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1,989 results for “fires”
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
<p>This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following:</p> <p>Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), <em>Atmos. Chem. Phys. Discuss.</em>, https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019.</p> <p>See Readme for more information.</p>
DATASET : Thresholds of fire response to moisture and fuel load differ between tropical savannas and grasslands across continents
<p><strong>Abstract </strong></p> <p><strong>Aim:</strong> An emerging framework for tropical ecosystems states that fire activity is either ‘<em>fuel build-up limited</em>’ or ‘<em>fuel moisture limited</em>’ i.e. as you move up along rainfall gradients, the major control on fire occurrence switches from being the amount of fuel, to the moisture content of the fuel. Here we used remotely sensed datasets to assess whether interannual variability of burned area is better explained by annual rainfall totals driving fuel build-up, or by dry season rainfall driving fuel moisture.</p> <p><strong>Location:</strong> Pantropical savannas and grasslands</p> <p><strong>Time period:</strong> 2002-2016</p> <p><strong>Methods:</strong> We explored the response of annual burned area to interannual variability in rainfall. We compared several linear models to understand how <em>fuel moisture </em>and <em>fuel build-up effect </em>(accumulated rainfall during 6 and 24 months prior to the end of the burning season respectively) determine the interannual variability of burned area and explore if tree cover, dry season duration and human activity modified these relationships.</p> <p><strong>Results:</strong> Fuel and moisture controls on fire occurrence in tropical savannas varied across continents. Only 24% of South American savannas were <em>fuel build-up limited</em> against 61% of Australian savannas and 47% of African savannas. On average, South America switched from fuel limited to moisture limited at 500 mm yr<sup>-1</sup>, Africa at 800 mm yr<sup>-1</sup> and Australia at 1000 mm yr<sup>-1 </sup>of mean annual rainfall.</p> <p><strong>Main conclusions:</strong> In 42% of tropical savannas (accounting for 41% of current area burned) increased drought and higher temperatures will not increase fire, but there are savannas, particularly in South America, that are likely to become more flammable with increasing temperatures. These findings highlight that we cannot transfer knowledge of fire responses to global change across ecosystems/regions – local solutions to local fire management issues are required, and different tropical savanna regions may show contrasting responses to the same drivers of global change.</p>
Locked Shields Partners Run 23 (LSPR23): A novel IDS dataset from the largest live-fire cybersecurity exercise
<p>IDS Dataset from the Largest Live Fire Cybersecurity Exercise Using Virtual Blue Team Network Traffic.<br><br></p> <ul> <li> <p>LSPR23 is derived from Locked Shields 2023, a major live-fire cyber defense exercise.</p> </li> <li> <p>LSPR23 includes ~16M network flows, of which ~1.6M are labeled malicious.</p> </li> </ul> <p> </p> <p>Please cite our research article:"LSPR23: A novel IDS dataset from the largest live-fire cybersecurity exercise" when using our dataset:<br>https://doi.org/10.1016/j.jisa.2024.103847<br><br><br></p>
Blaze Fire Classification – Segmentation Dataset
<p>The dataset is destined to be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’.</p> <p>Details on acquiring the dataset can be found <strong><a href="https://aiia.csd.auth.gr/blaze-fire-classification-segmentation-dataset/" target="_blank" rel="noopener">here</a></strong>. </p> <p> </p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Kingdom of Saudi Arabia. al-Ḥasāʾ, Jabal al-Qāra (جَبَل ٱلْقَارَة). Fired ware being inspected.
<p>Kingdom of Saudi Arabia. al-Ḥasāʾ, Jabal al-Qāra (جَبَل ٱلْقَارَة). Fired ware being inspected, 1969.</p>
Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent alpha5-GABAA receptors
<p>The ZIP file consists of folders, containing RAW data used for analysis and used to prepare the Figures 1-6 of the paper published in Cell Reports 2021, with the title mentioned above.</p> <p>All recordings were made from acute hippocampal brain slices, obtained from adult C57BL6 mice. Whole-cell voltage-clamp and current-clamp recordings of mature and adult-born young hippocampal granule cells were performed as outlined in methods and recordings were digitized using a power1401 interface from CED (Cambridge Electronic Design, UK), and saved to file using the CFS library support from CED. The contents of the different files is described in File_Description.pdf.</p>
Dataset for "Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra"
<p>Data that support the findings of the study "<strong>Fire disturbance promotes biodiversity of plants, lichens and birds in the Siberian subarctic tundra</strong>".</p>
Data used in the paper: Heatwaves, droughts, and fires: Exploring compound and cascading dry T hazards at the pan-European scale
<p>These datasets were used to analyze European compound and cascading dry hazards. The scripts are publicly available on GitHub: https://github.com/sjsutanto/Dryhazards.git.</p> <p>File Daily_SM_drought_WB_Converted.nc is for soil moisture drought, fwi_1990_2016_binary_95th_lowThreshold.nc is for wildfires, and datacube_2mtpp_19902016_HW.nc is for heatwaves.</p> <p> </p>
Winter Season Spectral Snowpack Albedo Data For the Caldor and Creek Fires
<p>* Description: The file "Caldor_Creek_Fires_Winter_Snow_Albedo_Spectrometer_Dataset.csv" is a comma-delimited file containing the spectral snowpack albedo measurements from the Caldor and Creek Fires in California and the associated burn severities at the location of each measurement.</p> <p> </p> <p>Data and File Overview</p> <p>======================</p> <p>Summary Metrics</p> <p>---------------</p> <p>* File count: 1</p> <p>* Total file size: 909 KB </p> <p>* Range of individual file sizes: 909 KB </p> <p>* File formats: .csv</p> <p> </p> <p>Naming Conventions</p> <p>------------------</p> <p>* File naming scheme: One file that includes all dates, all burn severities, all wavelengths.</p> <p>* Format(s): Comma-separated value (.csv) file</p> <p>* Size(s): 909 KB</p> <p>* Dimensions: 17,209 rows x 6 columns</p> <p>* Variables:</p> <p> * Measurement_Number: An index of the measurement number, unitless</p> <p> * wavelength: The wavelength of light (units in nanometers) for which albedo sample ranging from 350-2500 nm</p> <p> * Type: Measurement type is albedo in various burn severity environments (_hb is high burn severity, _mb is moderate burn severity, _ub is unburned, with the last letter corresponding to the month (J is January, F is February, A is April). NA corresponds to estimated January unburned data for the Caldor Fire where unburned albedo for April in the Creek Fire was adjusted downwards by 0.04 to account for less grain-size growth (Colbeck 1982, Rev. Geophys., https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/RG020i001p00045). </p> <p> * Albedo: The albedo (unitless), is a measurement of the solar radiation reflected by the snow surface divided by the radiation incident on its surface. In other words, albedo is the fraction of the incident sunlight reflected by the snow.</p> <p> * month: The month when the measurement was taken</p> <p> * burn: Burn severity at measurement location based upon classifications (High, Medium,, Unburned) from the Monitoring Trends in Burn Severity Dataset (https://mtbs.gov/). NA corresponds to the estimated unburned data for the Caldor Fire using April unburned data in the Creek Fire.</p> <p> * Missing data codes: No missing data is presented.</p> <p> </p> <p>Dates and Locations</p> <p>-------------------</p> <p>* Dates of data collection: Surface albedo collected in 27-28 February 2021, 1 April 2021, and 21 January 2022</p> <p>* Geographic locations of data collection: Data collected within the Caldor Fire perimeter in the central Sierra Nevada, California-Nevada (January 2022) and the Creek Fire perimeter, California (February and April 2021).</p> <p> </p> <p>Setup</p> <p>-----</p> <p>* Recommended software/tools to open file: parentage- R Studio; file can be opened using any text editor or programming language (e.g., R Studio, MATLAB, Python, TextMate, Microsoft Excel, etc)</p> <p> </p>
UAV data of post fire dynamics, Quesenbank, Harz, 2022 (orthomosaics, topography, point clouds)
<p>Unoccupied aerial vehicles (UAVs) were used to investigate a burnt forest site situated in the Harz National Park area near the location Schierke, east of the Brocken, called Quesenbank (<a href="https://www.google.com/maps/place/51%C2%B046'07.6%22N+10%C2%B041'30.2%22E/@51.7682386,10.6905312,489m/data=!3m1!1e3!4m5!3m4!1s0x0:0xd8d7f64a3ff9acf1!8m2!3d51.76877!4d10.69173">51.76877 °N, 10.69173 °E</a>) which is a spruce stand stand severely affected by bark-beetle and windfall. Most of the trees are dead such that they provide high fuel loads for potentially occurring wildfires. The small river Wormke crosses the Quesenbank and separates the survey area between a hiking path and the forest stand.</p> <p>During the 12.08.2022, inhabitants reported a <a href="https://www.ndr.de/nachrichten/niedersachsen/braunschweig_harz_goettingen/Waldbrand-im-Harz-Polizei-geht-von-Brandstiftung-aus,waldbrand882.html">fire</a> near Schierke which was quickly contained by the authorities. Two months after the fire, on 13.10.2022, a team of scientists from GAU Göttingen and TU Berlin was accompanied by a National Park representative for investigation. The site was surveyed by using modern UAVs and by sampling soil strata, ash and vegetation. This report will briefly describe UAV-based surveys and the derived data products.</p> <p>For an overview, see the <strong>report </strong>or download the Maps.zip folder.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong>Projekt ”Postfeuerdynamik auf Brandflächen im Nationalpark Harz”</strong></p> <p>Dr. Simon Drollinger, Georg-August Universität Göttingen</p> <p>Marlene Düngelhoef, Nationalparkverwaltung Harz</p> <p>Thomas Glinka, Nationalparkverwaltung Harz</p>
Precipitation and fire history at landslide sites
<p>These data include precipitation and burned area histories for events listed in the NASA Global Landslide Catalog. Each landslide includes a location uncertainty estimate. Precipitation values are the mean of all values within the uncertainty radius, while the fraction burned is computed for burned area.</p> <p>These data are intended to be used with the an RMarkdown notebook available at <a href="http://doi.org/10.5281/zenodo.7653683">this GitHub repository</a></p>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Creating Safe Environments: Optimal Acoustic Alarming of Laypeople in Fire Prevention - Online Supplement
<p>This online supplement contains datasets (raw data) and study material collected in an experimental study by the University of Münster, Germany. The study is part of a larger research project (<a href="https://www.brawa.ovgu.de/en/">https://www.brawa.ovgu.de/en/</a>) and examined the perception of acoustic fire alarm signals.</p> <p>Hazards like fires occur regularly and can cost people’s lives. Optimal auditory alarm signals enable laypeople to recognize dangers and to protect themselves. Existing fire alarm sound research focuses on alarm sounds and voice alerts presented singularly. We explored a combination of both and aimed to identify alarm signals that work optimally in everyday life. Thus, we conducted two online experiments: In Study 1 (<em>N</em> = 379), we tested eight alarm sounds regarding their typicality, their familiarity, their arousal, their valence, and their dominance. Siren-like alarm sounds were most effective. In Study 2 (<em>N</em> = 206), we combined the four most effective alarm sounds with a voice alert. The voice alert reinforced ambiguity reduction, action motivation, and action intention. Hence, we suggest using alarm sounds with siren-like patterns. They should be combined with a voice alert to foster a quick and specific (target task-oriented) reaction.</p> <p>The ethics committee of the University of Münster approved the studies (ID 2021-57-MT), and we preregistered both studies with AsPredicted.org under numbers #77031 and #81137 (see <a href="https://aspredicted.org/vx2rr.pdf">https://aspredicted.org/vx2rr.pdf</a> and <a href="https://aspredicted.org/mt9g3.pdf">https://aspredicted.org/mt9g3.pdf</a>). The studies were supported by the German Federal Ministry of Education and Research (grant numbers 13N15416 and 13N15419).</p> <p><strong>This online supplement includes: </strong></p> <ul> <li>Two codebooks describing all instructions and items in Study 1 and in Study 2</li> <li>Raw data (anonymized) and analysis scripts (Note: The raw data contains only the information of persons who were included in the analysis and who gave their informed consent. Some demographic information was deleted to ensure anonymity.)</li> <li>Study material: <ul> <li>Alarm signal example</li> <li>Hearing test implemented in both studies</li> </ul> </li> </ul>
Global Fire Emissions Database (GFED5) Burned Area
<p>The monthly GFED5 burned area data produced in this study are available in netCDF files. For years between 2001 and 2020, five layers of burned area (Norm: normal type, Crop: cropland burning, Defo: deforestation burning, Peat: peatland burning, Total: the sum of all burning) are provided at 0.25°×0.25° resolution. The ‘Norm’ layer contains burned areas in each grid cell (in km<sup>2</sup>) separated by 17 major land cover types. For the pre-MODIS era (1997-2000), only the ‘Total’ burned area layer with reduced spatial resolution (1°×1°) is provided. We also provide two global maps of burnable area (with water and snow/ice cover excluded) in each grid cell (0.25°×0.25° for the MODIS era and 1°×1° for the pre-MODIS era). Please refer to readme.html or readme.pdf for more detail about the dataset.</p>
Fire INventory from NCAR (FINN) v2.5(MODIS), MOZART VOC speciation
<p>The Fire INventory from NCAR (FINN) provides daily global fire emissions at high spatial resolution. The FINN model uses satellite detection of active fires (thermal anomalies) and the land cover type to determine the emission estimates. These emission estimates are based on MODIS active fire detection. Other versions of FINNv2.5 use MODIS+VIIRS fire detections. Please find additional VOC speciations and gridded emissions files at: https://doi.org//10.5065/XNPA-AF09</p>
Data for "Climate change is shifting and narrowing prescribed fire windows in the Western United States"
<p><strong>The data archived here represent two types of information used in the publication “Climate change is shifting and narrowing prescribed fire windows in the Western United States” by Swain et al. 2023:</strong></p> <p>1) The meteorological and vegetation dryness/fuel moisture data (as described within the file) extracted from prescribed fire burn plans around the Western United States (drawn from entities such as the U.S. Forest Service, U.S. National Park Service, and The Nature Conservancy) between 2002 and 2022. This data is provided in tabular form, both as an .xlsx file and a .csv file for the convenience of the user. In addition to the specific values from each burn plan, summaries of median values for forested and non-forested landscapes are provided as well.</p> <p>2) The number of days on which environmental conditions (i.e., weather and vegetation fuel moistures) are acceptable for prescribed fire according to the composite metric described in Swain et al. 2023 (known as “RxDays”). Underlying RxDay definitions are different for forest and non-forested landscapes. This data is provided in geospatially explicit (gridded) form as NetCDF files, which are a self-describing file format. Each file is provided as a single 3-dimensional hypercube (i.e., dimensions of time, latitude, and longitude, respectively; units and details described within the file) corresponding to the number of RxDays per calendar month.</p> <p>One file is provided for each climate model iteration (as identified in each filename); these represent projected RxDays at monthly scale between 1981 and 2060 using an RCP 4.5 climate forcing trajectory. An additional file (rx_gridMET.nc) is provided that represents the same values from an atmospheric reanalysis dataset, which represents a best estimate of observed RxDay values (1981-2020).</p>
Bastrop (TX, USA) forest fire cross-sensor change detection images
<p><strong>Abstract. </strong></p> <p>This dataset is composed of a set of four images acquired by different sensors over the Bastrop County, Texas (USA). On September 4, 2011, the region has been struck by ‘‘the most destructive wildland-urban interface wildfire in Texas history’’ which caused 2 casualties, more than 1300 destroyed buildings and almost burned entirely the Bastrop county state park.</p> <p>This dataset is composed of a pair of pre- and post-event images from the same sensor, the Landsat 5 TM (L5T1 and L5T2), which are completed by a post-event of another sensor, the Advanced Land Imager (ALI) from the Earth Observing (EO-1) mission, acquired very shortly after the L5T2 (denoted ALIT2). These three scenes are very similar to each other and no apparent changes between L5T2 and ALIT2 are visible, since images were acquired within 1 day. Differences between these pre- and post-event pairs are only due to burned forest since they were acquired at a 16 days interval during summer. We also dispose of a fourth image of the same area acquired one year and 9 months after the forest fire by the Landsat 8 Operational Land Imager (OLI, L8T2 hereafter). The differences between L5T1 and L8T2 are significant, due both to sun/sensor angles and the long temporal interval between acquisitions. A whole new series of building has been constructed in the burn scar and many cultivated crops are at a different stage of growth.</p> <p>Table : Dataset description</p> <pre><code class="language-markdown">| | Pre-event | Post-event 1 | Post-event 2 | Post-event 3 | |--------- |--------------|--------------|--------------|---------------| | Filename | t1_L5 | t2_L5 | t2_ALI | t2_L8 | | Sensor | Landast 5 TM | Landsat 5TM | EO-1 ALI | Landsat 8 OLI | | Channels | 7 | 7 | 9a | 7b | | GSD | 30, 120 | 30, 120 | 30 | 30 | | Sp. Range | [0.45–2.35, | [0.45–2.35, | [0.4–2.4] | [0.43–2.25] | | [\mu m] | 10.40–12.50] | 10.40–12.50] | | |</code></pre> <p>We prepared the ground truth for pairs of change detection problems by photo-interpretation and relying on the maps provided on the emergency response website [1]. In the ground truth involving the L5T1-L8T2 problem we also included changes related to vegetation density and vegetation/bare soil transitions, since these changes are of the same spectral class as those related to the burned scar.</p> <p>This data has been collected from the NASA LP DAAC Program [2], we are free to redistribute the data. For this reason we provide the Bastrop data and the ground truth we defined and used in these experiments (subset of original crops and ground truth).</p> <p><strong>Data Files</strong></p> <p>The archive contains the following files:</p> <pre><code>data ├── t1_L5.tif ├── t2_L5.tif ├── t2_ALI.tif ├── t2_L8.tif ├── ROI_1.tif ├── ROI_2.tif ├── Cross-sensor-Bastrop-data.mat </code></pre> <p>Where:</p> <ul> <li>`t1_L5.tif` is the pre-event image acquired by Landsat 5 TM</li> <li>`t2_L5.tif` is the post-event image acquired by Landsat 5 TM - `t2_ALI.tif` is the post-event image acquired by EO-1 ALI</li> <li>`t2_L8.tif` is the post-event image acquired by Landsat 8 OLI</li> <li>`ROI_1.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L5.tif`</li> <li>`ROI_2.tif` is the ground truth for the change detection problem between `t1_L5.tif` and `t2_L8.tif`</li> <li>`Cross-sensor-Bastrop-data.mat` is a Matlab file containing the data in a more convenient format for Matlab users</li> </ul> <p><strong>Citation</strong></p> <p>If you are using this dataset, please cite the following paper:</p> <pre><code>@article{volpi2015jisprs, author = {Michele Volpi and Gustau Camps-Valls and Devis Tuia}, title = {Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {107}, pages = {50-63}, year = {2015}, doi = {https://doi.org/10.1016/j.isprsjprs.2015.02.005}, url = {https://www.sciencedirect.com/science/article/pii/S0924271615000404}, issn = {0924-2716}, }</code></pre> <p>Volpi, M., Camps-Valls, G., Tuia, D. (2015). Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 107, 2015, Pages 50-63. https://doi.org/10.1016/j.isprsjprs.2015.02.005</p>
Datasets for examining perceptions of fire resilient landscapes
<p>Datasets used to answer the question 'what is a fire resilient landscape?'. Included is the data used for a literature review from Scopus and Web of Science containing entries surrounding resilience to landscape fires. Also included is survey responses. Participants came from two groups, students of the Pyrogeography course at Wageningen University, and professionals working within the fire community (both academia and practice). The participants were asked about their perception of a fire resilient landscape, and the responses coded with thematic analysis.</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.