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499 results for “fuels”
Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications
<p>This dataset contains extended data files for the publication entitled "Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications."</p> <p>Data files includes: <br>- Dataset description.txt (provides abbreviations or codes of samples and their properties)<br>- Extended data.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characterisitics of raw material (two-phase olive pomace slurry) and hydrochars<br>- 13C-NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars)<br>- TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars)<br>- FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars) </p> <p>Checksum numbers for enclosed data files: <br>- MD5 Checksum number for file named Extended dataset v1.0 = 6c542debe0a8b4297b8730830e7d5b58<br>- MD5 Checksum number for file named 13C-NMR data = bb25d31771d281ce1ca6518c4dc1cb41 <br>- MD5 Checksum number for file named FTIR data = 0df81ec3a25b4d130be09cef59736d30<br>- MD5 Checksum number for file named TGA-DTA data = 7205cc5a24daa94e60c82a43ae748fb2</p>
FireCaster Wildland Fire Fuels Database for Corsican - Mediterranean Forest stand types
<p>This database includes wildland fuels data for Mediterranean basin vegetation stands types and in particular for those in Corsica, for fire/forest management, risk assessment and decision-support purposes. It gathers together some of the most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). It has been conceived by using a layering approach, this is, assuming that a vegetation stand type is constituted by one or more structurally distinct pseudo-homogenous layers of vegetation. Vegetation stand types considered are based on the fuel classification and mapping of the BDForêt® <em>2.0 – Corsica.</em> Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The building methods and the different data sources have been detailed in a paper which is under review.</p> <p> The attached dataset consists of two tables and one text document:</p> <p> - The first table (<em>FuelLayersData.csv</em>) contains fuel layers and fuel elements attributes for each vegetation stand type. The table has 15 columns. The first one (<em>CODE_TFV</em>) corresponds to the code assigned to each vegetation stand type following the BDForêt®<em> </em>nomenclature. Next columns, refer to the layer numbering, the stratum of the layer and the species scientific name. After that, next six columns correspond to the layer attributes and four columns correspond to the fuel element attributes. The last column is the diameter at breast height (DBH) for canopy layers. The empty cells in the table indicate that the corresponding attribute is not applicable for this particular layer.</p> <p> - The second table (<em>FuelParticlesData.csv</em>) contains the particle attributes, this is, the surface-to-volume ratio, particles density and low heat content.</p> <p> - The text document (<em>StandTypesDescription.docx</em>) is derived from BDForêt®<em> version 2.0 – Corsica</em> (https://geo.isula.corsica/wp-content/uploads/2021/01/descriptif-contenu-bd_foret-IGN.pdf) and contains a short description of the different stand types considered according to the CODE_TFV.</p> <p>This work was supported by the Agence Nationale de la Recherche, France (grant number ANR-16-CE04-0006 FIRECASTER) and by H2020-EU.3.5. Programme (FIRE-RES, Grant agreement ID: 101037419).</p> <p> </p> <p>Pérez-Ramirez Y, Ferrat L, Filippi JB. (2024) Wildland Fire Fuels Database for Corsican – Mediterranean Forest stand types. Forest Ecology and Management, 565, 122002.</p>
Dataset for the publication: Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions
<p>The dataset covers the research data of the publication in Catalysis Science & Technology with the title "Impact of unintentionally formed CH2O in oxygenated fuel exhausts on DeNOx-SCR at different NO2/NOx ratios under close to real conditions" (DOI: 10.1039/d2cy01935c).</p>
Dataset: Clean Energy Fuels Corp. (CLNE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Westport Fuel Systems Inc. (WPRT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Verde Clean Fuels, Inc. (VGAS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Verde Clean Fuels, Inc. (VGASW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fusion Fuel Green PLC (HTOO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fusion Fuel Green PLC (HTOOW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Lattice Boltzmann simulation of liquid water transport in gas diffusion layers of proton exchange membrane fuel cells: Impact of gas diffusion layer and microporous layer degradation on effective transport properties
<p><span>Underlying data to publication Sarkezi-Selsky et al., <em>J. Pow. Sour.</em> 556 (2023) 232415,<span> https://doi.org/10.1016/j.jpowsour.2022.232415</span> <br><br>Polymer Electrolyte Membrane Fuel Cells (PEMFCs) represent a promising technology for clean drivetrain solutions, in particular for heavy-duty applications. However, lifetime requirements demand high durability of each cell component.<br></span><span>In this work, transport of liquid water through pristine and degraded gas diffusion layers (GDL) was simulated with a 3D Color-Gradient Lattice Boltzmann model. The GDL microstructure was reconstructed </span><span>from high-resolution X-ray micro-computed tomography (</span><span>μ</span><span>-CT) of an impregnated Freudenberg H14. The </span><span>effect of a microporous layer (MPL) was considered by reconstruction of an impregnated and MPL-coated H14. Aged microstructures were generated artificially, assuming loss of polytetrafluoroethylene (PTFE) within the GDL and increase of MPL macroporosity as main degradation mechanisms. Liquid water transport within aged microstructures was simulated by imposing a liquid phase flow rate until breakthrough was reached. Subsequently, the GDL microstructures were analyzed for their breakthrough characteristics by means of saturation and effective gas transport properties. When the MPL was pristine, no distinct GDL degradation effect was observable, this was attributed to the MPL dominating capillary transport. MPL aging, however, led to increased saturations and thus to a deterioration of the effective gas transport. With a partially degraded MPL, aging of the GDL then appeared to affect the breakthrough characteristics.</span></p>
Supplementary Material of "Fuel Starvation in Automotive PEMFC Stacks: Hydrogen Stoichiometry and Electric Cell-to-Cell Interaction"
<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., Schwämmlein, J. N., & Hölzle, M. (2024). Fuel starvation in automotive PEMFC stacks: hydrogen stoichiometry and electric cell-to-cell interaction. <em>Journal of Physics: Energy</em>. <br><a title="https://doi.org/10.1088/2515-7655/ad5f54" href="https://doi.org/10.1088/2515-7655/ad5f54">https://doi.org/10.1088/2515-7655/ad5f54</a></strong></p> <p>Version 01: Video as .MKV file. Quite large and not supported for in-browser visualization by zenodo.</p> <p>Version 02: Changed video format from .MKV to .MP4 to reduce file size and allow in-browser visualization by zenodo. Identical content as Version 01.</p>
Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe
<p>This dataset contains primary, intermediate, and output data for "Climate change could fuel urinary schistosomiasis transmission in Africa and Europe". In this paper, we use mechanistic and correlative modelling to predict the distribution of schistosomiasis intermediate host snail <em>Bulinus truncatus.</em> Model projections suggest the suitable habitat for <em>B. truncatus</em> will increase by 17%, with new suitable habitat in Southern Europe and Central Africa, and a reduction in suitable habitat in the Sahel region.</p>
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Dataset to: Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles
<p>This is the dataset to the published article "Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles" (DOI: 10.1016/j.ijhydene.2023.08.292) in which the degradation of two commercial PEM fuel cell stacks was analyzed. <strong>Please cite this publication if you use the dataset in a publication as follows</strong>:</p> <p>P. Thiele, Y. Yang, S. Dirkes, M. Wick, S. Pischinger, Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles, Int. J. Hydrogen Energy 52 (Part D) (2024) 1065–1080, https://doi.org/10.1016/j.ijhydene.2023.08.292.</p>
Data, plotting scripts, and figures for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures"
<p>This bundle of files contains all the data and plotting scripts for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures", as well as the figures themselves.</p> <p>These results are part of the paper:</p> <p>Tejas Chandrashekhar Mulky and Kyle E. Niemeyer. "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures," 2018. Accepted for publication in <em>Proceedings of the Combustion Institute</em>, available via <a href="https://arxiv.org/abs/1806.08396">https://arxiv.org/abs/1806.08396</a></p>
Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation
<p>This dataset contains supplemental tables and tracks for the study entitled: "Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation".</p> <ul> <li>Files <ul> <li>PMDs_CGIs.zip <ul> <li>The included files contain</li> <li>Genome positions of detected PMDs with their mean methylation (weighted mean, see Methods)</li> <li>Genome positions of CpG islands with their mean methylation (weighted mean)</li> <li>The "Brinkman" directory contains files from breast cancer data produced in this study</li> <li>The "normals" directory contains files from normal tissues (external data) analyzed in this study</li> <li>The "tumors" directory contains files from tumors (external data) analyzed in this study</li> <li>All genome positions are based on GRCh37/hg19 </li> <li>All files are TAB-delimited text files (.tsv)</li> </ul> </li> <li>DNAme_bigwigs.zip <ul> <li>The included files are BIGWIG files (http://genome.ucsc.edu/goldenPath/help/bigWig.html) for viewing the DNA methylation profiles in a genome browser such as UCSC (http://genome.ucsc.edu). Each file represents a whole-Genome Bisulfite Sequencing (WGBS) DNA methylation profile from one tumor used in this study. The used genome build was GRCh37/hg19. For every CpG with a coverage of at least 4 reads, the DNA methylation value (range: 0-1) is included.</li> </ul> </li> </ul> </li> <li>Methods <ul> <li>Detection of partially methylated domains (PMDs) in all whole-genome bisulfite sequencing (WGBS) methylation profiles throughout this study was done using the MethylSeekR package for R (1). Before PMD calling, CpGs overlapping common SNPs (dbSNP build 137) were removed. The alpha distribution (1) was used to determine whether PMDs were present at all, along with visual inspection of WGBS profiles. After PMD calling, the resulting PMDs were further filtered by removing regions overlapping with centromers (undetermined sequence content).</li> <li>Mean methylation values from WGBS inside CGIs were calculated using the ‘weighted methylation level’ (2).</li> <li>Mean methylation values from WGBS inside PMDs were calculated using the ‘weighted methylation level’ (2). Calculation of mean methylation within PMDs involved removing all CpGs overlapping with CpG island(-shores) and promoters, as the high CpG densities within these elements yield unbalanced mean methylation values, not representative of PMD methylation. </li> </ul> </li> <li>References <ul> <li>(1) Burger, L., Gaidatzis, D., Schübeler, D. & Stadler, M. B. Identification of active regulatory regions from DNA methylation data. Nucleic Acids Research 41, (2013).</li> <li>(2) Schultz, M. D., Schmitz, R. J. & Ecker, J. R. ’Leveling’ the playing field for analyses of single-base resolution DNA methylomes. Trends in Genetics 28, 583–585 (2012).</li> </ul> </li> </ul>
Code and data for "Current fossil fuel infrastructure does not yet commit us to 1.5°C warming"
<p>This package generates all of the model runs and plotting code for "Current infrastructure does not yet commit us to 1.5°C warming".</p> <p>See enclosed README file for dependencies and how to run.</p>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </p>
Data for figures in the article "Thermal-integration in Photoelectrochemistry for Fuel and Heat Co-Generation"
<p>This repository contains the data used to generate figures in the article "<span>Thermal-integration in Photoelectrochemistry for Fuel a</span><span>nd Heat Co-Generation" in 2024 in Sustainable Energy and Fuels.</span></p> <p><span>Authors of this data and the article are Evan F Johnson and Sophia Haussener.</span></p>
Production of hydrochar fuel by microwave-hydrothermal carbonisation of olive pomace slurry from olive oil industry for combustion application
<p><span>Extended dataset for research article titled “Production of hydrochar fuel by microwave-hydrothermal carbonisation of olive pomace slurry from olive oil industry for combustion application.” It includes the following data files: </span></p> <p><span>(1) Dataset description.txt (provides abbreviations or codes of samples and their properties); Checksum MD5 code = a2fc3f46e9d3ef61f390f93f4a45fa97 </span></p> <p><span>(2) Extended dataset v1.0.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characteristics of raw material (two-phase olive pomace slurry) and hydrochars; Checksum MD5 code = 01469e10c1133367d41c5ab5553a9616</span></p> <p><span>(3) 13C Solid NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars); Checksum MD5 code = dd446a11e92cef9d63ee5e2f5786ee7c</span></p> <p><span>(4) TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars); Checksum MD5 code = 0bb2a59e6cc3c2b3de0492ac057fa626</span></p> <p><span>(5) FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars); Checksum MD5 code = ff20a5545198ec7b395506ccdd841e82</span></p> <p> </p> <p> </p>
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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.