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2,009 results for “Global data”
MCR LTER: Coral Reef: Dead coral skeletons impair key recovery processes following coral bleaching; data for Kopecky et al., 2024 Global Change Biology
The data included in this data package were collected on the North shore of Moorea, French Polynesia, from 2015-2023 to explore how dead coral skeletons (e.g,, left after coral bleaching events) influence critical processes tied to coral reef resilience. Together, these various datasets were used for analyses in the manuscript entitled "Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching", published in Global Change Biology. These data are in support of a publication Kopecky et al. (2024) Global Change Biology, and were a part of the thesis of K. Kopecky. The manuscript title and author list are as follows: Changing disturbance regimes, material legacies, and stabilizing feedbacks: dead coral skeletons impair key recovery processes following coral bleaching. Kai Kopecky, Russell J. Schmitt, Sally J. Holbrook. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 22-24354 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
Data set discussed in "Beyond Fortune 500: Women in a Global Network of Directors"
<p>Bipartite graph of directors and companies. Generated from information on the Financial Times website (<a href="https://markets.ft.com/data/equities/results">https://markets.ft.com/data/equities/results</a>), retrieved on 17 September 2016.</p> <p>Blank fields are used for missing data.</p> <p><strong>comp_nodes.csv:</strong></p> <ul> <li>id: unique identifier</li> <li>ft_country: name of the country</li> <li>ft_sector: segment of the economy in which a company operates</li> <li>ft_industry: specific business (i.e., subset of sector) in which a company operates</li> <li>ft_employees_num: number of company's employees. "NA" if the vertex represents a person or if the company's number of employees is unknown.</li> </ul> <p><strong>comp_people_edges.csv:</strong></p> <ul> <li>person_id:</li> <li>comp_id: company identifier. It matches the identifier in comp_nodes.csv</li> </ul> <p><strong>people_one_mode_edges.csv:</strong></p> <p>Edges in the one-mode projection, in which two directors are connected if and only if they sit together on at least one board. Numbers correspond to the identifiers in unique_people_nodes.csv.</p> <p><strong>unique_people_nodes.csv:</strong></p> <ul> <li>ID: unique identifier</li> <li>age: years of age</li> <li>gender_base: "Male" or "Female"</li> </ul>
Metabolism dataset: one year of high-frequency temperature, dissolved oxygen, wind, photosynthetically active radiation observations and low-frequency nutrient data for 58 lakes in the Global Lake Ecological Observatory Network
Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.
Daily phenocam image data and derived timeseries for global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains daily data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data included here comes from phenocams installed in two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and this dataset includes color values extracted from shrub and grass regions in these images. Further analyses, including daily values of calculated greenness (green chromatic coordinate), precipitation, and temperature, for all the plots included in the study are in EDI dataset knb-lter-jrn.210574002. This study is ongoing.
Data set for Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types
<p>Dataset used in the publication: " Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types". The dataset gathers estimates of ecosystem standing stocks (biomass, organic carbon, detritus), fluxes (GPP, ER, NEP) and process rates (decomposition and carbon uptake rates) for eight broad ecosystem types (forest, grassland, agroecosystem, desert, stream, lake, pelagic and benthic marine ecosystems) in five broad climatic zones (arctic, boreal, arid, temperate, tropical, arid).</p> <p>The scripts to produce the figures and the statistics of the publication are released along with the txt version of the data, which file is uploaded when running the script.</p>
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The chapter on<em> Examples of implementing the IPBES data management Policy</em> contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. <strong>Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here:</strong> <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a></p> <p>This session <em>Literature review from the Global Assessment chapter 4 </em>walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment. </p>
Data for: Global political responsibility for the conservation of albatrosses and large petrels
<p>Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). </p>
TCOM-HF : Daily global gap-free stratospheric hydrogen fluoride (HF) profile data set based on TOMCAT CTM and Occultation Measurements
<p><strong>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated hydrogen fluoride (HF) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time hydrogen fluoride profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used.</strong></p> <p><strong>Dataset also includes two files containing daily mean zonal mean hydrogen fluoride profiles on height (10-50 km) and pressure (300-0.1 hPa) levels:</strong></p> <p><strong>zmhf_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 50 km)</strong></p> <p><strong>zmhf_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa)</strong></p> <p><strong>Daily 3D profiles on height and pressure levels would be made available on request.</strong></p>
Global Naturalized Alien Flora (GloNAF). Open access data to support research on understanding global plant invasions.
<p>This dataset is a snapshot of the Global Naturalized Alien Flora (GloNAF) database, version 2.02. GloNAF is a continuously updated, curated compilation of alien naturalized vascular plant inventories for geographic regions from around the world. The dataset has 16,429 unique taxa reported as naturalized or invasive and covers 1,343 regions (including 427 islands) from 336 data sources. For each region, the status (invasive, naturalized) is provided as listed in the original source. We provide the scientific names included with the original data source, and the matching accepted name or synonym of the taxon as given in the World Checklist of Vascular Plants (WCVP) Version 12. In addition, we provide an ESRI shapefile of polygons for each region. We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources.</p> <p>The 'glonaf_flora2.csv' file lists the IDs ('taxon_wcvp_id') of all naturalized taxa contained in GloNAF and the regions they occur in. The 'glonaf_taxon_wcvp.csv' lists the original taxon names provided in the source data along with the corresponding accepted taxon name from the WCVP (version 12) for all alien taxa in GloNAF, regardless of their naturalization status. To link taxon names with naturalization records, join the 'id' column of the 'glonaf_taxon_wcvp.csv' file to the 'taxon_wcvp_id' column in 'glonaf_flora2.csv' . Additional information regarding the original source of the data ('glonaf_reference.csv'), specific attributes of the taxon lists ('glonaf_list.csv') and the region ('glonaf_region.csv') can also be joined similarly to 'glonaf_flora2.csv '. </p> <p> </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>
Raw planetary images and boulder labels data (as shapefiles) collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 64 large images of craters on the lunar and martian surfaces and 3 images of boulder fields on Earth (see manuscript <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a> for more information on those terrestrial locations). The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024.</p> <p>For each image, the boulder outlines within specific tiles within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). </p> <p>For each location, you will find a raster with a .tif format, and three shapefiles:</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a tiles-completely-mapped file, which depicts the patches/tiles/windows on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches/tiles/windows (pick the term you are the most familiar with) within a raster.</p> </li> </ul> <p>In addition you will find .pkl (which stands for pickle), which contains some information about the patches/tiles/windows if you would need to clip those windows out from the original raster. You can find more information in the way we process this raw data into a format which can be ingested in a deep learning model (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>) in the two following github repositories (<a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth</a> and <a href="https://github.com/astroNils/MLtools/tree/main" target="_blank" rel="noopener">https://github.com/astroNils/MLtools</a>). If you don't plan in adding more training data, you can directly used the pre-processed database (see <a href="https://zenodo.org/records/14250874" target="_blank" rel="noopener">https://zenodo.org/records/14250874</a>).</p> <p>There are multiple locations/images per planetary body. Cold spots are located on the Moon, but they are saved in a folder of their own. </p> <p>Note that the cold spots boulder mapping shapefiles are partially manually mapped, and partially originating from predictions made from a deep learning model (which explains the outline of boulders are predicted within one pixel).</p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── raw_data/ ├── coldspots/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ │ ├── <image_name>-tiles-completely-mapped.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</code></pre>
Global Ocean Heat Content Anomalies and Ocean Heat Uptake based on mapping Argo data using local Gaussian processes
<p>Monthly Ocean Heat Content Anomalies (OHCA) in the top 2000 dbar of the ocean are calculated (during 2004-2024, equatorward of 65 degree latitude) subtracting the mean over the period 2004-2024 from the monthly time series of OHC. Yearly OHCA time series are then calculated that include 1. one point per year, i.e., from averaging Jan to Dec (see files ending in “yearly.nc”), and 2. two points per year, i.e., from averaging Jan to Dec and Jul to Jun, respectively (see files ending in “yearly2.nc”). OHC fields are mapped using locally stationary Gaussian processes (defined over space and time) with data-driven decorrelation scales (Kuusela and Stein, 2018). A linear time trend was included in the estimate of the mean field (along with spatial terms and harmonics for the annual cycle). Mapping is done separately for different vertical sections: 15-20 dbar, 15-300 dbar, 300-700 dbar, 700-1850 dbar, 1800-1850 dbar. The 15-20 dbar (1800-1850 dbar) section is used to estimate OHCA for 0-15 dbar (1850-2000 dbar), where observations are sparser. Different vertical sections are combined to estimate global OHCA time series for 0-2000 dbar, 0-700 dbar, 700-2000 dbar (as indicated in the file names). The attribute "area" is included in the netcdf files and it tells the corresponding surface area for the estimates. Regions of the ocean that are shallower than 300 m or are not sufficiently well sampled by the Argo array are not included. Maps of the ocean masks used for the different vertical sections can be found in the .png files (blue shading indicates the area used for the horizontal integral); the bathymetry mask by Roemmich and Gilson (included in the file RG_ArgoClim_Temperature_2019.nc at https://sio-argo.ucsd.edu/RG_Climatology.html) is also used to define the ocean mask. Ocean Heat Uptake is calculated from the monthly OHCA and then averaged as described above to produce yearly time series included in the files for the different layers.</p> <p>For the uncertainty at each time point, the standard deviation of each OHCA/OHU value in the time series is included. When plotting a time series, the user may consider, e.g., shading plus/minus 1* or 1.96*standard deviation (corresponding to a confidence level of 68% or 95% respectively). These standard deviations in the files are estimated using spatially and temporally dependent conditional simulations of monthly gridded anomalies. When combining different layers, the standard deviation of the sum is conservatively estimated as the sum of the standard deviations. </p> <p>Finally, OHCA/OHU trends are estimated via a least-squares fit and reported in the variable metadata with uncertainties (confidence level of 68%). Trend uncertainties are estimated by repeating the fit for each member of the conditional simulation ensemble described above.</p> <p> </p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Data for "Globally widespread and increasing violations of environmental flow envelopes"
<p>Data and code for</p> <p><strong>Globally widespread and increasing violations of environmental flow envelopes</strong></p> <p>Vili Virkki*#, Elina Alanärä#, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu*</p> <p># equal contribution to the article<br> * Correspondence to: Vili Virkki (vili.virkki@aalto.fi), Matti Kummu (matti.kummu@aalto.fi)</p> <p><br> link to published version: https://hess.copernicus.org/articles/26/3315/2022/</p> <p><strong>Please cite the published version of the article when using these data.</strong></p> <p><strong>See readme.txt in data for a detailed description of attached files.</strong></p>
New maps of global geologic provinces and tectonic plates: global tectonics data and QGIS project file
<p>The global tectonics data compilation is a set of raster and vector data that are useful for investigating tectonics past and present. The datasets are useful on their own or can be used in GIS software, which includes the QGIS project file for convenience. The datasets include our new models for tectonic plate boundaries and deformation zones, geologic provinces and orogens. Additional datasets include earthquake and volcano locations, geochronology, topography, magnetics, gravity, and seismic velocity.</p> <p>The global tectonics collection is suitable for research and educational purposes.</p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
ScienceDex guides
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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.