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Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Burn severity data from: The heterogeneity of burn severity affects bird density in an abandoned mountain landscape of the Atlantic-Mediterranean transition
<p><strong>[Abstract]</strong></p> <p>Fire regimes in mountain landscapes of southern Europe have been shifting from their baselines due to the accumulation of fuel fostered by long-standing rural abandonment and fire exclusion policies. Understanding the role of fire on biodiversity is paramount to implement adequate management to mitigate the impacts of altered fire regimes and land abandonment on biodiversity. Here, we explored to what extent the spatiotemporal variation in burn severity has affected bird abundance of a mountain abandoned landscape located in the Atlantic-Mediterranean transition (NW Iberia). We took advantage of: (1) satellite images of Sentinel 2 and Landsat missions to compute burn severity indicators from 2010 to 2020, and (2) standardized bird surveys carried out over 206 point-counts along the breeding season of 2021. Bird abundance models were built from burn severity metrics together with well-known fire regime attributes (% of burnt area and time since fire). Our results showed that the spatiotemporal variation of burn severity significantly correlated with the abundance of the 39% of the modeled species, supporting the role of pyro-diversity in driving bird populations in our region. The burnt area also explained abundance patterns for 28% of species. Time since fire only correlated with the abundance of 3 species. Our findings confirm the importance of incorporating burn severity indicators into the toolkit of decision makers to anticipate the response of birds to fire management.</p> <p><strong>[Dataset Description]</strong></p> <p>For each year between 2010 and 2020, we used a pair of satellite images, one before (April - July) and one after (September - November) the fire season. In order to use the best available information, we selected different satellites along the study period: for years 2010 and 2011, we used Landsat 5 imagery, whereas for 2012 we used Landsat 7, since Landsat 5 imagery was not useful due to high cloudiness. Since its launch in 2013, we shifted to Landsat 8 data, and finally to Sentinel 2 data from 2015 onwards. For each of these images we calculated the Normalized Burn Ratio (NBR), which is the normalized ratio between near infrared (NIR) and short wave infrared (SWIR) radiation (Eq. 1). NIR and SWIR bands of satellite sensors respond in opposite ways to burned vegetation, allowing to identify burned areas.</p> <p>NBR =(NIR - SWIR) / (NIR +SWIR) (1)</p> <p>To obtain a quantitative measure of change for each year, we calculated the dNBR by subtracting the NBR of the post-fire season image from the NBR of the pre-fire season image (Eq. 2). Finally, dNBR values were used as an estimate for fire severity.</p> <p>dNBR = NBRprefire- NBRpostfire (2)</p>
Validation of Sepsis-3 using survival analysis and clinical evaluation of quick SOFA, SIRS, and burn-specific SIRS for sepsis in burn patients with suspected infection
<p><span><strong>Purpose</strong>: Sepsis-3 is a life-threatening organ dysfunction caused by dysregulated host responses to infection; and defined using the Sepsis-3 criteria, introduced in 2016, however, the criteria need to be validated in specific clinical fields. We investigated mortality prediction and compared the diagnostic performance of quick Sequential Organ Failure Assessment (qSOFA), systemic inflammatory response syndrome (SIRS), and burn-specific SIRS (bSIRS) in burn patients.</span></p> <p><span><strong>Methods</strong>: This single-center retrospective cohort study examined burn patients in Seoul, Korea during January 2010–December 2020. Overall, 1,391 patients with suspected infection were divided into four sepsis groups using SOFA, qSOFA, SIRS, and burn-specific SIRS. </span></p> <p><span><strong>Results</strong>: Hazard ratios (HRs) of all unadjusted models were statistically significant; however, the HR (0.726, p = 0.0080.001) in the SIRS ≥2 group is below 1. In the adjusted model, HRs of the SOFA ≥2 (2.426, p < 0.001), qSOFA ≥2 (7.198, p < 0.001), and SIRS ≥2 (0.575, p < 0.001) groups were significant. The diagnostic performance of dichotomized qSOFA, SIRS, and bSIRS for sepsis was defined by the Sepsis-3 criteria. The mean onset day was 4.13±2.97 according to Sepsis-3. The sensitivity of SIRS (0.989, 95% confidence interval [CI]: 0.982–0.994) was higher than that of qSOFA (0.841, 95% CI: 0.819–0.861) and bSIRS (0.803, 95% CI: 0.779–0.825). Specificities of qSOFA (0.929, 95% CI: 0.876–0.964) and bSIRS (0.922, 95% CI: 0.868–0.959) were higher than those of SIRS (0.461, 95% CI: 0.381–0.543).</span></p> <p><span><strong>Conclusion</strong>: Sepsis-3 is a good alternative diagnostic tool because it reflects sepsis severity without delaying diagnosis. SIRS showed higher sensitivity than qSOFA and bSIRS and may therefore more adequately diagnose sepsis.</span></p>
Data for: Sierra Nevada mixed-conifer regeneration response to repeated burning varies by species
<p>Fire-exclusion has acted as a major perturbation on dry conifer forests in the western U.S., increasing tree density and, in mixed-conifer forests, the dominance of shade-tolerant species. Restoration efforts aim to reverse these effects by reducing stand density, restoring relative proportions of tree species, and reintroducing recurrent fire, but there are limited long-term data on the effects of repeated burning on tree regeneration. We analyzed two decades of seedling and overstory data from the Teakettle Experimental Forest in the southern Sierra Nevada, California, USA to determine how thinning and repeated burning affect seedling establishment and overstory recruitment. The treatments included three levels of thinning (no thin, understory thin, overstory thin) crossed with two levels of burning (burn, no burn). Treatments were replicated three times in 4 ha treatment units. Seedlings were surveyed at gridpoints within the treatment units for the pre-treatment period (1999-2000), following the first-entry burn (2004-2006), at 10 years post-treatment (2011-2012), and before (2016-2017) and after (2018-2020) the second-entry burn. We analyzed seedling response using mixed models. Across treatments, pine seedling densities remained much lower than shade-tolerant seedling densities. We found that repeated burns led to modest increases in sugar pine (<em>Pinus lambertiana</em>) and substantial increases in incense-cedar (<em>Calocedrus decurrens</em>) seedling densities four years post-burn. No significant differences in seedling densities among repeated burning treatments were detected for Jeffrey pine (<em>P. jeffreyi</em>) or white fir (<em>Abies concolor</em>). We used overstory stem map data and seedling data to estimate recruitment into the mid-story and found that estimates of natural mid-story recruitment were much higher among white fir and incense-cedar than pines, even following treatments. </p>
Biophysic and socioeconomic drivers of burned area and carbon emissions from fires in the Pantropical tropical dry forests
<p><span>The global burned area declined by nearly one-quarter between 1998 and 2015. Drylands contain a large proportion of these global fires but there are important differences within the drylands, e.g., savannas and tropical dry forests (TDF). Savannas, a biome fire-prone and fire-adapted, have reduced the burned area, while the fire in the TDF is one of the most critical factors impacting biodiversity and carbon emissions. Moreover, under climate change scenarios TDF is expected to increase its current extent and raise the risk of fires. Despite regional and global scale effects, and the influence of this ecosystem on the global carbon cycle, little effort has been dedicated to studying the influence of climate (seasonality and extreme events) and socioeconomic conditions of fire regimen in TDF. Here we use the Global Fire Emissions Database and, climate and socioeconomic metrics to better understand long-term factors explaining the variation in burned area and biomass in TDF at the Pantropical scale. On average, fires affected 1.4% of the total TDF' area (60,208 km<sup>2</sup>) and burned 24.4% (259.6 Tg) of the global burned biomass annually at Pantropical scales. Climate modulators largely influence local and regional fire regimes. Inter-annual variation in fire regime is shaped by El Niño and La Niña. During El Niño and the forthcoming year of La Niña, there is an increment in extension (35.2 and 10.3%) and carbon emissions (42.9 and 10.6%). Socioeconomic indicators such as land management and population were modulators of the size of both, burned area and carbon emissions. Moreover, fires may reduce the capability to reach the target of "half protected species" in the globe, i.e., high-severity fires are recorded in ecoregions classified as nature could reach half protected. These observations may contribute to improving fire management.</span></p>
Development of volatility distributions for organic matter in biomass burning emissions
<p>We present a novel filter-in-tube sorbent tube method to collect S/I-VOC samples from a range of biomass burning experiments and find that volatility distributions are relatively consistent with prior findings and across the tested combustion types.</p>
A method for creating a burn severity atlas: an example from Alberta, Canada
<p>Relativized Burn Ratio (RBR) grids derived from Landsat imagery for large fires in Alberta and adjacent national parks (1985 - 2018).</p>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
Dataset: Proposed Origin of the Burns Formation (Meridiani Planum, Mars) by Erosion, Reworking, and Diagenetic Alteration of a Grasberg-like Precursor
<p>Dataset for the study entitled "Proposed Origin of the Burns Formation (Meridiani Planum, Mars) by Erosion, Reworking, and Diagenetic Alteration of a Grasberg-like Precursor" submitted to JGR-Planets. Contents include: (1) a compilation of chemical compositions for the Burns, Grasberg, and Murray formations, basaltic rocks, and soils measured by rovers on Mars; (2) geochemical mass-balance models for diagenetic addition of MgO and SO3 to the Burns formation; (3) diagrams and additonal calculations based on the compiled set of chemical compositions.</p>
Is green the new black? Black-backed Woodpecker vital rates do not differ between unburned and burned forests within a pyrodiverse landscape
<p>Woodpeckers can reflect rapid changes to forest health and often serve as indicator species to help guide forest management decisions. The Black-backed Woodpecker (<em>Picoides</em> <em>arcticus</em>) is known for its strong association with recently burned forests and is a species of conservation concern due to habitat loss stemming from post-fire management of burned forest. Recently, several studies have found the Black-backed Woodpecker occupying extensive areas of unburned (i.e., green) forests in the western part of its range during the breeding season, raising questions about whether green forests can support viable nesting populations in this region. We studied breeding Black-backed Woodpeckers in southern Oregon, USA to evaluate whether two vital rates critical to population recruitment – nest survival and post-fledging survival – differed between green and burned forests. During 2018, 2019, and 2021, we monitored 91 Black-backed Woodpecker nests (<em>n</em> = 34 in green forest, <em>n</em> = 57 in burned forest) and found that neither daily nest survival rate nor reproductive output (i.e., the number of fledglings per successful nest) differed between nests located in green and burned forest; however, nestling body condition was slightly enhanced in green forest. We also quantified survival of recently fledged individuals using VHF radio telemetry and found that the survival rate of birds in green forest was nearly identical to those in burned forest, with most mortalities occurring within 4 weeks of fledging. Taken together, our results indicate that Black-backed Woodpeckers in green forests were equally successful at breeding as conspecifics in recently burned forest, although nesting densities in the green forest we studied were lower than those in burned forest. Our findings indicate certain types of green forest, particularly mature lodgepole pine, can support viable populations of the Black-backed Woodpecker in the western portion of its range. This finding has conservation implications given that green forest occupies the majority of the forested landscape in this region and is often juxtaposed to areas subjected to high-severity fire. Therefore, practices that promote pyrodiversity – landscape-level spatial and temporal variability in fire effects – as well as connectivity between green and burned forest within fire-prone landscapes are likely to provide the greatest conservation benefit for this species. </p>
Data from: Resolving a heated debate: the utility of prescribed burning as a management tool for biodiversity on lowland heath
<p>We investigated the impact of prescribed burning and vegetation cutting on a chronisequence of heathland sites (107 in total) in the New Forest National Park, Hampshire, UK. We present data for vegetation cover, canopy active invertebrates collected with sweep nets and ground active invertebrates collected using pitfall traps.</p> <p>Using a multi-trophic approach, we compared the ecological impact of prescribed burning with two types of vegetation cutting (swiping and baling) as management tools for biodiversity outcomes for up to 20 years after management. Indicators included: Common Standards Monitoring assessment (CSM); vegetation species assemblage; invertebrate biodiversity; and available food resources for two characteristic heathland birds – the Dartford Warbler <em>Sylvia</em> <em>undata</em> and the Nightjar <em>Caprimulgus</em> <em>europaeus</em>. When compared with swiped sites, areas managed by prescribed burning resulted in: better habitat condition (assessed by CSM); higher cover of heathers; lower bracken cover; more areas of bare ground. We found no evidence that burning is detrimental for the investigated components of biodiversity. Cutting by swiping did not replicate the benefits of burning. Swiping supported grassland conditions that suit non-heathland species. Baling resulted in habitat conditions similar to prescribed burning but restricted replication of baled sites limited our conclusions. However, swiped sites supported high invertebrate abundance and diversity, including food resources for Dartford Warbler and Nightjar.</p>
Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations
<p><strong>Aim</strong>: A unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM<sub>2.5</sub>). PM<sub>2.5</sub> concentrations can display strong seasonal variation originating from natural and anthropogenic processes. How these processes affect seasonal associations with ALAN and PM<sub>2.5</sub> for nocturnally migrating birds has not been documented.</p> <p><strong>Location</strong>: Western Hemisphere</p> <p><strong>Time</strong> <strong>period</strong>: 2021</p> <p><strong>Major taxa studied</strong>: Nocturnally migrating passerine (NMP) bird species</p> <p><strong>Methods</strong>: We combined monthly estimates of PM<sub>2.5</sub> and ALAN with weekly estimates of relative abundance for 164 NMP species within the Western Hemisphere derived using bird observations from eBird. We identify groups of species with shared associations with PM<sub>2.5</sub>.</p> <p><strong>Results</strong>: PM<sub>2.5</sub> was lowest in North America, especially at higher latitudes during the boreal winter. PM<sub>2.5</sub> was highest in the Amazon Basin, especially during the dry season (August-October). ALAN was highest within eastern North America, especially during the boreal winter. For the NMP species, PM<sub>2.5</sub> associations reached their lowest levels during the breeding season (<10 μg/m<sup>3</sup>) and highest levels during the nonbreeding season, especially for species that winter in Central and South America (~20 μg/m<sup>3</sup>). Species that migrate through Central America in the spring encountered similarly high PM<sub>2.5 </sub>concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm<sup>2</sup>/sr) or spend the nonbreeding season (~15 nW/cm<sup>2</sup>/sr) in eastern North America.</p> <p><strong>Main conclusions</strong>: We did not find evidence that the disorienting influence of ALAN enhances PM<sub>2.5</sub> exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM<sub>2.5</sub> concentrations for an extended period of their annual life cycles. </p>
30m resolution global forest burned area products, 2014-2021
<p>Global forest burned area data produced based on the high-precision global burned area product GABAM.The product was projected in a Geographic (Lat/Long) projection at 0.00025° (approximately 30 meters) resolution, with the WGS84 horizontal datum and the EGM96 vertical datum, consisting of 10° x 10° tiles spanning the range 180W–180E and 80N–60S.</p><p>contacts: zhangzhaoming@aircas.ac.cn / zhangzm@radi.ac.cn</p>
Gulf War Burned Nintendo Gameboy
 Lots of history to this one! Sanned at the nintendo world store in NYC with polycam Source: Objaverse 1.0 / Sketchfab
Wood burning stove
low poly wood stove / aga with internal chambers and ducts. Made for game Lina Belina. In this scene 6 year old Lila wants to cook some baby food from milk and biscuits but needs to light the stove first. She needs to be careful not to fill the kitchen with smoke or drop the pan in the fire. Source: Objaverse 1.0 / Sketchfab
FireSR: A Dataset for Super-Resolution and Segmentation of Burned Areas
<p><br># FireSR Dataset</p> <p>## Overview</p> <p>**FireSR** is a dataset designed for the super-resolution and segmentation of wildfire-burned areas. It includes data for all wildfire events in Canada from 2017 to 2023 that exceed 2000 hectares in size, as reported by the National Burned Area Composite (NBAC). The dataset aims to support high-resolution daily monitoring and improve wildfire management using machine learning techniques.</p> <p>## Dataset Structure</p> <p>The dataset is organized into several directories, each containing data relevant to different aspects of wildfire monitoring:</p> <p>- **S2**: Contains Sentinel-2 images.<br> - **pre**: Pre-fire Sentinel-2 images (high resolution).<br> - **post**: Post-fire Sentinel-2 images (high resolution).</p> <p>- **mask**: Contains NBAC polygons, which serve as ground truth masks for the burned areas.<br> - **pre**: Burned area labels from the year before the fire, using the same spatial bounds as the fire events of the current year.<br> - **post**: Burned area labels corresponding to post-fire conditions.</p> <p>- **MODIS**: Contains post-fire MODIS images (lower resolution).</p> <p>- **LULC**: Contains land use/land cover data from ESRI Sentinel-2 10-Meter Land Use/Land Cover (2017-2023).</p> <p>- **Daymet**: Contains weather data from Daymet V4: Daily Surface Weather and Climatological Summaries.</p> <p>### File Naming Convention</p> <p>Each GeoTIFF (.tif) file is named according to the format: `CA_<year>_<province>_<id>.tif`, where:<br>- `CA` stands for Canada.<br>- `<year>` is the year of the wildfire event.<br>- `<province>` is the province code (e.g., AB for Alberta, BC for British Columbia).<br>- `<id>` is a unique identifier for the wildfire event.</p> <p>### Directory Structure</p> <p>The dataset is organized as follows:</p> <p>```<br>FireSR/<br>│<br>├── dataset/<br>│ ├── S2/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── mask/<br>│ │ ├── post/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ │ ├── pre/<br>│ │ │ ├── CA_2017_AB_204.tif<br>│ │ │ ├── CA_2017_AB_2418.tif<br>│ │ │ └── ...<br>│ ├── MODIS/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── LULC/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>│ ├── Daymet/<br>│ │ ├── CA_2017_AB_204.tif<br>│ │ ├── CA_2017_AB_2418.tif<br>│ │ └── ...<br>```</p> <p>### Spatial Resolution and Channels</p> <p>- **Sentinel-2 (S2) Images**: 20 meters (Bands: B12, B8, B4)<br>- **MODIS Images**: 250 meters (Bands: B7, B2, B1)<br>- **NBAC Burned Area Labels**: 20 meters (1 channel, binary classification: burned/unburned)<br>- **Daymet Weather Data**: 1000 meters (7 channels: dayl, prcp, srad, swe, tmax, tmin, vp)<br>- **ESRI Land Use/Land Cover Data**: 10 meters (1 channel with 9 classes: water, trees, flooded vegetation, crops, built area, bare ground, snow/ice, clouds, rangeland)</p> <p>**Daymet Weather Data**: The Daymet dataset includes seven channels that provide various weather-related parameters, which are crucial for understanding and modeling wildfire conditions:</p> <p>| Name | Units | Min | Max | Description |</p> <p>|------|-------|-----|-----|-------------|</p> <p>| dayl | seconds | 0 | 86400 | Duration of the daylight period, based on the period of the day during which the sun is above a hypothetical flat horizon. |</p> <p>| prcp | mm | 0 | 544 | Daily total precipitation, sum of all forms converted to water-equivalent. |</p> <p>| srad | W/m^2 | 0 | 1051 | Incident shortwave radiation flux density, averaged over the daylight period of the day. |</p> <p>| swe | kg/m^2 | 0 | 13931 | Snow water equivalent, representing the amount of water contained within the snowpack. |</p> <p>| tmax | °C | -60 | 60 | Daily maximum 2-meter air temperature. |</p> <p>| tmin | °C | -60 | 42 | Daily minimum 2-meter air temperature. |</p> <p>| vp | Pa | 0 | 8230 | Daily average partial pressure of water vapor. |</p> <p>**ESRI Land Use/Land Cover Data**: The ESRI 10m Annual Land Cover dataset provides a time series of global maps of land use and land cover (LULC) from 2017 to 2023 at a 10-meter resolution. These maps are derived from ESA Sentinel-2 imagery and are generated by Impact Observatory using a deep learning model trained on billions of human-labeled pixels. Each map is a composite of LULC predictions for 9 classes throughout the year, offering a representative snapshot of each year.</p> <p>| Class Value | Land Cover Class |</p> <p>|-------------|------------------|</p> <p>| 1 | Water |</p> <p>| 2 | Trees |</p> <p>| 4 | Flooded Vegetation |</p> <p>| 5 | Crops |</p> <p>| 7 | Built Area |</p> <p>| 8 | Bare Ground |</p> <p>| 9 | Snow/Ice |</p> <p>| 10 | Clouds |</p> <p>| 11 | Rangeland |</p> <p><br>## Usage Tutorial</p> <p>To help users get started with FireSR, we provide a comprehensive tutorial with scripts for data extraction and processing. Below is an example workflow:</p> <p>### Step 1: Extract FireSR.tar.gz</p> <p>```bash<br>tar -xvf FireSR.tar.gz<br>```</p> <p>### Step 2: Tiling the GeoTIFF Files</p> <p>The dataset contains high-resolution GeoTIFF files. For machine learning models, it may be useful to tile these images into smaller patches. Here's a Python script to tile the images:</p> <p>```python<br>import rasterio<br>from rasterio.windows import Window<br>import os</p> <p>def tile_image(image_path, output_dir, tile_size=128):<br> with rasterio.open(image_path) as src:<br> for i in range(0, src.height, tile_size):<br> for j in range(0, src.width, tile_size):<br> window = Window(j, i, tile_size, tile_size)<br> transform = src.window_transform(window)<br> outpath = os.path.join(output_dir, f"{os.path.basename(image_path).split('.')[0]}_{i}_{j}.tif")<br> with rasterio.open(outpath, 'w', driver='GTiff', height=tile_size, width=tile_size, count=src.count, dtype=src.dtypes[0], crs=src.crs, transform=transform) as dst:<br> dst.write(src.read(window=window))</p> <p># Example usage<br>tile_image('FireSR/dataset/S2/post/CA_2017_AB_204.tif', 'tiled_images/')<br>```</p> <p>### Step 3: Loading Data into a Machine Learning Model</p> <p>After tiling, the images can be loaded into a machine learning model using libraries like PyTorch or TensorFlow. Here's an example using PyTorch:</p> <p>```python<br>import torch<br>from torch.utils.data import Dataset<br>from torchvision import transforms<br>import rasterio</p> <p>class FireSRDataset(Dataset):<br> def __init__(self, image_dir, transform=None):<br> self.image_dir = image_dir<br> self.transform = transform<br> self.image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith('.tif')]</p> <p> def __len__(self):<br> return len(self.image_paths)</p> <p> def __getitem__(self, idx):<br> image_path = self.image_paths[idx]<br> with rasterio.open(image_path) as src:<br> image = src.read()<br> if self.transform:<br> image = self.transform(image)<br> return image</p> <p># Example usage<br>dataset = FireSRDataset('tiled_images/', transform=transforms.ToTensor())<br>dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)<br>```</p> <p>## License</p> <p>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material as long as appropriate credit is given.</p> <p>## Contact</p> <p>For any questions or further information, please contact:<br>- Name: Eric Brune<br>- Email: ebrune@kth.se</p>
BEST (Burn Center Evaluation of Standard Therapies) Ventilator Mode Study-
ClinicalTrials.gov study NCT00351741. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Comparison of Active vs Passive Neural Mobilizations Effects in Improving Burning Pain, Muscular Strength, and Range of Motion in Patients With Diabetic Neuropathy
ClinicalTrials.gov study NCT07141992. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparison Between Carbapenems and Noncarbapenem Beta-lactam Antibiotics in Septic Burn Patients
ClinicalTrials.gov study NCT07096310. IPD Sharing: NO. Countries: 1. Publications: 6.
CO2 Fractional Laser With Tranexamic Acid As An Effective Tool For Post Burn Hyperpigmentation.
ClinicalTrials.gov study NCT07195539. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
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.