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4,230 results for “Energie”
Future Projections and Life Cycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability
<p>The dataset presents the findings of the study "Future Projections and Lifecycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability". The data results are contained in the files "Results_data.xlsx" and "LCIs and LCA results.zip," while the "Figures data.xlsx" file includes the data needed for plotting. </p>
SST data from JAXA for "Importance of Strains in Kinetic Energy Conversion for Submesoscale Processes from an Anisotropic Perspective"
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Test Results Multi energy system NS
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Consumer Readiness for EV Green Energy
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Contributions of Tropical Cyclones and Internal Tides to Deep Near-Inertial Kinetic Energy Under Eddy Modulation
<p>This dataset contains the velocity data observed by the mooring at 130°E, 15°N. It is supplementary of the paper "<em><strong>Contributions of Tropical Cyclones and Internal Tides to Deep Near-Inertial Kinetic Energy Under Eddy Modulation</strong></em>" submitted to the <em>Geophysical Research Letters</em>. If you need any more information or want to utilize the data for research purpose, please contact the author first (Dr. Zhixiang Zhang; E-mail: zzx@qdio.ac.cn).</p>
Biexciton binding energy and line width of single quantum dots at room temperature
<p>Data and vector images for main-text figures of S.J.W. Vonk, Bart A.J. Heemskerk, Robert C. Keitel, Stijn O.M. Hinterding, Jaco J. Geuchies, Arjan J. Houtepen & Freddy T. Rabouw, Biexciton binding energy and line width of single quantum dots at room temperature, <em>Nano Lett.</em> <strong>21</strong>, 5760–5766 (2021).</p>
MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing
<p><span>This is the dataset for the NCST project "</span><span>MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing</span><span>"</span></p> <p> </p> <p><span>The Georgia Tech research team developed MOVES-Matrix 3.0 based on the EPA's MOVES3 (version 3.1.0) energy use and emission rate model by running MOVES3 thousands of times on the PACE supercomputing cluster across all combinations of input variables and storing the output as lookup tables.<span> </span>MOVES-Matrix 3.0 allows on-road energy consumption and emissions modeling to be conducted more than 800 times faster than running MOVES, while it generates the exact same results, as verified in this report. <span> </span>MOVES-Matrix 3.0 was designed similarly to its predecessor, MOVES-Matrix 2014, but required extensive code modifications to accommodate changes in the MOVES3 environment (including a shift from MySQL to MariaDB and incorporation of new vehicle source sub-types and operating parameters). <span> </span>The review of the fuel and I/M scenarios indicated that MOVES3 now defines 122 modeling regions, as compared with 109 regions in MOVES 2014b (different matrices need to be developed each modeling region).<span> </span>The development of matrices for each modeling region takes approximately 15-20 days on the PACE supercomputing cluster given our assigned resources (compared with only 5-7 days to develop matrices for MOVES 2014). <span> </span>A case study of 3,000 roadway links using Atlanta's matrices confirmed that MOVES-Matrix 3.0 produces the exact same energy consumption and emissions results as MOVES3, but execution modules operate 800 times faster using MOVES-Matrix lookups than running MOVES for any single run.</span></p>
Baroclinic energy conversion in Mars Reanalyses
<p>Vertically integrated baroclinic energy conversion in the Mars Analysis Correction Data Assimilation (MACDA), Open-Access to Mars Simulated Remote Soundings (OpenMARS), and Ensemble Mars Atmospheric Reanalysis System for the ensemble average (EMARS) and representative member (EMARSmemb) for the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) eras. Note MACDA does not have an MCS era, so there are seven (7) files. Included in each file are the BCEC (time x lat x lon), Mars year (time), Areocentric Longitude (time), latitude (lat), and longitude (lon) in each file. The files are named using the convention "BCEC_[reanalysis]_[instrument].mat". BCEC units are W/m-2; areocentric longitude, latitude (north), and longitude (east) units are degrees.</p> <div> <div>See the publication for more information: Battalio, J.M., 2022. Transient Eddy Kinetic Energetics on Mars in Three Reanalysis Datasets. Journal of the Atmospheric Sciences 79, 361–382. <a href="https://doi.org/10.1175/JAS-D-21-0038.1">https://doi.org/10.1175/JAS-D-21-0038.1</a></div> </div>
Google Trend Enhanced Deep Learning Dataset for Renewable Energy Asset Price Prediction
<h3>Overview</h3> <p>This dataset accompanies the research paper titled <strong>“<a href="https://doi.org/10.1016/j.knosys.2024.112733">A Google Trend Enhanced Deep Learning Model for the Prediction of Renewable Energy Asset Price</a>”</strong> by Dr. Nachiketa Mishra, Dr. Lalatendu Mishra, Balaji Dinesh, P M Kavyassree . The study investigates the predictive efficiency of various forecasting models using oil prices and investor sentiment for renewable energy assets, specifically focusing on renewable energy ETFs such as ICLN, PBD, and QCLN.</p> <p>The dataset contains the processed inputs and raw data used in the analysis, including sentiment indices derived from Google Trends and traditional financial indices.</p> <h3>Citation :</h3> <p>Please cite this dataset as:</p> <ul> <li>Mishra, L., Dinesh, B., Kavyassree, P.M. and Mishra, N., 2024. A Google Trend enhanced deep learning model for the prediction of renewable energy asset price. <em>Knowledge-Based Systems</em>, p.112733.</li> </ul> <pre><code>@bibtex<br><br>@article{MISHRA2025112733,<br>title = {A Google Trend enhanced deep learning model for the prediction of renewable energy asset price},<br>journal = {Knowledge-Based Systems},<br>volume = {308},<br>pages = {112733},<br>year = {2025},<br>issn = {0950-7051},<br>doi = {https://doi.org/10.1016/j.knosys.2024.112733},<br>url = {https://www.sciencedirect.com/science/article/pii/S0950705124013674},<br>author = {Lalatendu Mishra and Balaji Dinesh and P.M. Kavyassree and Nachiketa Mishra},<br>}</code><code><br></code></pre> <h2>Code : </h2> <p>Refer Repository URL provided</p> <h2>Directory Structure and Description</h2> <pre><code>📦 data ├── 📂 etf-data │ ├── 📜 ICLN_INPUT.csv # Input data for ICLN │ ├── 📜 PBD_INPUT.csv # Input data for PBD │ ├── 📜 QCLN_INPUT.csv # Input data for QCLN │ └── 📂 raw-data # Original unprocessed data │ ├── 📂 market-data # ETF market prices and oil volatility (OVX) │ ├── 📂 navs # Net Asset Value (NAV) data │ └── 📂 volatility # Volatility data (GARCH and Moving Average models) ├── 📂 google-trends │ ├── 📜 keys.txt # Keywords for Google Trends search │ ├── 📂 trends │ ├── 📂 first-principal-components # Final Google Trend Index (PCA) │ ├── 📂 formatted-trends # Cleaned trends data │ └── 📂 raw-google-trends # Raw fetched Google Trends data</code></pre> <pre>Key Files</pre> <ul> <li><strong>ICLN_INPUT.csv</strong>, <strong>PBD_INPUT.csv</strong>, <strong>QCLN_INPUT.csv</strong>: Processed inputs for the prediction models of each ETF.</li> <li><strong>raw-data</strong>: Contains original data for market prices, NAVs, and volatility measures (GARCH, Moving Average).</li> <li><strong>google-trends</strong>: Data related to Google search trends, including raw, formatted, and the final index derived using Principal Component Analysis (PCA).</li> </ul> <h3>Usage Notes</h3> <ol> <li><strong>Google Trends Data</strong>: The Google Trend Index constructed from the keywords can be found in the <code>first-principal-components</code> folder. This index was a key input in the predictive models and used to construct modified indices in data>*_INPUT.csv’s.</li> <li><strong>Reproducibility</strong>: For reproducing the results from the study, you can directly use the inputs provided under <code>/data</code> to build predictive models.</li> <li><strong>Modifications</strong>: If you aim to modify or extend the dataset, be cautious of the index construction process, particularly around Principal Component Analysis (PCA) in the Google Trends data.</li> </ol> <h2>License</h2> <p>This dataset is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. You are free to share and adapt the data, provided appropriate credit is given.</p> <h2>Contact Information</h2> <p>For any questions or further information, please contact:</p> <ul> <li><strong>Dr. Nachiketa Mishra</strong>: Department of Mathematics, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India</li> <li><strong>Dr. Lalatendu Mishra</strong>: Department of Management Sciences, Indian Institute of Technology Kanpur, India</li> <li><strong>Balaji Dinesh</strong>: Department of Computer Science, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. email : <a href="mailto:balajidinesh918@gmail.com">balajidinesh918@gmail.com</a></li> </ul>
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Data associated with the study titled "Tailored anharmonic potential energy surfaces for infrared signatures"
<p><br>This repository contains the files for the computational study on "Tailored anharmonic potential energy surfaces for infrared signatures". The repository is organised into different folders as described below: </p> <p><br>================================================================<br>catechol <br>================================================================</p> <p>=============<br>1_opt: This folder contains the optimized xyz structure of catechol (B2PLYP-D3/aug-cc-pVTZ) <br>=============</p> <p>=============<br>2_pes: This folder contains all calculated potential energy surfaces (PES) and dipole moment surfaces (DMS) of catechol for the applied high-level (hl - B2PLYP), low-level (ll - r2SCAN-3c), and multilevel (ml) for all applied underlying coordinate types (FALCON and normal modes). The PES and DMS are provided in the MidasCPP sum-over-product format with the ending ".mop". The respective applied coordinates can be found in the subdirectory declared with "mol" and are given in the respective "Molecule.mmol" file format of MidasCpp. Folder declaration with e.g. "2mode, 6mode" etc. refer to a number of coordinates the PES is generated on. "2mc, 3mc" etc. refers to the respective mode-coupling level. </p> <p>/fc: refers to FALCON generated coordinates.</p> <p>/fc/full: refers to PES and DMS in the hl, ll, ml for the vibrational space with 36 FALCON coordinates. </p> <p>/fc/red: refers to PES and DMS in the hl, ll and ml for the reduced vibrational spaces of 2-mode, 6-mode and 12-modes generated with the FALCON growing scheme. </p> <p>/nc: refers to PES and DMS in the hl, ll, ml on normal modes of catechol. <br>============= </p> <p><br>===============================================================<br>uracil<br>===============================================================</p> <p>=============<br>1_opt: This folder contains the optimized xyz structure of uracil (B2PLYP-D3/aug-cc-pVTZ)<br>=============</p> <p>=============<br>2_pes: This folder contains all calculated potential energy surfaces (PES) and dipole moment surfaces (DMS) of catechol for the applied high-level (hl - B2PLYP), low-level (ll - r2SCAN-3c), and multilevel (ml) for all applied underlying coordinate types (FALCON and normal modes). The PES and DMS are provided in the MidasCPP sum-over-product format with the ending ".mop". The respective applied coordinates can be found in the subdirectory declared with "mol" and are given in the respective "Molecule.mmol" file format of MidasCpp. Folder declaration with e.g. "2mode, 6mode" etc. refer to a number of coordinates the PES is generated on. "2mc, 3mc" etc. refers to the respective mode-coupling level.</p> <p>/fc: refers to PES and DMS generated of created vibrational subspaces with the FALCON growing scheme.</p> <p>/nc/full: refers to PES and DMS in the HL, LL and ML with all normal coordinates.</p> <p>/nc/red/: refers to PES and DMS in the HL and LL for a selected number of normal coordinates.<br>=============</p> <p> </p> <p>==============================================================<br>falcon<br>==============================================================</p> <p>Input and output files for the generation of all FALCON coordinates in this work. </p>
Data and R File - The Role of Nutrient and Energy Limitation on Microbial Decomposition of Deep Podzolized Carbon: A Priming Experiment
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Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments
<p>This package contains the survey data, programs and instructions to replicate manuscript "Information Nudges, Subsidies, and Crowding Out of Attention: Field Evidence from Energy Efficiency Investments" by Matthias Rodemeier & Andreas Löschel forthcoming at JEEA.</p>
Supplementary files:TSH Promotes Right Ventricle Energy Metabolism Remodeling and Hypertrophy
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Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve
<p>Data and results of paper "Optimising Grid-Connected PV-Battery Systems for Energy Arbitrage and Frequency Containment Reserve"</p>
Using Machine Learning With Supplementary NC Code To Predict Machining Energy - Excel Documents
<p>The Excel Files Housed within this DOI represent the raw data collected during machining each of the test parts, and the excel documents made which prevent model summaries for each model created., during the execution of the, "Using Machine Learning With Supplementary NC Code to Predict Machining Energy. These files were created by Samuel D. Stencel, a Graduate Research Assistant and Purdue University.</p>
Building thermal and electrical energy hourly time series
<p>The results reported are the <strong>hourly thermal and electrical energy use</strong> for one year of a renovated five-floor small Multi-Family house with ten dwellings in different EU climates.</p> <p>A simulation-based database organized in Excel files (one per each climate) collects the hourly thermal and electrical energy use in the considered reference building (a small Multi-Family house) in three EU climates: Nordic, EU Continental, and Mediterranean considering the HVAC system solution with room booster heat pumps (HPs) developed within Horizon 2020 HAPPENING project and two reference systems. </p> <p>The building, HVAC and on-site renewable energy production systems are modeled and simulated using TRNSYS as dynamic simulation software. </p> <p>The fact that this simulation work is performed in different EU climates allows to extrapolate indications about how the HVAC system solutions work in different EU climatic contexts.</p> <p>Regarding the <strong>building</strong>, a small Multi-Family house is considered as it represents the target building for the HP-base solutions developed within the HAPPENING project. The building has five floors with two dwellings per floor with a floor area of 50 m<sup>2</sup> each, for a total floor area of 500 m<sup>2</sup>. In terms of thermal envelope performance, a renovated building has been considered, with a yearly space heating thermal demand in the range of 70 kWh/m<sup>2</sup>/y in the Nordic and EU Continental climates.</p> <p>Regarding the <strong>HVAC system</strong> solutions, the dataset reports the results obtained considering the cascade HP-based solution with room booster HPs developed in the project HAPPENING. In addition to that, a typical (reference) air-water HP system, and a gas boiler system are used as reference systems to assess and compare the HAPPENING system solution performance.</p> <p>Regarding the <strong>on-site renewable energy production system</strong>, a PV plant has been considered. The presence of an electric battery is also accounted for to allow a more complete assessment of the interactions between on-site renewable production and HVAC system consumption, highlighting the effects, advantages, and disadvantages of having also an electric storage. The PV and battery system is considered in all HVAC system solutions (HAPPENING solution, reference air-water HP system, gas boiler system) to highlight the differences in the interactions between different HVAC system solutions with PV and battery.</p> <p>The project deliverable D2.7, available in the material, reports more details about all the quantities included in the dataset, as well as a series of system performance analyses, both over representative weeks and on a yearly basis.</p> <p> </p> <p>The research leading to these results has received funding from the European Community's Horizon 2020 Programme (H2020) under grant agreement n° 957007.</p>
Frequency of sustainability indicators in open access literature of house energy components
<p>Processed data obtained from open access papers showing frequency of sustainability indicators in open access literature about PV systems and heatpumps in houses.</p> <p>Data used in paper "Systematic literature review of low energy housing electric component material sustainability".</p>
Two dataset created by APROS recording the operational condition variations of IES integrated energy system
<p>Using the APROS software, we set up two IESs: a simpler system comprising a microgrid, a steam network, and a compressed air network, where the steam network powers both the microgrid’s generator and the compressor in the compressed air network through a turbine; and a more complex system that builds on this by adding a district heating system fed by the steam network, along with battery storage and photovoltaic units in the microgrid, and a steam storage tank in the steam network. The datasets primarily consist of time-series data reflecting the status of various IES components, recorded at a 1-second resolution to capture how the system’s equipment states respond to external influences.</p> <p>The code corresponding to the dataset can be found on <a href="https://zenodo.org/records/15331160">https://zenodo.org/records/15331160</a></p>
Data from: Functional traits and environmental conditions predict community isotopic niches and energy pathways across spatial scales
1. Despite ongoing research in food web ecology and functional biogeography, the links between food-web structure, functional traits and environmental conditions across spatial scales remain poorly understood. Trophic niches, defined as the amount of energy and elemental space occupied by species and food webs, may help bridge this divide. 2. Here, we ask how the functional traits of species, the environmental conditions of habitats and the spatial scale of analysis jointly determine the characteristics of trophic niches. We used isotopic niches as a proxy of trophic niches, and conducted analyses at spatial scales ranging from local food webs and metacommunities to geographically distant sites. 3. We sampled aquatic macroinvertebrates from 104 tank bromeliads distributed across five sites from Central to South America, and compiled the macroinvertebrates' functional traits and stable isotope values (δ15N and δ13C). We assessed how isotopic niches within each bromeliad were influenced by the functional trait composition of their associated invertebrates and environmental conditions (i.e., habitat size, canopy cover, and detrital concentration). We then evaluated whether the diet of dominant predators and, consequently, energy pathways within food webs, reflected functional and environmental changes among bromeliads across sites. Finally, we determined the extent to which the isotopic niches of macroinvertebrates within each bromeliad contributed to the metacommunity isotopic niches within each site, and compared these metacommunity-level niches over biogeographic scales. 4. At the bromeliad level, isotopic niches increased with the functional richness of species in the food web and the detrital concentration in the bromeliad. The diet of top predators tracked shifts in prey biomass along gradients of canopy cover and detrital concentration. Bromeliads that grew under heterogeneous canopy cover displayed less trophic redundancy and therefore combined to form larger metacommunity isotopic niches. Finally, the size of metacommunity niches depended on within-site heterogeneity in canopy cover. 5. Our results suggest that the trophic niches occupied by food webs can predictably scale from local food webs to metacommunities to biogeographic regions. This scaling process is determined by both the functional traits of species and heterogeneity in environmental conditions.
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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.