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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>
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>
Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based cathodes used in CO2 electrolysers"
<p>Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based CO2 electrolysers", published in Journal of Materials Chemistry A 2023 11:5083–5094, doi: <a href="https://doi.org/10.1039/D2TA06965B">10.1039/D2TA06965B</a></p> <p>Folder names describe the type of data content.</p>
Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers
<p>Dataset for the publication "Unintended cation crossover influences CO2 reduction selectivity in Cu-based zero-gap electrolysers"</p>
Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'
<p>Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)</p> <p> </p> <p> </p>
Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''
<p>Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: <a href="https://doi.org/10.1016/j.apcatb.2023.122885">10.1016/j.apcatb.2023.122885</a></p> <p>Folder names describe the type of data content.</p>
Hybridization of Fossil- and CO2-Based Routes for Ethylene Production using Renewable Energy
<p>Dataset associated with the publication "Hybridization of Fossil- and CO<sub>2</sub>-Based Routes for Ethylene Production using Renewable Energy" by Iasonas Ioannou, Sebastiano C. D'Angelo, Antonio J. Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1002/cssc.202001312">https://doi.org/10.1002/cssc.202001312</a>. The dataset includes the numeric data associated with most of the scenarios described in the main manuscript and in the Supporting Information (SI), as well as the tables presented in the main manuscript and in the SI converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>MS-Results</strong>: numerical values associated with the economic and environmental results included in both the main manuscript and the SI, for all the considered scenarios. The results include the total price for the assessed scenarios, with and without externalities, with uncertainty ranges, as well as the environmental results for human health, ecosystems, resources, and global warming potential (GWP).</li> <li><strong>MS-Tables</strong>: table reported in the main manuscript associated with the price and breakeven point of four assessed scenarios dependent on different CO<sub>2</sub> source assumptions.</li> <li><strong>SI-Tables-Economics</strong>: tables reported in the SI associated with the economic assessment of all the scenarios.</li> <li><strong>SI-Tables-LCI</strong>: tables reported in the SI associated with the environmental assessment of all the scenarios.</li> <li><strong>SI-Tables-AdditionalResults</strong>: tables reported in the SI associated with additional results presented in the work.</li> </ul>
Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling (data for figures)
<p>This is the data used to create the figures in the article "Pathways to enhance electrochemical CO2 reduction identified through direct pore-level modeling".</p> <p>Published in EES Catalysis</p> <p>DOI: 10.1039/d3ey00122a<br> Evan Johnson<br> Etienne Boutin<br> Shuo Liu<br> Sophia Haussener</p> <p><br> Additional notes are given in the "ReadMe.txt" file.</p>
Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies
<p>Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication <em>Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO<sub>2</sub> Capture Technologies </em>by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information. </p> <p> </p>
SeaFlux v2023: harmonised sea-air CO2 fluxes from surface pCO2 data products using a standardised approach
<p><strong>BE SURE TO DOWNLOAD 2023.02</strong></p> <p>See the additional notes for updates on the products. </p> <p>Fluxes calculated using the standardized approach:</p> <p> \(F\text{CO}_2=K_0 \cdot K_w \cdot (p\text{CO}_2^\text{sea} - p\text{CO}_2^\text{atm})\ \cdot (1 - [ice])\).</p> <p>We provide each of the components to this equation to reduce the potential for errors in fluxes due to methodological differences.</p> <p>The netCDF files contain the following data (<strong>note that only bold names have been updated in v2023</strong>): </p> <ul> <li>fgco2_all_winds_products: the sea-air CO2 flux for all spCO2 products (6) and <em>kw</em> from all wind products (5). </li> <li>fgco2_global:<strong> </strong>the globally integrated sea-air CO2 fluxes for all spCO2 products (6) and <em>kw</em> from all wind products (6)</li> <li><strong>sol:</strong> \(K_0\) is calculated using the Weiss (1974) parameterization with EN4 salinity and OISST temperatures </li> <li><strong>kw:</strong> \(k_w\) is calculated for winds with each being scaled independently to a 14-C bomb flux estimate of 16.5 cm/hr using the quadratic formulation by Wanninkhof (1992). <ul> <li>CCMPv2</li> <li>ERA5</li> <li>JRA55</li> <li>NCEP1</li> <li>NCEP2</li> </ul> </li> <li>spco2_SOCOM_unfilled<em>: </em>\(p\text{CO}_2^\text{sea}\) downloaded from various sources contains the following products: <ul> <li>CMEMS_FFNN</li> <li>CSIR_ML6</li> <li>JENA_MLS</li> <li>JMA_MLR</li> <li>MPI_SOMFFN</li> <li>NIES_FNN</li> </ul> </li> <li>spco2_filler<em>: </em>scaled version of the Landschützer et al. (2020) climatology used to fill missing regions of <em>spco2_SOCOM_unfilled</em></li> <li><strong>fco2atm: </strong>\(p\text{CO}_2^\text{atm}\) is calculated from NOAA's marine boundary layer product with ERA5 mean sea level pressure corrected for pH2O. The virial coefficient is then applied to pCO2atm</li> <li><strong>ice: </strong>\([ice]\) is the ice fraction from the OISST product</li> <li><strong>area_ocean:</strong><em> </em>the surface area of the ocean including the fractional area of the coastal regions</li> <li><strong>seafrac: </strong>the fraction of a pixel that is ocean</li> </ul> <p><strong><em>Units are listed in the metadata of each of the netCDF variables. </em></strong></p>
Dataset for publication "Enhancing C≥2 product selectivity in electrochemical CO2 reduction by controlling the microstructure of gas diffusion electrodes"
<p>Data used for publication:</p> <p>Broad topic: electrochemical reduction of CO2 using gas diffusion electrodes and neutral electrolyte</p> <p>Data is devided in subfolders named after the figure of the paper.</p> <p>Raw data, processed data, and Origin/Power Point files are all contained in the subfolders </p> <p>A subfolder corresponding to a sample contains: data from a potentiostat, gas and liquid chromatograms, recording of flow, pressure and temperature, tables of calculated Faradaic efficiency (FE), png image of the FE vs t, zipped raw files.</p> <p>.json file was created using a yadg scheme (https://dgbowl.github.io/yadg/master/index.html), and data was processed by a dgpost scheme (<a href="https://pypi.org/project/dgpost/">https://dgbowl.github.io/dgpost/master/index.html</a>)</p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Chamber measurements of carbon dioxide (CO2) and methane (CH4) in Everglades following Hurricane Irma: 2017 - 2019
On September 9, 2017, highwinds from Hurricane Irma impacted the Florida Everglades. With 24 hours of heavy rain along with strong winds and storm surge, the storm caused higher than normal water levels, wind-induced defoliation, uprooting of plants and soil disturbance in Everglades short-stature freshwater wetlands. The hurricane redistributed short-stature vegetation into dead mats in Everglades National Park. The high post-storm water levels saturated many of these dead mats, slowing decomposition and increasing their persistence on the landscape. In this study, we measure carbon dioxide (CO2) and methane (CH4) fluxes at dead mats and compared them to the ridge and slough of freshwater marsh and to the marl prairie.
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.
Summer high frequency measurements of dissolved O2 and CO2 concentrations and water temperature at the surface of 11 northern lakes
This dataset includes high frequency paired measurements of dissolved O2 and CO2 concentrations at the surface (0.5 to 2 m depth) of 11 lakes in the Northern Hemisphere. Measurements were taken every 2 hours in summer (July and August) of various years depending on lakes (between 2011 and 2014). The dataset is used to test a conceptual framework on the controls of coupling and decoupling of these two gases. In all lakes dissolved CO2 was measured with infrared analyzer coupled with a diffusion membrane and dissolved O2 with optodes. All gas measurements are paired with water temperature provided by one of the gas probe (usually from the O2 probe).
Stream and hyporheic carbon dioxide (CO2) measurements in a headwater catchment, Watershed 1, and throughout Lookout Creek watershed at the HJ Andrews Experimental Forest, July 2013 to July 2014
We examined the spatial and temporal variability of stream carbon dioxide (CO2) in a headwater catchment. We measured stream and hyporheic pCO2 at at 30-minute intervals over 11 months in 95.9-ha Watershed 1, HJ Andrews Experimental Forest. We also measured stream temperature, pH and alkalinity which we used to calculate CO2 at monthly intervals from July 2013 through July 2014 at 38 locations across the 6400-ha HJ Andrews Experimental Forest.
Plant species percent cover data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Soil percent nitrogen and carbon: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Long-term species richness and evenness trends in response to N addition and elevated CO2 in BioCON
The data and code in this package are associated with the analysis for a manuscript titled "Elevated CO2 first dampens but then amplifies diversity loss due to N enrichment over 24 years". The files include 24 years of data on species cover, total aboveground biomass, species richness, evenness and environmental variables from the BioCON experiment. The complete BioCON (Biodiversity, CO2, and N) experiment includes 371 2 x 2 m plots in six circular 20-meter diameter rings, located at the Cedar Creek Ecosystem Science Reserve in Minnesota, USA. Plots were established on secondary successional grassland on a sandy outwash soil after removing the prior vegetation. The BioCON project includes several overlapping and nested experiments.
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.