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499 results for “fuels”
Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea
<p>This data submission is connected to a scientific paper submitted to<br> Atmospheric Chemistry and Physics ("Measurement report: Characterization of uncertainties of fluxes and fuel sulfur content from ship emissions at the Baltic Sea" by Walden et al.). It consists of measurement results conducted beside the ship routs at the Baltic Sea near Helsinki, Finland. The gaseous and particle concentrations were measured along with the meteorological parameters, and the fluxes were calculated by the micrometeorological methods. The content of sulfur in the marine fuel, FSC, used by the passing ships was also calculated. We paid attention to calculate the uncertainties of the measurement results, both for the fluxes and for the FSC.</p> <p>The released data of:<br> 1. Gases, particles and met data (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles) as minute values. </p> <p>Data_ACP_Fig4_acbd.xlsx.</p> <p> <br> 2. Size distribution of nanoparticles (number concentration of nanoparticles at size class). Data_ACP_Fig6.xlsx</p> <p> <br> 3. Profiles of 30 min averages of gases, nanoparticles and meteorological parameters (SO<sub>2</sub>, NO, NO<sub>2</sub>, O<sub>3</sub>, CO<sub>2</sub>, and N<sub>tot</sub> (number concentration of nanoparticles), wind direction and wind speed, friction velocity, stability parameter and Monin-Obukhov length. Calculated values of atmospheric turbulence parameters and calculated fluxes of CO2 and nanoparticles by gradient and/or eddy covariance method.</p> <p>Data_ACP_Fig8_abcd_Fig9_abcd.xlsx<br> <br> 4. CO2 fluxes by Eddy covariance method from land based and sea based measurements. Concentration of CO2 in seawater and in air.</p> <p>Data_ACP_Fig10_ab.xlsxEngl</p>
Fine fuel biomass in grasslands and shrublands of the Intermountain West
<p>The Great Basin Coordination Center partners with Bureau of Land Management field offices in the Intermountain West to monitor fine fuel loading to make firefighting resource allocation decisions in the early spring. Fine fuel loads, a measurement of the small, herbaceous, and flammable biomass in a system can vary greatly inter-annually in grass and shrub dominated systems. These data are a compilation of those measurements from 11 district offices spanning from 1996-2020 years over 164 sites and the methods used to collect those measurements, which varies between field offices.</p>
Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes
<p>This file contains the data reported in the paper:</p> <p>A Bertei, F Tariq, V Yufit, E Ruiz-Trejo, N P Brandon, <em>Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes</em>, <strong>Journal of the Electrochemical Society</strong> (2016)</p> <p>All the data here reported can be reproduced by solving the equations reported in the manuscript with the corresponding parameters.</p>
Forest restoration and fuels reduction work: Different pathways for achieving success in the Sierra Nevada
Fire suppression and past selective logging of large trees have fundamentally changed frequent-fire adapted forests in California. The culmination of these changes produced forests that are vulnerable to catastrophic change by wildfire, drought, and bark beetles, with climate change exacerbating this vulnerability. Management options available to address this problem include mechanical treatments (Mech), prescribed fire (Fire), or combinations of these treatments (Mech + Fire). We quantify changes in forest structure and composition, fuel accumulation, modeled fire behavior, inter-tree competition, and economics from a 20-year forest restoration study in the northern Sierra Nevada. All three active treatments (Fire, Mech, Mech + Fire) produced forest conditions that were much more resistant to wildfire than the untreated control. The treatments that included prescribed fire (Fire, Mech + Fire) produced the lowest surface and duff fuel loads and the lowest modeled fire hazards. Mech produced low fire hazards beginning 7-years after the initial treatment and Mech + Fire had lower tree growth than controls. The only treatment that produced inter-tree competition similar to historical California mixed-conifer forests was Mech + Fire, indicating that stands under this treatment would likely be more resilient to enhanced forest stressors. While Fire reduced modeled fire hazard and reintroduced a fundamental ecosystem process, it was done at a net cost to the landowner. Using Mech that included mastication and commercial thinning resulted in positive revenues and was also relatively strong as an investment in reducing modeled fire hazard. The Mech + Fire treatment represents a compromise between the desire to sustain financial feasibility and the desire to reintroduce fire. One key component to long-term forest conservation will be continued treatments to maintain or improve the conditions from forest restoration. Many Indigenous people speak of 'active stewardship' as one of the key principles in land management and this aligns well with the need for increased restoration in western US forests. If we do not use the knowledge from 20+ years of forest research and the much longer tradition of Indigenous cultural practices and knowledge, frequent-fire forests will continue to be degraded and lost.
Dataset for the publication: Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine
<div> <p>The dataset covers the research data of the publication "Potential of alcohol fuels in active and passive pre-chamber applications in a passenger car spark-ignition engine" in International Journal of Engine Research (DOI: 10.1177/14680874211053168).</p> </div>
Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944
<p>Dataset for publication "Chemical-Dealloying-Derived PtPdPb-Based Multimetallic Nanoparticles: Dimethyl Ether Electrocatalysis and Fuel Cell Application" in ACS Applied Materials & Interfaces 2023, 15, 49, 56930–56944, https://doi.org/10.1021/acsami.3c11003</p> <p>Dataset contains XRD, SAXS, Particle size distribution, HRTEM, Electrochemical characterisation, Fuel cell current-voltage curve, DFT calculations, Elemental maps and electrode stability measurements made on pristine and chemically dealloyed carbon supported Pt2PdPb2 nanoparticles.</p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
Supplementary Material of "Fuel Starvation in Automotive PEMFC Stacks: Stack Current and Bipolar Plate Resistance"
<p>This video contains the discussed experimental data of the following journal publication, which explains experimental setup, test cycle and the shown data in detail.</p> <p><strong>Nissen, J., Boye, J. P., Schrievers, M., Schwämmlein, J. N., & Hölzle, M. (2025). Fuel Starvation in Automotive PEMFC Stacks: Stack Current and Bipolar Plate Resistance. <em>Journal of Physics: Energy</em>. </strong></p> <p><strong><a href="https://doi.org/10.1088/2515-7655/ada184">https://doi.org/10.1088/2515-7655/ada184</a></strong></p> <p>The time-dependent behavior of the respective fuel cell is furthermore discussed in a follow-up publication:</p> <p><strong>Nissen, J., Boye, J. P., Schwämmlein, J. N., Willich, C., & Hölzle, M. (2025). Fuel starvation in automotive PEMFC stacks: A self-enhancing overheating mechanism. <em>Journal of Physics: Energy</em>. <br><a href="https://doi.org/10.1088/2515-7655/ade288">https://doi.org/10.1088/2515-7655/ade288</a></strong></p>
Data Storage for Baylis and Boomhower (2022): LANDFIRE Aspect, Elevation, Slope, and Anderson 13 Fuel Models
<pre># Description Zenodo data storage for large, non-proprietary data used in "The Economic Incidence of Wildfire Suppression in the United States", by Patrick Baylis and Judson Boomhower. Main OpenICPSR repository (contains code and main README.txt): https://www.openicpsr.org/openicpsr/workspace?goToPath=/openicpsr/144601 # Contents This storage mirrors the following offline directories used in the code. Each .tar file contains a directory of the same name. To replicate the existing code, users should decompress each directory into raw/, following the structure used in the code. (Note: as described in the main README, running most of the code requires access to proprietary data which is not included in this storage). ## Resulting directory structure To be consistent with the original source code, included the .tar files should be decompressed into the following directory structure within the directory designated by the RAW global in 01_Code/globals.R in the main reposistory. LANDFIRE/Aspect/ LANDFIRE/DEM_Elevation/ LANDFIRE/Slope/ LANDFIRE/US_140FBFM13_12052016/</pre>
Data from Environmental Outcomes of the U.S. Renewable Fuel Standard
<p>Data and figures associated with the paper "Environmental Outcomes of the U.S. Renewable Fuel Standard" by Lark et al.</p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
TIMES-Sweden Fuel production technologies database
<p>This is a database containing techno-economic data for fuel production technologies, including data for stand-alone technologies, technologies co-producing district heating and technologies producing heat for integration with industries. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Industry database (<a href="https://doi.org/10.5281/zenodo.4139800">10.5281/zenodo.4139800</a>), and TIMES-Sweden (Industrial) Heat generation technologies database (<a href="https://doi.org/10.5281/zenodo.6372930">10.5281/zenodo.6372930</a>).</p> <p>This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
Data generated by the model presented in the research article entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell"
<p>This repository provides all the data and scripts necessary to reproduce the line plots shown in the manuscript entitled "Simulation of mass and heat transfer in an evaporatively cooled PEM fuel cell".</p>
Canadian fossil fuel production and greenhouse gas emissions compared to predictions following the 2.0°C scenario
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. McGlade and Ekins (2015) proposed quotas for the production of each type of fossil fuel in order to provide a 67% chance to limit warming to 2.0°C by 2100. The proportion of each quota that is already spent is calculated. Emissions targets from 21 scenarios originating from five effort-sharing studies are compared with Canadian 2020 emissions to evaluate the difference. Carbon budgets from 18 scenarios originating from seven studies are compared with Canadian cumulative emissions to evaluate the percentage of the budgets already emitted within the 2010-2050 period. Emissions from five database are used in the calculations.</p>
Source data and code for: Existing fossil fuel extraction would warm the world beyond 1.5°C
<p>Source data and code for the study, "Existing fossil fuel extraction would warm the world beyond 1.5°C." Datasets 1-4 include mine-level data collected for China (Dataset 1), India (Dataset 2), and five other countries (Dataset 3) that are among the world's top nine coal producers - the United States, Indonesia, Australia, South Africa, and Poland. Dataset 4 includes global and country-level output data from the 1,000-run Monte Carlo simulation. <Committed_Reserves_Monte_Carlo_Input_Data.zip> includes data and code to replicate the Monte Carlo simulation.</p>
Accelerated Stress Tests for Solid Oxide Cells via Artificial Aging of the Fuel Electrode
<p><strong>AD ASTRA: Data from Experiments for Artificial Aging of the Fuel Electrode in Solid Oxide Cells via Redox Cycling</strong></p> <p>One of the big hurdles towards fast deployment of Solid Oxide Cells (fuel cells or electrolyzers) is durability. Although intensive works are carried out for life time improvement, they meet a serious problem concerning long term electrochemical tests for accumulation of reliable data that may continue several years, which is unaffordable. A problem-solving approach is the introduction of Accelerated Stress Tests (AST) applying high levels of stress for a shorter period thus reducing the test time for degradation qualification.</p> <p>Since there are no definite criteria for the level of acceleration of a specific degradation phenomenon, the selection of aggravating conditions is a critical moment which is under studies in the FCH JU Project AD ASTRA (GA 825027).</p> <p>Herein we present data accumulated during the development of a procedure for artificial accelerated aging of the fuel electrode via redox cycling. They include electrochemical testing (current-voltage curves and impedance measurements) and microstructural characterization (SEM-BSE image analysis), as well as procedure for redox cycling.</p>
Fuel Consumption Patterns linked to Evolution of India's PM2.5 Pollution Between 1998 and 2020
<p>These datasets are part of Supplementary information for the journal article<br> "<a href="https://doi.org/10.1039/D2EA00027J">Evolution of India’s PM2.5 Pollution Between 1998 and 2020 Using Global Reanalysis Fields Coupled with Satellite Observations and Fuel Consumption Patterns</a>"</p> <p>Pollution data summaries are listed <a href="https://doi.org/10.5281/zenodo.7115052">here</a>.<br> <a href="https://doi.org/10.5281/zenodo.7115052">https://doi.org/10.5281/zenodo.7115052</a></p> <p>Fuel consumption and activity data over the years is collected from indiastats.<br> Excel files include absolute activity data.<br> Figures are showing the ratio to year 2000 for each of the categories<br> Individual figures are included in the power point for reference and use.<br> Individual figures are also included in the respective excel files</p> <p>Files listed below</p> <ol> <li>coal.consumption.total.xlsx (total coal)</li> <li>electricity.consumption.xlsx (industry, domestic, railways, agriculture, commercial, and total)</li> <li>fuel.consumption.xlsx (LPG, petrol, diesel, kerosene, ATF, others, and petcoke)</li> <li>pass.freight.movement.xlsx (freight as tonne/km and passenger as pass/km)</li> <li>vehicle.registrations.xlsx (2-wheelers+4-wheelers, buses, freight, and all vehicles)</li> </ol>
Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996
<p>Here is a dataset of live fuel moisture content (LFMC, computed as the water mass over dry mass of living shoots) time series, collected in the French Mediterranean area by the French National Forest Organization (“Office National des Forêts”) for operational fire prevention. A network of 53 sites (called the "Reseau Hydrique" network) were sampled, among which 35 are geolocalized for a maximum period extending from 1996 to 2016. For each site and year available, LFMC is measured during the summer season on shrub species (between one and three species per site) at a weekly to biweekly frequency depending on site and year.</p> <p>The dataset can be used to validate or calibrate fire danger model, assess remote sensing drought indices and understand the physiological and climatic determinants of LFMC. There are 584 site*year data (a total of 22787 individual data) for several shrub species of the French Mediterranean area.</p> <p>From the raw dataset, researchers from the Ecology of Mediterranean Forest Unit at INRA (French National Institute for Research in Agronomy) of Avignon (France) have produced an improved dataset that includes corrections, outlier identifications and error estimations. Preliminary validation assessments of the data quality were also produced.</p> <p>A data paper describing in detail the method and all the modifications, error estimations and evaluations of the raw dataset that were performed is under review in Annals of Forest Science. Both the raw and improved datasets are made available on Zenodo (Cabane et al 2017, DOI 10.5281/zenodo.162978).</p> <p>The attached dataset consists of four tables:</p> <ol> <li>The first table (<em>LFMC_final_Table.csv</em>) contains the live fuel moisture content (LFMC) on a dry weight basis (see Supplementary S1 for details). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method fully described above described in a data paper under revision (Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Each row in the table describe the LFMC at a given date, for a given species and a given site. The table has eleven columns. The first eight columns indicate the site identifier (SiteCode and SiteName), the species (Species), a unique identifier for a given species at a given site (SitexSpecies), the date (Date, Year, Month, Day of Year). The last three columns are respectively the robust LFMC (labelled RobustLFMC), the standard error <em>SE</em> (labelled RobustStandErrLFMC) and the number of valid measurements that were not identified as outliers (labelled RobustNval). RobustStandErrLFMC, and that can be used to estimate confidence limits depending on the desired confidence rate.</li> </ol> <p> </p> <ol> <li>The second table (<em>RainTable.csv</em>) contains rainfall measurements. The site identifiers are given (SiteCode and SiteName) and the rainfall amount (rainfall) corresponding to rainfall occurring between the day of year of the previous measurement (PreviousDoy) and the day of year when the measurement was performed (Doy). The last column enables to identify the doubtful measurements (RainFlag = 1), when the discharge of the gauge during the previous measurement was uncertain.</li> </ol> <p> </p> <p> </p> <ol> <li>The fourth table (<em>InfoSite_ReseauHydrique.csv</em>) contains a basic description of each site. It includes the identifier of the site (SiteCode and SiteName), the coordinates of the site in WGS84 (Longitude and Latitude), a flag indicating whether the site is still active (1= active, 0= inactive), the names of measured species (SpeciesName1 and up to SpeciesName3), the first and last year of measurement, as well as the number of measurement year available, for each species (StartYear, EndYear, NbYears).</li> </ol> <p> </p> <ol> <li>The third table contains raw data as produced by the French National Forest Organization (<em>LFMC_raw_Table.csv</em>). The first twelve columns indicate site name, species name, and date, as in the first table. The six following columns indicate individual LFMC values (LFMC1 to LFMC5), and the mean LFMC value (FFSLFMC) released by the French Forest Service. The last six columns correspond to flags identifying outliers (LFMC1Flag to LFMC5Flag). Flags were attributed either manually or automatically (see Martin-StPaul <em>et al</em>., under review in Annals of Forest Science). Missing values (e.g. following an unforcasted rain event, see Methods) were represented by the symbol “NA“ (Not Available).</li> </ol> <p>Note that the initiative was funded by a French organization dedicated the protection of the Mediterranean forest (The "Délégation à la Protection de la Forêt Méditerranéenne") and the raw dataset is available on a French website (http://www.reseau-hydrique.org/). However the raw dataset is not fully adapted to scientific purposes for several reasons. The dataset is not referenced (<em>i.e.</em> does not have a DOI) and its description is in French. In addition, the labels of sampling sites have evolved over time and some species were given a vernacular name. Finally, raw data are expressed on fresh mass basis (instead of dry mass as generally done in scientific studies) and present some outliers, duplications and inconsistencies. Additionally, uncertainties were not provided in the raw datasets. This is why INRA researchers recommend the usage of the improved dataset. In the forthcoming month, additional data regarding the environmental description, the ecology and history of the sites will be provided.</p> <p> </p> <p>Martin-StPaul, N; Pimont, F; Dupuy JL; Rigolot E; Ruffault J; Fargeon H; Cabane E; Duché Y; Savazzi R; Toutchkov M. Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean area since 1996. Under revision in Annals of Forest Science.</p> <p> </p>
Role of bark beetle disturbance and fuel types on fire radiative power and burn severity in the Bohemian-Saxon Switzerland - Data and Material.
<p>This data repository includes different datasets for fuel types, burn severity, fire radiative power and burned area, which were analysed and used in our paper on the <strong>Role of bark beetle disturbance and fuel types on fire radiative power and burn severity in the Bohemian-Saxon Switzerland</strong>.</p> <p>Study area: National Park Bohemian and Saxon Switzerland and conservation areas, Germany and Czech Republic.</p> <p>Burn severity:<br>dnbr_fire22.nc – Burn severity data covering the burned area, which has been calculated with the Difference Normalized Burn Index (dNBR) using Sentinel-2 and Landsat 8, 9 images. Remote sensing images were reprojected and resampled to 10 m to ensure harmonization before index calculation.</p> <p>cbi.csv – Burn severity surveyed in the field in autumn 2022 as validation data for the dNBR. Contains: ID, coordinates, CBI, CBI values separated for different strata (A to E) and individual strata variables, forest type and species for intermediate trees (strata D) and tall trees (strata E), and the dNBR value that covered the plot extent.</p> <p>Burned area:<br>burned_area.shp – Burned area was mapped by rangers in the Saxon Switzerland National Park and was taken from the dataset provided by the Copernicus Emergency Management Service (EMS) for the Bohemian Switzerland National Park.</p> <p>FRP:<br>frp.nc - Fire Radiative Power gridded to 300 m and clipped to the burned area. FRP during the main fire spread 24/07/22 - 29/07/22. </p> <p>Fuel:<br>fueltype_bohemiansaxonswitzerland.nc/fueltype_bohemiansaxonswitzerland_postfire.nc – Raster datasets (10m spatial resolution, EPSG:32633) of fuels present in the area before and after the fire. The fuel classification system can be found in the fuel_classification.xlsx.</p> <p>fuel_classification.xlsx – The fuel type classification system for the study area. Fuel type ID's as seen in fueltype_bohemiansaxonswitzerland.nc (pre- and postfire).</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.