregating the indicators to country (or region) level, for both hazards and exposure, population and land-area weighted<br><br>The .zip archives ‘table_output_climate_exposure_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_climate_exposure_land_air_pollution.zip’ contains the table data for theland and air pollution indicators. <br><br></li> <li>Tabular data (.csv) for avoided impacts by mitigating to 1.5 °C (land and population exposure)<br><br>The .zip archives ‘table_output_avoided_impacts_{aggregation_level}.zip’ contain the tabular data for all indicators. Four different aggregation levels are provided: country level, R10 regions and the EU, IPCC AR6-WGI reference regions, and UN R5 regions. A separate file named ‘table_output_avoided_impacts_land_air_pollution.zip’ contains the table data for the land and air pollution indicators.</li> </ul> <p> </p> <p>Further details are available on the Data Story page – <a href="http://www.climate-solutions-explorer.eu/story/data">www.climate-solutions-explorer.eu/story/data</a>. A detailed description of the methodology and the calculation of the ISIMIP-derived indicators has been published in <a title="Global warming levels indicators of climate change and hotspots of exposure" href="https://doi.org/10.1088/2752-5295/ad8300" target="_blank" rel="noopener">Werning, M. et al. (2024).</a></p> <p> </p> <p><strong>Release notes (v1.1)</strong></p> <p>Changes in this version:</p> <ul> <li>Only table output data for the land and air pollution indicators have been changed, all other indicator data remain unchanged from v1.0</li> <li>Updated land and air pollution indicators to use scaled population data to match the latest SSP population projections from the Wittgenstein Center from 2023</li> <li>Fixed issue with the region mask for the EU</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> </ul> <p> </p> <p><strong>Release notes (v1.0)</strong></p> <p>Changes in this version:</p> <ul> <li>Fixed calculation of the indicator “Drought intensity” (both for the version using discharge and run-off)</li> <li>Masked out the Greenland ice sheet and the desert areas for the global gridded projections for the hydrology indicators in the final output files</li> <li>Added table output data for the IPCC AR6-WGI reference regions and the UN R5 regions</li> <li>Used scaled population data to match the latest SSP population projections from the Wittgenstein Center from <a>2023</a></li> <li>Added the indicator ‘Heatwave days’</li> <li>Added intermediate outputs for all ensemble members for energy, hydrology, precipitation, and temperature indicators<br><br></li> </ul> <p><strong>Release Notes (v0.4)</strong></p> <p>Changes in this version:</p> <ul> <li>Removed ssp and metric from variable name in netCDF files</li> <li>Removed obsolete coordinates in netCDF files for 'Drought intensity'</li> <li>Added intermediate outputs for energy, hydrology, precipitation, and temperature indicators</li> </ul> <div> </div>
Tycho Region Database of impact craters >=21 meters on the Moon.
<p>This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (<strong>see Version V2 for the global catalog at 100m/px</strong>) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:</p> <ol> <li>La Grassa, Riccardo, et al. <strong>"YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces."</strong> Remote Sensing 15.5 (2023): 1171, https://doi.org/10.3390/rs15051171.</li> <li>La Grassa, R, et al. <strong>"LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon"</strong>, ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.<strong><br></strong></li> </ol> <p><br>The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.</p> <p>The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].</p> <p>This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.</p> <p>Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in <strong>version V2</strong> of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.</p> <p>*********************************************************************🌕🌖🌗🌘🌑🌒🌓🌔🌕 **********************************************************************</p> <p>Release of a Tycho area at 21m/px of craters catalog with more than 6.8 million craters.</p> <ul> <li><em>File source csv</em></li> <li><em>Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.</em></li> <li><em>The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. <br></em></li> <li><em>Diameters (km)</em></li> </ul> <p> </p>
Water levels at tide gauges from: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "<strong>Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution</strong>" published in Earth System Science Data (ESSD). </p><p>Note that in this repository, water levels are only provided tide gauge locations which were used for the analysis presented in the paper. The full Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><h2>File Descriptions</h2><h4>HCC_analysis_and_plots.ipynb</h4><p>This jupyter-notebook contains all scripts to produce the plots presented in the paper. Make sure that all necessary python packages are installed. The script assumes all netCDF files from this repository to be stored in a sub-directory called "data".</p><h3>hcc_gesla3_99pctl_surge_2011_2015.nc</h3><p>Extreme surge levels from 2011-2015 at 999 GESLA-3 tide gauge stations with at least 90 percent of data in the considered period. As astronomical tides are removed from the modeled and observed water levels to yield the surge component. The file also contains monthly relative water levels and monthly geocentric water levels from 1900-2015 from the HCC dataset.</p><h4>Variables:</h4><ul><li><i>observed_99pctl_surge_level_anomaly</i> -- 99th percentile of daily maximum surge level anomalies from 2011-2015</li><li><i>hcc_99pctl_surge_level_anomaly -- </i>HCC surge level anomalies at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_counterfactual_99pctl_surge_level_anomaly</i> -- HCC counterfactual surge levels at the same time steps as <i>observed_99pctl_surge_level_anomaly</i></li><li><i>hcc_water_level_monthly</i> – Monthly relative water level from 1900-2015</li><li><i>hcc_geocentric_water_level_monthly</i> – Monthly geocentric water level from 1900-2015</li></ul><h3>hcc_hr_psmsl_water_level_monthly_1900_2015.nc</h3><p>Monthly water levels at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. The file contains data from the HCC, HR and PSMSL datasets. To align PSMSL and HR with HCC, the 1993-2012 average from PSMSL and HR is removed from each of those datasets respectively and the 1993-2012 average of HCC is added. The average is calculated only over all time steps where the associated observational record has valid data.</p><h4>Variables:</h4><ul><li><i>hcc_water_level_monthly</i> – Monthly relative water level from the HCC dataset</li><li><i>hr_aligned_water_level_monthly</i> -- Monthly relative water level from the HR dataset, aligned with <i>hcc_water_level_monthly</i></li><li><i>psmsl_aligned_water_level_monthly</i> -- Monthly relative water level from the PSMSL database, aligned with <i>hcc_water_level_monthly</i></li></ul><h3>hcc_codec_hr_gesla3_water_level_hourly_monthly_1979_2015.nc</h3><p>Hourly water levels at 1040 GESLA-3 tide gauge stations which have at least 30 percent of valid observations between 1979 and 2015. The file contains data from the HCC, CoDEC, HR and GESLA-3 datasets. The different records are not vertically aligned.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li><li><i>codec_water_level_hourly</i> -- Hourly relative water level from the CoDEC dataset</li><li><i>hr_water_level_monthly</i> -- Monthly relative water level from the HR dataset</li></ul><h3> </h3><h3>hcc_gesla3_water_level_hourly_2011_2015.nc</h3><p>Water levels from the HCC and GESLA-3 datasets, only for tide gauge stations with a complete record in the period 2011-2015 and associated HCC grid points.</p><h4>Variables:</h4><ul><li><i>gesla3_water_level_hourly</i> -- Hourly relative water level from the GESLA3 database</li><li><i>hcc_water_level_hourly</i> -- Hourly relative water level from the HCC dataset</li></ul><h3>slr_ds_psmsl_selected.nc</h3><p>Linear estimates of relative sea level rise from 1900 to 2015. Data is provided at 663 PSMSL tide gauge stations of at least 20 year length and with at least 30 percent data coverage in the 1993-2012 period. Estimates are calculated for the HCC, HR and PSMSL datasets.</p><h4>Variables:</h4><ul><li><i>psmsl_rslr, psmsl_rslr_lower, psmsl_rslr_upper</i> -- Relative sea level rise for PSMSL with lower and upper bounds for a 95 percent confidence interval</li><li><i>hcc_long_rslr, hcc_long_rslr_lower, hcc_long_rslr_upper </i>-- Relative sea level rise for HCC with lower and upper bounds for a 95 percent confidence interval</li><li><i>hr_rslr, hr_rslr_lower, hr_rslr_upper</i> -- Relative sea level rise for HR with lower and upper bounds for a 95 percent confidence interval</li></ul><h3>reg_mask_xr.nc</h3><p>Split of the world into 7 ocean basins: Indian Ocean - South Pacific, Northwest Pacific, East Pacific, South Atlantic, Subtropical North Atlantic, Subpolar North Atlantic West and Subpolar North Atlantic East.</p><h4>Variables:</h4><p><i>reg_mask</i> – Float value, representing the ocean basins</p><p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.