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Small mammal captures per 100 trap nights at the 2007 Anaktuvuk River fire scar and nearby unburned site, sampled in 2014, 2017-2019.
Small mammals (rodents and shrews) were sampled 7-12 years following the Anaktuvuk River Fire to examine how post-fire ecological changes influence small mammal abundance. Small mammals were snap-trapped in August 2014, 2017-2019 at the site of the 2007 Anaktuvuk River Fire, and a nearby unburned control site. At each site, 120 traps were set in 3 parallel lines spaced 40m apart. Each trap was spaced 10m apart, baited, and set to rodent sign within one meter of the trap station. Traps were checked the following two mornings with all captures collected and sprung traps reset. Abundance estimates (captures per 100 trap nights) are presented for tundra voles (Microtus oeconomus), red-backed voles (Myodes rutilus) and shrews (Sorex spp.) The goals of the project were to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Eriophorum vaginatum tiller nitrogen content at the 2007 Anaktuvuk River fire scar and nearby unburned tundra measured in 2019.
Tillers from 24 Eriophorum vaginatum individuals were sampled in late July 2019 to examine differences in percent nitrogen between previously burned (Anaktuvuk River Fire) and unburned tussocks at a nearby unburned control site. At the burned site tussocks exhibiting evidence of rodent grazing were also sampled to separate herbivore effects from those of the fire. From each tussock, 3-4 new leaves were sampled (as indicated by lack of brown tips) and dried at 60°C for 24 hours, before being ground and analyzed for percent nitrogen. The goals of the project were to examine the impact of post-fire changes in plant community composition, nutrient quality and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Bonanza Creek LTER: Annual Active Layer Depths from 2004 to Present in the Boundary Fire Fireline near Fairbanks, Alaska
In 2004 the Boundary Fire burned an area within the Caribou Poker Creeks Research Watershed providing an opportunity to study various fire effects. When wildfire burns through a northern black spruce forest there is usually a subsequent increase in depth of thaw, due to the reduction in the depth of the organic layer. The construction of firelines with heavy machinery involves the complete removal of the organic layer and results in an even greater increase in active layer. This study was designed as a long-term comparison between depth of thaw on firelines, burned and unburned black spruce forest underlain by ice rich permafrost. This study will allow us to compare thaw depths from recent firelines to those studied at the Wickersham and Bonanza Creek fireline study sites. Within the fireline bulldozers were used to knock down and in some cases remove, the trees and organic layer. Within the safety zone an area approximately 30 m x 30 m was cleared to mineral soil. After the fire was out an excavator was used to return the organic material to the fireline and safety zones.
Bonanza Creek LTER: Annual Active Layer Depths from 2002 to Present in the Survey Line Fire near Fairbanks, Alaska
In 2001 the Survey Line Fire burned an area of black spruce forest along the Tanana River adjacent to the Bonanza Creek Experimental Forest. In 2002 two research sites were established within the burn, one in a dry area and one in a wet area. When wildfire burns through a northern black spruce forest there is usually a subsequent increase in depth of thaw, due to the reduction in the depth of the organic layer. Thaw depth is being measured annually at tweny points within each of these sites.
Toklat River Fire in Denali National Park and Preserve: Site level environmental, soil, tree, vegetation, and fire characteristics measured in 2016
This dataset contains site-level average estimated of environmental, soil, tree, vegetation, and fire characteristics measured in 2016, three years after the Toklat River Fire in Denali National Park and Preserve. Measured parameters include latitude, longitude, slope, aspect, elevation, moisture classification, bulk density of the surface soil, residual organic soil depth, thaw depth, burn depth, density and basal area of all tree species pre-fire, the density of all tree species post-fire, estimates of above- and below-ground carbon combustion, and understory vegetation turnover from pre-fire to post-fire. There is also data on seed trap collection and experimental regeneration of seedlings collected in 2017 and 2018 at a subset of sites.
Alaska 2004 Burns: Growth and survival of tree seedlings in post-fire experimental transplant study across 39 sites
This dataset contains measurements of tree seedlings growth for an experimental transplant study started in 2005 at sites that burned in 2004 in interior Alaska. Records are from a set of 39 intensive study sites that were formerly dominated by black spruce along the Steese, Taylor, and Dalton highways. Seedlings were monitored for 10 years, with detailed measurements in 2006, 2008, 2011, 2013, and 2015. Aboveground biomass was harvested in 2011.
Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Post-fire Seedling Recruitment Collected 2023
This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Within each site, three plots were established. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the seedling recruitment data.
Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Residual Soil Organic Layer Depth, Burn Depth and Thaw Depth Collected 2023
This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Within each site, three plots were established. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the soils data.
Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Pre-fire Tree Species Density and Combustion Collected 2023
This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the pre-fire tree species density and combustion data.
Gaviota Fire Perimeter (Santa Barbara County, CA), June 9, 2004 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gaviota Fire burned from 2004-06-05 to 2004-06-12, 15 miles west of Santa Barbara, Santa Barbara County. Approximately 7440 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2004-06-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Tea Fire Perimeter (Santa Barbara County, CA), November 15, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Tea Fire burned from 2008-11-13 to 2008-11-17, Montecito, Cold Springs Creek and Hot Springs Road, Santa Barbara County. Approximately 1940 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-11-15, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Jesusita Fire Perimeter (Santa Barbara County, CA), May 10, 2009 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Jesusita Fire burned from 2008-05-05 to 2008-05-18, Northwest of Mission Canyon and Santa Barbara City, Santa Barbara County. Approximately 8733 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-05-10, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Effects of Fire Seasonality on Chihuahuan Desert Grasslands at the Sevilleta National Wildlife Refuge, New Mexico (2007-2020)
Desert grassland vegetation is a key resource upon which rangelands in the southwestern US are built, and managing these ecosystems remains a critical challenge today. This experimental fire seasonality research project, in collaboration with the USFWS, USFS Rocky Mountain Research Station, and the Sevilleta LTER, is intended to provide land management agencies with information about vegetation recovery following fire under different seasonal conditions and burning treatments. This experimental research will enable the FWS to more effectively set project objectives for prescribed burning on the Sevilleta NWR to benefit not only wildlife habitat, but to better align the timing and intensity of fire to benefit the reestablishment of the dominant native grama grasses Bouteloua eriopoda and B. gracilis. Since its creation in 1973, management has been devoted to restoring the Sevilleta NWR to the natural conditions that might have been seen around the turn of the century. The Sevilleta NWR is an ideal place for research because climatic conditions, plant species composition and net primary production following wildfire have been well documented by the Sevilleta LTER. Additional experimental research is needed, however, to better inform managers about the timing and use of fire as an ecosystem restoration and management tool. This is an on-going, long-term experiment under the auspices of the Sevilleta LTER program.
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
LAVESI-FIRE simulation output at Lake Satagay, Central Yakutia, Siberia
<p>Simulation output data for long-term fire-vegetation simulations with the individual-based, spatially explicit model LAVESI-FIRE. The individual, numbered simulation folders are described in the included .docx file, as well as in the related research paper. In each folder, following files can be found (_18224XX refers to the climate input used for the simulation):</p> <ul> <li><strong>datatrees_currencies_18224</strong>XX<strong>.csv</strong>: Timeseries data. Simulation area-wide summarized data on tree abundance, climate, environment and fire occurrence. Each row of the table represents an individual annual simulation timestep.</li> <li><strong>databiomassgrid_1_18224</strong>XX<strong>_</strong>XX<strong>00_1_</strong>X<strong>.csv</strong>: Spatial data. Each file includes tree abundance for one species (_1 = <em>Larix gmelinii</em>; _2 = <em>Larix sibirica</em>; _3 = <em>Larix cajanderi</em>; _4 = <em>Picea obovata</em>; _5 = <em>Pinus sylvestris</em>; _6 = <em>Pinus sibirica</em>), summarized in grid cells with x- and y-coordinates.</li> <li><strong>datatrees_Treedensity</strong>XX<strong>00_18224</strong>XX<strong>.csv</strong>: Spatial data. Each file includes tree density, environment, and fire occurrence, summarized in grid cells with x- and y-coordinates.</li> </ul> <p>For more detail, please refer to the linked research paper:</p> <p>Glückler, R., Gloy, J., Dietze, E., Herzschuh, U., & Kruse, S. (2024). Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum. Fire Ecology, 20(1), 1. https://doi.org/10.1186/s42408-023-00238-8</p>
Map of Swedish Forest Fires >10ha from 2016-2023
<p><span>A vector map of perimeters of forest fires in Sweden above 10ha from 2016 to 2023 and the Sala Megafire from 2014. Fire locations were identified from multiple sources: the <em>Skogsstyrelsen Skogsbränder Map 2020</em>, <em>EFFIS burned area map</em> and the<em> NASA FIRMS MODIS or VIIRS Fire/Hotspot Data</em> thermal anomalies products. <br>For fires from 2018 to 2020, fire perimeters were sourced directly from <em>Skogsbränder Map 2020</em> or the <em>EFFIS burned area map</em>. The <em>EFFIS burned area map</em> provided fire occurrence dates. For fires lacking occurrence dates (from <em>Skogsbränder Map 2020</em>), we cross-referenced locations and assigned the dates from either the <em>EFFIS burned area map</em> or the <em>MSB incident reports database</em>, using ArcGIS.</span></p> <p><span>Fire locations (points) from 2016, 2017, 2021, 2022 and 2023 were identified from <em>NASA FIRMS MODIS or VIIRS Fire/Hotspot Data</em> thermal anomalies products, the fire date was set as the earliest detection date. We then digitized these fire perimeters using post-fire images from Sentinel-2 (10m x10m resolution). First fire locations were manually examined </span><span>using Sentinel Hub EO Browser as false colour composites (band combination Near Infrared – Red – Green) and image chips downloaded for fires over 10ha. We then manually delineated fire perimeters in ArcGIS; unburned areas located within the fires were excluded from fire perimeters. Fire perimeters include areas burned that were not forest land cover; however, fires that occurred on entirely non-forested land were excluded.</span></p> <p><span> </span></p> <p><span>The dataset is in vector (.shp) format with single part polygons representing burned areas; each associated with an occurrence date. This means that fire events with non-continuous burned areas are represented by several single-part polygons, which share the same fire ID number and date.</span></p> <p><span>Data References</span></p> <p><span>European Forest Fire Information System. 2020. EFFIS Burned Area Product. European</span></p> <p><span>Forest Fire Information System -Copernicus. (Available here </span><span><a href="https://forest-fire.emergency.copernicus.eu/applications/data-and-services"><span>https://forest-fire.emergency.copernicus.eu/applications/data-and-services</span></a></span><span>)</span></p> <p><span> </span></p> <p><span>NASA FIRMS MODIS or VIIRS Fire/Hotspot Data,</span> <span>Country Yearly Summary. 2024. (Available here </span><span><a href="https://firms.modaps.eosdis.nasa.gov/country/"><span>https://firms.modaps.eosdis.nasa.gov/country/</span></a></span><span>)</span></p> <p><span><br></span><span>Myndigheten för samhällsskydd och Beredskap. </span><span>2020. Incident reports from municipal</span></p> <p><span>fire brigades. (Available through direct contact with MSB)</span></p> <p><span> </span></p> <p><span>Skogsstyrelsen. 2020. Skogsbränder. (Accessed via Skogsstyrelsen FTP service, under https://www.skogsstyrelsen.se/sjalvservice/karttjanster/geodatatjanster/ftp/)</span></p>
Chemical sensors for fire detection and nuisance rejection under EN-5420 standard conditions and reduced-scale chamber
<p>The dataset was acquired using a gas sensor array placed in the celling of a validated standard fire room (240 m3) located in Minimax Company. The dataset includes measurements of three different campaigns that were performed over 15 months. The dataset includes standard EN-54 smoldering fires and non-standard smoldering fires (such as plastic fires; PVC, cables Fire). In order to generate scenarios that may result in false-positive alarms when gas sensors are used, different nuisance experiments were also performed (such as cleaners, and air fresheners). Additionally, an additional measurement campaign was performed in a small chamber. The small-scale experiments dataset includes scale-down replicates of the fire and nuisances experiments performed in the standard fire room (EN-54 smoldering fire experiments, non-standard fires, and nuisance experiments).</p> <p>Citation request: Ana Solórzano et al, Early fire detection based on gas sensor arrays: Multivariate calibration and validation, Sensors and Actuators B: Chemical, 2021, <a href="https://doi.org/10.1016/j.snb.2021.130961">https://doi.org/10.1016/j.snb.2021.130961</a>.</p>
Simulation of Fire Propagation in Cable Tray Installations - Data Set
<p>This repository contains simulation data used for a conference paper at ISTSS 2018, with the title "<a href="https://www.researchgate.net/publication/323999819_Simulation_of_Fire_Propagation_in_Cable_Tray_Installations_for_Particle_Accelerator_Facility_Tunnels?ev=auth_pub">Simulation of Fire Propagation in Cable Tray Installations for Particle Accelerator Facility Tunnels</a>". Furthermore, the plots are provided, including the Python 3 scripts to create the plots, used in this paper.</p> <p>With the Fire Dynamics Simulator FDS, in the versions 6.3.2 and 6.5.3, simulations of cable fire tests have been performed. Experimental data from micro-combustion calorimetry and Cone Calorimeter tests were used to calibrate a material parameter set, aming to predict the fire spread in a cable tray installation. The simulations are based on experimental data from the CHRISTIFIRE Phase 1 campaign.</p> <p>The authors want to thank Kevin B. McGrattan for providing access to the CHRISTIFIRE data.</p> <p> </p> <p><strong>Some remarks on the usage:</strong></p> <p>Unfortunately, for some unclear reason, Zenodo does right now not support the creation of folders within the repository. In an effort to maintain the structure of the data, ZIP archives have been created. Note that specifically the MT-3 simulations are quite large and take about 3.5 GB of space after extraction.</p> <p>It is only necessary to reproduce the file structure, if the user wants to utilise the provided Python scripts "as is". It is, of course, also possible to adjust the file pathes in the scripts to the users desire.</p> <p>To recreate the original file structure, one needs to copy all files of this repository into a single directory. The ZIP archives are sub-directories within that basic directory. The names of the archives contain the information of how the sub-directory structure looks like. Triple underscores '___' are placeholders indicating the file path, thus need basically changed to '/'. For example, the ZIP archive ''Cone___CoarseCone___ArrCHRISTIFIRE.zip' translates to the path 'Cone\CoarseCone\ArrCHRISTIFIRE\'.</p> <p> </p>
Projected fire cycle (yrs) for Canada at a 0.25 degree resolution
<p>These rasters represent the projection of future fire cycles for Canada at a 0.25 degree of resolution. The data was produced in three steps:</p> <ol> <li>Future fire cycles were obtain by projecting annual area burned as in Boulanger et al. (2014) (https://cdnsciencepub.com/doi/full/10.1139/cjfr-2013-0372) at the homogeneous fire regime zone scale. Models used here were improved from those used in Boulanger et al. (2014). Projections were conducted for specific time periods (baseline, 2011-2040, 2041-2070 and 2071-2100) under specific anthropogenic climate forcing scenarios (RCP 4.5 and RCP 8.5). Three Earth System models were used i.e., CanESM2, HadGEM2-ES and MIROC-ESM-CHEM.</li> <li>Values obtained at the homogeneous fire regime zone scale were further "downscaled" at a 250m resolution according to vegetation type (cover x age class) following Bernier et al. (2016) (https://www.mdpi.com/1999-4907/7/8/157) using forest attributes of 2011 as assessed in Beaudoin et al. (2014) (https://cdnsciencepub.com/doi/10.1139/cjfr-2013-0401). </li> <li>Values obtained at a 250m resolution were averaged in 0.25x0.25 degree cells.</li> </ol>
Sentinel-2 Satellite Imagery Based Forest Fire Monitoring
<p><strong>Forest Fire in Villages near Berlin - Normalized Burn Ratio (NBR)</strong></p> <p>Villages in Treuenbrietzen (Frohnsdorf, Klausdorf and Tiefenbrunnen) around 50 km southwest of Berlin have been severely affected by recent unpredicted wildfire and the size of the burned area is about of 400 hectares, which started to spread on 23rd of August, 2018. More than 500 people had to leave their homes as a result of the fire in Treuenbrietzen and the burning fire with dense smoke continued for days. This year Europe has faced a long hot dry summer with almost no rain and as a consequence some European countries like Germany are on high alert regarding possible forest fires.</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.