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1,543 results for “co2”
SBC LTER: Ocean: Time-series: Mid-water SeaFET and CO2 system chemistry at Alegria (ALE), 2011-2014
Calibrated pH (Total scale, SeaFET sensor) data was collected from Alegria in the Santa Barbara Channel (site ID: ALE) along with in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2011-06-21 to 2014-01-07.The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: pH time series: Water-sample pH and CO2 system chemistry, ongoing since 2011
Data are a time-series of pH and carbonate chemistry in manually-collected sea water samples from sixteen near-shore locations and 20 locations along the Santa Barbara Channel, California, USA, and intended to benchmark data from moored pH instruments. Data collection began in June 2011 and is ongoing. Time-series for pH sensors deployed in the field (e.g., SeaFET) are available elsewhere. Discrete seawater samples were collected by hand or using a Niskin bottle. In the laboratory, samples were analyzed for pH (total scale) and total alkalinity. Two parameters were used to calculate the full suite of carbonate chemistry parameters using the CO2calc algorithms. Program outputs are included for laboratory conditions as input, and adjusted for situ temperature. See Methods for more information, e.g., Lunden, et al. Data were collected by a consortium of research groups with the intent to leverage their collective assets and expertise. These groups (and sponsors) include Santa Barbara Coastal LTER (NSF), OMEGAS Project (NSF), Passow Lab (Calif. State Water Board), Hofmann Lab (NSF, CINMS, US NPS).
CO2 NEE and ER + air and soil meteorological and climate parameters in Alpine grasslands, Gran Paradiso National Park, 2017-2019
<p>The dataset “fluxes_meteoclimate_nivolet_V0” is a .csv file reporting CO<sub>2</sub> Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2700 m.a.s.l.) using the flux chamber method, during the 2017, 2018 and 2019 vegetative seasons (July-September), approximately twice a month. NEE is measured with a transparent flux chamber, while ER with a shaded chamber. Data represent the average values and the corresponding standard deviations obtained from four sites at different altitudes and geological substrate of the soil. Each average value is obtained as a mean over a set of more than 20 point-measures for each site and each sampling date. Flux data are complemented by measurements of soil temperature and volumetric water content, air temperature and moisture, and solar radiance. The four sites are characterized by soils developed over carbonates (carb) (45.500212N-7.152213E), glacial deposits (glac) (45.490167N-7.139916E), gneiss rocks (gnei) (45.490256N-7.149253E) and alluvial deposits (allu) (45.492656 N-7.146092 E).</p> <p>Other relevant shortcuts used in the .csv table: Std = Standard deviation; VWC% = Volumetric Water Content %. Meteorological and climate variables recorded during the measurement of NEE and during the measurement of ER bring the suffix NEE and ER respectively (es. Pressure_NEE (hPa) = atmospheric pressure recorded during the measurement of Net Ecosystem Exchange).</p>
Data for "Soil CO2 efflux errors are lognormally distributed - Implications and guidance."
<p>Soil CO2 flux data at site ES-LMa of four automatic chambers in the control-openLand-subplot for the period from 2015-11-10 to 2016-11-10.</p> <p>These data were used for the publication:</p> <p>Wutzler, et al. (2020) "Soil CO2 efflux errors are lognormally distributed - Implications and guidance." Geoscientific Instrumentation, Methods, and Data Systems</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p> <p> </p>
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
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>
Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.
<p>Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5).</p> <p>Details on simulations and alkalinisation strategies are given in the reference article below.</p> <p> </p> <p>Reference:</p> <p>Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a></p>
Paleo-CO2 Data Archive
<p>These data were compiled from published paleo-CO<sub>2</sub> data that have been assembled by an international group of proxy experts supported through an NSF-funded Research Coordination Network. It brings together paleo-CO<sub>2</sub> reconstruction data from terrestrial and marine samples, and the compilation includes data derived from multiple proxies including Phytoplankton, Boron, Stomatal Frequencies, Leaf Gas Exchange, Liverworts, Land Plant Carbon Isotopes, Paleosols, and Nahcolite. These are used in the interactive archive plot made available on the Paleo-CO2 project web page at (https://www.paleo-co2.org) and are also archived in the NCDC database (<a href="https://www.ncdc.noaa.gov/paleo/study/35079">https://www.ncdc.noaa.gov/paleo/study/35079</a>). </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>
Data Product for "Toward a Cenozoic history of atmospheric CO2"
<p>These data were vetted and revised from published paleo-CO2 data (original estimates archived at https://zenodo.org/uploads/8052599) by an international group of proxy experts supported through an NSF-funded Research Coordination Network. It brings together paleo-CO2 reconstruction data from terrestrial and marine archives, and the compilation includes estimates derived from multiple proxies including Phytoplankton, Boron, Stomatal Frequencies, Leaf Gas Exchange, Liverworts, Land Plant d13C, Paleosols, and Nahcolite. Data are visualized in an interactive product plot made available on the Paleo-CO2 project web page at (https://www.paleo-co2.org) and are also archived in the NCDC database (<a href="https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/">https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/</a>). </p>
Belowground nitrogen cycling in a montane grassland exposed to elevated CO2, warming and drought
<p>#### Data description<br> Data from a multi-factorial global change experiment (elevated CO<sub>2</sub>, warming and drought) in a montane grassland experiment in Austria (ClimGrass). Variables presented are soil nitrogen cycling rates measured using isotope pool dilutions.</p> <p>Companion paper will be linked following manuscript publication.</p> <p>#### Metadata<br> climgrass_soil_N_cycling.csv data description</p> <p>Year: 2017<br> Harvest: three harvests (May 30, July 25, October 3)<br> Season: numerical column for harvest number<br> Plot: location of plot within the ClimGrass experiment<br> Treatment: eight treatment levels<br> c0t0 (ambient CO<sub>2</sub>, ambient temperature)<br> c0t1 (ambient CO<sub>2</sub>, + 1.5°C)<br> c0t2 (ambient CO<sub>2</sub>, + 3°C)<br> c1t1 (+150 ppm, +1.5°C)<br> c2t0 (+300 ppm, ambient temperature)<br> c2t2 (+300 ppm, +3°C)<br> c0t0-d (ambient CO<sub>2</sub>, ambient temperature, extended drought)<br> c2t2-d (+300 ppm, +3°C, extended drought)<br> CO2_ppm: three values of carbon dioxide enrichment treatment (+0, +150, or +300 ppm)<br> Temp_C: three values of elevated temperature treatment (+0, +1.5, +3°C)<br> Drought: two levels (control, drought)<br> Prot_depoly: gross protein depolymerization rates (micrograms nitrogen per grams dry soil per day = µg N g-1 d-1)<br> FAA_uptake: gross free amino acid uptake rates (µg N g-1 d-1)<br> MRT_FAA_hrs: mean residence time of free amino acids (hours)<br> FAA: free amino acids (µg N g-1)<br> Mineralization: gross mineralization rates (µg N g-1 d-1)<br> Nitrification: gross nitrification rates (µg N g-1 d-1)</p> <p>#### References<br> Additional information on the experimental design can be found in the following paper:<br> Piepho, H.-P., Herndl, M., Pötsch, E.M., Bahn, M., 2017. Designing an experiment with quantitative treatment factors to study the effects of climate change. Journal of Agronomy and Crop Science 203, 584–592. doi:https://doi.org/10.1111/jac.12225</p> <p>More information on the isotope pool dilution method used can be found in the following paper:<br> Wanek, W., Mooshammer, M., Blöchl, A., Hanreich, A., Richter, A., 2010. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15 N isotope pool dilution technique. Soil Biology and Biochemistry 42, 1293–1302. doi:10.1016/j.soilbio.2010.04.001</p>
Supplementary Data to Kinetic oxygen isotope fractionation between water and aqueous OH- during hydroxylation of CO2
<p>In this dataset, we provide analytical data to <strong>Kinetic oxygen isotope fractionation between water and aqueous OH<sup>-</sup> during hydroxylation of CO<sub>2</sub></strong> by Bajnai and Herwartz (2021). Also deposited here is the R code that was used to generate the figures in the manuscript.</p>
Rising CO2 and warming reduce global canopy demand for nitrogen
<ul> <li>Nitrogen (N) limitation has been considered as a constraint on terrestrial carbon uptake in response to rising CO<sub>2</sub>and climate change. By extension, it has been suggested that declining carboxylation capacity (<em>V</em><sub>cmax</sub>) and leaf N content in enhanced-CO<sub>2</sub>­ experiments and satellite records signify increasing N limitation of primary production.</li> <li>We predicted <em>V</em><sub>cmax </sub>using the coordination hypothesis, and estimated changes in leaf-level photosynthetic N for 1982–2016 assuming proportionality with leaf-level <em>V</em><sub>cmax</sub> at 25˚C. Whole-canopy photosynthetic N waas derived using satellite-based leaf area index (LAI) data and an empirical extinction coefficient for <em>V</em><sub>cmax</sub>, and converted to annual N demand using estimated leaf turnover times.</li> <li>The predicted spatial pattern of <em>V</em><sub>cmax </sub>shares key features with an independent reconstruction from remotely-sensed leaf chlorophyll content. Predicted leaf photosynthetic N declined by 0.28 %/year, while observed leaf (total) N declined by 0.2–0.25 %/year. Predicted global canopy N (and N demand) declined from 1997 onwards, despite increasing LAI.</li> <li>Leaf-level responses to rising CO<sub>2</sub>, and to a lesser extent temperature, may have reduced the canopy requirement for N by more than rising LAI has increased it. This finding provides an alternative explanation for declining leaf N that does not depend on increasing N limitation.</li> </ul>
Supplementary dataset for "Rising CO2 and warming reduce global canopy demand for nitrogen"
<p>This repository contains the dataset used for “<strong>Rising CO<sub>2</sub> and warming reduce global canopy demand for nitrogen” </strong></p> <p>The deposition consists of:</p> <ol> <li>An satellite-derived leaf chlorophyll vcmax25 database (Luo<em> et al.</em>, 2019)</li> <li>Simulated <em>V<sub>cmax</sub></em> with all the factors based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax </sub></em>with CO<sub>2</sub> fixed at 340 ppm based on the coordination hypothesis</li> <li>Simulated <em>V<sub>cmax</sub> </em>with fixed climate based on the coordination hypothesis</li> <li>Simulated turnover time.</li> <li>Simulated leaf-level <em>N</em><sub>rubisco</sub> (g m<sup>–2</sup> leaf area), canopy-level <em>N<sub>rubisco</sub></em> (g m<sup>–2</sup> ground area), annual leaf-level <em>N<sub>rubisco</sub></em>demand (g m<sup>–2</sup> leaf area year<sup>–1</sup>), and annual canopy-level of <em>N<sub>rubisco</sub></em> demand (g m<sup>–2</sup> ground area year<sup>–1</sup>) in figure 4.</li> <li>Lifespan of evergreen</li> </ol> <p>Note. </p> <ol> <li>LAI products used in the paper , such as TCDR LAI during 1982­–2016; GLASS LAI during 1982–2014; and GLOBMAP LAI during 1982–2011 are public available, the details information see Jiang <em>et al </em>(2017).</li> <li>Evergreen, deciduous and herbaceous vegetation fractions data derived from ESA CCI land cover products is publicly available, the details information see Li <em>et al </em>(2018).</li> <li>The climate force for <em>V<sub>cmax </sub></em>simulation was used CRU TS4.3 (Harris <em>et al,</em> 2020) for 1982–2016 at 0.5° resolution, which is publicly available at </li> </ol> <p><a href="https://crudata.uea.ac.uk/cru/data/hrg/">https://crudata.uea.ac.uk/cru/data/hrg/</a>.</p> <p>The data files are all in netcdf format at 0.5 resolution </p> <p>Reference:</p> <ol> <li><strong>Luo X, Croft H, Chen JM, He L, Keenan TF. 2019.</strong> Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. <em>Global Change Biology</em> <strong>25</strong>(7): 2499-2514.</li> <li><strong>Jiang C, Ryu Y, Fang H, Myneni R, Claverie M, Zhu Z. 2017.</strong> Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. <em>Global Change Biology</em> <strong>23</strong>(10): 4133-4146.</li> <li><strong>Li W, MacBean N, Ciais P, Defourny P, Lamarche C, Bontemps S, Houghton RA, Peng S. 2018.</strong> Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015). <em>Earth Syst. Sci. Data</em> <strong>10</strong>(1): 219-234.</li> <li><strong>Harris I, Osborn TJ, Jones P, Lister D. 2020.</strong> Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. <em>Scientific Data</em> <strong>7</strong>(1): 109.</li> </ol>
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
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>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
BIOLOGICAL DATA in CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system
<p>BIOLOGICAL DATA to estimate the carbon dioxide budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system.</p> <p>Álvarez-Salgado et al. (2022) estimate the carbon dioxide and total alkalinity budgets due to the Mediterranean mussels (Mytilus galloprovicialis) growing in suspended culture in a low seston environment such as the Galician Rías (NW Spain). This database contains the biological data needed to estimate the carbon dioxide fluxes and changes in total alkalinity induced by the different biological processes involved in mussel growth. </p> <p>Manuscript available at: <a href="https://doi.org/10.1016/j.scitotenv.2022.157867">https://doi.org/10.1016/j.scitotenv.2022.157867</a></p> <p>Álvarez-Salgado, X.A., Fernández-Reiriz, M.J., Fuentes-Santos, I., Antelo, L.T., Alonso, A.A., Labarta, U., 2022. CO2 budget of cultured mussels metabolism in the highly productive Northwest Iberian upwelling system. Sci. Total Environ. 849, 157867.</p>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
data-base of CO2, CH4, N2O and ancillary data in the Congo River
<p>data-base of CO2, CH4, N2O and ancillary data in the Congo River relative to paper "Variations of dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity" by Borges et al. (https://doi.org/10.5194/bg-2019-68)</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.