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111 results for “CO2 emissions”
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2018
<p>GCP-GridFED (version 2019.1) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2018 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, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. 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.</p> <p>GCP-GridFED was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR; version 4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2019 global carbon budget (GCB-NAE) for the years 1959-2018 (Friedlingstein et al., 2019).</p> <p>The data description article is under review.</p>
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
<p><span>Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (</span><span>5 m<sup>2</sup> area and 1.5 m soil depth) </span><span>in an ecotron facility, continuously measuring ecosystem-level CO<sub>2</sub>, N<sub>2</sub>O, and H<sub>2</sub>O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO<sub>2</sub> emissions. Endogeic earthworms reduced N<sub>2</sub>O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.</span></p>
Unpublished data: Quantifying CO2 Emissions and Carbon Sequestration from Digestate-Amended Soil Using Natural 13C Abundance as a Tracer
<p>Unprocessed data of CO2 evolution measured daily on cavity ring-down spectroscopy analyser (G2201-i CRDS isotopic CO2/CH4 analyser, Picarro, Santa Clara, CA, USA).</p>
Scout Benchmark Scenarios for U.S. Building Energy and CO2 Emissions to 2050
<p><strong>Overview and Intended Use Cases</strong></p> <p>These scenarios establish a range of futures for U.S. buildings sector energy use and CO<sub>2</sub> emissions to 2050 using <a href="https://scout-bto.readthedocs.io/en/latest/">Scout</a>, a reproducible and granular model of U.S. building energy use, emissions, and consumer costs developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office (BTO).</p> <p>Scout benchmark scenario data are suitable for the following example use cases:</p> <ul> <li>Setting high-level policy goals for U.S. buildings sector energy use, electricity demand, and CO<sub>2</sub> emissions over both the near- and long-term (e.g., X% building CO<sub>2</sub> emissions reductions vs. 2005 levels by 2030, Y% reductions vs. 2005 levels by 2050);</li> <li>Exploring the effects of key deployment dynamics driving U.S. buildings sector energy and CO<sub>2</sub> emissions to 2050 that could be affected by policy levers (e.g., raising minimum technology performance levels; improving market penetration of commercially available technologies; accelerating electrification and/or retrofit rates; introducing breakthrough technologies to the market);</li> <li>Determining priority segments (regions, building types, and end use/technology types) and sequencing of U.S. buildings sector energy and CO<sub>2</sub> emissions reductions and/or changes in total consumption by fuel type to 2050 under a given set of assumptions;</li> <li>Identifying the energy and CO<sub>2</sub> impacts or cost effectiveness of specific technologies or operational approaches of interest—in isolation or after considering competition with other measures in a scenario portfolio; and/or</li> <li>Exploring the total cost of deploying different portfolios of building energy efficiency and end-use electrification measures, as well as the total consumer energy cost savings potential of those portfolios. </li> </ul> <p><strong>Scenario Summary</strong></p> <p>A total of 5 scenarios explore total building energy use, CO<sub>2</sub> emissions, and technology and energy costs from 2024–2050 under varying levels of demand-side deployment of building efficiency and electrification measures and parallel decarbonization of buildings’ electricity supply. Narrative descriptions of these scenarios are as follows:</p> <ul> <li><strong>Stated Policies: </strong>Existing policies and regulations (mainly IRA for buildings) lead to modestly accelerated deployment of HPs/HPWHs but not other efficiency measures in the buildings sector. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> <li><strong>Mid: </strong>Policy makers rely mostly on market-based instruments to moderately increase deployment of efficient technology and fuel switching to heat pumps. The power sector decarbonizes consistent with a “Mid-case with 95% Decarbonization by 2050 (without tax credit phaseout)” scenario.</li> <li><strong>High: </strong>Policy makers use both regulations and market-based instruments to dramatically accelerate deployment of high efficiency technologies and fuel switching to heat pumps, though building technologies with breakthrough increases in performance at low cost do not materialize on the market. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Breakthrough: </strong>Research and innovation breakthroughs lead to market availability of cost-effective, high-performance building technologies by 2030; these, coupled with accelerated deployment of high efficiency technologies and fuel switching to heat pumps, lead to aggressive buildings sector transformation. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case with 100% Decarbonization by 2035 (without tax credit phaseout)</a>” scenario.</li> <li><strong>Inefficient Electrification Sensitivity: </strong>Policy makers use regulations and market-based instruments to encourage fuel switching but do not include provisions that require switching to efficient heat pumps, resulting in a substantial amount of switching to inefficient electric resistance heating and water heating technologies. The power sector decarbonizes consistent with a “<a href="https://www.nrel.gov/docs/fy23osti/84916.pdf">Mid-case (with tax credit phaseout)</a>” scenario.</li> </ul> <p>The key input dimensions that are varied to produce the above range of scenarios are as follows:</p> <ul> <li><u>Market-available technology performance range:</u> the energy performance levels of building technologies available for purchase by end use consumers, bounded by a minimum performance “floor” and maximum performance “ceiling”;</li> <li><u>Load electrification rate and efficiency:</u> the rate at which fossil-fired equipment is converted to electric service, and the efficiency level of the electric equipment; </li> <li><u>Early retrofits:</u> the fraction of consumers that choose to replace existing building equipment and/or envelope components before the end of their useful lifetimes; and</li> <li><u>Power grid decarbonization:</u> the annual average CO<sub>2</sub> emissions intensity of the electricity supplied to the buildings sector across the modeled time horizon (2024–2050), resolved by grid region. </li> </ul> <p>Refer to the attached “Scenario_Guide" PDF for further scenario details and results; instructions for reproducing scenario results are available in “Scenario_Execution” XLSX.</p> <p>Results data are reported as an annual time series (2024–2050) at both a national and regional (<a href="https://www.eia.gov/outlooks/aeo/pdf/nerc_map.pdf">EMM grid region</a>) spatial resolution. While not reflected in this dataset, annual time series data may be further translated to a sub-annual, hourly resolution for integration with grid modeling—please contact the authors for more information.</p> <p><strong>What's New in This Version</strong></p> <p><strong><em>Note: v6.1 updates the file ./Results/Results_Summary.xlsx to reflect the latest scenario runs. Please disregard the outdated version of this file that was posted in v6.</em></strong></p> <p>This set of benchmark scenarios provides an update to <a href="../records/8087519">Version 5</a> of the Scout Benchmark Scenarios (June 2023) using the same scenario definitions but an updated set of baseline and measure input data alongside several minor methodological changes. </p> <p>The following scenario features are new in this dataset:</p> <ul> <li>Reference case data and energy use projections updated to <a href="https://www.eia.gov/outlooks/aeo/">AEO 2023</a>, including updates to energy and stock and technology cost, performance, and lifetime data; updated site-source energy conversions, CO2 emissions intensities, and energy prices; and revised peak and take period definitions that are consistent with 2023 EMM projections.</li> <li>Integration of federal and state cost incentives from AEO 2023 (see <a href="https://www.eia.gov/outlooks/aeo/IIF_IRA/pdf/IRA_IIF.pdf">AEO2023 Issues in Focus: Inflation Reduction Act Cases</a> in the AEO2023 for details); these incentives reduce the initial cost of upgrades for applicable measures.</li> <li>Revised method for allocating end use electricity baselines in AEO from census divisions to EMM regions and states by using <a href="https://www.nrel.gov/buildings/end-use-load-profiles.html">End Use Load Profiles</a> (EULP) data. EULP data now also underpin updated, EMM-resolved hourly load baseline shapes.</li> <li>Retail price projections for grid scenarios are updated to match those produced by NREL under the Department of Energy’s DECARB Initiative (these are similar to but differ in slight ways from NREL’s <a href="https://www.nrel.gov/analysis/standard-scenarios.html">Standard Scenarios</a>). Three scenarios are included: <ul> <li><em>Stated Policies</em>: includes moderate estimates for inputs such as technology costs, fuel prices, and demand growth with no nascent technologies and electric sector policies that match current federal laws and regulations (including IRA & BIL); achieves an 88% reduction in building site electricity emissions <em>intensity</em> (Mt CO2/quad site) from 2005 levels by 2050.</li> <li><em>Mid</em>: consistent with<em> Stated Policies</em> except achieves 97% reduction in building site electricity emissions intensity from 2005 levels by 2050.</li> <li><em>High:</em> includes low demand growth projections with advanced inputs for technology costs and allowance of transmission expansion between regions (without limitations based on historical build rates); federal policies are consistent with implemented laws (including IRA & BIL); building electricity is fully decarbonized after 2035.</li> <li>The previous version of the benchmark datasets used retail price data from EIA’s <a href="https://www.eia.gov/outlooks/aeo/">Annual Energy Outlook</a> scenarios.</li> </ul> </li> <li>In contrast to <a href="https://doi.org/10.5281/zenodo.8087519">Version 5</a>, measures in the “best available” measure tier are not deployed with load flexibility features. </li> </ul>
Modelling CO2 emissions of cultivated and rewetted peat soils with SWAP-ANIMO - Dataset
<p><span>Three locations in Europe (wet river valley (Denmark), coastal peatland (The Netherlands) and broad river floodplain (Switzerland)) were selected for which two to three years of measurements of hydrological variables and CO2 exchange fluxes were available for some period between 2015 and 2023. The hydrology, grass growth and CO2 fluxes of these sites were modelled with the SWAP-ANIMO model using the available measurement period for model input and calibration. Model simulations were used to improve the understanding of the hydrological drivers of each site and to obtain estimates of the different pools contributing to the measured CO2 fluxes using a period of 10 years (2014-2023). Rewetting was considered either by calibration on direct measurements of an actual rewetting measure (Denmark, The Netherlands) or extrapolation of the reference simulation (Switzerland). Also, the potential impact of climate change on the rate of peat oxidation was modelled for these sites for both the reference and rewetting measure. </span></p> <p><span>The dataset contains the relevant detailed, daily model output of the 10 year simulation period and aggregated, yearly model output of the scenario simulations which are detailed in the corresponding report (van de Craats et al., 2024), available at <a href="https://doi.org/10.5281/zenodo.14041243">https://doi.org/10.5281/zenodo.14041243</a>.</span></p>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
Data published in manuscript "Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4" by Castro-Morales et al.
<p>This data is published in the manuscript<strong>:</strong></p> <p>Castro-Morales, K., Canning, A., Körtzinger, A., Göckede, M., Küsel, K., et al. (2022). Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO<sub>2</sub> and CH<sub>4</sub>. <em>Journal of Geophysical Research: Biogeosciences</em>, 127, e2021JG006485. <a href="https://doi.org/10.1029/2021JG006485">https://doi.org/10.1029/2021JG006485</a>.</p> <p>The data contains the water properties and gases data measured at a site in Ambolikha River, meteorological data measured at an eddy covariance tower located in the neighbor floodplain, and data from the analysis of dissolved organic matter in river water samples. The data was collected between 26 June, 2019 and 02 August, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_Ambolikha_River.txt" should be read before accessing the data. The authors recommend downloading Version 2.0 because it is the most up to date data.</p> <p>For questions contact the main and corresponding author Dr. Karel Castro-Morales at: karel.castro.morales@uni-jena.de</p>
Data for Deciphering-the-CO2-emissions-and-emission-intensity-of-cement-sector-in-China-through-decomposition-analysis
<p>The dataset contains data for Figure 7-13 in our article "<em>Deciphering the CO<sub>2</sub> emissions and emission intensity of cement sector in China through decomposition analysis</em>", and data for part of<em> China Cement Industry Dataset (CCID)</em>. </p>
Burnt forest area and CO2 emissions from fires in Russian forests by fire protection zones in 2010-2020
<p>The dataset is Supplementary Materials for the article ''<em>Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2010-2020</em>'' in the Carbon Balance and Management journal. It contains files with burnt forest area and carbon dioxide emissions from fires data in Russia 2010-2020.<br> Article Supplementary materials are stored in the file Supplementary Tables and contains:</p> <p>- Table 1. Burnt forest area from NIR and MODIS (MCD64A1) in 2010-2020;</p> <p>- Table 2. Burnt forest area in the ground, aviation and the control (no fire protection) zones in 2010-2020 using MCD64A1;</p> <p>- Table 3. Carbon emissions from forest fires from National Inventory Report (NIR) and Copernicus Atmosphere Monitoring Service (CAMS) in 2010-2020</p> <p>Also, there is Supplementary Figure 1 with the Federal Districts of Russia schematic map.</p> <p>There are 22 files in GeoTIFF format for every year: </p> <p>1. Burnt forest area obtained using MODIS product MCD64A1 (250 m pixel, ESRI:102025). Coverage: -4064059.5401764437556267,1967242.6686790268868208 : 3658440.4598235562443733, 6012242.6686790268868208</p> <p>2. CO2 emissions using Copernicus Atmosphere Monitoring System (CAMS) (0.1 degrees, VGS 84). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594<br> <br> In addition, we share Shapefiles:</p> <p>1. Russian borders (necessary to cut Russia from CO2 GeoTIFFs), EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992;</p> <p>2. Forest Fire Protection zoning in 2019: ground zone, aviation zone, the so-called control zone (no fire protection), EPSG:4326. Coverage: 27.4019779002987676,41.3483353426717599 : 173.8255532772949721,72.6575707670955353.</p>
Partial cutting of a boreal nutrient-rich peatland forest causes radically less on-site CO2 emissions than clear-cutting
<p>This package contains the data used in the research article: "Partial cutting of a boreal nutrient-rich peatland forest causes radically less on-site CO2 emissions than clear-cutting" published in Agricultural and Forest Meteorology.</p> <p>LAI_data.xlsx - Contains Leaf Area Index data and their standard deviations for all the measured areas</p> <p>WTL_data.csv - Contains the mean water table level data for pre-harvest, partial harvest and clearcut areas.</p> <p>Lettosuo_2010-2015_Section_A_fluxes.csv - Contains the pre-harvest (2010-2015) carbon flux data for Section A.</p> <p>Lettosuo_2010-2015_Section_BCD_fluxes.csv - Contains the pre-harvest carbon flux data for Section BCD.</p> <p>Lettosuo_2016-2021_Section_AB_(partialcut).csv - Contains the carbon flux data for the partial cut area (2016-2021, Section AB).</p> <p>Lettosuo_2016-2021_Section_D_(Clearcut).csv - Contains the carbon flux data for the clear-cut area (2016-2021, Section D)</p> <p>The carbon flux data files contain the following columns:</p> <p>Gapfilled PAR - Gapfilled photsynthetically active radiation</p> <p>Gapfilled air temperature - Gapfilled air temperature</p> <p>Measured NEE - Filtered NEE data</p> <p>Modelled TER - Modelled total ecosystem respiration</p> <p>Modelled GPP - Modelled gross primary production</p> <p>Modelled NEE - Modelled NEE calculated from the modelled TER and GPP</p> <p>Gapfilled NEE - A combination of measured and modelled NEE. Gaps in the measured data are filled with modelled NEE</p> <p>Modelling uncertainty - Uncertainty of the modelled NEE</p> <p>Measurement uncertainty - An estimation of the uncertainty of the measured NEE</p>
Analysis of public transport in Vienna with Co2 emissions in Austria's households
<p>This repository serves as a backup and longterm storage for the datasets and plots resulting from the analysis on means of transport in Vienna in the time period 2010 - 2021 together with the Co2 emissions of Austria's households.</p>
An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions - Dataset
<p>These data support the journal article : An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions (Journal of Advances in Modeling Earth Systems).</p> <p>The files provided are as follows:</p> <p>SITE_RMSE* - These files provided the computed RMSE values for SYNOP site evaluation using the control IFS and the urban IFS. Results are given for different forecast lead times, different seasons and for both 2 m and 10 m wind speed. </p> <p>DIURNAL* - These files provide the diurnal 2 m temperature output from the model and the comparison of those with observations.</p> <p>For more information please contact or access to alternative data related to the publication please contact: joe.mcnorton@ecmwf.int</p> <p> </p>
Net-zero CO2 emissions scenarios for Switzerland
<p>This dataset accompanies the relevant article in Communications Earth and Environment. It contains the key assumptions used in the energy system modelling with the Swiss TIMES energy systems model (STEM) for assessing net-zero carbon dioxide emissions scenarios for Switzerland. In addition, contains extensive results from STEM for each one of the core scenarios and variants assessed in the study. </p>
Herbarium specimens reveal century-long trait shifts in poison ivy due to anthropogenic CO2 emissions
<p>Dataset for manuscript entitled "Herbarium specimens reveal century-long trait shifts in poison ivy due to anthropogenic CO<sub>2</sub> emissions." Contains one spreadsheet file ("Ng et al 2023 Poison Ivy trait data.xlsx"). Note that metadata can be found in first tab.</p>
Worldwide CO2 emissions and natural disasters from 1960 to 2021
<p>A PDF file containing a plot visualizing 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>
China's fossil fuel CO2 emissions estimated using surface observations of co-emitted NO2
<p>We employed an EnKF-based Regional Multi-Air Pollutant Assimilation System (RAPAS) to assimilate <em>in-situ</em> NO<sub>2</sub> observations, allowing us to combine observation-constrained NO<em><sub>x</sub></em> emissions co-emitted with FFCO<sub>2</sub> and grid-specific CO<sub>2</sub>-to-NO<em><sub>x</sub></em> emission ratios for inferring daily <strong>FFCO<sub>2</sub> emissions</strong> over China.</p> <p><strong>cnemc_obs.nc </strong>includes assimilated and verified observations.</p> <p><strong>emission.tar.gz</strong> includes inferred daily posterior NO<em><sub>x</sub></em> and FFCO<sub>2</sub> emissions for the year 2016.</p>
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
Open the record for dataset details and reuse information.
Collected data on early estimates of global fossil CO2 emissions
<p>This dataset collects together a number of early estimates made of global emissions of fossil CO2, starting with Arvid Högbom in 1894. Microsoft Excel files for original data sources where these are time series, including images of the tables from the original sources, in addition to one CSV file of CO2 emissions from all sources. Sources that only reported emissions for a short period are not included in the Excel collection, but are included in the CSV file. In addition the original United Nations energy data used by several sources is included as UN1956.xlsx.</p>
CO2 emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>Monthly CO2 emission projections, modified by the country-specific impacts of COVID-19 lockdown. </p> <p>This repository holds the netcdf files for CO2 emissions projected by the 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, projected out for 3 years after 2020 before returning to baseline. The details of these activity estimates can be found in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions, and version numbers used here are consistent with the data seen in that database. We present only a single scenario (called 2-year blip, featuring a one year recovery after the end of the 2 years) compared to the baseline. </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>
Global, Regional, and National Fossil-Fuel CO2 Emissions: 1751-2017
<p>This data product is a time series of Carbon Dioxide (CO2) emissions from fossil fuel combustion and cement manufacture. Estimates of CO2 emissions are included for the globe and by nation back to 1751, and include emissions from solid fuel consumption, liquid fuel consumption, gas fuel consumption, cement production, and gas flaring. Per capita CO2 emissions and emissions from international trade (bunker fuels) are included as well; bunker fuels are not included in country totals, but are assigned to the country in which loading took place. Estimates are generated using the United Nations Energy Statistics database and the United States Geologic Survey’s cement statistics. Datasets produced from this group at Appalachian State University are located at https://data.ess-dive.lbl.gov/view/doi:10.15485/1712447, and are also located at https://energy.appstate.edu/research/work-areas/cdiac-appstate. Historic CDIAC data from Oak Ridge National Laboratory are located here: https://data.ess-dive.lbl.gov/view/doi:10.3334/CDIAC/00001_V2017. This dataset is the foundational dataset for the annual global carbon budget and other carbon cycle analyses that need relevant fossil fuel CO2 data. Within this data package are spreadsheets (.csv) of global and national estimates of CO2 emissions as well as text files of the ranking of each country’s total CO2 emissions and per capita for that year</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.