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Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
<p>CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories of anthropogenic CO2 emissions developed in the framework of the CoCO2 project (<a href="https://coco2-project.eu/">https://coco2-project.eu/</a>). CoCO2-MOSAIC 1.0 provides gridded (0.1˚×0.1˚) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors: energy_s (super-emitting sources above 7.9e-6 kg/m2/s), energy_a (average emitters), manufacturing, settlements, transport, aviation land/take-off (LTO) and other. The regional inventories included are CAMS-GHG-REG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0 and CAMS-GLOB-SHIP 3.1 are used for gap-filling missing sectors and regions. CAMS-GLOB-TEMPO 3.1 is used for temporal disaggregation of inventories providing annual emissions. Aviation emissions from climb, descent, and cruise are not covered by regional inventories and are provided as a separate file. Note that 2015 is the only year when all regional inventories are simultaneously available. </p> <p>Compared to global inventories, CoCO2-MOSAIC 1.0 includes all the regional information available without the limitation of providing spatially consistent emissions. Therefore, CoCO2-MOSAIC 1.0 can be used as a global baseline inventory due to the higher level of detail, higher spatial resolution, and country-specific information included by regional inventories. </p> <p>For further details see Urraca et al. 2023 (ESSD submitted). The paper (i) describes the CoCO2-MOSAIC methodology and (ii) uses the mosaic to inter-compare the most widely used global inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0/7.0, ODIAC v2020b, and CEDS v2020_04_24.</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>
Open-source traffic and CO2 emission dataset for commercial aviation
<p>This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. <br>It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. </p> <p>Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>).<br>A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a><br>The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.<br> </p> <h4><br><strong>1- DISCLAIMER</strong></h4> <p><br>The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. </p> <p>Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use.</p> <p>As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance.</p> <h4><br><strong>2- DESCRIPTION</strong></h4> <p>Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.</p> <p><em>Please </em>refer<em> to </em>the<em> support article for more details (see above).</em></p> <p>The dataset contains the following columns:</p> <ul> <li>"First column" : index</li> <li><strong>airline_iata : </strong>IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li><strong>acft_icao : </strong>ICAO code of the aircraft type</li> <li><strong>acft_class : </strong>Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li><strong>seymour_proxy: </strong>Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li><strong>source: </strong>Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li><strong>seats: </strong>Number of seats available for the data entry, AFTER airport residual scaling</li> <li><strong>n_flights: </strong>Number of flights of the data entry, when available</li> <li><strong>iata_departure</strong>, <strong>iata_arrival : </strong>IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li><strong>departure_lon</strong><em>, </em><strong>departure_lat</strong><em>, </em><strong>arrival_lon</strong><em>, </em><strong>arrival_lat : </strong>Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li><strong>departure_country, arrival_country</strong>: Origin and destination country ISO2 code. <strong>WARNING: </strong>disable NA (Namibia) as default NaN at import</li> <li><strong>departure_continent, arrival_continent: </strong>Origin and destination continent code. <strong>WARNING: </strong>disable NA (North America) as default NaN at import</li> <li><strong>seats_no_est_scaling: </strong>Number of seats available for the data entry, BEFORE airport residual scaling</li> <li><strong>distance_km: </strong>Flight distance (km)</li> <li><strong>ask: </strong>Available Seat Kilometres</li> <li><strong>rpk: </strong>Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li><strong>fuel_burn_seymour: </strong>Fuel burn <em>per flight</em> (kg) when seymour proxy available</li> <li><strong>fuel_burn: </strong>Total fuel burn of the data entry (kg)</li> <li><strong>co2: </strong>Total CO2 emissions of the data entry (kg)</li> <li><strong>domestic: </strong>Domestic/international boolean (Domestic=1, International=0)</li> </ul> <p> </p> <h4><strong>3- Citation</strong></h4> <p>Please cite the support paper instead of the dataset itself. </p> <blockquote> <p>Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. <em>Journal of Open Aviation Science</em>. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a></p> </blockquote>
Code for "New land-use-change emissions indicate a declining CO2 airborne fraction"
<p>Data and programming scripts for reproducing the results from the Nature publication titled:</p> <p>"New land-use-change emissions indicate a declining CO2 airborne fraction".</p> <p>Authors: Margreet J. E. van Marle*, Dave van Wees*, Richard A. Houghton, Robert D. Field, Jan Verbesselt, and Guido R. van der Werf<br> * These authors contributed equally.</p> <p>DOI: https://doi.org/10.1038/s41586-021-04376-4</p> <p> </p> <p>This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"):</p> <p>- "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series.</p> <p>- "Script.py":<br> BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files.<br> NOTE: This script requires the Python module: 'pymannkendall'<br> Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below).</p> <p>- "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum.</p> <p>- "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach.</p> <p>- "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/</p> <p>- Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.</p> <p> </p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories
<p><strong>Data Access Notice</strong></p> <p>Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link:</p> <p><a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a></p> <p><strong>Product Description</strong></p> <p>See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below.</p> <p>GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023).</p> <p>GCP-GridFEDv2024.0 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO<sub>2</sub>. Note that positive values in GridFED signify a surface-to-atmosphere CO<sub>2 </sub>flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink.</p> <p>GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021).</p> <p><strong>Core Methodology in Brief</strong></p> <p>GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024). </p> <p>GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp.</p> <p>For full details of the core methodology, see Jones et al. (2021).</p> <p><strong>Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards</strong></p> <p>The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): </p> <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> <p>Seasonality is determined in the following ways for those countries/sources:</p> <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> <p>For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021).</p> <p><strong>Other New Features of GCP-GridFEDv2024.0</strong></p> <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul> <p> </p>
The Global Carbon Project's fossil CO2 emissions dataset
<p>The <a href="https://www.globalcarbonproject.org/">Global Carbon Project</a> (GCP) has been publishing estimates of global and national fossil CO2 emissions since 2001. In the first instance these were simple re-publications of data from another source, but over subsequent years refinements have been made in response to feedback and identification of inaccuracies. In this article (PDF document) we describe the history of this process leading up to the methodology used in the 2025 release of the GCP's fossil CO2 dataset.</p> <p>The fossil CO2 emissions dataset is included in both its standard, absolute form, and per capita, with associated metadata files in JSON format. A file indicating the source(s) of each data point is also provided.</p> <p>This is the initial release of the 2025 dataset.</p>
Contribution of CO2 and CH4 emissions at ice-melt to annual emissions from 450 and 270 lakes, respectively, 1986 to 2014
The ice-covered period on lakes in the northern hemisphere can be extensive, lasting up to 7 months of the year. During this time, C cycling in lakes is altered affecting CO2 and CH4 dynamics below ice. Lake ice impedes atmospheric exchange, trapping CO2 and CH4 in the lake over winter. As lake ice-melts, CO2 and CH4 that has accumulated over winter is emitted from the into the atmosphere. To investigate the importance of CO2 and CH4 emissions during the ice-melt period, we conducted a literature search for studies that had CO2 and CH4 emission estimates for both the ice-melt and open water period. From these literature values, we could calculate the percent contribution of the ice-melt period to annual CO2 and CH4 emissions. We obtained data for 271 (n= 258) and 447 (n= 689) individual lakes, for CH4 and CO2, respectively.
Carbon Monitor - Global Daily CO2 Emissions in Near-Real-Time
<p><strong><em>Carbon Monitor: A near-real-time global daily CO2 emission dataset</em></strong></p> <p>Carbon dioxide (CO<sub>2</sub>) emissions from the use of fossil fuels and the production of cement are the main driving force of climate change. Carbon Monitor is an international initiative providing for the first time regularly updated, science-based estimates of daily CO<sub>2</sub> emissions.</p> <ul> <li>Website:</li> </ul> <p><a href="https://carbonmonitor.org">https://carbonmonitor.org</a></p> <ul> <li>Citation:</li> </ul> <p>Liu, Z., Ciais, P., Deng, Z. <em>et al.</em> Near-real-time monitoring of global CO<sub>2</sub> emissions reveals the effects of the COVID-19 pandemic. <em>Nat Commun</em> <strong>11, </strong>5172 (2020). https://doi.org/10.1038/s41467-020-18922-7</p> <ul> <li>Data file description:</li> </ul> <table> <thead> <tr> <th scope="col">Field</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>country</td> <td>Brail, China, EU27 & UK, France, Germany, India, Italy, Japan, ROW, Russia, Spain, UK, US, WORLD *</td> </tr> <tr> <td>co2</td> <td>CO2 emissions from fuel combustion and cement production process (unit: kt CO2)</td> </tr> <tr> <td>sector</td> <td>Power, Industry, Residential, Ground Transport, Domestic Aviation, International Aviation, International Shipping, Total **<sup>,</sup>***</td> </tr> <tr> <td>date</td> <td>From 2019/1/1, every day</td> </tr> </tbody> </table> <p>* WORLD = China + US + EU27 & UK + India + Russia + Japan + Brazil + ROW + International Aviation (WORLD) + International Shipping (WORLD)</p> <p>** Total (country level) = Power + Industry + Residential + Ground Transport + Domestic Aviation</p> <p>** Total (WORLD) = Power + Industry + Residential + Ground Transport + Domestic Aviation + International Aviation + International Shipping</p>
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Soil greenhouse gas emissions (CO2 and N2O) data and metadata derived from H2020 Diverfarming project
<p>Soil greenhouse gas emissions (CO<sub>2</sub> and N<sub>2</sub>O) data and metadata of an almond crop diversified with <em>Thymus hyemalis </em>(diversification 1) and with<em> Capparis spinosa </em>(diversification 2). This data comes from WP5 "Environmental impact and delivery of ecosystem services by crop diversification", derived from H2020 Diverfarming project. This workpackage has been designed to provide sound and robust scientific understanding of the benefits and drawbacks of the tailored diversified cropping systems for improvement of the environmental quality and delivery of ecosystem services in each pedoclimatic region. http://www.diverfarming.eu</p>
Dataset Cumulative CO2 Emissions of International Transport
<p>This dataset considers the year 1783, when the first steamship was built, as the first year of the international transport CO2 emissions.</p> <p>The global cumulative CO2 emissions including international transport are converted to the 1875 baseline, similar to the Global Warming baseline (1850-1900). </p>
TNO-CAMS European CO2 emissions 2000-2014 v1
<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO<sub>2</sub> from fossil fuels and CO<sub>2</sub> from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO<sub>2</sub> estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage. The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv) & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO<sub>2</sub> for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p>
CO2 emissions, water table and temperature time series from an undrained tropical peatland
<p>Supplement to: Hoyt, A. M., Gandois, L. , Eri, J. , Kai, F. M., Harvey, C. F. and Cobb, A. R. (2019), CO2 emissions from an undrained tropical peatland: Interacting influences of temperature, shading and water table depth. <em>Global Change Biology</em>. https://doi.org/10.1111/gcb.14702</p>
Global CO2 emissions from cement production
<p><strong>GCP-CEM: The Global Carbon Project CEMent-process emissions dataset</strong></p> <p>This is an update of the dataset documented in:</p> <blockquote> <p>Andrew, R.M., 2019. Global CO2 emissions from cement production, 1928–2018. Earth System Science Data 11, 1675–1710. <a href="https://doi.org/10.5194/essd-11-1675-2019">https://doi.org/10.5194/essd-11-1675-2019</a>.</p> </blockquote> <p>Data in this release cover the period 1880–2024.</p> <p>Note that emissions from use of fossil fuels in cement production are not included in this dataset since they are usually included elsewhere in global datasets of fossil CO2 emissions. The process emissions in this dataset, which result from the decomposition of carbonates in the production of cement clinker, amounted to ~1.5 Gt CO2 in 2024 while emissions from combustion of fossil fuels to produce the heat required amounted to an additional ~0.9 Gt CO2 in 2024.</p> <p><strong>October 2025 release (251007): Changes</strong></p> <ul> <li>The 2025 editions of Annex 1 parties' reports to the UNFCCC have been included, as well as any Biennial Transparency Reports submitted by non-Annec 1 parties since the last update in February</li> <li>Various revisions to recent years' estimates based on newly published data</li> <li>New activity data sources for Egypt, Indonesia, Iran, Sri Lanka</li> <li>US emissions 1880-1924 are restored</li> </ul> <p><strong>The Cement Production dataset</strong></p> <p>Annual cement production data by country are assembled from a number of sources. Prioritisation is given to national sources, whether directly from statistical offices or activity data reported in official emissions reports submitted to the UNFCCC. Where official sources are not used, data are sourced from the USGS Minerals Yearbooks. Some data points in the USGS dataset are corrected based on either sense-checks or information from alternative sources. For data before 1990, USGS data are obtained via back-calculation from the 2019 edition of the CDIAC emissions dataset. The first year for most countries in the USGS data is 1928; where the combined dataset shows zeros before 1928 and non-zero data from 1928, these zeros are assumed to be artefacts and are set to NODATA. Using available data for some former Soviet states before the dissolution of the Soviet Union, Soviet states are disaggregated for all years before dissolution. Every data point in the cement production dataset has its source indicated in the accompanying source file. Blank cells should be interpreted as NODATA.</p> <p><strong>The Clinker Production dataset</strong></p> <p>Annual clinker production data by country are assembled from a number of sources. No such multi-country dataset exists elsewhere to our knowledge. Many countries report 'activity data' in their emissions reporting to the UNFCCC, and for the Cement Production sector (2.A.1), this is often clinker production. For all Annex 1 countries this is the case, and clinker production for these countries are obtained from their Excel-format reporting files (CRTs), although New Zealand (and Hungary in recent years) exceptionally has withheld these data for reasons of confidentiality. The new BTRs for non-Annex 1 countries also include CRTs, and these have been used where available. Many other countries report time-series of clinker production in their official emissions reporting, and for some countries data are available (sometimes with monthly frequency) from official websites. Every data point in the clinker production dataset has its source indicated in the accompanying source file. Blank cells should be interpreted as NODATA. Not all countries are present in the dataset.</p> <p><strong>Emissions calculation</strong></p> <ul> <li>Emissions for all UNFCCC Annex I ("developed") countries are taken directly from their official submissions to the UNFCCC (or EIONET) in Common Reporting Format (structured Excel files), for which data are available from 1990 (slightly earlier for some Economies in Transition). <ul> <li>Australia, Austria, Belgium, Bulgaria, Belarus, Canada, Switzerland, Cyprus, Czechia, Germany, Denmark, Spain, Estonia, Finland, France, United Kingdom, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Japan, Kazakhstan, Liechtenstein, Lithuania, Luxembourg, Latvia, Malta, Netherlands, Norway, New Zealand, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Sweden, Turkey, Ukraine, United States of America.</li> </ul> </li> <li>Country-specific methods are used for Brazil, India, South Africa, Thailand, USA, Vietnam. <ul> <li>For Brazil, emissions are published from 1990, and clinker ratios are reported starting in 1970, allowing more accurate estimation before 1990.</li> <li>Little information is available about clinker production in India since the Cement Manufacturers' Association was forced to stop collecting these data. Various sources are used to estimate how the clinker ratio has changed over time in India.</li> <li>South Africa's reported emissions appear to be calculated assuming limestone sales statistics are cement production statistics. An alternative method is used here.</li> <li>For Thailand, cement production and clinker trade data are available from 1990, and these are used to estimate clinker production in the period 1990-2015.</li> <li>The US publishes clinker production data beginning in 1925, and cement production data from 1880.</li> <li>Vietnam is a significant producer but doesn't collect or publish clinker production data. High levels of exports mean that applying a clinker ratio to cement production would be inappropriate. Here we follow Vietnam's own method of using cement production combined with clinker trade data to estimate clinker production.</li> </ul> </li> <li>The combined_cement_data.xlsx file is used to overwrite emissions with superior data, in most cases as reported in official reporting to the UNFCCC, e.g. Biennial Update Reports, National Communications, and National Inventory Reports. Where more than one data source has been found for a country (e.g. subsequent reports), a comparison is automatically made of overlapping data, and they are combined only if they are in very close agreement (i.e., significant revisions mean that previous estimates will be ignored).</li> <li>Clinker production data have been obtained for some countries in addition to those available from Annex 1 parties' CRFs, either from reporting to the UNFCCC or directly from official agencies. The period available varies by country. Emissions for these countries are calculated directly from these clinker production data where official emissions estimates are not available. <ul> <li>Afghanistan, Argentina, Armenia, Bangladesh, Brazil, Chile, China, Spain, Jamaica, Japan, Moldova, Norway, Paraguay, Poland, Rwanda, Turkey, Saudi Arabia, South Korea, Taiwan, Thailand, Togo, Tunisia, Ukraine, United Kingdom, USA, Uzbekistan.</li> </ul> </li> <li>Some countries do not report time-series of emissions, but do supply some isolated estimates in their official reporting to the UNFCCC, and these are used in some cases to constrain estimates.</li> <li>A number of countries state in their official reporting to the UNFCCC that they have never produced clinker, so emissions are set to zero for all years for these countries. In other cases, statements are made that no clinker was produced before or after a certain year, and this information is also incorporated. <ul> <li>Never produced clinker: Mauritania, Sierra Leone, Côte d'Ivoire, Brunei Darussalam, Tuvalu, Papua New Guinea, Guinea, Andorra, Singapore, Réunion, Guadeloupe, French Guiana, Martinique, Mayotte, Macao.</li> <li>Stopped or started producing clinker: Cambodia, Estonia, Fiji, Ghana, Iceland, the Netherlands (see file zero_before_after.csv).</li> </ul> </li> <li>The information available usually covers a number of years, up to 3 decades. These are then extrapolated by combining available data and assumptions about historical developments in clinker ratios to produce longer time series of emissions based on the longer cement production dataset. More details on this method are given in the accompanying journal paper.</li> <li>For any non-Annex I countries for which time-series data of neither emissions or clinker are available, and cement production is non-zero, clinker ratios derived from the Getting the Numbers Right (GNR) cement sustainability initiative are applied to the cement production dataset to derive approximate clinker production by country, from which emissions are calculated using IPCC default factors.</li> <li>Where emissions are estimated from clinker (or apparent clinker) production data, IPCC default factors are used, with the exception of China and Argentina, for which officially reported factors are used. The factor for CO2 emitted per tonne of clinker exhibits only small variations between countries, so using the default factor introduces very little uncertainty.</li> </ul> <p>This dataset contributes to:</p> <ul> <li>The <a href="https://globalcarbonbudget.org">Global Carbon Budget</a> (republished by <a href="https://ourworldindata.org/grapher/annual-co2-cement">Our World in Data</a>)</li> <li>The <a href="https://www.energyinst.org/statistical-review">Energy Institute</a> Statistical Review of World Energy</li> <li>The <a href="https://doi.org/10.5281/zenodo.4479171">PRIMAP-hist </a>emissions dataset</li> <li>The <a href="https://github.com/JGCRI/CEDS/wiki/Release-Notes">CEDS</a> emissions dataset</li> </ul> <p>See also:</p> <ul> <li>"Monthly global cement production data": <a href="https://doi.org/10.5281/zenodo.10277408">https://doi.org/10.5281/zenodo.10277408</a></li> </ul> <p>You may contact the author here: <a href="https://forms.gle/jeuyvoeXqBQMnsGX8">https://forms.gle/jeuyvoeXqBQMnsGX8</a></p>
Dataset CO2 Emission per Capita Forecast 2020-2100
<p>The dataset includes Business As Usual (BAU) forecast of the world's global CO2 emissions per capita (CpC) for 2020-2100.</p> <p>The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [3]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase.</p> <p>The population forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world population will change from 7,795 million in 2020 to 15,206 million in 2100, a 95% increase.</p> <p>CO2 emissions per capita (CpC) are calculated by dividing the CO2 emissions per year by the population in the same year.</p> <p>The world CpC was 4.3366 tCO2/y,cap in 2020. The CpC forecast for 2100 is 4.6160 tCO2/y,cap, 6.4% increase.</p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Worldwide CO2 emissions and natural disasters from 1960 to 2021
<p>A CSV file containing worldwide CO2 emissions as well as the number of natural disasters per year.</p> <p>Sources:</p> <ul> <li>Global Carbon Atlas <ul> <li>DOI: <a href="http://doi.org/10.17616/R3434K">http://doi.org/10.17616/R3434K</a></li> <li>URL: <a href="http://www.globalcarbonatlas.org/en/CO2-emissions">http://www.globalcarbonatlas.org/en/CO2-emissions</a></li> <li>Last accessed: 2023-05-09</li> </ul> </li> <li>EM-DAT <ul> <li>DOI: <a href="http://doi.org/10.17616/R3QQ1X">http://doi.org/10.17616/R3QQ1X</a></li> <li>URL: <a href="https://public.emdat.be/data">https://public.emdat.be/data</a> (registration necessary)</li> <li>Last accessed: 2023-05-14</li> </ul> </li> <li>GitHub Project <ul> <li>DOI: <a href="http://doi.org/10.5281/zenodo.7934702">http://doi.org/10.5281/zenodo.7934702</a> </li> <li>URL: <a href="https://github.com/jkopec/global-emission-and-disaster-analysis">https://github.com/jkopec/global-emission-and-disaster-analysis</a></li> </ul> </li> </ul>
Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions
<p>The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af</p> <p><br> The data are provided as .csv and .nc files<br> There are different types of data</p> <p><br> 1) TIMESERIES DATA (Fig. 1 and 2)<br> =================================<br> The name of the files indicates the variable:<br> co2_ts.csv change in atm. co2 [ppm]<br> cumulativeEmissions_ts.csv cumulative emissions [GtC]<br> cumulativeAOflux_ts.csv cumulative atm-ocean C flux [GtC]<br> cumulativeABflux_ts.csv cumulative atm-land C flux [GtC]<br> sat_ts.csv change in surface air temperature [degC]<br> ohc_ts.csv change in ocean heat content [10^24 J]<br> amoc_ts.csv change in Atlantic meridional overturning circulation strength [Sv]<br> seaice_ts.csv fraction of pre-industrial sea-ice area remaining [fraction of PI]<br> <br> The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows:<br> c4k#_###</p> <p>c4 indicates the maximum co2 as times pre-industrial (4 times)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> ### indicates the rate of CDR:<br> 010: 0.1% yr^-1<br> 010: 0.3% yr^-1<br> 010: 0.5% yr^-1<br> 010: 0.7% yr^-1<br> 100: 1% yr^-1<br> 200: 2% yr^-1<br> 400: 4% yr^-1<br> 600: 6% yr^-1</p> <p><br> 2) HYSTERESIS DATA (Fig. 3)<br> ===========================<br> The name of the files indicates the variables:<br> cumulativeEmissions_sat.csv cumulative emissions and change in surface air temperature [degC]<br> cumulativeEmissions_OHCsurf.csv cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]<br> cumulativeEmissions_o2thermo.csv cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]<br> cumulativeEmissions_OM_arag.csv cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction]</p> <p>each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention)</p> <p><br> 3) SPATIAL DATA (Fig. 4 and 5)<br> ==============================<br> All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:<br> c4k2_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]<br> c4k3_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]<br> c4k5_100_sat hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]<br> <br> c4k2_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]<br> c4k3_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]<br> c4k5_100_o2thermo hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]<br> <br> c4k3_100_Om_arag_up mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]<br> c4k3_100_Om_arag_do mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]</p> <p> </p> <p> </p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
CO2 concentrations and emissions from subtropical headwater streams, São Carlos, Brazil, 2018
The data were collected in the municipalities of São Carlos, Itirapina, and Brotas in the state of São Paulo, southeastern Brazil. Six sandy/rocky-bottom headwater streams (1st to 2nd order) were selected based on the main land use in the catchment. Three streams drained sugarcane plantations, and three streams drained native vegetation catchments (Cerrado vegetation). The catchment drainage areas were determined using digital elevation models. Land use was classified based on satellite images from LANDSAT using ArcGIS software. The data were collected to study the impact of different land uses (sugarcane plantations vs. native vegetation) on the headwater streams. These streams have previously been studied for methane dynamics, indicating a focus on understanding environmental and ecological impacts. Three samples were collected from each stream during spring, summer, and winter using the headspace extraction technique. Due to access issues, samples from one stream were not collected in spring and summer 2018. Syringes filled with ultrapure nitrogen were used to collect stream water samples, which were then shaken to equilibrate gases. The gas was analyzed using a Shimadzu GC-2014 gas chromatograph equipped with various detectors. Concentrations were compared with standards to calculate CO2 levels, and CO2 emissions were calculated based on gas transfer velocity and dissolved concentrations.
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.