Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
64
datasets available to search
ShareScore release 0.9.0
Dataset results
64 results for “biomass burning”
Modeling actinic flux and photolysis frequencies in dense biomass burning plumes - Data asset
<p>Dataset is related to the paper by Tirpitz et al.: Modeling actinic flux and photolysis frequencies in dense biomass burning plumes</p> <p>Contains the preprocessed model input data and the modeling results (actinic fluxes and photolysis frequencies) for the Shady wildfire on July 25, 2019. The VPC model is on a private Github-Repository. Access is provided by the authors on request. See paper and INFO.txt in Data.zip for more information.</p> <p>Correspondence:<br> Jan-Lukas Tirpitz: jltirpitz@atmos.ucla.edu<br> Jochen Stutz: jochen@atmos.ucla.edu</p> <p>Other contributors:<br> Santo Fedele Colosimo<br> Nathaniel Brockway<br> Robert Spurr<br> Matthew Christi<br> Samuel Hall<br> Kirk Ullmann <br> Johnathan Hair<br> Taylor Shingler<br> Rodney Weber<br> Jack Dibb<br> Richard Moore<br> Elizabeth Wiggins<br> Vijay Natraj<br> Nicolas Theys</p>
Estimation of biomass combustion carbon emissions data for 2016 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2016, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2015 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2015, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Tree regeneration after fire: Delta 1994 burn surveys, data to develop allometric equations re. seedling height or diameter to dry biomass
Data for this study were collected in 2001 and 2002 by Jill Johnstone (University of Alaska Fairbanks) and Eric Kasischke (University of Maryland). Sites were located within the perimeter of the 1994 burn southeast of Delta Junction Alaska, USA, bordering the Alaska Highway to the North and the Gerstle River to the West. Sites were selected from satellite classifications prepared by Eric Kasischke to represent different levels of burn severity and post-fire vegetation canopy greenness (NDVI). Site selection was constrained by road access, and only areas where all trees had been killed by the fire were selected. At each site, a central point was located in an area of visually homogeneous vegetation. Five parallel transects, each 50 m long, were laid out as follows: 1) the first transect started at the central point and followed a randomly-selected compass direction, 2) two additional transects were established parallel to the first, but at a random distance from the central transect up to 25 m distant. Vegetation was sampled in a 2-m wide belt centered on each transect, and soil samples were made at intervals along the transect line. Vegetation measurements included: a) basal diameters of all pre-fire trees greater than 1.3 m in height, b) counts of all post-fire tree seedlings, and c) basal diameters of tree seedlings and willows, measured in a randomly chosen 5x2 m portion of each transect. General notes were made on visual percent cover of different vegetation growth forms at the site. Destructive measurements of tree seedlings and willows made in 2001 were used to develop allometric equations to predict dry biomass from basal diameter. Measurements of soil organic layer depth were made at 5 m intervals with the use of a spade to excavate small chunks of sod. At one randomly-selected sample point per transect, a 10x10 cm sample of the organic layer was collected for bulk density measurements. Bulk density samples were dried in a 60degC oven for 48 hours and then w
SGS-LTER Nitrogen content of aboveground biomass on and off US Forest Service Burns on the Pawnee National Grassland, Colorado, USA 1997-2004
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Most investigators studying grasslands have assumed that the low standing biomass of the SGS created a system with a low probability of carrying fire, and thus a minimal historical role of fire. Nonetheless, there are years with aboveground biomass equivalent to the mixed grass prairie, and a high frequency of lightening storms. Regardless of the historical role of fire in SGS, there are new questions regarding its utility in managing for the presence of the threatened mountain plover, which only nests in areas of low plant biomass. United States Forest Service, Pawnee National Grassland recently initiated a burning program in the mid 1990s to address questions about using fire to increase plover habitat; we have collected data on some of these plots to investigate the influence of fire on SGS vegetation. Several datasets were created between 1999 and 2004 by SGS-LTER researchers, including measurements of shrub and cactus mortality rates, aboveground net primary production, amounts of litter and standing dead, and aboveground nitrogen dynamics in burned and control plots in the western section of the Pawnee National Grassland. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83326.
SGS-LTER Monthly Nitrogen content of aboveground biomass on and off US Forest Service Burns on the Pawnee National Grassland, Colorado, USA 1999-2003
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Most investigators studying grasslands have assumed that the low standing biomass of the SGS created a system with a low probability of carrying fire, and thus a minimal historical role of fire. Nonetheless, there are years with aboveground biomass equivalent to the mixed grass prairie, and a high frequency of lightening storms. Regardless of the historical role of fire in SGS, there are new questions regarding its utility in managing for the presence of the threatened mountain plover, which only nests in areas of low plant biomass. United States Forest Service, Pawnee National Grassland recently initiated a burning program in the mid 1990s to address questions about using fire to increase plover habitat; we have collected data on some of these plots to investigate the influence of fire on SGS vegetation. Several datasets were created between 1999 and 2004 by SGS-LTER researchers, including measurements of shrub and cactus mortality rates, aboveground net primary production, amounts of litter and standing dead, and aboveground nitrogen dynamics in burned and control plots in the western section of the Pawnee National Grassland. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83326.
Measurement report: quantifying source contribution of fossil fuels and biomass-burning black carbon aerosol in the southeastern margin of the Tibetan Plateau
<p>Anthropogenic emissions of Black carbon (BC) aerosol are transported from Southeast Asia to the southwestern Tibetan Plateau (TP) during the pre-monsoon; however, the quantities of BC from different anthropogenic sources and the transport mechanisms are still not well constrained because there have been no high-time-resolution BC source apportionments. Intensive measurements were taken in a transport channel for pollutants from Southeast Asia to the southeastern margin of TP during the pre-monsoon to investigate the influences of fossil fuels and biomass burning on BC. A receptor model coupled multi-wavelength absorption with aerosol species concentrations was used to retrieve site-specific Ångström exponents (AAE) and mass absorption cross-sections (MAC) for BC. An ‘aethalometer model’ that used those values showed that biomass burning had a larger contribution to BC mass than fossil fuels (BCbiomass = 57% versus BCfossil = 43%). The potential source contribution function indicated that BCbiomass was transported to the site from northeastern India and northern Burma, The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) model indicated that 40% of BCbiomass originated from Southeast Asia, while the high BCfossil was transported from the southwest of sampling site. A radiative transfer model indicated that the average atmospheric direct radiative effects (DRE) of BC was +4.6 ± 2.4 W m<sup>-2</sup> with +2.5 ± 1.8 W m<sup>-2</sup> from BCbiomass and +2.1 ± 0.9 W m<sup>-2</sup> from BCfossil. The DRE of BCbiomass and BCfossil produced heating rates of 0.07 ± 0.05 and 0.06 ± 0.02 K day<sup>-1</sup>, respectively. This study provides insights into sources of BC over a transport channel to the southeastern TP and the influence of the cross-border transportation of biomass burning emissions from Southeast Asia during the pre-monsoon.</p>
Contrasting Activation Characteristics of Biomass Burning and Fossil Fuel Combustion Aerosols in Fogs and Clouds: Implications for Regional Air Quality and Climate
<p>The key 'jul' in data use 2021-01-01 as the referece day, for example, 2021-01-02 12:00:00 corresponding to jul of 2.5. </p>
Data for the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"
<p>The data for the 3 figures in the journal article "Brown Carbon from Biomass Burning Imposes Strong Circum-Arctic Warming"</p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
Figure source data for Riddell-Young et al., 2024 "Abrupt changes in biomass burning during the last glacial period"
<p>These are the source data files for all of the data presented in main text figures 1 through 4 in Riddell-Young et al., 2024: "Abrupt changes in biomass burning during the last glacial period". Each file contains several sheets that correspond to the data presented in each subplot of the figure. Please refer to the manuscript for a detailed description of the data that was collected and analysis that was performed as part of the study. The Data and Code Availability statements discuss where the official dataset and code publications can be found.</p>
Impacts of biomass burning in peninsular Southeast Asia on PM2.5 concentration and ozone formation in southern China during springtime – A case study
<p>Abstract: Biomass burning (BB) affects fine particulate matter (PM<sub>2.5</sub>) and ozone (O<sub>3</sub>) formations by emitting their gaseous precursors and primary aerosols. Impacts of BB in peninsular Southeast Asia (BB-PSEA) are evaluated on the PM<sub>2.5</sub> and O<sub>3</sub> formations in southern China, using a source-oriented WRF-Chem model to simulate an air pollution episode from 21 to 25 March 2015. The source-oriented model separates the emission from the BB-PSEA and other sources and is able to evaluate the effect of aerosol-radiation interactions (ARI) and aerosol-photolysis interactions (API) from the BB-PSEA. Comparisons with observations reveal that the model performs well in simulating the air pollution episode. Sensitivity experiments show that BB-PSEA increases PM<sub>2.5</sub> concentrations by 39.3 μg m<sup>-3</sup> (68.0%) in Yunnan Province (YNP) and 8.4 μg m<sup>-3</sup> (24.1%) in other downwind areas (ODA) in southern China (including the provinces of Guizhou, Guangxi, Hunan, Guangdong, Jiangxi, Fujian, and Zhejiang) on the regional average. The PM<sub>2.5</sub> enhancement is mainly contributed by primary aerosols in YNP but by secondary aerosols in the ODA. The BB-PSEA increases O<sub>3</sub> concentrations of 18.1 μg m<sup>-3 </sup>(19.4%) in YNP and decreases O<sub>3 </sub>concentrations in the ODA by 3.7 μg m<sup>-3</sup> (5.3%). The O<sub>3</sub> increase in YNP is contributed by the gaseous emissions of the BB-PSEA, and the O<sub>3</sub> decrease in the ODA is caused by the effects of ARI and API of the BB-PSEA. The NH<sub>3</sub> emissions from the BB-PSEA plays a key role in enhancing secondary inorganic aerosols in southern China, and also determine the PM<sub>2.5</sub> increase in the ODA.</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Development of volatility distributions for organic matter in biomass burning emissions
<p>We present a novel filter-in-tube sorbent tube method to collect S/I-VOC samples from a range of biomass burning experiments and find that volatility distributions are relatively consistent with prior findings and across the tested combustion types.</p>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations
<p><strong>Aim</strong>: A unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM<sub>2.5</sub>). PM<sub>2.5</sub> concentrations can display strong seasonal variation originating from natural and anthropogenic processes. How these processes affect seasonal associations with ALAN and PM<sub>2.5</sub> for nocturnally migrating birds has not been documented.</p> <p><strong>Location</strong>: Western Hemisphere</p> <p><strong>Time</strong> <strong>period</strong>: 2021</p> <p><strong>Major taxa studied</strong>: Nocturnally migrating passerine (NMP) bird species</p> <p><strong>Methods</strong>: We combined monthly estimates of PM<sub>2.5</sub> and ALAN with weekly estimates of relative abundance for 164 NMP species within the Western Hemisphere derived using bird observations from eBird. We identify groups of species with shared associations with PM<sub>2.5</sub>.</p> <p><strong>Results</strong>: PM<sub>2.5</sub> was lowest in North America, especially at higher latitudes during the boreal winter. PM<sub>2.5</sub> was highest in the Amazon Basin, especially during the dry season (August-October). ALAN was highest within eastern North America, especially during the boreal winter. For the NMP species, PM<sub>2.5</sub> associations reached their lowest levels during the breeding season (<10 μg/m<sup>3</sup>) and highest levels during the nonbreeding season, especially for species that winter in Central and South America (~20 μg/m<sup>3</sup>). Species that migrate through Central America in the spring encountered similarly high PM<sub>2.5 </sub>concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm<sup>2</sup>/sr) or spend the nonbreeding season (~15 nW/cm<sup>2</sup>/sr) in eastern North America.</p> <p><strong>Main conclusions</strong>: We did not find evidence that the disorienting influence of ALAN enhances PM<sub>2.5</sub> exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM<sub>2.5</sub> concentrations for an extended period of their annual life cycles. </p>
Estimates of black carbon emissions from global biomass burning for the period 1997–2023
Open the record for dataset details and reuse information.
Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations
Open the record for dataset details and reuse information.
Development of volatility distributions for organic matter in biomass burning emissions
Open the record for dataset details and reuse information.
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.