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499 results for “fuels”
Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response"
<p>Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response", in review at Atmospheric Chemistry and Physics, 2024, same authors.<br>The simulations were done using MIMICA version 4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Sample of Reanalysis Dead Fuel Moisture Content Dataset of California (2000-2020)
Open the record for dataset details and reuse information.
Chemical composition and mixing state of elemental carbarn-containing particles from solid fuel combustion
<p>The above data set contains the mass spectrum information of EC-containing particles obtained from the single particle aerosol mass spectrometer (SPAMS) and some related processing methods. Specifically, it includes: (1)Average positive and negative mass spectra of 5 solid fuels, (2)Average mass spectra for each particle cluster from 5 solid fuels, (3)Cluster composition of EC-containing particles, (4)cluster composition of EC-containing particles, (5)Particle size distribution of EC-containing and (6)Mixing state characteristics of EC-containing particles.</p>
Fossil Fuel CO₂ Emissions for the OCO2 Model Intercomparison Project (MIP)
<p>These are fossil CO<sub>2</sub> fluxes updated through August 2024 for atmospheric CO<sub>2</sub> modeling. They were constructed primarily to be used for the OCO2 Model Intercomparison Project (MIP).</p> <ul> <li>For 2000-2022, they're based on <a href="https://db.cger.nies.go.jp/dataset/ODIAC/DL_odiac2023.html">ODIAC 2023</a>, which in turn uses BP's energy use statistics for 2021 and 2022.</li> <li>ODIAC monthly emissions have been disaggregated to hourly using the TIMES emission factors for day of week and time of day (<a href="https://urldefense.us/v3/__https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196__;!!PvBDto6Hs4WbVuu7!YsQP_T-Vf3Fv83toql-90HY0NO5e92fR0D9kAi10tTzUd0Ugum9d3CTUMBp22qA0M-vYoU_fvd4$">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012JD018196</a>).</li> <li>For 2023 onwards, ODIAC's 2022 emissions have been scaled by the ratio of that month to 2022 emissions reported by <a href="https://www.nature.com/articles/s41597-020-00708-7">Carbon Monitor</a>, downloaded on October 15, 2024 from <a href="https://carbonmonitor.org">https://carbonmonitor.org/</a>. <ul> <li>ODIAC does not have sectoral decomposition to the degree provided by Carbon Monitor, so total ODIAC emissions for each region have been scaled by the total emission change between 2022 and each extended year reported by Carbon Monitor, i.e., power, ground transport, etc. have <strong>not</strong> been separately scaled.</li> <li>Carbon Monitor data are daily, but ODIAC emissions are monthly. So Carbon Monitor data have been aggregated to monthly totals before deriving scaling factors between 2022 and the extended years.</li> <li>Carbon Monitor reports international aviation emissions by country of origin, while ODIAC reports aviation emissions on a grid. Since there is no way to derive the points of emission for Carbon Monitor aviation emissions , all Carbon Monitor international aviation was aggregated to create a single number for each month, then that number was used to scale ODIAC's bunker fuel for each month in 2023-2024.</li> <li>CarbonMonitor data used for deriving 2023 and later emissions are now included in this dataset for convenience as netcdf files (converted from original CSV files).</li> </ul> </li> <li>Hourly global totals are given in the files as a check, in case you want to verify your units and file reading.</li> </ul> <p>These files can be downloaded from the browser, or from the command line following guides such as <a href="https://ict.ipbes.net/ipbes-ict-guide/data-and-knowledge-management/technical-guidelines/zenodo#b.-programmatically-using-r" target="_blank" rel="noopener">this</a>.</p>
Linked collectors and determiners for: FUEL - Herbário da Universidade Estadual de Londrina.
Natural history specimen data linked to collectors and determiners held within, "FUEL - Herbário da Universidade Estadual de Londrina". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/78dcee36-b0a2-4679-802d-bc0b5016518b">https://bionomia.net/dataset/78dcee36-b0a2-4679-802d-bc0b5016518b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/78dcee36-b0a2-4679-802d-bc0b5016518b">https://gbif.org/dataset/78dcee36-b0a2-4679-802d-bc0b5016518b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: UEL - Coleção Ficológica da Universidade Estadual de Londrina (FUEL-Algae).
Natural history specimen data linked to collectors and determiners held within, "UEL - Coleção Ficológica da Universidade Estadual de Londrina (FUEL-Algae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/46a95f2f-4f0c-4002-8e1a-68724e63f01d">https://bionomia.net/dataset/46a95f2f-4f0c-4002-8e1a-68724e63f01d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/46a95f2f-4f0c-4002-8e1a-68724e63f01d">https://gbif.org/dataset/46a95f2f-4f0c-4002-8e1a-68724e63f01d</a>. Formatted as a Frictionless Data package.
Research data supporting "Multiscale Molecular Modelling of ATP-Fueled Supramolecular Polymerisation and Depolymerisation"
<p>Raw research data supporting the publication Perego C. et al., <em>ChemSystemsChem</em> <strong>2021</strong>, DOI: <a href="https://doi.org/10.1002/syst.202000038">https://doi.org/10.1002/syst.202000038</a></p>
Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles - summary results
<p>Summary results for submitted article: Trash to Treasure: How the Renewable Fuel Standard can use garbage to pay for electric vehicles</p> <p> </p>
Canadian fossil fuel production, greenhouse gas emissions, emissions targets and carbon budgets
<p>This spreadsheet shows the amounts of coal, oil and natural gas produced in Canada from 2010 to 2020 using governmental sources. It includes calculations of the corresponding emissions according to a life-cycle analysis. The total greenhouse gas emissions from fossil fuels extracted annually in Canada (including those burned abroad) are computed. McGlade and Ekins (2015) proposed budgets 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 budget 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 within the period 2010-2050 already emitted.</p>
Dataset for: Using the quasi-chemical model beyond the quadruplet approximation: Density and Viscosity Models for Molten Salt Fuel Systems
<p>Contains data plotted in the figures of the manuscript with the same title (submitted, 2021). </p>
A New Reactivity Control Approach for Circulating Fuel Reactors - Dataset
<p>Dataset associated to the conference paper "A New Reactivity Control Approach for Circulating Fuel Reactors", Proceedings of the International Conference Nuclear Energy for New Europe (NENE2021), Bled, Slovenia, September 6–9, 2021</p>
Data for: Frequent fire slows microbial decomposition of newly deposited fine fuels in a pyrophilic ecosystem
<p>Fire-plant feedbacks engineer recurrent fires in pyrophilic ecosystems like savannas. The mechanisms sustaining these feedbacks may be related to plant adaptations that trigger rapid responses to fire's effects on soil. Plants adapted for high fire frequencies should quickly regrow, flower, and produce seeds that mature rapidly and disperse post-fire. We hypothesized that offspring of such plants would germinate and grow rapidly, responding to fire-generated changes in soil nutrients and biota. We conducted an experiment using longleaf pine savanna plants that were paired based on differences in reproduction and survival under annual ("more" pyrophilic) vs. less frequent ("less" pyrophilic) fire regimes. Seeds were planted in different soil inoculations from experimental fires of varying severity. The "more" pyrophilic species displayed high germination rates followed by species-specific, rapid growth responses to soil location and fire severity effects on soils. In contrast, the "less" pyrophilic species had lower germination rates that were not responsive to soil treatments. This suggests that rapid germination and growth constitute adaptations to frequent fires and that plants respond differently to fire severity effects on soil abiotic factors and microbes. Further, variable plant responses to post-fire soils may influence plant community diversity and fire-fuel feedbacks in pyrophilic ecosystems.</p>
Experimental investigation on hydrogen-rich fuel mixtures (H2/CH4/CO) doped with C6H6 in a 20 kW semi-industrial scale furnace
<p>The effects of benzene doping H2-rich fuel mixtures have been investigated in a semi-industrial furnace integrated with a recuperative burner of 20 kW of nominal power. The tested fuels consist of an H2/ CH4/CO blend, doped with a progressive addition of C6H6 (up to 5% v/v). This fuel blend represents a surrogate of a more complex Coke Oven Gas (COG) industrial mixture, an attractive by-product of coal carbonization. The relative ratios of H<sub>2</sub>, CH<sub>4,</sub> and CO correspond to the ones of a typical COG mixture. The emissions, along with the OH* and CH* chemiluminescence emissions and the flame temperatures were monitored under a wide range of equivalence ratios, i.e. Φ=0.71, 0.80, 0.91, 1.00, 1.05, 1.10, 1.20. The thermal input was kept constant at 20 kW for all the investigated cases, hence the flow rate of the fuel was decreased when C<sub>6</sub>H<sub>6</sub> was added to the reference mixture due to the increase of the lower calorific value. The exhaust gas composition was monitored by means of a Fourier Transform Infrared Spectroscopy (FTIR) analyzer from HORIBA® (HORIBA MEXA-ONE), equipped with a paramagnetic analyzer (MPA) for O2 measurements. On the other hand, OH* and CH* chemiluminescence imaging was carried out by means of an IRO (Intensified Relay Optics) and a CCD (Charge-Coupled Device) camera 1.4 M (La Vision 1392 x 1040 pixels) coupled with UV 78mm f/3.8 lens and two interferential filters to collect the chemiluminescence emitted by OH* (310 ± 10 nm) and CH* (438 ± 24 nm). Finally, in-situ flame temperature measurements were also performed by using an air-cooled suction pyrometer probe equipped with a B-type thermocouple.</p> <p> </p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Oxy-fuel Cutting Task State Image Dataset
<p><strong>Associated Paper: </strong>CNN-based Task State Estimation for Safer Automation of Oxy-fuel Metal Cutting<br><strong>Paper Status:</strong> Published (IEEE CASE 2023, doi: <a href="https://doi.org/10.1109/CASE56687.2023.10260647" target="_blank" rel="noopener">10.1109/CASE56687.2023.10260647</a>)</p> <p><strong>PAPER ABSTRACT:</strong></p> <p>The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot’s actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8% with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at <a href="https://doi.org/10.5281/zenodo.7734951">doi.org/10.5281/zenodo.7734951</a>.</p> <p><strong>DATASET DESCRIPTION:</strong></p> <p>The Oxy-fuel Cutting Task State (OCTS) dataset contains image data from footage recorded during a series of robotic oxy-fuel metal cutting experiments labeled with one of four cutting task states, identified using their prominent feature:</p> <ul> <li>Torch flame (<strong>TF</strong>): Associated with the vision system calibration task.</li> <li>Preheating pool (<strong>PP</strong>): Associated with the surface conditioning task.</li> <li>Combustion pool (<strong>CP</strong>): Associated with the combustion control task.</li> <li>Not applicable (<strong>NA</strong>): Associated with halting operations since none of the previous elements are identified; this is an anomaly.</li> </ul> <p>The dataset files consist of:</p> <ul> <li><strong>Data: </strong>Available as a ZIP archive split into 5 volumes (~2.8 GB each).</li> <li><strong>Labels:</strong> Available in JSON format.</li> <li><strong>Metadata:</strong> Available in CSV and PDF formats, contains the individual experiment set IDs, their dates and times, their total frame counts, and their label-wise frame counts.</li> </ul> <p><strong>DATASET LICENSE:</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a> (CC BY-NC 4.0).</p> <p><strong>DATA INSTRUCTIONS:</strong></p> <p>To extract the ZIP archive, download all five ZIP volumes into a common directory and extract the file <strong>dataset.zip.001</strong>. After extraction, 50 directories are obtained ('S01', 'S02', …, 'S50'). These contains the raw data (image frames) of each of the 50 individual cutting experiments. The image filenames are their frame number ('000000.jpeg', '000001.jpeg', …). All images are in JPEG format. All image filenames are their 6-digit frame number (includes leading zeroes such as in '004021.jpeg') for a particular experiment. Essentially, this is the sequential image data from the footage of each experiment.</p> <p><strong>JSON INSTRUCTIONS:</strong></p> <p>The JSON file partitions the images of each experiment set into the four labels. The first JSON level contains the experiment set ID as a string ('S01', 'S02', …, 'S50'). The second JSON level contains the four labels ('TF', 'PP', 'CP', 'NA') for each set ID. The third JSON level contains arrays of strings containing the frame number (filename without extension) of each image (e.g., ['000000', '000001', , …]). Usage of the JSON file is illustrated in the following Python code snippet:</p> <pre><code>import json with open ("labels.json") as json_file: labels = json.load(json_file) # `labels` is a dictionary. # Get all image filenames (frame numbers) from experiment `S01` in the `NA` label. labels['S01']['NA'] # returns list of image filenames (strings) #output: ['003843', '003844', …, '003978', '003979']</code></pre> <p>Thus, the labels are retrieved for each of the images in each of the set IDs.</p> <p><strong>METADATA INSTRUCTIONS:</strong></p> <p>The metadata associates the set ID of each experiment to its recording date and time. In addition, it lists the total frames of each experiment and the frame count in each of the four labels ('TF', 'PP', 'CP', 'NA'). This is available in PDF format for convenient viewing but also in CSV format.</p>
Characterizing ground and surface fuels across Sierra Nevada forests shortly after the 2012–2016 drought
<p>These files include the main processed data and R script used in the analysis for the publication <strong>Characterizing ground and surface fuels across Sierra Nevada forests </strong><strong>shortly after the 2012</strong>–<strong>2016 drought </strong>accepted in the journal Forest Ecology and Management.</p> <p><strong>Abtract</strong></p> <p>The 2012–2016 hotter drought in the Sierra Nevada, California, USA led to the mortality of millions of trees. This disruption to the disturbance regime is an example of how climate extremes can exacerbate current and future wildfire risks. In this study, we used data from an extensive network of forest plots across 13 different sites in the Sierra Nevada to characterize ground (i.e., duff) and surface fuels and their potential drivers shortly after the drought (2016–2018), but before snag (standing dead tree) fall associated with this massive tree mortality event occurred. Overall, we found high biomass of fuels for most sites (138.2 ± 21.7 Mg ha-1, Mean ± 95% CI), especially in areas lacking recent fire or active management, with values up to five times higher than estimates for pre-settlement conditions for mixed-conifer forests in the region. Four major groups of forest overstory structure were identified with some distinct fuel characteristics. These ranged from a cluster of ponderosa pine (Pinus ponderosa Douglas ex Lawson)-dominated plots with the highest density of snags, lowest live tree basal area, and lowest total ground and surface fuel biomass (mean = 90.1 Mg ha-1) to a group of giant sequoia (Sequoiadendron giganteum (Lindl.) J. Buchholz)-dominated plots with the highest live tree basal area and highest total fuel biomass (mean = 547.6 Mg ha-1). Although we found relatively weak relationships between forest characteristics and fuel loads, their inclusion in a model selection framework that also considered biophysical variables and disturbance history explained more than 80% of the observed variation in litter + fine woody debris loads. The baseline fuel conditions described here will not only inform the management of drought-impacted forests in the Sierra Nevada, but also help identify key drivers of fuel succession in a changing environment. </p>
Composition and structure of Mediterranean shrublands for fuel characterization
<p>The Catalan Forest Fire Prevention Service (SPIF) and the Forest Science and Technology center (CTFC) present a relational database containing detailed and accurate information of woody composition and structure of shrub-like formations in the permanent 100-m<sup>2</sup> shrubland plots distributed in the NE of Iberian peninsula. The datasets provide valuable information to improve fire danger prediction, to study vegetation dynamics in relation to drought and fire, or to test aerial-based methodologies with ground-based information. </p> <p>The database includes information gathered in two projects: Combuscat and MatoSeg. <strong>Combuscat, </strong>with 571 100-m<sup>2</sup> plots, aimed to characterize shrubland structure and fuel load in Catalonia and was carried out by forest specialist members of Catalan Forest Rangers (Cos d’Agents Rurals, Generalitat de Catalunya). <strong>MatoSeg</strong> aimed to complement the database with information on vascular plant composition at different scales (1-m<sup>2</sup>,100-m<sup>2</sup>, and 400-m<sup>2</sup> ) collected in a set of 24 plots: 13 plots in Catalonia, and 11 in the nearby regions of Aragon and Valencia.</p> <p> </p>
Downscaled Base, Sector and Fuel based PM2.5 from Stretched Grid Simulations using GEOS-Chem High Performance over South Asia.
<p>The <a href="https://zenodo.org/api/files/aed6de20-c761-4560-9cb8-b8f9e4e7b038/Gridded_Base_Sector_Fuel_PM25_South_Asia.mat">Gridded_Base_Sector_Fuel_PM25_South_Asia.mat</a> file that contains LAT_South_Asia, LON_South_Asia, PM25_base, PM25_sectors,PM25_fuels</p> <p>This is the order of the gridded sectors*:</p> <p>PM25_sectors(:,:,1) = AFCID;</p> <p>PM25_sectors(:,:,2) = OPEN_FIRES;</p> <p>PM25_sectors(:,:,3) = INDUSTRY;</p> <p>PM25_sectors(:,:,4) = POWER GENERATION;</p> <p>PM25_sectors(:,:,5) = RESIDENTIAL COMBUSTION;</p> <p>PM25_sectors(:,:,6) = TRANSPORT;</p> <p>PM25_sectors(:,:,7) = WASTE ;</p> <p>PM25_sectors(:,:,8) = AGRICULTURE ;</p> <p>PM25_sectors(:,:,9) = OTHER ;</p> <p>PM25_fuels(:,:,1) = BIOFUEL;</p> <p>PM25_fuels(:,:,2) = REMAINING_SOURCES;</p> <p>PM25_fuels(:,:,3) = COAL;</p> <p>PM25_fuels(:,:,4) = OIL_AND_GAS;</p> <p>PM25_fuels(:,:,5) = DUST_AND_FIRES;</p> <p>* The sectors here have been customized to prioritize particular sectors by adding lesser contributing sectors together. Please contact the author for more information on this. </p> <p>The NetCDFs contain the scaled ratios between CEDS 2019 and 2017 for Emissions for 31 species that were used in the simulations.</p>
[DATASET 8] - MICROBIAL FUEL CELLS (MFCS)
<p>In the framework of GrowBot project, Task 7.1 aims to develop autonomous MFCs that can provide energy to the robot and, in addition, do not require continuous monitoring of the cell conditions.</p> <p>Bioo's objectives are: i) to have a cell that is easy to install, ii) that does not require of a continuous maintenance and iii) that is adaptable to non-wet lands. In order to achieve the objective described, different configurations for the MFC will be tested and adapted regarding to predominant plant species, soil pH, temperature, average humidity, organic matter and mineral salt composition. New electrode materials (combinations of polymers, metals, carbon) and surface treatments (i.e. doping with catalysts, new 2D materials, and functionalization with bacteria for on-demand activation) will be developed to improve their performance. Energy harvesting and storage will be established by considering low power harvesting technologies like tunnel FET and supercapacitors.</p> <p>DS8 aims at collecting all the experimental data gathered during these activities.</p>
NewSOC, supplementary information to WT2.3 "Development of doped lanthanum chromite based fuel electrodes", WT2.5 "Electrochemical characterization of single cells"
<p>These are experimental data related to CERTH participation in NewSOC project (874577) regarding WT2.3 “Development of doped lanthanum chromite based fuel electrodes”, WT2.5 “Electrochemical characterization of single cells”. The data set includes:</p> <ul> <li>physicochemical characterization (ICP, BET, XRD and SEM) of the powders and electrodes or cells produced (SEM)</li> <li>electrochemical characterization (iV characteristics, impedance, stability tests)</li> </ul> <p> </p> <p><strong>LSCrF0.1.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.9</sub>Fe<sub>0.1</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, (B) SEM images of electrodes and cells and (C) data set including iV, EIS and stability testing for button cells using different electrolyte substrates (formulation and thickness) and oxygen electrode materials operating under steam electrolysis, co-electrolysis of steam and carbon dioxide and reversible operation in steam or carbon dioxide cycle.</p> <p><strong>LSCrF0.5.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Fe<sub>0.5</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, (B) SEM images of electrodes and cells and (C) data set including iV and EIS for button cells operating under steam electrolysis and co-electrolysis of steam and carbon dioxide. </p> <p><strong>LSCrNi0.1.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.9</sub>Ni<sub>0.1</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, (B) SEM images of electrodes and cells and (C) data set including iV and EIS for button cells operating under steam electrolysis and co-electrolysis of steam and carbon dioxide. </p> <p><strong>LSCrNi0.5.rar:</strong> The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Ni<sub>0.5</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, and (B) data set including iV and EIS for button cells operating under steam electrolysis and co-electrolysis of steam and carbon dioxide.</p> <p><strong>LSCrMn0.1.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.9</sub>Mn<sub>0.1</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, (B) SEM images of electrodes and cells and (C) data set including iV and EIS for button cells operating under steam electrolysis and co-electrolysis of steam and carbon dioxide. </p> <p><strong>LSCrF0.05Ti0.05.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.9</sub>Fe<sub>0.05</sub>Ti<sub>0.05</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM and (B) data set including iV and EIS for button cells operating under steam electrolysis, co-electrolysis of steam and carbon dioxide.</p> <p><strong>LSCrF0.05V0.05.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.9</sub>Fe<sub>0.05</sub>V<sub>0.05</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM and (B) data set including iV and EIS for button cells operating under steam electrolysis, co-electrolysis of steam and carbon dioxide.</p> <p><strong>LSCrF0.25Ti0.25.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Fe<sub>0.25</sub>Ti<sub>0.25</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM and (B) data set including iV and EIS for button cells operating under steam electrolysis, co-electrolysis of steam and carbon dioxide. </p> <p><strong>LSCrF0.25V0.25.rar</strong>: The zip folder contains data related to La<sub>0.75</sub>Sr<sub>0.25</sub>Cr<sub>0.5</sub>Fe<sub>0.25</sub>V<sub>0.25</sub>O<sub>3-δ</sub> material. (A) powder physicochemical characterization including ICP, BET, XRD and SEM, (B) SEM images of electrodes and cells and (C) data set including iV and EIS for button cells operating under steam electrolysis and co-electrolysis of steam and carbon dioxide.</p> <p><strong>Ni-GDC.rar</strong>: The zip folder contains data related to button cell stability testing using a commercial Ni-GDC fuel electrode material operating under steam electrolysis. </p> <p><strong>LARGE CELLS.rar</strong>: Data set for large cells (5x5 cm2) including iV, EIS and short-term stability testing employing Keracell III cell, Ni-GDC and LSCrF0.1 fuel electrodes operating under steam electrolysis and co-electrolysis of steam and carbon dioxide.</p> <p> </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.