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.
54
datasets available to search
ShareScore release 0.9.0
Dataset results
54 results for “fire models”
Updated Smoke Exposure Estimate for Indonesian Peatland Fires using a Network of Low-cost PM2.5 sensors and a regional air quality model - Purple Air data
<p>Daily mean PM2.5 concentrations collected by Purple Air sensors between 2023-08-16 and 2023-12-01. Concentrations have been RH adjusted using the Nilson et al (2022) adjustment. </p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</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>
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
<p>Tropical savannas are characterised by high primary productivity and high fire frequency, such that much of the carbon captured by vegetation is rapidly returned to the atmosphere. Hence, there have been suggestions that management-driven reductions in savanna fire frequency and/or severity could significantly reduce greenhouse gas emissions and sequester carbon in tree biomass. However, a key knowledge gap is the extent to which savanna tree biomass will respond to modest shifts in fire regimes due to plausible, large-scale management interventions. Here, we: (1) characterise relationships between the frequency and severity of fires and key demographic rates of savanna trees, based on long-term observations in vegetation monitoring plots across northern Australia; (2) use these relationships to develop a process-explicit demographic model describing the effects of fire on savanna tree populations; and (3) use the demographic model to address the question: to what extent is it feasible, through the strategic application of prescribed burning, to increase tree biomass in Australian tropical savannas? Our long-term tree monitoring dataset included observations of 12,344 tagged trees in 236 plots, monitored for between 3 and 24 years. Analysis of this dataset showed that frequent high-severity fires significantly reduced savanna tree recruitment, survival and growth. Our demographic model suggested that: (1) despite the negative effects of frequent high-severity fires on demographic rates, savanna tree biomass appears to be suppressed by only a relatively small amount by contemporary fire regimes, characterised by a mix of low- to high-severity fires; and (2) plausible, management-driven reductions in the frequency of high-severity fires are likely to lead to increases in tree biomass of about 11.0 t DM ha<sup>–1</sup> (95% confidence interval: -1.2–20.8) over a century. Accounting for this increase in carbon storage could generate significant carbon credits, worth on average three times those generated annually by current greenhouse gas (methane and nitrous oxide) abatement projects, and has the potential to significantly increase the economic viability of fire/carbon projects, thereby promoting ecologically sustainable management of tropical savannas in Australia and elsewhere. This growing industry has the potential to bring much-needed economic activity to savanna landscapes, without compromising important natural and cultural values.</p>
Integration of a deep-learning-based fire model into a global land surface model
<p>JSBACH4+DL-fire simulation results & their comparison (DL-fire, JSB4-DL-fire, JSB4-simple)</p>
Low- and high-intensity fire in the riparian savanna: demographic impacts in an avian model species and implications for ecological fire management
<ol> <li><span>Climate change is driving changes in fire frequency and intensity, making it more urgent for conservation managers to understand how species and ecosystems respond. In tropical monsoonal savannas – Earth's most fire-prone landscapes – ecological fire management aims to prevent intense wildfires late in the dry season through prescribed low-intensity burns early in the dry season. Riparian habitats embedded within tropical savannas represent critical refuges for biodiversity, yet are particularly fire-sensitive. Better understanding of the impact of fire – including prescribed burns – on riparian habitats is therefore key but requires long-term detailed post-fire monitoring of species' demographic rates, as effects may persist and/or be delayed.</span></li> <li><span>We analyse impacts of (prescribed) low-intensity and (prescribed but escaped) high-intensity fire in northern Australian riparian and adjacent savanna habitat. We quantify multi-year impacts on density, survival, reproduction and dispersal of an Endangered riparian bird, the western purple-crowned fairy-wren (<em>Malurus coronatus coronatus</em>), in a well-studied individually-marked population.</span></li> <li><span>Following low-intensity fire, bird density was reduced by >20% in burnt compared to adjacent unburnt riparian habitat for ≥2.5 years. This was a result of reduced breeding success and recruitment for two years immediately following fire, rather than mortality or dispersal of adults.</span></li> <li><span>In contrast, high-intensity fire (in a dry year) resulted in a sharp decline in population density by 50% 2–8 months after fire, with no signs of recovery after 2.5 years. The decline in density was due to post-fire adult mortality, rather than dispersal. Breeding success of the (few) remaining individuals was low but not detectably lower than in unburnt areas, likely because breeding success was poor overall due to prevailing dry conditions.</span></li> <li> <span><em>Synthesis and applications</em>. </span><span>Even if there is no or very low mortality during fire, and no movement of birds away from burnt areas, both low- and high-intensity fire in the riparian zone reduce population density. However, the mechanism by which this occurs, and recovery time, differs with fire intensity. To minimise impacts of fire on riparian zones in tropical savannas, we suggest employing low-intensity prescribed burns under optimal conditions shortly after the breeding season.</span> </li> </ol>
Low- and high-intensity fire in the riparian savanna: demographic impacts in an avian model species and implications for ecological fire management
Open the record for dataset details and reuse information.
Data for: Using a demographic model to project the long-term effects of fire management on tree biomass in Australian savannas
Open the record for dataset details and reuse information.
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
Open the record for dataset details and reuse information.
Wildland fire PM2.5 modeled estimates for the US from 2008-2018
Open the record for dataset details and reuse information.
A model for regional-scale oak savanna management: the roles of fire, canopy, and soils for understory plant diversity
Open the record for dataset details and reuse information.
The role of fire in the carbon dynamics of the boreal forest II. - Eurasia model simulations of historical fire disturbance and carbon dynamics (1000-2002).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
The role of fire in the carbon dynamics of the boreal forest III. - North America model simulations of historical fire disturbance and carbon dynamics (1900-2100).
The boreal forest contains large reserves of carbon, and across this region wildfire is a common occurrence. To improve the understanding of how wildfire influences the carbon dynamics of this region, methods were developed to incorporate the spatial and temporal effects of fire into the Terrestrial ecosystem Model (TEM). The historical role of fire on carbon dynamics of the boreal region was evaluated within the context of ecosystem responses to changing atmospheric CO2 and climate. These results show that the role of historical fire on boreal carbon dynamics resulted in a net carbon sink; however, fire plays a major role in the interannual and decadal scale variation of source/sink relationships. To estimate the effects of future fire on boreal carbondynamics, spatially and temporally explicit empirical relationships between climate andfire were quantified. Fuel moisture, monthly severity rating, and air temperature explained a significant proportion of observed variability in annual area burned. These relationships were used to estimate annual area burned for future scenarios of climate change and were coupled to TEM to evaluate the role of future fire on the carbon dynamics of the North American boreal region for the 21st Century. Simulations with TEM indicate that boreal North America is a carbon sink in response to CO2 fertilization, climate variability, and fire, but an increase in fire leads to a decrease in the sink strength. While this study highlights the importance of fire on carbon dynamics in the boreal region, there are uncertainties in the effects of fire in TEM simulations. These uncertainties are associated with sparse fire data for northern Eurasia, uncertainty in estimating carbon consumption, and difficulty in verifying assumptions about the representation of fires that occurred prior to the start of the historical fire record. Future studies should incorporate the role of dynamic vegetation to more accurately represent post-fire successional pr
A classification scheme to determine wildfires from the satellite record in the cool grasslands of southern Canada: considerations for fire occurrence modelling and warning criteria
<p>This data set can be used to reproduce the results from the following paper: "A classification scheme to determine wildfires from the satellite record in the cool grasslands of southern Canada: considerations for fire occurrence modelling and warning criteria".</p> <p>The Landsat Images are a series of pngs obtained from <a href="https://landbrowser.airc.aist.go.jp/hotarea/">https://landbrowser.airc.aist.go.jp/hotarea/</a> representing agricultural fires in our study area (Kato et al., 2018).</p> <p><a href="https://zenodo.org/api/files/b1449276-d0e7-4ede-aa97-6c976e27215f/Model_Input_MODIS_Hotspot_Clusters_Revised.csv">Model_Input_MODIS_Hotspot_Clusters_Revised.csv</a> contains a list of clusters of MODIS hotspots and associated attributes classified as either agricultural or grassland wildfires and is used to create a GAM to explore the conditions of these fires.</p> <p><a href="https://zenodo.org/api/files/b1449276-d0e7-4ede-aa97-6c976e27215f/Model_Predicted_MODIS_Hotspot_Clusters_Revised.csv">Model_Predicted_MODIS_Hotspot_Clusters_Revised.csv</a> contains the complete list of MODIS hotspot clusters and associated attributes for our study area from 2002-2018. These clusters have been classified as either agricultural or grassland wildfires using the GAM mentioned above.</p>
Data for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa"
<p>This is the data used for "Modeling the short-term fire effects on vegetation dynamics and surface energy in southern Africa using the improved SSiB4/TRIFFID-Fire model". The data includes two folders: fireon and fireoff representing the scenarios with the fire model turned on and off. Each folder includes 14 years of data from 2000-2013.</p>
Fire Weather Index for Europe from Downscaled and Bias-Corrected CMIP6 Model Outputs
<p>This dataset contains the Canadian Forest Fire Weather Index (FWI) calculated from six downscaled and bias-corrected CMIP6 model outputs. The models included are:</p> <ul> <li>ACCESS-CM2 (Ziehn et al. 2020)</li> <li>CanESM5 (Swart et al. 2019)</li> <li>CNRM-ESM2-1 (Séférian et al. 2019)</li> <li>EC-EARTH3 (EC-Earth Consortium 2019)</li> <li>MPI-ESM1-2-HR (von Storch et al. 2017)</li> <li>MRI-ESM2-0 (Yukimoto et al. 2019)</li> </ul> <p>The dataset encompasses four Shared Socio-economic Pathway (SSP) projections:</p> <ul> <li>SSP1-2.6</li> <li>SSP2-4.5</li> <li>SSP3-7.0</li> <li>SSP5-8.5</li> </ul> <p>Each model output has been downscaled to a resolution of 0.0703135°, corresponding to approximately 9km×9km grids before the FWI calculation. The data covers Europe spatially and temporally spans from 1950 to 2080, offering comprehensive insights into past, present, and future fire weather conditions.</p> <p>This dataset supports the manuscript titled <strong>"The fire weather in Europe: large-scale trends towards higher danger" </strong>by Hetzer et al., currently under review in ERL. Detailed instructions for accessing the data can be found in the included README file. </p> <p>Note: Downloads are password protected. Please use "FWI_2024" for access. </p> <p>Funding: The authors acknowledge the financial support of the European Union’s Horizon 2020 research and innovation action for the FirEUrisk project under grant agreement ID: 101003890.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Evaluating the performance of fire rate of spread models in northern-European Calluna vulgaris heathlands - Supplemental Material
<p>Supplemental Data analysis tables and raw data from the publication submitted to the journal "Fire" (MDPI) "Evaluating the performance of fire rate of spread models in northern-European Calluna vulgaris heathlands".</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling
<p>Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.</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.