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4,775 results for “carbon”
Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines
<p>Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.</p> <p>The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).</p> <p>The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":</p> <p> 0: Open land (OL) [#440154]<br> 1: Scrub (S) [#404387]<br> 2: Young regenerating forest (YRF) [#29788e]<br> 3: Low density forest (LDF) [#22a884]<br> 4: Medium density forest (MDF) [#7ad251]<br> 5: High density forest (HDF) [#fde725]<br> 10: Oil palm [#fcffa4]<br> 11: Coconut [#a4feff]<br> 50: Urban [#fa0000]<br> 255: No data</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research articles. These citations are as follows:</p> <p>Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.</p> <p>Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. <em>Remote Sensing of Environment</em>, <em>261</em>, 112479.</p> <p>Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448</p> <p> </p>
Global health burden of ambient PM2.5 and the role of anthropogenic black carbon and organic aerosols
<p><strong>SI Dataset S1 (</strong><strong>SI DataS1)</strong></p> <p>Excess mortality from ambient PM<sub>2<em>.</em>5 </sub>exposure among adults, children, and neonates.</p> <p><strong>SI Dataset S2 (</strong><strong>SI DataS2)</strong></p> <p>Pie charts showing distribution of excess death by disease among adults, children, and neonates.</p> <p><strong>SI Dataset S3 (</strong><strong>SI DataS3)</strong></p> <p>Sector contribution to ambient PM<sub>2<em>.</em>5</sub>-related excess death under EqT and 2BSP assumptions</p> <p><strong>SI Dataset S4 (</strong><strong>SI DataS4)</strong></p> <p>Excess death from ambient BC exposure and contributions of major anthropogenic sectors.</p> <p><strong>SI Dataset S5 (</strong><strong>SI DataS5)</strong></p> <p>Excess death from ambient POA exposure and contribution of major anthropogenic sectors.</p> <p><strong>SI Dataset S6 (</strong><strong>SI DataS6)</strong></p> <p>Excess death from ambient aSOA exposure and contribution of major anthropogenic sectors.</p> <p><strong>SI Dataset S7 (</strong><strong>SI DataS7)</strong></p> <p>Sector contribution to excess death under EqT and 2BSP relative toxicity assumptions by major regions.</p>
Data and code for "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"
<p>Code and data for the paper "Carbon neutrality should not be the end goal: Lessons for institutional climate action from U.S. higher education"</p> <p>File descriptions:</p> <p>'HEI_analysis_OneEarth.Rmd' is the code with improved annotation and colorblind-friendly figures.</p> <p>All other data files are provided as excel and csv for convenience.</p> <p>'working_master_data' contains data from the Second Nature reporting platform on emissions by category for each institution analyzed in the paper (measured in metric tons). All adjustments necessary to fill in the data gaps in this file are documented at the beginning of 'HEI_analysis'.</p> <p>'offsets' contains data on the type(s) of offsets purchased by each school in their carbon neutral year (measured in metric tons). This data was assembled from a variety of sources which are documented at the beginning of 'HEI_analysis'.</p> <p>'carbon_neutral_years' contains yearly counts of higher education neutrality goals that were reported to Second Nature as of November 2020.</p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
Carbon Budget Scenarios for Ireland's Energy System, 2021-50
<p>Carbon Budget Scenarios for Ireland's Energy System, 2021-50, calculated with the TIMES-Ireland model.</p>
Data supplement for "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition"
<p>This data supplements the publication "Global agricultural trade and land system sustainability: implications for ecosystem carbon storage, biodiversity and human nutrition" by Thomas Kastner, Abhishek Chaudhary, Simone Gingrich, Alexandra Marques, U. Martin Persson, Giorgio Bidoglio, Gaëtane Le Provost, Florian Schwarzmüller, available here:</p> <p><a href="https://doi.org/10.1016/j.oneear.2021.09.006">https://doi.org/10.1016/j.oneear.2021.09.006</a></p> <p>For details, please refer to that publication.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Forest carbon removal factor variance by climate domain
<p>Uncertainty (variance) in removal factor (annual sequestration rate) for forest carbon in new and existing forests by climate domain (tropical, subtropical, temperate, boreal). Uncertainty analysis is from Harris et al. 2021 Nature Climate Change. Units are aboveground carbon Mg^2/ha^2/year^2. New and existing forest are distinguished by the presence or absence of Hansen et al. 2013 tree cover gain pixels. </p> <p>Note: Uncertainty for existing temperate forest removal factors is so high because the IPCC national greenhouse gas inventory guidelines have a very high uncertainty for these forests (2019 refinement of guidelines). </p> <p>Note: Uncertainty analysis is for published version of the model (v1.2.0).</p> <p>https://github.com/wri/carbon-budget</p>
Scores for calculating automated FAIR assessments in the low carbon energy domain
<p>Results for an automated FAIR assessment of 80 databases from the low carbon energy domain. The assessment was performed with the help of the FAIR maturity evaluation service of Wilkinson et al. The FAIR status with respect to 16 FAIR criteria is listed. The scores are defined to be consistent with the FAIR assessment tool of the Australian Research Data Commons. More details can be found in an additional publication on Zenodo as well as in an upcoming publication by Schwanitz et al.</p>
Data from: Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis
<p>Data and analysis in R for the publication "Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis" by Ossanna & Gornish (2023), <em>Journal of Applied Ecology</em>, <em>60</em>(2), 218-228. <a href="http://doi.org/10.1111/1365-2664.14324">https://doi.org/10.1111/1365-2664.14324</a>.</p>
Recovery of Lithium Carbonate from Dilute Li-Rich Brine via Homogenous and Heterogeneous Precipitation
<p>An extensive experimental campaign on Li recovery<br> from relatively dilute LiCl solutions (i.e., Li+ ∼ 4000 ppm) is<br> presented to identify the best operating conditions for a Li2CO3<br> crystallization unit. Lithium is currently mainly produced via solar<br> evaporation, purification, and precipitation from highly concentrated<br> Li brines located in a few world areas. The process requires<br> large surfaces and long times (18−24 months) to concentrate Li+<br> up to 20,000 ppm. The present work investigates two separation<br> routes to extract Li+ from synthetic solutions, mimicking those<br> obtained from low-content Li+ sources through selective Li+<br> separation and further concentration steps: (i) addition of<br> Na2CO3 solution and (ii) addition of NaOH solution + CO2<br> insufflation. A Li recovery up to 80% and purities up to 99% at 80<br> °C and with high-ionic strength solutions was achieved employing NaOH solution + CO2 insufflation and an ethanol washing step.</p>
Data for: "Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands"
<p>RAW DATA FOR ARTICLE</p> <p>DATE: NOVEMBER 2022</p> <p>TITLE: Carbon dioxide reduction by lanthanide(III) complexes supported by redox-active Schiff base ligands</p> <p>AUTHORS: Nadir Jori, Davide Toniolo, Bang C. Huynh, Rosario Scopelliti, and Marinella Mazzanti*</p> <p>JOURNAL: Inorganic Chemistry Frontiers (RSC) 2020</p> <p>DOI: <a href="https://doi.org/10.1039/D0QI00801J">10.1039/D0QI00801J</a> </p> <p> </p>
NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO2 exposure reduction potential
<p>In-vehicle and on-road (ambient) NO<sub>2</sub> measurements in different car cabin from Birmingham, UK. This dataset was used for the publication NO2 levels inside vehicle cabins with pollen and activated carbon filters: A real world targeted intervention to estimate NO<sub>2</sub> exposure reduction potential, Science of The total environment,160395 <a href="https://doi.org/10.1016/j.scitotenv.2022.160395">https://doi.org/10.1016/j.scitotenv.2022.160395</a></p>
Carbon data for: Evidence for the Multiple Benefits of Wetland Conservation in North America
<p>These data were synthesized as part of a rapid evidence assessment of the scientific literature on a wide range of benefits associated with wetland conservation and restoration. Our synthesis emphasized data from North America and especially the United States, although many of the high priority meta-analyses and reviews we incorporated were global in scope. The data in these files represent a range of metrics related to carbon sequestration, storage, or flux compiled from multiple sources for the purposes of summarizing the range of observed values and how they vary across wetland classes or by restoration status. For more detail on the synthesis methods and each set of metrics, please see the full report: </p> <p>Conlisk E, Chamberlin L, Vernon M, Dybala KE. 2022. Evidence for the Multiple Benefits of Wetland Conservation in North America: Carbon, Biodiversity, and Beyond. Point Blue Conservation Science, Petaluma, CA.</p>
Result data related to "Bersalli et al. (2023) -- Most industrialised countries have peaked carbon dioxide emissions during economic crises through strengthened structural change"
<p>This repository contains the result data of our study investigating the relationship between emission peaks and economic crises. The repository contains mainly two datasets:</p> <ul> <li>multiplicative-contributions.csv / .nc</li> <li>prepost-growth-rates.csv / .nc</li> </ul> <p>Both datasets exist in CSV and NetCDF file format for convenience. The dataset <em>multiplicative-contributions</em> contains year-to-year change factors of GDP, population, energy-intensity, and carbon-intensity for every country in our study. The dataset <em>prepost-growth-rates</em> contains growth over a multi-year period pre- and post- crisis for each country and each crises in our study.</p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.</p>
Carbon-specific remineralization rates of small and large organic particles in the North Atlantic
<p>This repository provides the carbon-specific remineralization rates of small and large organic particles in the North Atlantic which were calculated by using BGC-Argo observations of backscatter (a proxy of particulate organic carbon, POC) and dissolved oxygen. Details are given in the following article:</p> <p>Wang, B. and Fennel, K. (2022), Biogeochemical-Argo data suggest significant contributions of small particles to the vertical carbon flux in the subpolar North Atlantic. Limnol Oceanogr, 67: 2405-2417. <a href="https://doi.org/10.1002/lno.12209">https://doi.org/10.1002/lno.12209</a></p>
ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)
<p>The Global Soil Organic Carbon map (GSOCmap) published by the Food and Agriculture Organization<br> constitutes a baseline estimation of soil organic carbon stock (CS, ton ha–1) from 0 to 30 cm, on a grid at 30 arc-seconds<br> resolution (approximately 1 x 1 km). It has been produced for the Italian territory by the Italian Soil Partnership (ISP): a<br> national hub of institutions dealing with soils, either academic/research institutions, and regional soil services (RSS). The<br> RSS are the main soil data owners in Italy and play a central role in the elaboration of policies for soil management. The<br> RSS adhering to the ISP are: Calabria, Campania, Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardia, Marche,<br> Piemonte, Puglia, Sicilia, Toscana, and Veneto. A national soil database is maintained by the Consiglio per la Ricerca e<br> l'Analisi dell'Economia Agraria (CREA). The RSS contributed with soil data, with mean density of 1 point per 50 square<br> kilometres, selecting data analysed for soil organic carbon content (SOC, dag kg-1), which were representative and well<br> distributed for the following environmental covariates: land use, geomorphology, and climate. The data were selected inbetween<br> 1990 al 2013. This was necessary in order to exclude the effect of the new soil protection policies of the Rural<br> Development Programme 2014-2020. For the RSS not included in the ISP, the data were selected from the national soil<br> database. 6748 point data were finally selected. SOC values obtained with the Springer and Klee and flash combustion<br> elemental analyser methods were retained for elaborations, because the 2 methods, were found to give statistically<br> equivalent results. SOC values obtained with Walkey and Black method were, instead, corrected with an empirical factor<br> of 1.3. 2292 of the 6748 point data had also measured bulk density (BD, Mg m–3). Pedotransfer functions were calibrated<br> to estimate BD were measured BD were missing, with the following as auxiliary variables: land use, soil regions, texture,<br> and SOC. The carbon stock (CS, ton ha–1) was calculated by multiplying: 0.3 (m) * SOC (dag kg-1) * fine earth fraction (1 -<br> skeletal content expressed as daL m–3) * BD (Mg m–3). CS of the first 30 cm depth was calculated as depth-weighted<br> average. A spatial statistics method was used for the CS interpolation. The following auxiliary variables were used: soil<br> regions, soil subregions, Corine land cover 2006, lithology, soils affected by natural constrains (gleyic, histic, vertic,<br> coarse, shallow, arenic, sodic, and acid), sand content, silt content, 30-m aster-DEM, distance from coast, distance from<br> relieves, soil aridity index, annual mean precipitations, mean annual air temperature, soil inorganic carbon, and soil<br> depth. For the soil region of Po valley, the land units at 1:250,000 scale were also used. The interpolation method was a<br> general linear regression for the soil regions of Po valley, and a radial basis function for the remaining Italian territory.<br> The 6748 point data were divided, by spatial random sampling, into 10 subsets. Ten interpolations were produced, each<br> time leaving out 1/10 of the dataset. Average (fig. 1), standard deviation and confidence intervals of these 10<br> interpolations were calculated. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) were respectively<br> 25.5 and 36.4 Mg/ha.</p> <p>A.85 Italy Map source: Country submission Point data Number of samples: 6748 Sampling period: 1990-2013 SOC analysis method: SOC values obtained with the Springer and Klee and ’flash combustion elemental analyser’ methods were retained for elaborations. Uncorrected values obtained by the Walkey and Black method were corrected with an empirical linear equation, based on previous studies and as recommended by the Italian official methods. BD analysis method: Undisturbed sampling, core method and pit method Mapping method Mapping method details: Neural Networks and GLM, according to soil region Validation statistics: Mean Error (ME) of the prediction is 1.688 Mg/ha, MAE 25.57 Mg/ha, Root Mean Squared Error (RMSE) is 36.24 Mg/ha. Contact Data Holder: Research centre for agriculture and environment Contact: CREA Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria edoardo.costantini@crea.gov.it</p>
Products and Models for "Detection of carbon monoxide's 4.6 micron fundamental band structure in WASP-39b's atmosphere with JWST NIRSpec G395H"
<p>Overview:</p> <p>Carbon monoxide (CO) is predicted to be the dominant carbon-bearing molecule in giant planet atmospheres, and, along with water, is important for discerning the oxygen and therefore carbon-to-oxygen ratio of these planets. The fundamental absorption mode of CO has a broad double-branched structure composed of many individual absorption lines from 4.3 to 5.1 µm, which can now be spectroscopically measured with JWST. Here we present a technique for detecting the rotational sub-band structure of CO at medium resolution with the NIRSpec G395H instrument. We use a single transit observation of the hot Jupiter WASP-39b from the JWST Transiting Exoplanet Community Early Release Science (JTEC ERS) program at the native resolution of the instrument (R ~ 2700) to resolve the CO absorption structure. We robustly detect absorption by CO, with an increase in transit depth of 264 <span>\(\pm\)</span> 68 ppm, in agreement with the predicted CO contribution from the best-fit model at low resolution. This detection confirms our theoretical expectations that CO is the dominant carbon-bearing molecule in WASP-39b's atmosphere, and further supports the conclusions of low C/O and super-solar metallicities presented in the JTEC ERS papers for WASP-39b. </p>
Historical (1979 - 2020) data for anthropogenic inputs to a catchment and riverine mainstem exports for carbon, nitrogen, and phosphorus
<p>We estimated the difference in Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at the finest scale possible (the municipality) in the <em>Rivière du Nord</em> watershed (Québec, Canada) between 1981 and 2016. The dataset here reports the delta between those two years for each municipality in the watershed.</p> <p>Three sites along the mainstem of <em>Rivière du Nord </em>have been sampled ~bi-monthly from ~1979 - 2020 for dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP), from which we estimated annual riverine export at each site. We also include annual precipitation (as the sum of rain and snow), and NANI-NAPI interpolated for each sub-watershed for 1981, 1986, 1991, 1996, 2001, 2006, 2011, and 2016.</p> <p> </p> <p> </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.