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354 results for “budget”
Carbon Budget at the Harvard Forest 1992-2015
How, where, and why carbon (C) moves into and out of an ecosystem through time are long-standing questions in biogeochemistry. Here, we bring together hundreds of thousands of C-cycle observations at the Harvard Forest in central Massachusetts, USA, a mid-latitude landscape dominated by 80–120-year-old closed-canopy forests. These data answered four questions: (i) where and how much C is presently stored in dominant forest types; (ii) what are current rates of C accrual or loss; (iii) what biotic and abiotic factors contribute to variability in these rates; and (iv) is climate change affecting the forest’s C cycle? Harvard Forest is an active C sink resulting from forest regrowth following land abandonment. Soil and tree biomass comprise nearly equal portions of existing C stocks. Net primary production (NPP) averaged 750–970 g C m-2 yr-1; belowground NPP contributed 30–60% of the total. Mineral soil C measured in the same inventory plots in 1992 and 2013 were too heterogeneous to detect change in soil-C pools; however, radiocarbon data suggest a small but persistent sink of 10–30 g C m-2 yr-1. Net ecosystem production (NEP) in hardwood stands averaged ~300 g C m-2 yr-1. NEP in hemlock-dominated forests averaged ~450 g C m-2 yr-1 prior to infestation by the hemlock woolly adelgid (HWA) in 2013, and then became a net C source. Stand dynamics and climate change in the last three decades enhanced the C sink in hardwood stands; NPP increased 26% between 2000–2014 (p = 0.02) and NEP increased 93% between 1992–2015 (p = 0.13). Compared to long-term global change experiments at the Harvard Forest, the C sink in regrowing biomass equaled or exceeded C cycle modifications imposed by soil warming, N saturation, and hemlock removal. Median forest biomass in the surrounding ecoregion was only 78% of that at the Harvard Forest due to higher timber harvesting rates across the region. Results of this synthesis and comparison to simulation models suggest that forests across the reg
Whole-Tree Nonstructural Carbohydrate Budgets in Five Species at Harvard Forest 2014
We measured nonstructural carbohydrate (NSC) concentrations throughout the year in the branches, stemwood, and roots of five temperate tree species. These NSC concentrations were used in two ways. First, we scaled up concentrations to the whole-tree level using allometric equations and compared NSC storage between these five species to determine the size and seasonal fluctuation of whole-tree total NSC budgets as well as the contribution of individual organs. Second, for four of these species, we assessed the radial patterns and seasonality of NSC concentrations in the stemwood based on contrasting wood anatomy (ring-porous vs. diffuse-porous).
Trout Lake USGS Water, Energy, and Biogeochemical Budgets (WEBB) Stream Data 1975-2013
This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
FAB 1: Forests and Biodiversity Experiment - High density diversity experiment: carbon budget data
This data represents changes in above and belowground C pools in young stands six years after the initiation of the Forests and Biodiversity experiment (FAB1) in 2013, consisting of high density plots of one, two, five, or 12 tree species planted in a common garden. Trees were planted to represent a range of native functional diversity, including needle-leaf conifer and broadleaf deciduous species as well as ectomycorrhizal and arbuscular mycorrhizal species. We quantified the effects of species richness, phylogenetic diversity, and functional diversity on aboveground C accumulation, as well as on soil C accumulation, fine root C, and soil aggregation. To assess the role of the microbial community in mediating these effects, we further compared changes in soil C pools to phospholipid fatty acids (PLFAs) profiles collected in 2016.
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018. The water budget includes precipitation, evapotranspiration, stream flows, water withdrawals, sewer export and public water supply imports.
Nitrogen budget in a Chihuahuan desert grassland long-term nitrogen fertilization experiment at the Sevilleta National Wildlife Refuge
Although the negative consequences of increased nitrogen (N) supply on plant communities and soil chemistry are well known, most studies have focused on mesic grasslands, and the fate of added N in arid and semi-arid ecosystems remains unclear. To study the impacts of long-term increased N deposition on ecosystem N-pools, we sampled a 26-year-long fertilization (10 g N m-2 yr-1) experiment in the northern Chihuahuan Desert at the Sevilleta National Wildlife Refuge (SNWR) in New Mexico. To determine the fate of the added N, we measured multiple soil, microbial, and plant N pools in shallow soils at three time points across the 2020 growing season.
A Lagrangian study of the contribution of the Canary coastal upwelling to the nitrogen budget of the open North Atlantic
<p>The attached datasets constitute the particle trajectory data produced in the experiment for Hailegeorgis et al..</p> <p>The "traj_upwell_1d_70m_1d-variables.nc" contains variables that describe different aspects of each upwelled particle (mostly regarding a particle's release or its initial or final conditions).</p> <p>The rest of the files with the format "traj_upwell_1d_70m_XXX-traj.nc" describe an attribute XXX (location or nutrient concentration) along the trajectory of upwelled particles tracked as part of the experiment.</p> <p>With ARIANE, particles are released and tracked in a ROMS simulation of the Canary coastal upwelling region. Out of the ~10M particles, the trajectories of the ~353K (~3.6%) that upwell are included. The variable "index_in_full_exp" in file "traj_upwell_1d_70m_1d-variables.nc" shows the index of each of these upwelling particles in the larger pool of released particles. For each upwelled particle, out of the 720-day trajectories starting from its release into the coast, the values from its upwelling step to its exit from the experiment are included, with the values outside this range being filled with a generic value (1.e20). An upwelled particle exits the experiment when it leaves the regional ROMS simulation altogether or when it leaves the coast and returns to the coast to re-upwell (more details in the paper).</p> <p>Be mindful of the different values of time. In "traj_upwell_1d_70m_1d-variables.nc", the variable "release_time" tells each particle's release time, in days since onset of the ROMS simulation, while variable "coast_exit_time" tells each particle's day of exiting coast, in days since its release. In each particle's trajectory (in traj_lon, traj_lat, etc), the first and last steps with valid values are the same as the days of its upwelling and its exit, respectively, since its release.</p> <p>The files contain the name and description of each variable. Along with the details in the publication, the descriptions here should be enough to fully interpret the information and replicate our analysis.</p>
BIOLOGICAL DATA in CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system
<p>BIOLOGICAL DATA to estimate the carbon dioxide budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system.</p> <p>Álvarez-Salgado et al. (2022) estimate the carbon dioxide and total alkalinity budgets due to the Mediterranean mussels (Mytilus galloprovicialis) growing in suspended culture in a low seston environment such as the Galician Rías (NW Spain). This database contains the biological data needed to estimate the carbon dioxide fluxes and changes in total alkalinity induced by the different biological processes involved in mussel growth. </p> <p>Manuscript available at: <a href="https://doi.org/10.1016/j.scitotenv.2022.157867">https://doi.org/10.1016/j.scitotenv.2022.157867</a></p> <p>Álvarez-Salgado, X.A., Fernández-Reiriz, M.J., Fuentes-Santos, I., Antelo, L.T., Alonso, A.A., Labarta, U., 2022. CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system. Sci. Total Environ. 849, 157867.</p>
Sediment Budget for Timber Harvest in a California Coastal Watershed
<p>Dataset for Publication: Sediment Budget for Timber Harvest in a California Coastal Watershed</p> <p> </p>
Global Carbon Budget 2022, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual Global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (data-products).</strong><br> There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of data-products and GOBMs and with the adjustments described in the Global Carbon Budget 2022 (https://doi.org/10.5194/essd-14-4811-2022, section C3), are available in the Global Carbon Budget 2022 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2022 paper (https://doi.org/10.5194/essd-14-4811-2022), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.17 GtC yr-1, Tropics: 0.16 GtC yr-1, South: 0.32 GtC yr-1, see GCB 2022 paper, section 2.4.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):</p> <p><br> fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br> fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude<br> area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A (‘contemporary simulation’, including effects of rising CO2, climate change and variability) and simulation B (‘control simulation’, constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br> sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br> area: Area per pixel, dimensions: latitude, longitude</p> <p><br> (3) One file ‘GCB-2022_OceanModel_RegionalBreakdown_1959-2021.nc’ with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p> <p><br> <strong>Fair data use statement:</strong><br> The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br> <strong>Citation:</strong> Please cite the Global Carbon Budget 2022 (Friedlingstein et al., 2022, ESSD, https://doi.org/10.5194/essd-14-4811-2022) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2022 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br> <strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: “We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output.”<br> <strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p><br> Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudget.org/</p> <p> </p>
Activity budgets and space use for two common Pacific parrotfish (Chlorurus Sp.) at Palmyra Atoll National Wildlife Refuge
Data was collected at Palmyra Atoll National Wildlife Refuge located in the Central Pacific in 2014, and the work focuses on two species of Parrotfish, Chlorurus microrhinos and Chlorurus spilurus (formerly Cholrurus sordidus). For C. microrhinos, the project was designed to collect fine-scale spatial behavior data, focusing on territory size, species interactions, and benthic impact (i.e. feeding behavior). Focal follow data was collected by one or two observer(s), either on snorkel or SCUBA, and recording focal activity down to the second. Simultaneously, the observer would be towing a GPS that was recording a location every 5 seconds and each location was then associated with a particular behavior. Throughout this study individual fish were identifiable and successive follows were possible on individuals. For C. spilurus the focus of the project was to collect behavioral time budget data on feeding, territorial defense, and spawning behavior. The ‘Chlorurus_Area_Palmyra_2014.csv’ data only covers C. Microrhinos and gives the 95% KUD area estimation for GPS towed tracks, as well as the 95% KUD area for locations where feeding was occurring. We also report the step length between successive points for the entire follow as well as where feeding was occurring. The ‘Chlorurus_Activity_Palmyra_2014.csv’ is for both C. Microrhinos and C. spilurus and reports the start and stop time of each focal follow as well as the start and stop time of each activity. Activity descriptions can be found in the metadata and the total length and phase for each individual in our study can be found in ‘Fish_Information_Chlorurus_Data_2014.csv.
Atlantic sea scallop energy budget data on the Northeast U.S. Shelf, monthly in 2010 and 2012
This dataset includes monthly Atlantic sea scallop energy budget data from Georges Bank to the Mid-Atlantic Bight based on Scope For Growth (SFG) model results in 2010 and 2012. Results were supported in part by Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER). For more details please see: Zang, Z., et al. (2022) Modeling Atlantic sea scallop (Placopecten magellanicus) scope for growth on the Northeast U.S. Shelf. Fisheries Oceanography, https://doi.org/10.1111/fog.12577.
Global Carbon Budget 2023, surface ocean fugactiy of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogechemical models and surface ocean fCO2-based data-products
<p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p><p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2023 (https://doi.org/10.5194/essd-15-5301-2023), are available in the Global Carbon Budget 2023 spreadsheet.</strong></p><p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 13 of the Global Carbon Budget 2023 paper (https://doi.org/10.5194/essd-15-5301-2023), the river flux adjustment needs to be added to the CO2 flux estimated from the data-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2023 paper, section 2.5.1). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p><p><strong>What is in the files?</strong></p><p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p><p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p><p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br><br>(3) One file 'GCB-2023_OceanModel_RegionalBreakdown_1959-2022.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Temporal resolution: annual.</p><p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2023 (Friedlingstein et al., 2023, ESSD, https://doi.org/10.5194/essd-15-5301-2023) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2023 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).<br><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p><p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Global Carbon Budget 2024, surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux of individual global ocean biogeochemical models and surface ocean fCO2-based data-products
<p><strong>v2 update: </strong></p> <ul> <li>update to data in UoEX-UEPFFNU fCO2-product</li> <li>fix of lat-lon issue in Jena-MLS fCO2-product</li> <li>minor fixes to metadata in fCO2-products</li> </ul> <p><br>The v2 data is used for the final published version of the Global Carbon Budget 2024.</p> <p>-----------------</p> <p><strong>Surface ocean fugacity of CO2 (fCO2) and air-sea CO2 flux data from individual Global Ocean Biogeochemistry Models (GOBMs) and surface ocean fCO2-based data-products (fCO2-products).</strong><br>There are three types of files: (1) one file per fCO2-product with gridded fields and regionally-integrated CO2 flux time-series, (2) one file per GOBM with gridded fields, and (3) one file with the regionally-integrated time-series for the GOBMs. </p> <p><strong>Note: </strong>These provided gridded outputs from fCO2-based data-products and GOBMs are regridded datasets, without adjustments. <strong>The best estimates of the annual global ocean carbon sink, based on the native grids of fCO2-products and GOBMs and with the adjustments described in the Global Carbon Budget 2024 (https://essd.copernicus.org/preprints/essd-2024-519), are available in the Global Carbon Budget 2024 spreadsheet.</strong></p> <p>The regionally-integrated time-series are as provided by the contributing groups, i.e. integrated from their native grids. In order to reproduce Figure 14 of the Global Carbon Budget 2024 paper (https://essd.copernicus.org/preprints/essd-2024-519), the river flux adjustment needs to be added to the CO2 flux estimated from the fCO2-products (North: 0.14 GtC yr-1, Tropics: 0.42 GtC yr-1, South: 0.09 GtC yr-1, see GCB 2024 paper). The sum of the regional fluxes may differ from the global estimates as reported in the GCB spreadsheet, because some adjustments were applied only for global fluxes.</p> <p><strong>What is in the files?</strong></p> <p>(1) The files for the fCO2-based data-products contain the following variables (temporal resolution: monthly):<br><br>fgco2_reg: Regionally integrated air-sea CO2 flux (positive downward), monthly, for regions: global, north, tropics, south<br>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude<br>area_reg: Total surface ocean area covered by native grid, for global, north, tropics, south</p> <p>(2) The files for the GOBMs contain the following fields, for simulation A ('contemporary simulation', including effects of rising CO2, climate change and variability) and simulation B ('control simulation', constant CO2, no climate change and variability). Temporal resolution: monthly</p> <p>fgco2: Flux density of the total air-sea CO2 flux (positive downward), dimensions: time, latitude, longitude<br>sfco2: Surface ocean fCO2, dimensions: time, latitude, longitude<br>area: Area per pixel, dimensions: latitude, longitude</p> <p>(3) One file 'GCB-2024_OceanModel_RegionalBreakdown_1959-2023.nc' with the regionally-integrated CO2 flux time-series for all individual GOBMs, and for simulations A and B. Regions: North, tropics, south. Temporal resolution: annual.</p> <p><strong>Fair data use statement:</strong><br>The data and model output provided on this site are freely available and were furnished by individual scientists who encourage their use.<br><strong>Citation:</strong> Please cite the Global Carbon Budget 2024 (Friedlingstein et al., 2024, ESSD, https://essd.copernicus.org/preprints/essd-2024-519) for all data. In addition, please also cite the corresponding original reference for each dataset that has been used - see Table 4 in Global Carbon Budget 2024 for references of all the individual Global Ocean Biogeochemical Models and fCO2-based data-products. Further, for an overview of the Global Ocean Biogeochemical Model output, you may find it useful to cite Hauck et al. (2020, Frontiers, doi:10.3389/fmars.2020.571720).</p> <p><strong>Acknowledgement:</strong> Please add the following text in the acknowledgement of your paper: "We acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget and we thank the ocean modeling and fCO2-mapping groups for producing and making available their model and fCO2-product output."<br><strong>Co-authorship: </strong>An invitation of co-authorship to the contributing groups is encouraged if these data are the central data set of the publication.</p> <p>Besides the surface fCO2 and air-sea CO2 flux data that is made available open access, we make<strong> additional 3D output</strong> from the Global Ocean Biogeochemical models (GCB-ocean) available upon request and with its own data policy. Please refer to the Global Carbon Budget website for these additional data: https://globalcarbonbudgetdata.org/closed-access-requests.html</p>
Environment's Share in Total Government Budget Allocations for R&D
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget.</p> <p>This dataset calculates the share of the Environment objective compared to the total allocations.</p> <p><br> The source (raw) dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Government Budget Allocations for R&D in Environment
<p>Government Budget Allocations for R&D (GBARD). GBARD data are measuring government support to research and development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities.</p> <p>GBARD data are compiled using the guidelines laid out in the OECD Guidelines for collecting and reporting data on research and experimental development - Frascati Manual, OECD, 2015 (See related identifiers). </p> <p>GBARD data are broken down by:</p> <p> - Socio-economic objectives (SEOs) in accordance to the Nomenclature for the analysis and comparison of scientific programmes and budget. This dataset uses the Environment objective.<br> <br> The missing data are approximated, forecasted and backcasted by country. In our version of the dataset, 40% more countries, and a 23% larger dataset can be used for supervised and unsupervised learning models, such as machine learning, that require complete datasets compared to the source dataset released by Eurostat: <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=gba_nabsfin07&lang=en">GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07]</a></p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
Dataset associated with Banks et al.: "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol and associated radiative effects described by the paper "Radiative cooling and atmospheric perturbation effects of dust aerosol from the Aralkum Desert in Central Asia", written by Banks et al. and submitted to ACP in 2023. The paper was renamed "Dust aerosol from the Aralkum Desert influences the radiation budget and atmospheric dynamics of Central Asia" in 2024.</p>
Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages
<p>Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages. This was done using the Python scripts provided under <a href="https://github.com/JeremySilver/KeywordCountsPresidentialMessages">https://github.com/JeremySilver/KeywordCountsPresidentialMessages</a>. The raw text data is from <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> (<a href="http://www.ucsb.edu/">UCSB</a>), with some Presidential Budget Messages being extracted from US Federal Budget documents available through <a href="https://fraser.stlouisfed.org/">FRASER</a> (a digital library of U.S. economic, financial, and banking history) or, for the more recent documents the website of the <a href="https://www.whitehouse.gov/">White House</a>.</p> <p>The data headings are:</p> <ul> <li>pid: in most cases, this is the index for the text document as archived on <a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a> website. In some cases, this was the filename of a plain-text file read directly.</li> <li>year: Year that the message was delivered.</li> <li>date: Date that the message was delivered.</li> <li>name: Name of the US President delivering the message.</li> <li>count_of_all_words: Count of all words in the document.</li> <li>count_of_keywords: Count of all keywords encountered in that document.</li> <li>Keyword specific columns - three per keyword. For example, for the 'energy' keyword, the 'energy' column gives the number of times the 'energy' keyword was counted in the message, 'energy_pct_of_keywords' gives this count as a percentage of all keywords, and 'energy_pct_of_all_words' gives this count as a percentage of all words</li> </ul> <p>Below is the list of keywords that match when the search is applied to a dictionary file containing over 99,000 US English words.</p> <ul> <li>energy: 'energy'</li> <li>tax: 'nontaxable', 'overtax', 'overtaxed', 'overtaxes', 'overtaxing', 'surtax', 'surtaxed', 'surtaxes', 'surtaxing', 'surtaxs', 'tax', 'taxable', 'taxation', 'taxations', 'taxed', 'taxes', 'taxing', 'taxpayer', 'taxpayers', 'taxs'</li> <li>defense: 'defend', 'defense'</li> <li>education: 'education'</li> <li>employment: 'employ', 'employable', 'employe', 'employed', 'employee', 'employees', 'employer', 'employers', 'employes', 'employing', 'employment', 'employments', 'employs', 'underemployed', 'unemployable', 'unemployed', 'unemployeds', 'unemployment', 'unemployments'</li> <li>research: 'research', 'researched', 'researcher', 'researchers', 'researches', 'researching', 'researchs'</li> <li>shooting: 'shooting'</li> <li>space: 'space'</li> <li>nuclear: 'nuclear'</li> <li>natural resources: 'natural resources'</li> <li>racism: 'racism', 'civil rights'</li> <li>crime: 'crime', 'crimes', 'criminal', 'criminally', 'criminals', 'decriminalization', 'decriminalizations', 'decriminalize', 'decriminalized', 'decriminalizes', 'decriminalizing'</li> <li>environment: 'environment', 'environmental', 'environmentalism', 'environmentalisms', 'environmentalist', 'environmentalists', 'environmentally', 'environments'</li> <li>religion: 'faith', 'god', 'prayer', 'religion'</li> <li>health: 'health', 'healthful', 'healthfully', 'healthfulness', 'healthfulnesss', 'healthier', 'healthiest', 'healthily', 'healthiness', 'healthinesss', 'healths', 'healthy', 'unhealthful', 'unhealthier', 'unhealthiest', 'unhealthy'</li> <li>terror: 'terror', 'terrorism', 'terrorisms', 'terrorist', 'terrorists', 'terrorize', 'terrorized', 'terrorizes', 'terrorizing', 'terrors'</li> <li>war: 'war', 'warrior', 'warriors', 'wars'</li> <li>economy: 'economic', 'economical', 'economically', 'economics', 'economicss', 'economy', 'economys', 'microeconomics', 'microeconomicss', 'socioeconomic', 'uneconomic', 'uneconomical'</li> <li>jobs: 'jobs'</li> <li>business: 'agribusiness', 'agribusinesses', 'agribusinesss', 'business', 'businesses', 'businesslike', 'businessman', 'businessmans', 'businessmen', 'businesss', 'businesswoman', 'businesswomans', 'businesswomen'</li> <li>drugs: 'drugs', 'narcotics'</li> <li>inflation: 'inflation'</li> <li>climate: 'climate'</li> <li>science: 'science', 'sciences', 'scientific', 'scientifically', 'scientist', 'scientists'</li> <li>gun: 'gun', 'gunfire', 'gunman', 'guns', 'handgun', 'rifle', 'shotgun'</li> <li>tech: 'biotechnology', 'biotechnologys', 'technical', 'technological', 'technologically', 'technologies', 'technologist', 'technologists', 'technology', 'technologys'</li> <li>military: 'military'</li> <li>security: 'security'</li> <li>housing: 'housing'</li> <li>pollution: 'pollution'</li> </ul> <p>The dictionary file used is a standard file among Linux systems, and the version used was provided with version 7.1-1 of the Ubuntu 'wamerican' package. Two extra phrases, which do not appear in the dictionary file, are added to the list: 'civil rights' (under the 'racism' keyword) and 'natural resources' (under the 'natural resources' theme).</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.