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4,230 results for “Energie”
Oblisk Energy amplifier (projekt earth)
this oblisk sits in public parks. it send out and recieves energy from the universe. it can be found usually near lay lines where energy is at its most potent. it is made of solid copper which help balance neutrons of anyone who stands near by. its coated in 18k gold and at its base are some of the most powerful crystals the planet has to offer. Source: Objaverse 1.0 / Sketchfab
2D Germanane-MXene Heterostructures for Cations Intercalation in Energy Storage Applications
<p>Raw Data of the full Article "2D Germanane-MXene Heterostructures for CationsIntercalation in Energy Storage Applications"</p> <p> </p>
UFLUX European ensemble 0.25deg daily carbon, water, and energy fluxes from 2000 - 2020
<h3>UFLUX Ensemble Europe025ddaily (European 0.25° Daily)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> offers <strong>European daily fluxes at 0.25° spatial resolution</strong>, generated using <strong>Deep Forest machine learning models</strong>. It integrates <strong>satellite-based vegetation proxies </strong>— including MODIS NIRv, GOME-2 SIF, and OCO-2 SIF — with <strong>ERA5 climate reanalysis</strong>, and is trained against <strong>ICOS eddy covariance observations</strong>. The dataset includes five core flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
UFLUX global ensemble 0.25deg monthly carbon, water, and energy fluxes from 2001 - 2021
<p> </p> <h3>UFLUX Ensemble Globe025dmonthly (Global 0.25° Monthly, 13 Members)</h3> <p><strong>Overview</strong><br>The <strong>UFLUX ensemble dataset</strong> provides <strong>global monthly fluxes at 0.25° spatial resolution</strong>, incorporating <strong>13 ensemble members</strong> derived from different combinations of satellite-based vegetation proxies and climate reanalysis data. The dataset includes five key ecosystem flux components:</p> <ul> <li> <p>Gross Primary Production (<strong>GPP</strong>)</p> </li> <li> <p>Ecosystem Respiration (<strong>RECO</strong>)</p> </li> <li> <p>Net Ecosystem Exchange (<strong>NEE</strong>)</p> </li> <li> <p>Sensible Heat Flux (<strong>H</strong>)</p> </li> <li> <p>Latent Energy Flux (<strong>LE</strong>)</p> </li> </ul> <p><strong>Ensemble Members:</strong><br>Each member combines unique satellite vegetation indices with climate datasets:</p> <ol> <li> <p>MODIS-NIRv-CFSV2</p> </li> <li> <p>MODIS-NIRv-ERA5</p> </li> <li> <p>OCO-2-CSIF-ERA5</p> </li> <li> <p>GOME-2-SIF-ERA5</p> </li> <li> <p>GOSAT-755-SIF-ERA5</p> </li> <li> <p>GOSAT-772-SIF-ERA5</p> </li> <li> <p>MODIS-NDVI-ERA5</p> </li> <li> <p>MODIS-EVI2-ERA5</p> </li> <li> <p>AVHRR-NIRv-ERA5</p> </li> <li> <p>AVHRR-NDVI-ERA5</p> </li> <li> <p>AVHRR-EVI2-ERA5</p> </li> <li> <p>MODIS-NIRv-ERA5-WY</p> </li> <li> <p>MODIS-NIRv-ERA5-NT</p> </li> </ol> <p><strong>Background and Methodology</strong><br>The <strong>Unified FLUXes (UFLUX)</strong> initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.</p> <p>Key innovations of UFLUX include:</p> <ol> <li> <p><strong>Consistent Upscaling Framework</strong>: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.</p> </li> <li> <p><strong>Hybrid Explainable ML</strong>: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).</p> </li> <li> <p><strong>Uncertainty Quantification</strong>: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.</p> </li> <li> <p><strong>Multisource Integration</strong>: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.</p> </li> <li> <p><strong>Superior Gap-Filling</strong>: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.</p> </li> <li> <p><strong>High Performance</strong>: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.</p> </li> <li> <p><strong>Community Adoption</strong>: Already used by other global upscaling projects, highlighting its reliability and impact.</p> </li> </ol> <p><strong>Applications</strong><br>UFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.</p> <p><strong>Resources</strong></p> <ul> <li><strong>UFLUX Website: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://sites.google.com/view/uflux</a></strong></li> <li> <p><strong>Code Repository</strong>: <a href="https://github.com/soonyenju/uflux" target="_new" rel="noopener">https://github.com/soonyenju/uflux</a></p> </li> <li> <p><strong>Technical & Descriptive Publication</strong>: <a href="https://doi.org/10.1080/01431161.2024.2312266" target="_new" rel="noopener">https://doi.org/10.1080/01431161.2024.2312266</a></p> </li> </ul>
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
<p>Bats subject to high rates of fatalities at wind-energy facilities are of conservation concern, but the impact on broader bat populations is difficult to assess. One reason is the poor understanding of the geographic source of individual fatalities and whether they constitute local resident individuals or migrants. Here, we used stable hydrogen isotopes, trace elements and species distribution models to determine the summer geographic origins of three different bat species (<em>Lasiurus borealis</em>, <em>L. cinereus</em>, and <em>Lasionycteris noctivagans</em>) killed at wind-energy facilities in Ohio and Maryland in the eastern United States. In Ohio, 58.4%, 78.7%, and 97.8% of all individuals of <em>L. borealis</em>, <em>L. cinereus</em>, and <em>L. noctivagans</em>, respectively, lacked evidence of movement and were likely residents. In contrast, in Maryland 22.7%, 62.9% and 72.7% of these same species were classified as residents. Our results suggest that a substantial portion of bats killed at a given wind facility are likely derived from resident populations. Finally, there is variation in the proportion of residents killed between seasons for some species and evidence of philopatry to summer roosts. Overall, these results indicate that impact of wind-energy facilities on resident bat populations may be greater than previously appreciated, but this impact is likely to vary across species and sites. Similar studies should be conducted across a boarder geographic scale to understand the impacts on bat populations from wind-energy facilities.</p>
Aligning renewable energy expansion with climate-driven range shifts
<p>Fossil fuel dependence can be reduced, in part, by renewable energy (RE) expansion. Increasingly, RE siting seeks to avoid significant impacts on biodiversity but rarely considers how species ranges will shift under climate change. Here, we undertake a systematic literature review on the topic and overlay future RE siting maps with the ranges of two threatened species under future climate scenarios to highlight this potential conflict.</p>
Engineering the Compositional Architecture of Core-Shell Upconverting Lanthanide-Doped Nanoparticles for Optimal Luminescent Donor in Resonance Energy Transfer: The Effects of Energy Migration and Storage
<p>Förster Resonance Energy Transfer (FRET) between single molecule donor (D) and acceptor (A) is well understood from fundamental perspective and is widely applied in biology, biotechnology, medical diagnostics and bio-imaging. However, the reliability of molecular FRET measurements can be affected by numerous artefacts which eventually hamper quantitative and reliable analysis, mostly due to issues with the donor and acceptor molecules. Lanthanide doped upconverting nanoparticles (UCNPs) have demonstrated their suitability as alternative donor species. Nevertheless, while they solved most disadvantageous features of organic donor molecules, such as photo-bleaching, spectral cross-excitation and emission bleed-through, the fundamental understanding and practical realizations of bio-assays with UCNP donors remain challenging. Among others, the actual donor ions in individual donor UCNPs are the numerous activator ions randomly distributed in the nanoparticle at various distances to acceptors anchored on the nanoparticle surface. Further, the power dependent, complex energy transfer upconversion and energy migration between sensitizing and activating lanthanide ions within UCNPs complicate the decay based analysis of <strong><em>D</em></strong>-<strong><em>A</em></strong> interaction. In this work, the assessment of designed virtual core-shell nanoparticle (VNP) models led us to the new designs of UCNPs, such as …@Er, Yb@Er, Yb@YbEr, which were experimentally evaluated as donor nanoparticles and compared to the simulations. Moreover, the specific properties of lanthanide-based upconversion motivated us to analyze not only steady-state luminescence and luminescence decay responses of both the UNCP donor and the sensitized acceptor, but also the effects of their luminescence rise kinetics upon RET was discussed in newly proposed disparity measurements. The presented studies help to understand the role of energy-transfer and energy migration between lanthanide ion dopants (due to their concentration and spatial distribution) and how the architecture of core-shell UCNPs affects their performance as FRET donors to organic acceptor dyes.</p>
Latency and energy characterization of 5G LDPC FEC Decoding on CPU and GPU
<p>CloudRIC is a system that meets specific reliability targets in 5G FEC processing while sharing pools of heterogeneous processors among DUs, which leads to more cost- and energy-efficient vRANs. The details of the solution are presented in <a title="CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing" href="https://doi.org/10.1145/3636534.3649381">CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing</a>. These repository provides a dataset, analyzed therein, with experiments carried out with different 5G LDPC decoding processors: (i) Intel FlexRAN library and two open-source alternative libraries on an Intel Xeon Gold 6240R CPU, and (ii) a proprietary driver on an NVIDIA GPU V100.</p> <p>See README file for a description of the dataset.</p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
The energy bands predicted by the universal HamGNN model
<p>universal_Hamiltonian.ckpt is the network weights for the universal HamGNN model. flat_systems.zip is the file contains the structures and energy bands for crystals with flat bands in GeNOME dataset. Energy_bands_prediction.zip is the file contains the structures and predicted energy bands for crystals in the test dataset of Materials Project. Energy_bands_DFT.zip is the file contains the structures and DFT calculated energy bands for crystals in the test dataset of Materials Project. </p>
Measuring frontier orbital energy levels of OLED materials using cyclic voltammetry in solution - accompanying data
<p>This dataset contains the data and the Matlab scripts to create the main figures in the published manuscript.</p>
India Onshore Wind Energy Atlas Accounting for Altitude and Land Use Restrictions and Co-Located Solar
<p>India faces the simultaneous challenges of meeting rising energy demand and reducing carbon emissions. To address these, India must transition to renewable energy sources. These high-resolution maps are used to quantify available areas for wind farms, after accounting for restrictions, including airports, buildings, protected land use, military zones, railways, roads, water bodies, waterways, wildlife and nature, high elevation and slope, and existing solar farms, to which policy-informed setback distances are applied. This study finds the wind and solar potential within available areas considering three altitudes (100 m, 150 m, 200 m) and four wind speed thresholds (5-8 m/s), and modern wind turbine and solar array dimensions. The raster files included here indicate available areas after aggregating restrictions for different combinations of altitude and wind speed threshold. Availability is indicated with a binary system in which available land is designated with a value of zero and restricted land is designated with a value of one.</p>
Surface drifters and high resolution global simulations mapping of internal tide surface energy
<p>File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the Global Drifter Program dataset. </p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>For all files semidiurnal signal is obatined from band-pass filtering.</p>
CEC EPC-19-056 Long-Duration Energy Storage Modeling Dataset
<p>Modeling dataset to study the value of long-duration energy storage (LDES) as part of the California bulk electricity system. Based on California Public Utilities Commission (CPUC) Integrated Resource Planning (IRP) proceeding 2019-2020 cycle data.</p>
Price discounts on low energy dense foods on food intake and health status
<p>The objective of this study was to observe the effects of a multi-level (30%, 15%, and 0%) randomized discount on fruits, vegetables, and non-caloric beverages on changes in dietary intake. This randomized controlled trial (RCT) comprised an 8-week baseline, a 32-week intervention, and a 16-week follow-up. 24-hour dietary recalls were conducted during the baseline period and before the intervention midpoint. In-person clinical measures were analyzed from Week 8 (end of baseline) and 24 (midpoint). This report is from an interim analysis up to the intervention period midpoint at Week 24, as the study is still ongoing. Participants with BMIs of 24.5-50 kg/m<sup>2</sup> and ages 18-70 years old who were the primary household shoppers were recruited from several New York City supermarkets, starting in September 2018. Of these, we analyzed 20 in the 30% discount group, 25 in the 15% discount group, and 19 in the 0% discount group. The 30% discount group reported greater intake of vegetables (+98.4 g ± 48.9 SD, <em>P </em>= 0.049) and diet soda (+63.3 g ± 29.3, <em>P</em> = 0.035) relative to the baseline period, compared to the 0% discount group. The clinical measures including body weight remained unchanged. The participants who experienced the COVID-19 pandemic had a marginal increase in body weight of 1.5 kg, P = 0.053. In conclusion, we observed a significant increase in intake of vegetables and diet soda in the 30% discount group relative to the 0% discount group.</p>
Potential energy surface and rovibrational line lists for thiopropynal
<p>Molpro restart files (ASCII) for the XSURF program of the potential energy and dipole moment surfaces of thiopropynal. Rovibrational line list (ASCII) obtained from RVCI calculations. Data refer to the publication <em>Rovibrational calculations without model Hamiltonians: the infrared and microwave spectra of thiopropynal (https://doi.org/<span>10.1002/qua.27378</span>).<br></em></p>
Reference Energy System for the Residential Sector
<p>This diagram illustrates a reference energy system designed for the residential sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Reference Energy System for the Industry Sector
<p>This diagram illustrates a reference energy system designed for the industry sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Reference Energy System for the Services Sector
<p>This diagram illustrates a reference energy system designed for the services sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Reference Energy System for the Transport Sector
<p>This diagram illustrates a reference energy system designed for the transport sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </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.