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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.
Mycorrhizal fungal communities identified from seedlings planted in the Taylor, Dalton, and Boundary fire complexes which burned in 2004
This dataset contains the operational taxonomic unit table and taxonomic assignments for fungi that were associated with the roots of seedlings planted into the 2004 burn sites. There were 458 seedlings from 22 of the 32 established intensive sites (Johnstone and Hollingsworth 2019) consistenting of black spruce, white spruce, aspen, and lodgepole pine.
CGP01 Gall-insect densities on selected plant species in watersheds with different fire frequencies
Long-term monitoring of gall-insect densities on Solidago canadensis, Vernonia baldwinii, and Ceanothus herbaceous. Gall abundances are censused in watersheds burned at one- to twenty- year intervals to asses the role of fire frequency and time since fire on gall-insect population dynamics. The data sets contain the following: Watershed fire frequency, number of growing seasons since last fire, plant species, number of galled stems, and number of censused stems. Censuses conducted for the 1989-1996 growing seasons except 1992 and 1994, next scheduled census is fall 1997.
KFH01 Konza prairie fire history
The Konza burn history data is downloadable by year. Watershed names and codes listed are the current watershed designations (2010). Please note that several watershed designations have changed over the history of Konza. This is inevitable due to changes in research objectives but is problematic for those wanting to discover the full burn history of a given area. In some cases watersheds have simply been renamed to reflect changes in experimental burn treatments (e.g. R20A was formerly 1A). In other cases watersheds have been subdivided or aggregated from smaller watersheds (eg. in 1994 3B3UA was added to 20A (currently R1A) to form a larger watershed). In a few cases watershed names have been moved to new areas (e.g. 1D was moved from its original location in 1978 after the acquisition of new property. The original 1D watershed is now part of WB and 20C). Investigators should consult the proper watershed map for a given year to see watershed designations at the time of burning.
NSW01 Soil water chemistry from porous cup lysimeters on watersheds with different fire treatment
Soil water nitrogen composition is measured using porous cup lysimeters. Measurements include nitrate, ammonia, phosphate, and organic nitrogen and phosphorus. Variables of interest are rainfall patterns, vegetation types, and time since burning.
Update of: The Global Fire Atlas of individual fire size, duration, speed and direction
<p>This is an updated and extended record of the Global Fire Atlas introduced by Andela et al. (2019). Input data (burned area and land cover products) are updated to the MODIS Collection 6.1 (the original version featured in Andela et al. (2019) was based on collection 6.0 burned area and collection 5.1 land cover products, respectively). The timeseries is extended to cover the period 2002 to August 2024.</p> <h2><strong>Methodological Notes:</strong></h2> <p>The method employed to create the dataset precisely follows the approach described by Andela et al. (2019).</p> <p>The input burned area product is MCD64A1 Collection 6.1. It is described by Giglio et al. (2018) and available at: https://lpdaac.usgs.gov/products/mcd64a1v061/. </p> <p>The input land cover product is MCD12Q1 Collection 6.1. It is described by Sulla-Menashe et al. (2019) and available at: https://lpdaac.usgs.gov/products/mcd12q1v061/. </p> <p>Note that while the methods have remained the same compared to Andela et al. (2019), we do observe small differences between the Global Fire Atlas products originating from differences between the MCD64A1 collection 6.1 burned area data used here and the collection 6 data used in the original product. In addition, we observe more substantial differences in the dominant land cover class associated with each fire due to the differences between the MCD12Q1 collection 6.1 data used here and collection 5.1 data used in the original product. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. For each MODIS tile, the fire season is defined as the twelve months centred on the month with peak burned area (see Andela et al., 2019). For example, for a MODIS tile with peak burned area in December, the 2023 fire season would be defined as the period from July 2023 to June 2024, with the current record ending in August 2024. This is particularly relevant in the Southern extratropics and the northern hemisphere subtropics, where the fire seasons often span the new year. The local definition of the fire season is based on climatological peak in burned area as described by Andela et al. (2019).</p> <p>Here we extended the time-series to include the fire season of 2002, and extended the time-series until February 2025.</p> <h2> </h2> <h2><strong>Usage Notes:</strong></h2> <h3><strong>Incomplete Observations for the Latest Fire Seasons:</strong></h3> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. As such, the time-series can be incomplete for the latest fire season (e.g. the "2024 fire season") and also for the penultimate fire season (e.g. the "2023 fire season") due to the way that fire seasons are defined (see above). For example, if the month with peak burned area for a tile is December, then full data covering the 2023 fire season in that tile are not available until midway through the 2024 calendar year. This contrasts with the original dataset from Andela et al. (2019), which only included the data for entire fire seasons between 2003 and 2016. </p> <h3><strong>Observational Outages:</strong></h3> <p>For the purpose of time-series analysis, we note that the 2002 product may have been affected by outages of Terra-MODIS (most notably, June 15 2001 - July 3 2001 and March 19 2002 - March 28 2002), which affects the burn date estimates and Global Fire Atlas product. Following the launch of Aqua-MODIS in May 2002 burn date estimates are more reliable as estimated from both MODIS sensors onboard Terra and Aqua. </p> <h3><strong>File Naming Convention:</strong></h3> <p>GFA_v<em>{time-stamp}</em>_<em>{data-type}</em>_<em>{fire_season}</em>.<em>{file_type}</em></p> <p><em>{time-stamp}</em><strong> </strong>= Date that code was run.</p> <p><em>{data-type}</em><strong> </strong>= “ignitions” or “perimeters” for vector files; “day_of_burn”, “direction”, “fire_line”, or “speed” for raster files.</p> <p><em>{fire_season} </em>= the locally-defined fire season in which the fire was ignited (see more below).</p> <p><em>{file_type} </em>= ".shp" for vector files; ".tif" for raster files. </p> <p>Please note that the year string in filenames refers to the locally-defined fire season in which the fire ignited, not the calendar year. Hence the file GFA_v20240409_perimeters_2003.shp can include fires from the 2003 fire season that ignited in the calendar years 2002 or 2004. </p> <h3>Coordinate systems (Map Projections):</h3> <p>Vector data are provided on the WGS84 projection.</p> <p>Raster data are provided on the MODIS sinusoidal projection used in NASA tiled products. The WKT string defining this projection is:</p> <pre><code>'PROJCS["unnamed",GEOGCS["Unknown datum based upon the custom spheroid",DATUM["Not_specified_based_on_custom_spheroid",SPHEROID["Custom spheroid",6371007.181,0]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Sinusoidal"],PARAMETER["longitude_of_center",0],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</code></pre> <p> </p> <h2><strong>Data Layers:</strong></h2> <p><em><strong>Table 1: Overview of the Global Fire Atlas data layers. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire, while the underlying 500 m gridded layers reflect the day-to-day behavior of the individual fires. In addition, we provide aggregated monthly summary layers at a 0.25° resolution for regional and global analyses.</p> <table> <tbody> <tr> <td>File name</td> <td>Content</td> </tr> <tr> <td>SHP_ignitions.zip</td> <td>Shapefiles of ignition locations with attribute tables (see Table 2)</td> </tr> <tr> <td>SHP_perimeters.zip</td> <td>Shapefiles of final fire perimeters with attribute tables (see Table 2)</td> </tr> <tr> <td>GeoTIFF_direction.zip</td> <td>500 m resolution daily gridded data on direction of spread (8 classes)</td> </tr> <tr> <td>GeoTIFF_day_of_burn.zip</td> <td>500 m resolution daily gridded data on day of burn (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_speed.zip</td> <td>500 m resolution daily gridded data on speed (km/day)</td> </tr> <tr> <td>GeoTIFF_fire_line.zip</td> <td>500 m resolution daily gridded data on the fire line (day of year; 1-366)</td> </tr> <tr> <td>GeoTIFF_monthly_summaries.zip</td> <td>Aggregated 0.25° resolution monthly summary layers. These files include the sum of ignitions, average size (km2), average duration (days), average daily fire line (km), average daily fire expansion (km2/day), average speed (km/day), and dominant direction of spread (8 classes). </td> </tr> </tbody> </table> <p> </p> <p><em><strong>Table 2: Overview of the Global Fire Atlas shapefile attribute tables. </strong></em>The shapefiles of ignition locations (point) and fire perimeters (polygon) contain attribute tables with summary information for each individual fire.</p> <table> <tbody> <tr> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>lat, lon</td> <td>Coordinates of ignition location (°)</td> </tr> <tr> <td>size</td> <td>Fire size (km2)</td> </tr> <tr> <td>perimeter</td> <td>Fire perimeter (km)</td> </tr> <tr> <td>start_date, start_DOY</td> <td>Start date (yyyy-mm-dd), start day of year (1-366)</td> </tr> <tr> <td>end_date, end_DOY</td> <td>End date (yyyy-mm-dd), end day of year (1-366)</td> </tr> <tr> <td>duration</td> <td>Duration (days)</td> </tr> <tr> <td>fire_line</td> <td>Average length of daily fire line (km)</td> </tr> <tr> <td>spread</td> <td>Average daily fire growth (km2/day)</td> </tr> <tr> <td>speed</td> <td>Average speed (km/day)</td> </tr> <tr> <td>direction, direc_frac</td> <td>Dominant direction of spread (N, NE, E, SE, S, SW, W, NW) and associated fraction</td> </tr> <tr> <td>MODIS_tile</td> <td>MODIS tile id</td> </tr> <tr> <td>landcover, landc_frac</td> <td>MCD12Q1 dominant land cover class and fraction (UMD classification), provided for 2002-2023</td> </tr> <tr> <td>GFED_regio</td> <td>GFED region (van der Werf et al., 2017; available at https://www.globalfiredata.org/)</td> </tr> </tbody> </table> <p> </p> <p> </p>
Fire Weather Index - ERA5 HRES
<p>The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread. </p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Hersbach et al., 2019), and replaces the homonymous indices based on ERA-Interim (Vitolo et al., 2019). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. </p> <p>The dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately on Zenodo. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). The caliver R package (Vitolo et al. 2017, 2018) contains useful functions to process this dataset. </p> <p>Details: </p> <ul> <li>File format: netcdf4</li> <li>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326)</li> <li>Longitude range: [-180, +180]</li> <li>Latitude range: [-90, +90]</li> <li>Temporal resolution: 1 day (at 12 local noon)</li> <li>Spatial resolution: 0.28 degrees (~31 Km)</li> <li>Spatial coverage: Global</li> <li>Time span: from 1980-01-01 to 2019-06-30</li> <li>Stream: Deterministic forecasts</li> </ul>
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.
Fire in the Everglades, 1978-2023
In the Florida Everglades, fire is a key driver of ecological dynamics, interacting with hydrology and vegetation structure. Fires have been recorded by Everglades National Park and Big Cypress National Preserve (1978-2023) by mapping the outer boundary of fires. Annual fire perimeter data for the two regions were combined to generate a fire history database for the subtropical landscape. This dataset includes fire perimeters, binary burned layers, and years of fire layers that can be used to quantify fire dynamics across subtropical ecosystems.
Pre- and post-fire vegetation and fuel loading data from mixed conifer plots in Arizona and New Mexico: 2010-2023
A permanent plot network was installed in mixed conifer stands across the U.S. Southwest (Arizona and New Mexico) between 2010-2013, primarily to monitor the spread and severity of white pine blister rust (WPBR), a disease caused by the fungal pathogen Cronartium ribicola on southwestern white pine (Pinus strobiformis). Study sites were mid-to-high elevation mixed conifer stands composed of southwestern white pine, Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), quaking aspen (Populus tremuloides), Gambel oak (Quercus gambelii), blue spruce (Picea pungens), Engelmann spruce (Picea engelmannii), corkbark fir (Abies latifolia var. arizonica), Rocky Mountain bristlecone pine (Pinus aristata), and New Mexico locust (Robinia neomexicana). After plot installation, 6 fires occurred in the study area, burning an estimated total of 489,390 acres and 30 plots. We remeasured plots at 1-, 5- and 10-year intervals post-fire, quantifying burn severity via a composite burn index (CBI) at the first year post-fire. We also assessed regeneration, overstory mortality, and fuel loading. Overstory variables collected included tree species, status, diameter at breast height (DBH), and mortality, as well as height, height to live crown base, strata, and crown class on a subset of trees. Understory trees were tallied by species. Fuel load was measured via transect and calculated in megagrams per hectare categorically based on fuel type. Other variables such as basal area and trees per hectare were derived and calculated. This dataset was utilized in the manuscript "Climate, fire, and the future of mixed conifer ecosystems in the U.S. Southwest" (currently in review), and R code used for analyses is included in the dataset.
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.
Above ground plant and below ground stem biomass of samples from the unburned control site near the Anaktuvuk River fire scar.
Above ground plant and below ground stem biomass were measured in 2011 from three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. These samples were analyzed for carbon and nitrogen concentrations.
Above ground plant and below ground stem biomass of samples from the severely burned site of the Anaktuvuk River fire, Alaska
Above ground plant and below ground stem biomass were measured in 2011 from three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. These samples were analyzed for carbon and nitrogen concentrations.
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), and phosphate (PO4) in the upper 5-10 cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using ion-exchange resin membranes incubated in the soil during the growing season of 2016.
Point-frame measurments from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains point-frame measurements from a nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016 at a severely burned site and an unburned site. Pin-vegetation contact was recorded using a 0.75 m2 frame with 41 evenly spaced pin-drop points. Data was collected once during the height of the growing season in 2016 (when fertilization began) 2017, 2018 and 2019. This data was used to measure the impact of fertilization and fire on community composition.
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2019 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), phosphate (PO4), and total free primary amines (TFPA )in the upper 5-10cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using buried resin bags which incubated for 1 month during the peak of the growing season in 2019.
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Anaktuvuk River, Alaska, USA tussock tundra flowering in response to fire severity, 2008-2015
Eriophorum vaginatum flower counts from annual photographs at the severe, moderate, and unburned Anaktuvuk River, Alaska, USA flux tower sites during peak flowering season (6/17-7/20).
Point-frame measurement of maximum canopy height for plant growth forms at the 2007 Anaktuvuk River Fire scar measured in 2019.
This file contains maximum plant heights from point frame measurements made in the southern section of the 2007 Anaktuvuk River fire scar, at a severely burned site and a nearby unburned site. Pin-vegetation contact was recorded using a 0.56 m2 frame with 41 evenly spaced sampling points. Data were collected during peak green in summer 2019. These data were used 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 tussock tundra.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.