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263 results for “forest fire”
Raw microclimate data from plots at burned areas from the 2020 Holiday Farm fire in the Andrews Experimental Forest and Hagan Block, 2022-2024
This dataset includes a suite of microclimate sensor data from areas burned by the 2020 Holiday Farm fire within the McKenzie River basin. A total of 42 microclimate sensor suites were installed in July and August of 2022 distributed across RS01, RS08, RS15, WS02, WS09, WS01, HGBK. All sensor locations are within the Holiday Farm Fire footprint and within Permanent Sample Plots (PSPs). An additional sensor was placed within the Primary meteorological station (PRIMET) of the HJ Andrews Experimental Forest for comparison and calibration between open-air measurements. Sites are stratified across three treatment variables including 1) fire severity: high or low; 2) management: managed or unmanaged; 3) water balance: moister or drier topographic positions. This resulted in eight treatment blocks, each with 5 sensor suite replicates. Each microclimate site includes a suite of measurements: Hobo temperature and relative humidity sensor installed at 1.5m within a gill shield (recording at 30 minute interval), one TOMST TMS-4 temperature and soil moisture sensor was located within 1 m of plot center (recording at 15 minute interval). The TOMST sensors include air temperature sensors at 15cm, 2cm, and soil temperature at -6cm in soil near-surface. Soil moisture is measured across the ~10cm near surface zone.
Interviews with members of the HJA Community during the Lookout Fire, HJ Andrews Experimental Forest, 2023
This dataset records the interview instrument, analytical codebook, and summary of results for the 2023 Lookout Fire Qualitative Interviews. Data was collected in 2023 in Corvallis, Oregon, and over Zoom. Members of the H. J. Andrews Experimental Forest (HJA) community (e.g., university faculty and administrative professionals, agency scientists and personnel, students, alumni and emeritus from the aforementioned communities) were interviewed between September 26th and November 8th 2023. At the time, the fire had largely stopped growing (no significant runs occurred during the interview period), but the fire was not fully contained and the fire severity was not yet known by the community. Data collection is complete. The interview included questions about emotional reactions to the Lookout Fire, current and foreseen impacts to research at the HJA, social relationships and the fire, naturalness of the fire, and climate change, climate anxiety, and the fire. Interviews were semi-structured; while interviews were guided by the interview protocol, conversation was allowed to proceed organically. In total, 40 respondents were interviewed. Interviews were transcribed verbatim and analyzed inductively and deductively. A finalized codebook was developed iteratively; the included codebook are the final codes used to analyze the full dataset. Interview transcripts and other potentially identifying information is not available to protect respondent confidentiality and anonymity. This dataset summarizes the key interview results.
Rates and controls of nitrogen fixation in post-fire lodgepole pine forests, Greater Yellowstone Ecosystem, 2022
This dataset contains all the contents needed to reproduce the calculations and analyses done in the original paper associated with this dataset (Heumann et al. 2025 Ecology). The primary method used in this study was the Acetylene Reduction Assay (ARA) which measures the rate at which acetylene is reduced to ethylene in nitrogen-fixing organisms as a proxy for nitrogen fixation activity. We measured acetylene reduction rates in multiple cryptic niches (i.e., lichen, moss, pine litter, dead wood and mineral soil) in 34-year-old lodgepole pine stands in the Greater Yellowstone Ecosystem to explore the rates, temporal patterns, and climate controls on cryptic N fixation. Thus the foundation of this dataset is ethylene production rate measurements. All the data tables in this dataset contain either measured ethylene production rates or estimates of N fixation scaled from those ethylene production rates. Included with this are various physical measurements (e.g. dry mass, moisture content, incubation temperatures) that we included in our analyses in order to either scale up rates of N fixation using biomass estimates from field sites or explore temperature and moisture relationships with nitrogen fixation activity under controlled conditions. Included with this dataset are three R studio scripts used to run the calculations and analyses reported in the manuscript publication from this study.
Data for: Can fire exclusion zones enhance postfire tree regeneration? A simulation study in subalpine conifer forests
Postfire tree regeneration in forests adapted to infrequent, stand-replacing fire is compromised by climate change and novel fire regimes. We used the individual-based forest simulation model iLand to ask whether mimicking spatial patterns of historical fire mosaics can sustain tree regeneration in a warmer future with more fire. We simulated forest and fire dynamics in Grand Teton National Park under four different climate scenarios, and with eight different scenarios (i.e. spatial configurations) of "fire exclusion zones" (Fx zones). Data were simulated for 2020 - 2100 period, and analyzed early (2026-2050) and late (2076-2100) in the simulation. Here, we present these simulated data and R-scripts to reproduce analyses presented in the associated manuscript (Keller et al. 2025, Ecological Applications). Specifically, our data deposit reproduces analyses for 1) differences in regeneration among scenarios at two different times in the simulation, 2) spatial patterns of regeneration in 2100 as a result of the operational fire exclusion zone scenario, and 3) supplemental analyses found in the appendixes.
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>
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>
Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics
<p>Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, <em>Science</em>).</p> <p>The records include mapped pyromes and data and code used to delineate the pyromes.</p> <h3><strong>Mapped Pyromes</strong></h3> <p>The data records include mapped pyromes in three forms:</p> <ol> <li><strong>Shapefile</strong> (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields <em>pyrome ID</em> and <em>pyrome name</em>. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li><strong>Lower-resolution NetCDF </strong>(Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at quarter-degree resolution.</li> <li><strong>Higher-resolution NetCDF</strong> (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at 0.05 degree resolution.</li> </ol> <p>Shapefiles are accessible via GIS programmes such as QGIS or ArcGIS. All files .shp, .dbf, .prj, .shx files must be stored in a single directory</p> <p>NetCDF files can be access by a variety of programming languages such as Python and R. For quick visualisations and access to the data structure, we suggest using the Panoply tool https://www.giss.nasa.gov/tools/panoply/.</p> <h3><strong>Correlation Data</strong></h3> <p>The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables:</p> <ul> <li><em><strong>Fire weather index</strong></em></li> <li><em><strong>Atmospheric instability (continuous Haines index)</strong></em></li> <li><em><strong>Lightning flash density</strong></em></li> <li><em><strong>Soil moisture</strong></em></li> <li><em><strong>Vegetation productivity (Normalised Difference Vegetation Index)</strong></em></li> <li><em><strong>Population density</strong></em></li> <li><em><strong>Cropland cover</strong></em></li> <li><em><strong>Pasture cover</strong></em></li> <li><em><strong>Road density</strong></em></li> <li><em><strong>Potential fuel loads - surface fuels</strong></em></li> <li><em><strong>Potential fuel loads - shrub fuels</strong></em></li> <li><em><strong>Potential fuel loads - canopy and ladder fuels</strong></em></li> <li><em><strong>Terrain ruggedness index</strong></em></li> <li><em><strong>Forest area density</strong></em></li> </ul> <p>The BA data derive from MODIS MCD64A1 collection 6.1 (Giglio et al., 2018). BA data for forests is masked using the MODIS MOD44B product (DiMiceli et al., 2021) with a 30% tree cover threshold. The predictor data derive from multiple sources as desribed by Jones et al. (2024). See Supplementary Methods and Materials.</p> <p>The gridded correlations data are provided in Hierarchical Data Format version 5 (.hdf5) files, zipped to Correlation_Qdeg.zip. File names describe the variables used.<em> Cropland_Pasture_Qdeg.hdf5 </em>contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value.</p> <p>As explained and justified by Jones et al. (2024), the correlation structure used depends on the variable (see Supplementary Methods and Materials) as per the following categories:</p> <ul> <li><strong><em>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density:</em></strong> Monthly correlation between forest BA and each variable across all fire season months in the period 2001-2021 at the quarter-degree resolution.</li> <li><strong><em>Soil Moisture:</em></strong> Inter-annual correlation between (i) mean soil moisture during the fire season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Vegetation Productivity (NDVI):</em></strong> Inter-annual correlation between (i) mean NDVI during the prior growing season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Population Density, Cropland Cover, Pasture Cover, Road Density, T</em></strong><strong><em>errain Ruggedness Index, Forest Area Density: </em></strong>Spatial correlation between mean annual forest BA and each variable across the 0.05° cells within each quarter-degree cell during 2001-2021.</li> </ul> <p>Note that these grids are provided for insights into spatial variation in the input correlation data. Pyromes are defined based on correlations fitted on the spatial scale of Olson ecoregions, not quarter-degree grid cells (see further details below).</p> <h3><strong>Clustering Code</strong></h3> <p>DEMO_Clustering.zip contains R Statistics code for clustering forest ecoregions into pyromes based on correlations observed between forest BA and 14 predictors at regional level. The <em>Input</em> directory contains a .RData data frame with correlations between forest BA and each predictor for ecoregions. For demonstrative purposes the code is applied to cluster forest ecorgions of North America into pyromes. The <em>Regions</em> directory contains ecoregions of North America in shapefile format. The <em>Output</em> directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.</p>
The Fire and Fire Surrogate Study: Berkeley Forests, 2001-2020
Dry forests throughout the western United States are fire-dependent ecosystems, and much attention has been given to restoring their ecological function. For this reason, land managers often are tasked with reintroducing the fire process via prescribed fire and fire-surrogate treatments (such as thinning and mastication). During planning, managers are expected to anticipate the effects of management actions on forest structure, ecological function, and future fire behavior. In the case of fire surrogate treatments, managers must understand which components or processes are changed or lost, and with what effects, if treatments such as thinning and mastication are used instead of fire or in combination with fire. As such, a nationwide research effort, The Fire and Fire Surrogate Study, commenced in 2001 to evaluate the impacts of prescribed fire and mechanical fuel reduction treatments. As part of this national effort, a suite of fuel treatments was implemented in 2001 at Blodgett Forest Research Station, which is owned and operated by UC Berkeley Forests, in the northern Sierra Nevada. The Fire and Fire Surrogate Study at Blodgett Forest Research Station is comprised of a network of twelve stands, ranging in size from 35-70 acres, each of which was randomly assigned one of four possible treatments, which represent the basic range of forest restoration and fire hazard reduction options. The treatment options initiated at Blodgett Forest were: 1) Control: no active management. 2) Fire-only: prescribed fire applied to the forest stand. 3) Mechanical-only: Crown thinning followed by commercial thinning from below, which removed mid and larger sized trees, followed by mastication, which chipped/shredded smaller trees in place leaving 10% of them in clumps throughout the forest stand. 4) Mechanical + fire: same mechanical treatment described above, followed by prescribed fire. This dataset contains longitudinal information about forest and fuels composition, as well as und
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022
This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022
This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022
This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022
This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022
This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
NDSI values in a region affected by a forest fire
<p>This dataset includes maps with NDSI values that come from Landsat 5 Surface Reflectance collection images available in Google Eath Engine. TIFF images cropped to a region of Lanjaron where different post-fire treatments were established are presented. This area was affected by a forest fire in September 2005. That is why NDSI values and binary layers are presented for two periods, before and after the fire (hese binary layers are generated based on a threshold of 0.35). Comparisons are made in three forest management treatments after the fire. These treatments were No Intervention (NI), Partial Court (PCL) and Salvage Logging (SL).</p><p>This dataset also includes a grid with the sizes of the Landsat pixels that includes as information the treatment to which it belongs (column Trat_1).</p><p>And a folder of outputs where each cell has an associated value of ancillary variables (such as elevation, slope, shadows) and another where they present the NDSI values extracted from the images of the dataset</p><p> </p>
PM2.5 emissions from Siberian forest fires 2004-2021
<p>The dataset contains Supplementary Materials for the article <em>''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis''</em> in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis. <br> <br> <strong>Supplementary Figures</strong>:<br> - Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line); </p> <p>- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha. </p> <p>- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);</p> <p>- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;</p> <p>- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;</p> <p>- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region; </p> <p>- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory; </p> <p>- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory. <br> </p> <p>We share Copernicus Atmosphere Monytoring Service <strong>PM2.5 emissions maps</strong> (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594. </p> <p><br> To determine emissions from the territory of Russia, we provide <strong>shapefiles</strong> with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334). </p> <p>Also, there are<strong> initial dataset</strong> for analysis (Initital data_SARIMAX archive) and <strong>SARIMAX model settings</strong> (doc.). </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.