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1,989 results for “Fires”
Data from: Spatial transitions in tree cover are associated with soil hydrology, but not with grass biomass, fire frequency, or herbivore biomass in Serengeti savannahs
1. Although there is a well-known association between tree cover and soil texture in savannahs, the hydrological drivers of tree cover variation have not been systematically explored, particularly in parallel with factors such as fire, herbivory, and tree-grass interactions. The relationship between hydrological factors and tree cover is important for resolving the relative contribution of bottom-up vs. top-down factors in structuring savannah vegetation. 2. We quantified soil moisture dynamics across eight 1-km transects spanning tree cover gradients from open to woody savannah in Serengeti National Park in Tanzania using soil moisture sensors coupled with dataloggers. We mapped tree cover at two spatial scales through supervised classification of high-resolution satellite imagery. We simultaneously produced water retention curves in open and woody habitats within each transect to compare soil hydrological properties and to convert volumetric water content (θ) from dataloggers to plant-available water over the course of an annual cycle. We also quantified grass biomass at 100 locations per transect, estimated fire frequency from MODIS satellite data, and quantified herbivore occupancy with paired camera traps situated in open and woody habitats within each transect. 3. We found a positive relationship between tree cover and soil moisture drainage rate, and found that open habitats had more negative water potentials than woody habitats for a given value of θ. In contrast, we found no evidence for a consistent relationship between grass biomass or fire frequency and tree cover. We found evidence for higher browser occupancy in woody than open habitats, but no habitat effects on herbivores as a group (browsers plus grazers), suggesting that herbivory is unlikely to be the dominant factor explaining variation in tree cover. 4. Our results suggest that variation in tree cover is partly driven by hydrological (edaphic) factors unrelated to fire, herbivory, tree-grass interactions or mean annual precipitation at these spatial scales in Serengeti. We contrast our findings with previous work attributing tree cover shifts in Serengeti to precipitation gradients.
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
Data from: Past tree influence and prescribed fire exert strong controls on reassembly of mountain grasslands after tree removal
Woody-plant encroachment represents a global threat to grasslands. Although the causes and consequences of this regime shift have received substantial attention, the processes that constrain reassembly of the grassland state remain poorly understood. We experimentally tested two potentially important controls on reassembly—the past influence of trees and the effects of fire—in conifer-invaded grasslands (mountain meadows) of western Oregon. Previously, we had reconstructed the history of tree invasion at fine spatial and temporal resolution. Using small subplots (10 × 10 m) nested within larger (1-ha) experimental plots, we characterized the fine-scale mosaic of encroachment states, ranging from remnant meadow openings (minimally altered by trees) to century-old forests (lacking meadow species). Subsequently, we removed trees from six plots, of which three were broadcast burned and three remained unburned (except for localized burn piles). Within each plot, subplots were sampled before and periodically after tree removal to quantify the individual and interactive effects of past tree influence and fire on grassland community reassembly. Adjacent, uninvaded meadows served as references sites. 'Past tree influence' was defined as the multivariate (structural or compositional) distance of subplots to reference meadows prior to tree removal. 'Reassembly' was defined as the distance, or change in distance, to reference meadows at final sampling. Consistent with theory, we observed greater reassembly of plant community structure than of composition, as loss of meadow specialists was offset by establishment of disturbance-adapted meadow generalists of similar growth form. Nevertheless, 8 years after tree removal, most subplots remained structurally and compositionally distinct from reference meadows. Furthermore, fire had both destabilizing and inhibitory effects: it reduced survival of meadow specialists across the range of encroachment states and, where past tree influence was greater, it stalled reassembly by promoting expansion of a highly competitive native meadow sedge. The slow pace of reassembly, despite abundant open space, suggests strong seed limitation—a condition exacerbated by burning. We present a novel test of the importance of past tree influence and fire for restoration of tree-invaded grasslands, offering insights into how constraints on community reassembly vary along a continuum of tree-altered states.
Data from: Climate will increasingly determine post-fire tree regeneration success in low-elevation forests, Northern Rockies, USA
Climate change is expected to cause widespread shifts in the distribution and abundance of plant species through direct impacts on mortality, regeneration, and survival. At landscape scales, climate impacts will be strongly mediated by disturbances, such as wildfire, which catalyze shifts in species distributions through widespread mortality and by shaping the post‐disturbance environment. We examined the potential for regional shifts in low‐elevation tree species in response to wildfire and climate warming in low‐elevation, dry mixed‐conifer forests of the northern Rocky Mountains, USA. We analyzed interactions among climate and wildfire on post‐fire tree seedling regeneration 5–13 yr post‐fire at 177 sites burned in 21 large wildfires during two years with widespread regional burning. We used generalized additive mixed models to quantify how the density of Douglas‐fir and ponderosa pine seedlings varied as a function of climate normals (30‐yr mean temperature, precipitation, soil moisture, and evapotranspiration) and fire (tree survivorship, burn severity, and seed source availability). Mean summer temperature was the most important predictor of post‐fire seedling densities for both ponderosa pine and Douglas‐fir. Seed availability was also important in determining Douglas‐fir regeneration. As mean summer temperature continues to increase, however, seed availability will become less important for determining post‐fire regeneration. Above a mean summer temperature of 17°C, Douglas‐fir regeneration is predicted to be minimal regardless of how close a seed source is to a site. The majority (82%) of our sampled sites are predicted to exceed a mean summer temperature of 17°C by mid‐century, suggesting significant declines in seedling densities and potential forest loss. Our results highlight mechanisms linking climate change to shifts in the distribution of two widely dominant tree species in western North America. Under a warming climate, we expect post‐fire tree regeneration in these low‐elevation forests to become increasingly unsuccessful. Such widespread regeneration failures would have important implications for ecosystem processes and forest resilience, particularly as wildfires increase in response to climate warming.
Oval scaraboid intaglio in lapis lazuli; engraved with a fire altar and an inscription in four characters.
<p>Oval scaraboid intaglio in lapis lazuli; engraved with a fire altar and an inscription in four characters. British Museum 1892,1103.116.</p>
Oval intaglio in cornelian engraved with a fire-altar and a brāhmī inscription below.
<p>Oval intaglio in cornelian engraved with a fire-altar and a brāhmī inscription below. British Museum 1892,1103.118.</p>
3D Fire House Museum Exhibit
This is a test scene for a 3D Fire House Museum Exhibit we're playing with in an upcoming grant proposal. Everything you see in this scene is freely downloadable in our Indianapolis Fire Fighters Museum collection, as well as the Digital Indy Content DM site. Indianapolis Fire Fighters Museum 3D Collection: https://skfb.ly/6SzYz Complete Indianapolis Fire Fighters Museum Collection: http://www.digitalindy.org/cdm/landingpage/collection/ffm Source: Objaverse 1.0 / Sketchfab
Fire Extinguisher
This item was 3D scanned using a Creaform Go Scan 50. For more information about this object, feel free to visit: https://www.visitindy.com/indianapolis-firefighters-museum-historical-society For more information about the 3D Digitization Program at IUPUI visit: https://www.ulib.iupui.edu/digitalscholarship/3ddig Source: Objaverse 1.0 / Sketchfab
Gotland Stone Fire Pit - RealityScan
Stone fire pit scanned in Gotland, Sweden. Made with RealityScan (private beta version). Source: Objaverse 1.0 / Sketchfab
German Fire Hydrant 3D Scan
# This is a 3D Scan of a German Fire Hydrant * Scan from ~200 Pictures * Corrected White Balance with a Color Checker * Decimated from a 6 Mio Polygon Mesh * Real World Scaling and Alignmend * Height of the Hydrant is 150cm / 23.622 inch * Texture Resolution 4096 x 4096 px # -- Included -- **Datatype** * FBX * OBJ **PNG Files ** * Basecolor 8-Bit * Roughness 8-Bit * Metallic 8-Bit **TIFF Files** * Basecolor 16-Bit * Roughness 16-Bit * Metallic 16-Bit Did you have any questions, feel free to contact me. Source: Objaverse 1.0 / Sketchfab
Data set of CA1 pyramidal cell recordings using an intact whole hippocampus preparation, including recordings of rebound firing (V2)
<p>The frequency-current (f-I) profiles and an example of rebound firing of pyramidal cells are presented. Four .abf files contain the f-I curve data for the respective cell (as labelled PYR1, PYR2, PYR3 and PYR4 for Pyramidal cell 1, Pyramidal cell 2, Pyramidal cell 3 and Pyramidal cell 4). That is, they contain the cell's response to the application of a series of depolarizing current steps of 1 s duration while the cells are held in current clamp, as well as the current clamp data itself. Each recording is 2 s total. Amplitudes of the input were increased incrementally with step sizes of 10 pA for PYR1, PYR3, and PYR4, and a step size of 25 pA for PYR 2. PYR1 first spikes on the 5th of 30 steps with 38.7 pA of depolarizing input. PYR2 first spikes on the 3rd of 13 steps with 1.2 pA of input. PYR3 first spikes on the 7th of 34 steps with 62.0 pA of input, and PYR4 first spikes on the 7th of 30 steps with 12.1 pA of input. In the .abf file labelled PYR5_rebound, an example of rebound firing of a pyramidal cell following hyperpolarizing input is given. While the cell was held at -52 mV in current clamp, as series of 1 s hyperpolarizing steps (10 steps, 25 pA increments) were used to record the post- hyperpolarization rebound spiking. For the figure showing this rebound spiking (Figure 1), the first two hyperpolarizing steps and the respective firing are shown. For visualization purposes, the spike artifact in the current clamp input trace was removed and replaced with the mean current in the Figure. </p>
iTFM Matlab Code for Improved formulation of travelling fires
<p>This is the iTFM code for calculations in Matlab of gas temperature in improved formulation of travelling fires. It is written by Egle Rackauskaite and Guillermo Rein, Imperial College London, UK, and it is based on the journal paper (doi:10.1016/j.istruc.2015.06.001):</p> <p>E Rackauskaite, C Hamel, A Law, G Rein, <em>Improved formulation of travelling fires and application to concrete and steel structures</em>, <strong>Structures</strong>, 2015. http://dx.doi.org/10.1016/j.istruc.2015.06.001</p> <p>Contact authors at g.rein@imperial.ac.uk and reingu@gmail.com<br /> Work funded by Engineering and Physical Sciences Research Council and Arup<br /> File published under a Creative Commons license CC BY 4.0</p>
Fire-D: Analysis and ML-Ready NASA-Centric Remote Sensing of Wildfire and Smoke
<p>Earth science remote sensing imagery is rich in structural and spectral information, making such data an ideal platform for benchmarking for a broad range of machine learning (ML) tasks, from pattern retrieval to physics-informed classification to anomaly detection to transfer learning. Nevertheless, the utility of Earth science remote sensing data remains largely unexplored by the broader ML community. Our goal is to bridge this gap and bring a rich variety of multisource multi-resolution Earth image data to a wider range of ML researchers who are non-experts in remote sensing, thereby increasing the utility and societal impact of such data products. In particular, motivated by the emerging wildfire crisis, we present radiometrically and geometrically calibrated radiance data from airborne and orbital instruments from the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Korean Meteorological Administration (KMA).</p> <p>Given the scarce occurrence of wildfires and complex spatio-temporal dependencies in radiance data, these datasets are especially well suited for benchmarking unsupervised and self-supervised learning tasks both on images and non-Euclidean objects. Our experiments on these datasets indicate that contrastive learning and transfer learning algorithms can capture the structures of views and scenes, map pixel space of multi-sensor imagery to a high-level embedding space for further downstream tasks, and facilitate more cohesive integration of the state-of-the-art ML approaches into wildfire risk analytics.</p> <p>All NASA-based observations are freely usable under the <a href="https://science.data.nasa.gov/license/">Creative Commons Zero License</a>.There are also no restrictions on the use of <a href="https://registry.opendata.aws/noaa-goes/">GOES Data</a>. <a href="https://registry.opendata.aws/noaa-gk2a-pds/">GK2A data</a> are also open data without any restrictions on its use.<br><br>For the Planet data, we cannot not share the Radiances, but all masks within this dataset are freely usable with no restrictions.</p> <p> </p> <p>Use:</p> <p>On the data input, input geometrically and radiometrically calibrated radiance data has been pulled from various NASA, NOAA, Planet, and KMA archives. For instruments that have multiple different spatial resolutions within their spectral bands (GOES and GK2A), all bands have been resampled to the lowest collective spatial resolution.</p> <p>Geometric and radiometric calibration has been done by the science data processing pipelines of the various missions, and would not need to be done by anyone else looking to curate the same data. Further information for each instrument can be found in each of the publicly available Level-1 algorithm theoretical basis documents (ATBDs)</p> <p>All input and label data have been put in GeoTiff format. Each band is in a separate raster band and each scene is in a separate GeoTiff file. Label files and input files are in separate tar files, labeled respectively, and the file names match for input and labels, with the exception of an additional .fire and .smoke in the respective label filenames and subfolders.<br><br>The <a href="https://www.earthdata.nasa.gov/about/esdis/esco/standards-practices/geotiff">GeoTiff</a> data format natively contains geolocation metadata internally, and can be interfaced with via C/C++/Python <a href="https://gdal.org/en/stable">GDAL</a> packages, or other python packages that wrap GDAL, like <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> and <a href="https://corteva.github.io/rioxarray/stable/">rioxarray</a> . The documentation for <a href="https://nicks-personal-organization-2.gitbook.io/sit-fuse">SIT-FUSE</a> , the package with which the labels were generated, also has examples on how to read and interface with various data formats, including GeoTiffs. Lastly, this data can be interfaced with using Geographic Information Systems (GIS), like the free and open-source <a href="https://qgis.org/">QGIS</a>.</p> <p>An example of programmatic data access and usage can be found in the dataset's associated <a href="https://github.com/Fire-D-Dataset/FIRE-D">GitHub repository</a>. </p> <p>A working example using data from this repository for ML tasks is available <a href="https://drive.google.com/drive/folders/16aJO6LhrxJ3gsWoTU9BNN3hsb8W0refG?usp=sharing">here</a>.</p> <p>Timing information can be found in the file names, which all use the standard formats from the various instruments' L1B datasets.</p> <p>V2 includes additional GOES-18 radiance data and associated smoke and fire labels for the recent LA fires (Palisades and Eaton fires in January of 2025).</p> <p>V3 provides a reorganization of all data, and an inclusion of improved and additional data from airborne and satellite platforms in 2019, associated with this study: https://arxiv.org/pdf/2501.15343 . </p> <p>V4 provides additional AVIRIS-C Radiances and fixes the spatial range of the GOES-17 radiances to match that of the associated labels. The AVIRIS-C radiances are split across 5 tar files, ordered temporally - all associated labels are in a single tar file.</p> <p><br>Current fire coverage includes:</p> <ul> <li>2019: Williams Flats, Sheridan, Horsefly, and Mosquito (US)</li> <li>2022: Uljin Forest Fire (S. Korea; largest fire on record in S. Korea)</li> <li>2025: Palisades and Eaton Fires (US)</li> </ul> <p>Additional data for the 2025 Palisades and Eaton fires from the TEMPO instrument is currently being validated and will be released in a V4 shortly.</p> <p>Croissant file for dataset metadata specification is also included</p> <p>Validation:</p> <p>These labels have been extensively validated and further information can be referenced in associated publications:<br><a href="https://doi.org/10.3390/rs13122364">https://doi.org/10.3390/rs13122364</a><br><a href="https://doi.org/10.3390/rs17071267">https://doi.org/10.3390/rs17071267</a></p> <p> </p> <p> </p>
GFAS4HTAP vegetation fire emissions 2003-2023
<h1>Overview</h1> <p>This dataset contains emission flux from wildfires for various species and combustion rate. The data based on daily dry matter burnt estimates (DM) from CAMS GFASv1.2, downloaded at https://ads.atmosphere.copernicus.eu/datasets/cams-global-fire-emissions-gfas. Subsequently, an updated spurius signal mask is applied and emissions are calculated with a new land cover map derived from ESA CCI and PEATMAP for 2018, and emission factors from NEIVAv1.1 (<a href="https://gmd.copernicus.org/articles/17/7679/2024/">https://gmd.copernicus.org/articles/17/7679/2024/</a>) and further literature.</p> <p>Each archive contains a folder with the daily emissions for one species in the *_daily.nc file. Other NetCDF files with approximative fields at monthly, annual and 21-year resolution and plots have been added for illustration.</p> <p>The archives of the injection height parameters MAMI and APT contain the Mean Altitude of Maximal Injection and Altitude of Plume Top, respectively. They have been downloade from CAMS GFAS and converted to the standard date format of this reository. A detailed description is available in Remy et al. (2017) at <a href="https://acp.copernicus.org/articles/17/2921/2017/">https://acp.copernicus.org/articles/17/2921/2017/</a>.</p> <p>The data is available in netCDF4 format, where the data for each species is contained in individual files.<br>The dataset contains daily data from 01/01/2003 to 31/12/2023 on a regular lat-lon grid with 0.1deg resolution.<br>The date of the time coordinate identifies the validity period. For example for daily data, "2003-01-01 00:00:00" denotes emissions during 00:00:00-23:59:59 UTC of the first of January 2003.<br>All emission data is in [kg m**-2 s**-1].</p> <p>The CO2 data is the instantaneous emission of CO2 from wildfires. On a longer timescale CO2 will increase due to oxidation of, primarily, CO and CH4.</p> <p>C, PM2.5 and TPC, and only these, constitute a double-counting with other included species.</p> <p>Due to the data volume restriction of Zenodo, "toxic" emissions are provided in this sister repository: <a href="https://doi.org/10.5281/zenodo.15721938">10.5281/zenodo.15721938</a></p> <p>A paper explaining the methods and data used in the creation of the dataset is being worked on. Until it has been published, please cite <a href="https://gmd.copernicus.org/articles/18/3265/2025/">https://gmd.copernicus.org/articles/18/3265/2025/</a> when using GFAS4HTAP.</p> <h1>Calculating emissions locally</h1> <p>The G4H archive contains software and static data (emission factor table and land cover mask), with which users can calculate emission consistently with GFAS4HTAP from any dry matter burnt field: Install and activate the conda environment env_g4h.yml, adapt the configuration section in the main() routine of the emissions.py file and run the script.</p> <h1>Q&As</h1> <h3>Q1: Are emissions in beta and v2 for their common periods/species the same?</h3> <p>No, all emissions have changed: All are shifted by one day (fixing a "feature" of the CAMS ADS netCDF conversion) and the emmission factor for CO in savannah has been updated. Use of the beta version is discouraged. If bandwidth is an issue consider calculating the emissions locally. Additionally, the metadata in the NetCDF files has been completed.</p> <h3>Q2: Should VOCs not explicitly treated in the used model chemistry be ignored or lumped with other species to preserve the total mass? Is NMOC_g the total VOC mass?</h3> <p>The NEVIA database includes measurements of a lot of gaseous emissions of larger organic molecules, which have not been represented in emissions estimates or chemical mechanisms in the past. These are reported in the database as NMOC_g. Thus, NMOC_g is the mass of gaseous non methane organic carbon that is NOT included in the mass of other individual or lumped species. It is what is left over in the unspeciated bin after individual species have been accounted for. It can be very large, around half of the organic mass. In other words, total gaseous non-methane organic carbon = sum of all individual VOC species provided + sum of lumped VOC species provided + NMOC_g</p> <p>So, what do you do with NMOC_g or other explicit species that are not in your mechanism when preparing emissions inputs? … It depends on the mechanism that you are putting it into. A reasonable default approach may be to represent as much of the mass of explicit species provided in GFAS4HTAP as makes sense for the proxy/lumping scheme in your mechanism. There is little understanding of how to represent NMOC_g and assigning its mass to other species in the mechanism may well create too much hydrocarbon reactivity. So, a reasonable default approach may be to ignore NMOC_g. </p> <p>Different modelers are going to make different choices, and it will be useful for each model to provide their emissions inputs (total VOC and if possible speciated VOC) along with the outputs for comparison.</p> <h3>Q3: The daily file has emission rate in kg/m2/s – Is this a flat rate for the day (GMT)?</h3> <p>Yes, this is correct.</p> <h3>Q4: Where is the vegetation map?</h3> <p>It is part of the package for calculating emissions locally, i.e. in the file G4H.tgz.</p> <h3>Q5: Why did the Zenodo link change?</h3> <p>Zenodo provides one link/DOI for all version of a repository, which ends with "1". Additionally, each version has its own link/DOI, counting up in the last digit.</p> <h3>Q6: How do I get total particulate matter (TPM)?</h3> <p>The GFAS4HTAP emissions are based on NEIVA EFs and GFED5 speciation. Most PM measurements are now operationally defined, e.g., based on inlet cutoffs, so TPM is rarely reported. However, if TPM or PM10 are needed, it is recommended take the provided PM2.5 emissions and multiply them by 1.2 (inflate by 20%).</p> <h3>Q7: Which enhancement factor should be used for aerosol/PM emissions?</h3> <p>It is recommended to tune PM/aerosol emissions to each model setup with (at least) one universal scaling/enhancement parameter. As reference, atmospheric observations targeted by the model can be used. If no such reference is available, the total atmospheric load from an aerosol observation-constrained (re)analysis, e.g. from CAMS, might be use4d as reference. The underlying reason for the need to tune is that the fast aerosol chemistry in the smoke plumes near fires are represented to different degrees in different models and model configurations.</p> <h1>List of included species</h1> <table><colgroup><col><col></colgroup> <tbody> <tr> <td><strong>species</strong></td> <td><strong>long_name</strong></td> </tr> <tr> <td>C</td> <td>carbon combustion (C in CO2, CO, CH4, TPC)</td> </tr> <tr> <td>CO2</td> <td>carbon dioxide</td> </tr> <tr> <td>CO</td> <td>carbon monoxide</td> </tr> <tr> <td>CH4</td> <td>methane</td> </tr> <tr> <td>NMOC_g</td> <td>gaseous non-methane organic compounds not included otherwise</td> </tr> <tr> <td>H2</td> <td>hydrogen</td> </tr> <tr> <td>NOx</td> <td>nitrogen oxides(NOx as NO)</td> </tr> <tr> <td>N2O</td> <td>nitrous oxide</td> </tr> <tr> <td>PM2p5</td> <td>PM 2.5 (particulate matter <2.5u)</td> </tr> <tr> <td>TPC</td> <td>total particulate carbon (OC+BC)</td> </tr> <tr> <td>OC</td> <td>organic carbon (carbon in organic matter)</td> </tr> <tr> <td>BC</td> <td>black carbon</td> </tr> <tr> <td>SO2</td> <td>sulfur dioxide</td> </tr> <tr> <td>C2H6</td> <td>ethane</td> </tr> <tr> <td>CH3OH</td> <td>methanol</td> </tr> <tr> <td>C2H5OH</td> <td>ethanol</td> </tr> <tr> <td>C3H8</td> <td>propane</td> </tr> <tr> <td>C2H2</td> <td>acetylene</td> </tr> <tr> <td>C2H4</td> <td>ethylene</td> </tr> <tr> <td>C3H6</td> <td>propylene</td> </tr> <tr> <td>C5H8</td> <td>isoprene</td> </tr> <tr> <td>C10H16</td> <td>terpenes</td> </tr> <tr> <td>C7H8</td> <td>toluene</td> </tr> <tr> <td>C6H6</td> <td>benzene</td> </tr> <tr> <td>C8H10</td> <td>xylene</td> </tr> <tr> <td>Higher_Alkenes</td> <td>C4H8 + c5H10 + C6H12 + C8H16 (1 butene + i butene + tr-2-butene + cis-2-butene + 1 pentene + 2 pentene + hexene + octene)</td> </tr> <tr> <td>Higher_Alkanes</td> <td>C4H10 + C5H12 + C6H14 + C7H16 (n-butane + i-butane + n-pentane + i-pentane(me-butane) + n-hexane + i-hexane + Heptane)</td> </tr> <tr> <td>CH2O</td> <td>formaldehyde</td> </tr> <tr> <td>C2H4O</td> <td>acetaldehyde</td> </tr> <tr> <td>C3H6O</td> <td>acetone</td> </tr> <tr> <td>NH3</td> <td>ammonia</td> </tr> <tr> <td>C2H6S</td> <td>dimethyl sulfide (DMS)</td> </tr> <tr> <td>HCN</td> <td>hydrogen cyanide</td> </tr> <tr> <td>HCOOH</td> <td>formic acid</td> </tr> <tr> <td>CH3COOH</td> <td>acetic acid</td> </tr> <tr> <td>MEK</td> <td>methyl Ethyl Ketone / 2-butanone</td> </tr> <tr> <td>CH3COCHO</td> <td>methylglyoxal</td> </tr> <tr> <td>HOCH2CHO</td> <td>hydroxyacetaldehyde</td> </tr> <tr> <td>PCDD2378</td> <td>2,3,7,8-TeCDD</td> </tr> <tr> <td>PCDD12378</td> <td>1,2,3,7,8-PeCDD</td> </tr> <tr> <td>PCDD123478</td> <td>1,2,3,4,7,8-HxCDD</td> </tr> <tr> <td>PCDD123678</td> <td>1,2,3,6,7,8-HxCDD</td> </tr> <tr> <td>PCDD123789</td> <td>1,2,3,7,8,9-HxCDD</td> </tr> <tr> <td>PCDD1234678</td> <td>1,2,3,4,6,7,8-HpCDD</td> </tr> <tr> <td>OCDD</td> <td>OctaCDD</td> </tr> <tr> <td>PCDF2378</td> <td>2,3,7,8-TeCDF</td> </tr> <tr> <td>PCDF12378</td> <td>1,2,3,7,8-PeCDF</td> </tr> <tr> <td>PCDF23478</td> <td>2,3,4,7,8-PeCDF</td> </tr> <tr> <td>PCDF123478</td> <td>1,2,3,4,7,8-HxCDF</td> </tr> <tr> <td>PCDF123678</td> <td>1,2,3,6,7,8-HxCDF</td> </tr> <tr> <td>PCDF123789</td> <td>1,2,3,7,8,9-HxCDF</td> </tr> <tr> <td>PCDF234678</td> <td>2,3,4,6,7,8-HxCDF</td> </tr> <tr> <td>PCDF1234678</td> <td>1,2,3,4,6,7,8-HpCDF</td> </tr> <tr> <td>PCDF1234789</td> <td>1,2,3,4,7,8,9-HpCDF</td> </tr> <tr> <td>OCDF</td> <td>OctaCDF</td> </tr> <tr> <td>NAP</td> <td>Naphthalene</td> </tr> <tr> <td>ACY</td> <td>Acenaphthylene</td> </tr> <tr> <td>ACE</td> <td>Acenaphthene</td> </tr> <tr> <td>FLO</td> <td>Fluorene</td> </tr> <tr> <td>PHE</td> <td>Phenanthrene</td> </tr> <tr> <td>ANT</td> <td>Anthracene</td> </tr> <tr> <td>FLA</td> <td>Fluoranthene</td> </tr> <tr> <td>PYR</td> <td>Pyrene</td> </tr> <tr> <td>BaA</td> <td>Benz(a)anthracene</td> </tr> <tr> <td>CHR</td> <td>Chrysene</td> </tr> <tr> <td>BbF</td> <td>Benzo(b)fluoranthene</td> </tr> <tr> <td>BkF</td> <td>Benzo(k)fluoranthene</td> </tr> <tr> <td>BaP</td> <td>Benzo(a)pyrene</td> </tr> <tr> <td>IcdP</td> <td>Indeno(1,2,3-cd)pyrene</td> </tr> <tr> <td>DahA</td> <td>Dibenz(a,h)anthracene</td> </tr> <tr> <td>BghiP</td> <td>Benzo(g,h,i)perylene</td> </tr> <tr> <td>Hg</td> <td>Mercury as Hg0+HgP</td> </tr> </tbody> </table> <h1>List of other parameters</h1> <table><colgroup><col><col></colgroup> <tbody> <tr> <td><strong>short name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>MAMI</td> <td>Mean Altitude of Maximal Injection</td> </tr> <tr> <td>APT</td> <td>Altitude of Plume Top</td> </tr> <tr> <td>G4H</td> <td>software for calculating enissions locally, including emission factor table and land cover mask</td> </tr> </tbody> </table>
The Data For Inside-Out versus Upside-Down: The Origin and Evolution of Metallicity Radial Gradients in FIRE Simulations of Milky Way-mass Galaxies and the Essential Role of Gas Mixing
<p>The files titled Graf et al. 2024b store the x-axis and y-axis values for each line in each figure. The files which end in .py are the scripts which produced the data in the figures.</p> <p>This data abides by CC-BY.</p>
Fire Events with Water Balance Shift for Western US during 2014-2020
<p>This dataset comprises fire events in the Western US during 2014-2020, with respective fire characteristics, centroid locations, and variables necessary to calculate water balance shift during pre- and post-fire years, as described in the manuscript (Ahmad et al., 2023)</p>
Data from: Fox control and fire influence the occurrence of invasive predators and threatened native prey
<p>It can be challenging to distinguish management impacts from other population drivers, including 'natural' processes and co-occurring threats. However, disentangling processes is important, particularly when management may have unintended consequences, such as mesopredator release. We explored the effects of long-term, broadscale poison-baiting programs on the distribution of red foxes <em>Vulpes vulpes</em> (targeted invasive predator), feral cats <em>Felis catus</em> (unmanaged invasive competitor) and two of their threatened native prey in two fire-affected regions of south-eastern Australia. We synthesised data from 3,667 camera-trap deployments at 1,232 sites (172,052 trap-nights), combining experimental manipulation of foxes and fire with space-for-time approaches. Fox control effectiveness––in terms of decreased probability of fox occurrence and increased probability of prey occurrence––depended on the duration and intensity of the poison-baiting program. The effects of fox control on prey occurrence also varied between the two native prey species: fox control was strongly beneficial to the long-nosed potoroo <em>Potorous tridactylus</em> but had no measurable effect on southern brown bandicoot <em>Isoodon obesulus</em> occurrence. Feral cat occupancy tended to be higher in landscapes with long-term fox control, although we found no effect of fox-bait density on fine-scale cat occurrence. Time since fire (0–80 years) was associated with the occurrence of each study species, but its association with invasive predators also differed among vegetation types. Invasive predators and altered fire regimes are key, often overlapping, biodiversity threats. Our work highlights the importance of fine-scale monitoring and consideration of multiple drivers in distribution models to develop effective, tailored conservation strategies.</p>
After the 'Black Summer' fires: faunal responses to megafire depend on fire severity, proportional area burnt, and vegetation type
<ol> <li>Climate change and human activities have disrupted historical fire regimes, leading to complex and far-reaching impacts on global ecosystems. Despite extensive research in fire ecology, studies exploring vertebrate responses to megafires, and to nuanced fire characteristics, remain limited.</li> <li>We collected camera trap data 3–27 months following Australia's 2019–20 'Black Summer' megafires from 30 burnt sites and 10 unburnt sites. Our data included 14 animal species/groups, encompassing mammalian predators, small and medium-sized mammals, large herbivores, and birds. We used generalised additive mixed models to assess the influence of time-since-the-fires, burn status, fire severity, proportional area burnt, and vegetation type on species' activity.</li> <li>Models that included fire variables were well-supported for all species. The proportional cover of low-moderate or high-extreme severity fire had substantial support for five species, particularly herbivores, which generally showed a preference for burnt sites but at differing fire severities. The proportional area burnt, disregarding severity, was well supported for four species. At highly burned sites, fox activity peaked shortly after the fires, while small to medium-sized mammal activity increased more gradually. Vegetation type strongly influenced the response of four species to fire; in particular, wet forest birds preferred unburnt areas.</li> <li> <em>Policy implications</em>. We document variable short- to medium-term responses of a range of species to fire which could help guide management interventions. We demonstrate that animal species' responses to fire are diverse and better captured using broader landscape-scale fire variables. We found that species were strongly influenced by proportional area burnt, fire severity, and vegetation type. Introduced foxes were attracted to recently burnt areas, so timely predator control may benefit vulnerable prey species. Wet forest species were sensitive to fires and could benefit from preservation and restoration of these habitats. Some species exploited low-moderate severity burnt areas, while others preferred high-severity burns. This suggests that species will face diverse challenges and opportunities in future extreme fire events. We emphasise the importance of using multi-faceted approaches to account for the complex responses of co-occurring species to fire events.</li> </ol>
The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976-2100
<p>There are 5 files uploaded.</p><p> </p><p>(1) fwi26.txt.gz, RCP26</p><p>(2) fwi45.txt.gz, RCP45</p><p>(3) fwi85.txt.gz, RCP85</p><p>(4) msk20231028.txt</p><p>(5) Shape_Canadian_Boreal_Forests.7z</p><p> </p><p>mask20231028.txt is an ascii file covering the Canadian Boreal Forests.</p><p>This file can be imported into GisMAP to generate a mask raster.</p><p> </p><p>Shape_Canadian_Boreal_Forests.7z is a compressed shape file cover the</p><p>Canadian Boreal Forests.</p><p> </p><p>fwi26.txt.gz, fwi45.txt.gz, and fwi85.txt.gz are the zipped file of FFMC</p><p>and DSR daily surfaces. There are 6 fields in the files:</p><p> </p><p>Field 1: row number in the mask file of mask20231028.txt.</p><p>Field 2: column number.</p><p>Field 3: Years; 1, 2, 3, ... 95 correspoind to 2006, 2007, 2008, ..., 2100.</p><p>Field 4: Days; 1, 2, 3, ..., 365 in a year.</p><p>Field 5: Daily FFMC values.</p><p>Field 6: Daily DSR values. </p>
Daily Fire Weather Index dataset over India - Current (2006-2015) and End Century (2091-2100)
<p>This is a gridded high-resolution fire weather index (FWI) dataset over India. This dataset is at 10km spatial and daily temporal resolution for two ten-year time slices i.e. Current (2006-2015) and Endcentury (2091-2100). FWI is calculated using the Canadian CFFDRS -FWI package implemented in MATLAB software (https://zenodo.org/records/10047237). The meteorological input to the system is taken from the 10km gridded bias-corrected and dynamically downscaled DSCESM dataset (https://www.wdc-climate.de/ui/entry?acronym=WRF10km_wbc_C5<i>forcoIndia). </i>The current file is named fwi-c-daily and the end-century file is named fwi-f-daily. The datasets are in MATLAB .mat format which is easily convertible in NetCDF format.</p>
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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.