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ShareScore release 0.7.1
Dataset results
23 results for “Greenhouse gases”
Data associated with the FLooded Upland Dynamics EXperiment (FLUDEX), conducted at the IISD Experimental Lakes Area 1997 to 2003, investigating reservoir flooding impacts on ecosystems, particularly the release of mercury and greenhouse gases.
The data included in this repository were collected over the course of the FLooded Upland Dynamics Experiment (FLUDEX) conducted at the IISD Experimental Lakes Area (IISD-ELA) from 1997 to 2003. A plethora of data was collected over five years of flooding three upland reservoir sites, in order to examine the relationship between the amount of flooded, and thus decomposed, terrestrial organic matter and the production of methylmercury (MeHg), total mercury (THg), and greenhouse gases (GHGs) in the reservoirs. Findings from this experiment suggest that the amount of organic carbon stored in a flooded site does not directly influence the amount of THg, MeHg, and GHGs produced, but it does affect the persistence of mercury in the reservoir and food web. This version of the repository contains data collected on water chemistry, benthic invertebrate (chironomid) emergence, mercury and methylmercury concentrations in the water and food web, stable isotopes of carbon and nitrogen in emerging insects and zooplankton, and abundance and biomass of zooplankton, phytoplankton, and bacteria. This data package contains only some of the data from the FLUDEX project. IISD-ELA hopes to add more data in subsequent versions.
Dissolved greenhouse gas concentrations derived from the NEON dissolved gases in surface water data product (DP1.20097.001)
This dataset contains partial pressure and molar concentration of dissolved carbon dioxide, methane, and nitrous oxide in 34 streams, rivers, and lakes calculated from headspace equilibration samples collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, in situ dissolved gas concentrations were calculated from the air and headspace mixing ratios provided by the NEON Dissolved gases in surface water data product (DP1.20097.001), adjusted for sample and water temperature (DP1.20097.001, DP1.20264.001, DP1.20053.001), barometric pressure (DP1.20097.001, DP1.00004.001), and alkalinity (DP1.20093.001). The final set of inputs is found in the file, input_file, and the processing scripts are available at https://github.com/kellyaho/NEON-GHG-processing. The file, output_file, contains the raw outputs from running the input_file through the processing scripts. There are three outputs for each gas for each sample, one for each of three different pre-equilibration headspace mixing ratios (paired atmospheric samples, loess smoothing of atmospheric samples, and site-specific median). The file, GHG_final, contains the final dataset. This GHG_final uses the outputs from output_file calculated with paired atmospheric samples, and substitutes 0.01 μatm, 0.001 μM, 0.001 μatm, and 0.001 μM for any negative instances of pCH4, [CH4], pN2O, and [N2O], respectively. See methods for more detail. Please cite the NEON data inputs (listed below), in addition to this dataset, when using the data. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
MAR2PROTECT - Coconut shell derived activated carbon for effective separation of greenhouse gases - DATASET
<p>The need for innovative and efficient adsorptive materials with enhanced structural characteristics that facilitate the selective capture of greenhouse gases (GHGs) is critical. Porosity and surface area play an important role in the adsorptive capture and separation of GHGs, enabling the design of processes that reduce GHGs emissions. This study shows how residual coconut shell (CS) biomass can be reused for the design of novel biomaterials (CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>) with structural characteristics that promote the selective adsorption of GHGs. Additionally, the results are compared with those obtained with activated carbon monoliths (ACM) and a Metal-Organic Framework (MOF Fe-BTC) to understand the impact of different porous solid matrices on adsorptive GHG capture. In this context, the adsorption performance of difluoromethane (R-32), pentafluoroethane (R-125), 1,1,1,1-tetrafluoroethane (R-134a), 1,1,1,1-trifluoroethane (R-143a), carbon dioxide (CO<sub>2</sub>), and methane (CH<sub>4</sub>) on CS-CO<sub>2</sub>, CS-ZnCl<sub>2</sub>, ACM and Fe-BTC were measured by gravimetry at 283.15 K, 303.15 K and 323.15 K. The experimental data are correlated using the dual-site Langmuir adsorption model, and the selectivities of the commercial mixtures R-410A, R-407C, R-404A and CO<sub>2</sub>/CH<sub>4</sub> are calculated using the Ideal Adsorption Solution theory (IAST). CS-ZnCl<sub>2</sub> has a higher selectivity for R-125 over R-32 in the separation of R-410A at low pressure, and also a higher selectivity for R-407C due to its larger pore volume. In the separation of the R-404A refrigerant blend, CS-CO<sub>2</sub> adsorbs predominantly R-134a and R-143a over R-125. Finally, the ACM material preferentially adsorbs CO<sub>2</sub> over CH<sub>4</sub>, owing to its large and elongated micropores that favour the adsorption of the smaller molecule. This study introduces novel and innovative materials to enhance the separation of GHGs mixtures, contributing to a reduction in their emissions.</p>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
Model output files for Ou et al. 2021 (Deep Mitigation of CO2 and non-CO2 Greenhouse Gases towards 1.5°C and 2°C Futures)
<p>GCAM model output files used to reproduce Ou et al. (Deep Mitigation of CO<sub>2</sub> and non-CO<sub>2</sub> Greenhouse Gases towards 1.5°C and 2°C Futures)</p>
(DATA) Adsorption Behavior of Greenhouse Gases on Carbon Nanobelts: A Semi-Empirical Tight-Binding Approach for Environmental Application
<ul> <li>Input structures.</li> <li>Scripts to generate some inputs.</li> <li>Script to run all the calculations.</li> <li>Scripts to run analysis.</li> <li>Molecular dynamics trajectories (in PDB and TRJ formats)</li> <li>Molecular dynamics animations (in MP4 format)</li> </ul>
Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG)
<p>This is a database called Cryosphere Inland Water Greenhouse Gases Database (CIWD-GHG), which provides spatial-temporally resolved greenhouse gases (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) data from cryosphere inland waters (lakes, ponds, reservoirs, rivers, and streams). The dataset was created through a synthesis procedure.</p> <p>To compile the dataset, we conducted searches in various sources including peer-reviewed papers, dissertations, theses, and public data repositories. The searches were conducted using platforms such as Web of Science, Google Scholar, ProQuest Dissertations & Theses Global, China National Knowledge Infrastructure, Arctic Data Center, Zenodo, Environmental Data Initiative, and PANGAEA. The search string is: (methane OR CH4 OR carbon dioxide OR CO2 OR nitrous oxide OR N2O OR greenhouse*) AND (river OR stream OR lake OR pond OR reservoir) AND (Arctic* OR Tibet* OR Greenland OR Antarctic OR glacier* OR permafrost). We ensured completeness by conducting multiple searches before April 2023.</p> <p>We applied a consistent criterion for screening and selecting the searched results. Specifically, we included data on greenhouse gas concentrations or fluxes measured in inland water systems such as streams, rivers, ponds, lakes, and reservoirs that are associated with permafrost or glaciers. The study focused on the cryosphere extent, excluding sites outside this extent. Wetland ecosystems and inland waters in cold regions without glaciers or permafrost were also excluded. Additionally, floodplain lakes connected with river channels were excluded, except for those barely connected with high-closure river channels, which were included as lake sites. Gas concentration data in permanently ice-covered water bodies were not included. We also collected auxiliary data on waterbody physical and chemical characteristics, climate, and land cover to the extent possible.</p> <p>The dataset was divided into two separate files: CryoLake.xlsx and CryoRiver.xlsx. CryoLake.xlsx contained data on lakes, ponds, and reservoirs, while CryoRiver.xlsx contained data on rivers and streams. Each file consisted of four sub-tables: source table (data sources), sites table (sites information), concentration table (concentration data), and flux table (flux data). All sub-tables were linked using unique source IDs, and the sites, concentration, and flux tables were further linked using unique site IDs. The temporal resolution varied, with daily data being the shortest resolution. Sub-daily measurements were averaged to daily data, and data reported in monthly, seasonal, and annual scales were also included. Considering the difficulty in obtaining cryosphere-related GHG data, we included all available data. The spatial resolution primarily focused on the plot scale, although aggregated sites were also included. Detailed spatiotemporal information of the measured data was recorded in the data table for further analysis. Due to regional heterogeneity, we did not have a uniform standard for dividing seasons. Instead, seasons were assigned based on the site descriptions provided in each study.</p> <p>The R script file is the multilevel bootstrap method used for Inland water greenhouse gas emissions upscaling. The ziped file contains bootstrap results for further calculating zonal and monthly GHG emissions.</p>
Andersen et al., 2018 - A UAV-based active AirCore system for measurements of greenhouse gases - Raw data
<p>Raw Picarro data sets and flight logs from the Lutjewad drone flights on September 13th 2016, along with the processed flight data, as presented in "A UAV-based active AirCore system for measurements of greenhouse gases" published in <em>Atmospheric Measurement Techniques </em>(<a href="https://doi.org/10.5194/amt-11-1-2018">https://doi.org/10.5194/amt-11-1-2018</a>)<em>. </em></p>
Sharing data and code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols"
<p>The public data repository contains the data and plotting code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols". The dataset includes annual maximum one-day precipitation (Rx1day), its proxy computed by a physical scaling diagnostic (scaling), and the decomposed components of the scaling (i.e., thermodynamic response, dynamic response, and their interaction). Several reanalyses (including ERA5 and JRA55) and CMIP6 simulations under several scenarios (including ALL, GHG, AER, and piControl) are applied to compute the historical Rx1day, scaling, and the associated components following a GitHub Python repository (<a href="https://github.com/oliverangelil/precip_extremes_scaling">https://github.com/oliverangelil/precip_extremes_scaling</a>). Note that the decomposed components are calculated as anomalies. </p> <p>The data results of the extreme precipitation decomposition in the NetCDF format are available in zip files “<em><strong>ERA5</strong></em>”, "<em><strong>JRA55</strong></em>", "<em><strong>ALL</strong></em>", "<em><strong>GHG</strong></em>", "<em><strong>AER</strong></em>", "<em><strong>NAT</strong></em>", and "<em><strong>piControl</strong></em>". Code for visualizations is available in the "<em><strong>Jupyter Notebooks</strong></em>" zip file.</p>
Impact of community size fraction on plants and greenhouse gases dataset
Open the record for dataset details and reuse information.
CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
Ambitious hydropower plans will accelerate greenhouse gases emissions from the Hindu-Kush Himalaya region
<p>01-<em>07: Database of GHG emisisons from existing and future hydropower plants in the Hindu-Kush Himalaya region. 08</em> : Source codes for simulating reservoir flooded area and GHG fluxes.</p>
(DATA) Exploring the potential of boron-nitride nanobelts in environmental applications: greenhouse gases capture
<ul> <li>Input structures.</li> <li>Scrip to run all the calculations.</li> <li>Output files from several calculations.</li> </ul>
Greenhouse gases (GHG) profiling over Cyprus with Aircore
<p>AirCore is a unique system for sampling air and rendering vertical profiles of greenhouse gases (GHGs) concentrations from the surface up to the stratosphere. Carried out by a weather balloon filled with helium, it can reach altitudes up to 35 km. On the ascending phase the tube empties out (via expansion along with decreasing atmospheric pressure) and samples the ambient air on the descending phase (via compression). Once at ground level the sample is analysed to measure CO2 CH4 and CO concentrations using a Cavity Ring Down Spectrometer (CRDS).</p> <p>In collaboration with LSCE and the French Aircore (AC) program, AC activities have been initiated in June 2020 in Cyprus within the framework of the EMME-CARE (Eastern Mediterranean and Middle East – Climate and Atmosphere Research). The EMME region has been identified as a global climate change "hot spot". Climate and atmosphere research is now taking place with the establishment of a regional Centre of Excellence.</p> <p> </p> <p>This project has received funding rom the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 856612 and the Cyprus Government.</p> <p> </p>
Greenhouse Gases Data for Lake Nokoué (Benin, West Africa)
<p><span>The file contains, mixing ratio of CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from bubbles, surface water concentrations, as well as both ebullitive and diffusive fluxes from Lake Nokoué, located in Benin (West Africa).</span></p>
Source data used to reproduce global maps for Ou et al. 2021 (Deep Mitigation of CO2 and non-CO2 Greenhouse Gases towards 1.5°C and 2°C Futures)
<p>Shape file and R code used to reproduce Fig 3d and SI figures with global maps, using an open-source R package "sf" (https://r-spatial.github.io/sf/ ) </p>
Ocean heat uptake and interbasin redistribution driven by anthropogenic aerosols and greenhouse gases
<p>Codes and data for the figures in the paper: Ocean heat uptake and interbasin redistribution driven by anthropogenic aerosols and greenhouse gases </p>
CARAFE: Regional Airborne Greenhouse Gases Eddy Covariance Measurements, 2016-2017
This dataset provides airborne eddy covariance (EC) fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution collected during the NASA Carbon Airborne Flux Experiment (CARAFE) airborne 2016 and 2017 campaigns. CARAFE utilized the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation. Deployment occurred across the Mid-Atlantic Region for the period 2016-09-07 through 2016-09-26 and 2017-05-03 through 2017-05-26. The data also include downwelling radiation, water vapor, pressure, temperature, wind, and aircraft navigation data. Airborne EC can quantify surface fluxes at local to regional scales, potentially helping to bridge gaps between top-down and bottom-up flux estimates and offering novel insights into biophysical and biogeochemical processes.
NACP Greenhouse Gases Multi-Source Data Compilation, 2000-2009
This data set is a collection of measurements of carbon dioxide (CO2) and non-CO2 greenhouse gases made across North America by nine independent atmospheric monitoring networks from 2000 - 2009. During this North American Carbon Program (NACP) sponsored activity, data were compiled from the following networks: AGAGE, COBRA, CSIRO, INTEX-A, INTEX B, Irvine Latitude Network, NOAA CMDL, SCRIPPS, and Stanley Tyler-UC Irvine. The files presented here are the products of merging multiple original measurement results files for selected sites across North America from each monitoring network. The primary focus of this effort was the compilation of non-CO2 greenhouse gases over North America, but numerous CO2 observations are also included. The data files for each network are accompanied by detailed readme documentation files prepared by the respective network investigators. Project descriptions, objectives, references, sampling and analysis methods, and data file descriptions are included in these READMEs. Table 1 in the documentation displays the monitoring network sites, sample types, analytes, and links to the detailed network README files. Network- and laboratory-specific data citations are included in the README documentation and should be used to acknowledge the use of these data as appropriate. The data files for each monitoring network and each sampling type (continuous or flasks) have been combined into one compressed (*.zip) file along with the detailed README document. There are 17 compressed files that when expanded contain data files which represent one year�s data for that specific campaign and sampling method. The number of annual files that were compiled from a network into this collection varies.
NACP: Urban Greenhouse Gases across the CO2 Urban Synthesis and Analysis Network, V2
This dataset provides hourly urban greenhouse gas measurements for cities in the CO2 Urban Synthesis and Analysis (CO2-USA) Data Synthesis Network for 2000 to 2019. Measurements include carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) concentrations measured at hourly intervals at multiple sites within the U.S. cities of Boston, Indianapolis, Los Angeles, Portland, Salt Lake City, San Francisco, and Washington DC/Baltimore, and Toronto, Canada.
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.