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499 results for “fuels”
HAEOLUS Project Fuel Cell Data.
<p>Dataset from a Fuel Cell test in the HAEOLUS European Project <a href="https://haeolus.eu/" target="_blank" rel="noopener">https://haeolus.eu/.</a></p> <p> </p>
Impact of Ambient Temperature on Light-duty Gasoline Vehicle Fuel Consumption under Real-World Driving Conditions
<p>This dataset includes the vehicle operating (speed, acceleration), fuel consumption rate, and weather condition (temperature, pressure, humidity, heat index) data of Beijing, China.</p> <p>See our published paper for more information: </p> <div> <div>Fan, P., Song, G., Lu, H., Yin, H., Zhai, Z., Wu, Y., Yu, L., 2024. Impact of ambient temperature on light-duty gasoline vehicle fuel consumption under real-world driving conditions. International Journal of Sustainable Transportation 1–16. <a href="https://doi.org/10.1080/15568318.2024.2385635">https://doi.org/10.1080/15568318.2024.2385635</a></div> </div>
3D micro/nano-CT datasets of sandstone rock, lithium-ion battery, and fuel cell used for testing P3T-Net
<p>These are the datasets used for training and testing P3T-Net for 3D unpaired domain transfer in .tif format. Images can be directly opened with ImageJ, Avizo, Python, Matlab, etc.</p> <p>Dataset contains overall 8 3D images of 4 cases. Each case contains images from the target domain and source domain. </p> <p>Case 1. Target domain: micro-CT image of a 9-hour long scan of a sandstone (voxel size: 2.15um); Source domain: micro-CT image of a 7-minute fast scan of a sandstone (voxel size: 2.15um).</p> <p>Case 2. Target domain: micro-CT image of a 7-hour long scan of a sandstone (voxel size: 3.28um); Source domain: synchrotron micro-CT image of a 24-second fast scan of a sandstone (voxel size: 6.75um).</p> <p>Case 3. Target domain: nano-CT image of a dual-mode scan of Lithium-ion battery cathode (voxel size: 128nm); Source domain: three nano-CT images of a single-mode scan of lithium-ion battery cathode (two absorption and one phase mode) (voxel size: 128nm).</p> <p>Case 4. Target domain: micro-CT image of a 3-hour long scan of a fuel cell (voxel size: 2.25um); Source domain: micro-CT image of a 10-minute fast scan of a fuel cell (voxel size: 2.25um).</p> <p>For any further questions or requirements, please contact kunning.tang@unsw.edu.au.</p>
Fossil Fuel Subsidy Reform - Data and Analysis
<p>This is raw data, the code for cleaning and compiling the data into a panel data, code for mapping and replication of main results for</p> <p>Droste, Chatterton, and Skovgaard (2024). A political economy theory of fossil fuel subsidy reforms in OECD countries. <em>Nature Communications 15</em>: <span>5452</span> </p>
Input Data for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary information accompanying "Energy Storage Profit Risk under Stochastic Fuel Prices" paper submitted to <a href="https://www.journals.elsevier.com/energy-economics/">Energy Economics</a>.</p>
A detailed flowsheet of a 100 MW Solid Oxide Fuel Cell plant modeled in ASPEN PLUS
<p>Files required for modeling a 100 MW Solid Oxide Fuel Cell Plant (Power-to-Gas Mode) in Aspen Plus. The plant was designed by Butera et al.[1]. A detailed capital cost estimate was made using the AspenTech Process Economic Analyzer and written in ESA.xlsx. </p> <p>Further details are available upon request: alexandru.botan@phystech.edu</p> <p>[1] Butera, G., Jensen, S.H., and Clausen, L.R. "A novel system for large-scale storage of electricity as synthetic natural gas" submitted to J. Energy</p> <p> </p> <p> </p>
Surrogate Model Optimisation of a 'micro core' PWR fuel assembly arrangement using deep learning models - Figures
<p>Figures for Physior 2020 paper</p>
Pitting corrosion of AA1050 in ethanol-containing fuels: Classification dataset
<p>This dataset was used to predict the occurrence of ethanol-based pitting corrosion in AA1050 using binary classification. It consists of five system descriptors:</p> <ul> <li>Temperature (°C)</li> <li>Chloride content (ppm)</li> <li>Water content (ppm)</li> <li>Grit size of polishing paper</li> <li>Ethanol content (% by volume) of the fuel</li> </ul> <p>The target variable, named "corr," indicates corrosion occurrence, where 1 means corrosion occurred and 0 means no corrosion occurred. The dataset comprises 115 samples in total: 105 samples where corrosion occurred and 10 samples where no corrosion occurred.</p>
SPARCS_WP3_Espoo_City_District heating production by fuel in Espoo, Finland
<p>District heat production in Espoo, Finland, divided by fuel. Provided by the Helsinki Region Environmental Authority. 2000-2023</p>
DATASET for Biomethanol production via electrolysis, oxy-fuel combustion, water-gas shift reaction, and LNG cold energy recovery
<p>DATASET for the paper entitled: Biomethanol production via electrolysis, oxy-fuel combustion, water-gas shift reaction, and LNG cold energy recovery</p>
Trends in global fuel economy of new vehicles: 2005 - 2022
<p>This data contains information on the fuel economy of new light-duty vehicle sales in major automotive markets and underpins the report - <strong>Trends in the global vehicle fleet 2023: managing the SUV shift and the EV transition.</strong> This report is the 2023 installment of the Global Fuel Economy Initiative (https://www.globalfueleconomy.org/) which, among other things, tracks progress in the efficiency of the global vehicle fleet. </p> <p>Yearly vehicle sales are defined by segment (small car, medium car, large car, small SUV, large SUV, Light Commercial Vehicle) and powertrain ( internal combustion engine, mild hybrid, hybrid, plug-in hybrid, battery electric, fuel cell hydrogen). For each combination of segment and powertrain the data contains information on sales, average weight (kg), average footprint (m2), and average specific energy consumption (lge/100 km). The data was obtained from a set of sources and processed to gain the best possible estimate of global trends in energy consumption for light duty vehicles. More information on the methodologies underpinning the data analysis can be found in the report.</p> <p>Files included:</p> <ul> <li>supplementary_information_GFEI2023_TDC.xlsx <ul> <li>data: sheet containing the the data - blank rows indicate missing data.</li> <li>unique values: contains the unique values taken by each column in sheet data</li> </ul> </li> <li> supplementary_information_GFEI2023_vizualisation.xlsx <ul> <li>raw_data: same data as that available in the file above</li> <li>country_map: tha mapping of individual countries to regions used in the report</li> <li>filter_data: combinations of powertrain-year-country that account for less than 0.5% of yearly sales that are not displayed in the Graphs tab</li> <li>pivots_country, pivots_powertrain, pivots_segment: intermediate step </li> <li>Powertrain: registrations, specific fuel consumption, weight and footprint by powertrain</li> <li>Segment: registrations, specific fuel consumption, weight and footprint by segment</li> <li>Graphs: Includes plots of the above two tabs, contains a country selector in I2</li> </ul> </li> <li>sdmx.zip <ul> <li>Data structures and data in SDMX-ML format, containing exactly the same information as supplementary_information_GFEI2023_TDC.xlsx, converted using the code at <a href="https://github.com/transport-data/gfei-2023" target="_blank" rel="noopener">https://github.com/transport-data/gfei-2023</a></li> </ul> </li> </ul> <p>Nota Bene: Older versions of Excel might encounter issues with titles in graphs, that is likely caused by the CONCAT function not being availble - substituting the CONCAT function with CONCATENATE should solve the problem.</p>
Data and scripts from: Balanced polymorphism fuels rapid selection in an invasive crab despite high gene flow and low genetic diversity
<p><em>Carcinus maenas</em> is a globally invasive species which spreads and thrives across a range of temperate environments. In the northwestern Pacific, the species has spread across >12 degrees of latitude in 10 years from a single source, following its introduction <35 years ago. Using six locations spanning >1,500 km, we examined genetic structure and selection to temperature using 9,376 Single Nucleotide Polymorphisms (SNPs) derived from cardiac transcriptome sequencing.</p> <p>Data in this repository includes information on sequenced samples (*.csv, *.txt), a cleaned transcriptome assembly after expression filtering (*.fasta), transcriptome annotation from EnTAP (*.tsv), list of transcripts removed from analysis after mapping (*.txt), high-quality SNPs identified from the transcriptome sequencing with GATK (seven files representing different SNP sets used in the analysis; *.vcf), and four custom scripts used in processing SNP data (*.py and *.R).</p> <p>Raw sequence data is archived in GenBank's SRA. 2015-2016 samples: BioProject ID PRJNA690934 and BioSample IDs SAMN17267686–SAMN17267781. 2011 samples: BioProject ID PRJNA283611 and BioSample IDs SAMN03653390–SAMN03653413.</p>
Dataset associated to paper: Nanoscaffold effects on the performance of air-cathodes for microbial fuel cells: Sustainable Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions
<p>This file contains the dataset associated to the published research article "Nanoscaffold effects on air-cathode performance in microbial fuel cells: Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions". The dataset contains X-ray powder diffraction, Inductively Coupled Plasma Emission Spectroscopy, elemental analysis, measurements of the specific surface area, transmission electron microscopies, x-ray photoelectron microscopy, electrochemistry and microbial fuel cells power outputs data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No. 799175 (HiBriCarbon) and No. 748968 (EDGE-FREEMAB). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213 and 19/FFP/6761. SI kindly acknowledges support by the Department of Social Justice State Government of Maharashtra, India. </p>
Model data 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 and burned area 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><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
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>
Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model
<p>Data Set: Renewable hydrogen fuels versus fossil fuels for trucking, shipping and aviation: A holistic cost model</p>
Data for: Plant roots fuel tropical soil animal communities
<p>Belowground life relies on plant litter, while its linkage to living roots had long been understudied, and remains unknown in the tropics. Here, we analysed the response of 30 soil animal groups to root trenching and litter removal in rainforest and plantations in Sumatra, and found that roots are similarly important to soil fauna as litter. Trenching effects were stronger in soil than in litter, with an overall decrease in animal abundance in rainforest by 42% and in plantations by 30%. Litter removal little affected animals in soil layers, but decreased the total abundance by 60% in rainforest and rubber plantations but not in oil palm plantations. Litter and root effects on animal group abundances were explained by body size or vertical distribution. Our study quantifies principle carbon pathways in soil food webs under tropical land use, providing the basis for mechanistic modelling and ecosystem-friendly management of tropical soils.</p>
TE invasion fuels molecular adaptation in laboratory populations of Drosophila melanogaster
<p>Transposable elements are mobile genetic parasites that frequently invade new host genomes through horizontal transfer. Invading TEs often exhibit a burst of transposition, followed by reduced transposition rates as repression evolves in the host. We recreated the horizontal transfer of <em>P</em>-element DNA transposons into a <em>D</em>. <em>melanogaster</em> host and followed the expansion of TE copies and evolution of host repression in replicate laboratory populations reared at different temperatures. We observed that while populations maintained at high temperatures rapidly go extinct after TE invasion, those maintained at lower temperatures persist, allowing for TE spread and the evolution of host repression. We also surprisingly discovered that invaded populations experienced recurrent insertion of P-elements into a specific long non-coding RNA, <em>lncRNA:CR43651</em>, and that these insertion alleles are segregating at unusually high frequency in experimental populations, indicative of positive selection. We propose that, in addition to driving the evolution of repression, transpositional bursts of invading TEs can drive molecular adaptation.</p>
Dataset for: Efficacy of prescribed fire as a fuel reduction treatment in the Colorado Front Range
<p>Prescribed fires are an important management tool for reducing fuels and returning fire to the landscape. However, rarely are changes in fuels fully quantified using pre- to post-prescribed fire measurements, and those studies that do exist show variable results. In the southern Rockies, little literature exists on the impacts of prescribed fires, thus we examined multiple prescribed fires in northern Colorado to understand fire effects and changes in fuel complexes. Most prominently, prescribed fires influenced litter, duff, and rotten coarse woody debris but did not influence other surface fuels. Crown base height increased and tree density decreased, while basal area was relatively unimpacted. Season of burning impacted fire effects as substrate burn severity, bole char, and crown volume scorched were highest in summer and fall. Continued monitoring of prescribed fires is critical to understand the influence of prescribed fire on wildfires and ultimately improving prescribed fire outcomes.</p>
Data used in the paper "High Performance H2−Mn Regenerative Fuel Cells through an Improved Positive Electrode Morphology "
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors:Javier Rubio-Garcia, Anthony Kucernak, Barun Kumar Chakrabarti , Dong Zhao , Danlei Li2, Yuchen Tang , Mengzheng Ouyang , Chee Tong John Low and Nigel Brandon </p> <p>Title:High Performance H2−Mn Regenerative Fuel Cells through an Improved Positive Electrode Morphology </p> <p>Journal:Batteries</p> <p>DOI:</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.7599405</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.