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111 results for “CO2 emissions”
Data from: Hydroxymethylbutenyl diphosphate accumulation reveals MEP pathway regulation for high CO2-induced suppression of isoprene emission
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Data from: Higher fungal diversity is correlated with lower CO2 emissions from dead wood in a natural forest
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Biochar increases tree biomass in a managed boreal forest, but does not alter N2O, CH4, and CO2 emissions
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Reductions in California's urban fossil fuel CO2 emissions during the COVID-19 pandemic
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TeRaCON eight years data - species composition, productivity (NPP), soil carbon emissions and plant carbon stocks:BioCON: Biodiversity, CO2, and Nitrogen
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
Set of European CO2 and CO emission grids representing emission uncertainties
<p>This dataset was prepared by TNO as a contribution to the H2020 project CHE and the H2020 project VERIFY. The basis is a high-resolution (~1x1 km) emission inventory providing CO<sub>2</sub> and CO (from fossil fuels and biofuels separately) over western Europe (2ºW - 19ºE, 47ºN - 56ºN). The reported emissions by European countries to UNFCCC (CO<sub>2</sub>) and to EMEP/CEIP (CO) have been used and where needed gap-filled or replaced with emission data from the GAINS model. These country-level emissions are disaggregated in space using a consistent spatial distribution methodology, whereas large point sources are listed with their exact locations. This approach is similar to the one described by Kuenen et al., (ACP, 2014). Emissions are reported per GNFR sector, with an extra split for road transport.</p> <p>The emission grids that are part of this dataset are a variation on the base grid, representing the uncertainty in the emission data. Each grid is equally plausible. The grids have been created using a Monte Carlo approach. The uncertainties in the underlying data used to create the base grid (emissions: activity data and emission factors, spatial proxies) have been collected (either from country reports or based on expert judgement). Through the Monte Carlo simulation these uncertainties, taking into account error correlations between some sub-sectors, are combined to create ten new emission grids. The spread in emissions between these emission maps gives an indication of the uncertainty in the emissions.</p> <p>The grid files (in .csv and .nc format) contain annual total emissions per grid cell for the year 2015. A separate file has been prepared for each ensemble member in the Monte Carlo simulation (indicated with M). The unit in the files is kg/yr.</p> <p>A detailed description of the Monte Carlo simulation is presented in:</p> <p>Super, I., Dellaert, S. N. C., Visschedijk, A. J. H., and Denier van der Gon, H. A. C.: Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-696, in review, 2019.</p> <p><strong>N.B. It is important to note that 10 maps are not sufficient to describe the sometimes complex uncertainty structures, for example in the case of lognormal uncertainty distributions. The interpretation of the uncertainty based on these 10 maps should therefore be done with care.</strong></p> <p><strong>NB. Despite efforts to prevent negative emissions to occur in the grid maps, some negative values are still present. In local studies this might cause some issues, and we recommend to set negative emissions to zero in those cases.</strong></p>
Data from: Impact of cognitive tasks on CO2 and isoprene emissions from humans
<p>The human body emits a wide range of chemicals, including CO<sub>2</sub> and isoprene. To examine the impact of cognitive tasks on human emission rates of CO<sub>2</sub> and isoprene, we conducted an across subjects, counterbalanced study in a controlled chamber involving 16 adults. The chamber replicated an office environment. In groups of four, participants engaged in 30 minutes each of cognitive tasks (stressed activity) and watching nature documentaries (relaxed activity). Measured biomarkers indicated higher stress levels were achieved during the stressed activity. Per-person CO<sub>2</sub> emission rates were greater for stressed than relaxed activity (30.3 ± 2.1 vs. 27.0 ± 1.7 g/h/p, <i>p </i>= 0.0044, mean ± standard deviation). Isoprene emission rates were also elevated under stressed vs. relaxed activity (154 ± 25 µg/h/p vs. 116 ± 20 µg/h/p, <i>p</i> = 0.041). Chamber temperature was held constant at 26.2 ± 0.49 ◦C; incidental variation in temperature did not explain variance in emission rates. Isoprene emission rates increased linearly with salivary-alpha amylase levels (<i>r<sup>2</sup></i> = 0.6, <i>p</i> = 0.02). These results imply the possibility of considering cognitive tasks when determining building ventilation rates. They also present the possibility of monitoring indicators of cognitive tasks of occupants through measurement of air quality. </p>
Gold Standard and Annotation Dataset for CO2 Emissions Annotation
<p>This repository contains the results of a research project which provides a benchmark dataset for extracting greenhouse gas emissions from corporate annual and sustainability reports. </p> <p>The zipped <code>datasets</code> file contains two datasets, <code>gold_standard</code> and <code>annotation_dataset</code>(inside the outer zip file there is a password-protected zip file containing the two datasets. To unpack, use the password is provided in the outer zip file).</p> <h3>Data collection</h3> <ol> <li>A Large Language Model (LLM) based pipeline was used to extract the greenhouse gas emissions from the reports (see columns prefixed with <code>llm_</code> in <code>annotation_dataset</code>). The extracted emissions follow the categories Scope 1, 2 (market-based) and 2 (location-based) and 3, as defined in the GHGP protocol (see variables <code>scope</code>).</li> <li>Annotation of the pipeline output was done in 3 phases: first by non-experts (see columns prefixed with <code>non_expert_</code> in <code>annotation_dataset</code>), then by expert groups (columns prefixed with <code>exp_group_</code> in <code>annotation_dataset</code>) in case of disagreement of non-experts and finally in a discussion of all experts (columns prefixed with <code>exp__disc</code> in <code>annotation_dataset</code>) in case of disagreement between expert groups. The annotation guidelines for the <a href="https://zenodo.org/api/records/15124118/draft/files/Non-Expert%20Annotation%20Guidelines.pdf/content">non-experts</a> and <a href="https://zenodo.org/api/records/15124118/draft/files/Expert%20Annotation%20Guidelines.pdf/content">experts</a> are also included in this repository.</li> <li>The annotation results from all three phases are combined to form the final benchmark dataset: <code>gold_standard</code>. Codebooks detailing each variable of each of the two datasets are also provided. More details about the annotation template or the data wrangling scripts can be found in the <a href="https://github.com/soda-lmu/gist-data-descriptor/" target="_blank" rel="noopener">GitHub repository</a>. </li> </ol> <h3>Merging of datasets</h3> <p>Users can match the two datasets (<code>gold_standard</code> and <code>annotation_dataset</code>) using the variable combination of <code>company_name</code>, <code>report_year</code> and <code>merge_id</code> (index column). The <code>merge_id</code> already includes the company name and report year implicitly, but to avoid column duplication in the join operation, it should be included as join variables. For example this is useful when comparing LLM extractions to gold standard data.</p>
Supporting data for Relative increases in CH4 and CO2 emissions from wetlands under global warming dependent on soil carbon substrates
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Data of satellite-estimated CO2 and CH4 emissions from 113 meso-eutrophic lakes of China's Yangtze and Huai River Basin
<p>This supporting information provides the data, which inlcude lake size, CO2 flux, CH4 flux, and eutrophic status such as chlorophyll a (Chl-a) concentration and trophic state index</p>
Implications of emission sources and biosphere exchange on temporal variations of CO2 and δ13C using continuous atmospheric measurements at Shadnagar (India)
<p>The data contains daily atmospheric CO2 mixing ratios and 13C of CO2 over the Shadnagar region of India during November 2018 to October 2019.</p>
Dataset for Projections of Forest Degradation and CO2 Emissions for the Brazilian Amazon
<p>Data and parameters used for generate scenarios of forest degradation and CO2 Emissions for the Brazilian Amazon.</p> <p> Spatial data are available and compiled into cellular spaces. We used LuccME land use modeling framework and INPE-EM emission model to generate theses scenarios. LuccME and INPE-EM versions used to build the model, the new LuccME components we developed and the scripts containing all the parameters are also available. </p> <p> </p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
COMPARING THE CO2 EMISSION INTENSITY OF THE STEEL INDUSTRIES IN THE EU AND CHINA RESULTING FROM TOP-DOWN AND BOTTOM-UP APPROACHES SUPPLEMENTARY
<p>The provided .xlsx file contains the following addtional data for the conference paper "Comparing CO<sub>2</sub> emission intensity of the steel industries in EU and China resulting from top-down and bottom-up approaches":</p> <ul> <li>The data used to plot Figure 1, 4, 5 and 6</li> <li>Scope adaption calculation</li> <li>EAF case study</li> </ul>
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods - data and weights
<p>Release for "Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods", submitted to "Atmospheric Chemistry and Physics<br><br>- Train, validation and test dataset for Lippendorf, Boxberg, and Turow CNN applications.<br>- Test dataset for OCO3 SAM application.<br>- Weights and architecture of the trained CNN.</p>
Supplemental data for the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe"
<p>This repository includes results discussed in the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe".</p>
OpenGHGMap - Europe - CO2 Emissions in 108,000 European Cities
<p>CO2 emissions for 116,000 administrative areas (108,000 cities/municipalities) in 34 European countries.</p> <p>Emissions are for the year 2018.</p> <p>The model homepage is https://openghgmap.net/</p> <p> </p> <p> </p> <p> </p>
Data included in "Radiocarbon Measurements Reveal Underestimated Fossil CH4 and CO2 Emissions in London"
<p>CO2 and CH4 concentrations measured at Imperial College, radiocarbon measurements of sample collected, radiocarbon simulations using the Met Office NAME model coupled with EDGAR emission inventories. </p>
Effects of biochar amendment on daily CO2 emission
<p>Poplar residue-derived biochars were larger in surface area and total pore volume but lower in nutrients and pH values than the rice straw-derived biochar. Increasing pyrolysis temperature led to a decrease in the total nitrogen content (TN) of poplar leaf- and rice straw-derived biochars, but enhanced the TN in the poplar twig- and poplar bark-derived biochars. After 180-day incubation, the total cumulative CO<sub>2</sub> emission decreased by 33.1–73.8% in the biochar amendments compared to their corresponding biomass residue addition, whereas the biochars derived from poplar twig and bark residues had more positive effects on reducing soil CO<sub>2</sub> emissions, but depended on the pyrolysis temperature.</p>
Data for the consolidated European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020
<p>The annual carbon dioxide fluxes used to create all graphs in the main text of McGrath et al, "European synthesis of CO2 emissions and removals for EU27 and UK: 1990-2020", submitted to Earth System Science Data.</p> <p> </p> <p>A second version was uploaded after responding to reviewer comments. The main difference is primarily that some data was discarded as the plot is no longer included; the units in the fossil fuel timeseries have been changed to Tg C instead of Tg CO2; and the uncertainties have been modified on the NGHGI data for FL, CL, and GL, resulting in a large change for GL (353.8% instead of 752%).</p>
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.