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229 results for “Energy use”
Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).</p> <p>GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a></p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption <em>per capita</em> by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita</em> by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)</span></p> <p>for income group<em> i</em> and service <em>j</em></p> <p> </p> <p><strong>Eq. 2</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)</span></strong></p> <p>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a></p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Empirical primary studies where gamification is used to study energy behavior
<p>The data set is used to conduct a literature review on primary studies where gamification is used to study energy behavior; it contains original search results and after-filter results. The data set also includes a Bib file for all reviewed studies.</p>
A meta-analysis investigating the effects of energy infrastructure proximity on grouse demography and space use
<p>The increased global demand for energy will require additional tools to help guide policy and management actions to conserve wildlife. Grouse (Tetraoninae) are adversely affected by infrastructure associated with energy development, but the magnitude of effects are difficult to quantify in a singular management prescription. Advancement in monitoring and analysis techniques have allowed researchers to evaluate complex questions surrounding the effects of infrastructure on grouse populations, rapidly increasing our knowledge. To better inform management decisions, especially with the emergence of renewable energy, a quantitative synthesis of previous research evaluating the effects of infrastructure on grouse populations is needed. We reviewed studies evaluating the effect of energy infrastructure on grouse, with the main objective to determine the magnitude of effect on grouse lek attendance, resource selection, and survival to help inform future conservation actions. We modeled slope coefficients for distance to energy infrastructure, standardized by scale, on various behaviors to determine overall effect sizes in a meta-analysis. We used 93 study-result combinations from 21 studies that directly evaluated resource selection, survival, or lek attendance relative to energy infrastructure. Trends in overall effect sizes suggest an adverse effect of distance to energy infrastructure on grouse behavior; however, the combination of non-significant pooled regression slopes and high among-study heterogeneity suggest the effect of distance to energy infrastructure is context dependent. While distance to infrastructure is a common metric used in many grouse management plans, our results suggest distance to infrastructure may not be a reliable predictor of grouse behavior and the effect is context dependent making management prescriptions based solely on distance to infrastructure in a one size fits all approach difficult. Our analysis points to numerous aspects that scientists can improve upon by evaluating density in conjunction with distance to energy infrastructure as well as reporting the necessary statistics for future meta-analyses.</p>
Data used for modeling in Energy-water-land-CCUS nexus model: carbon dioxide opportunities based on optimized regional development
<p>In this dataset, the data used for modeling technologies in an energy-water-land-CCUS nexus model in Khark Island in Iran, and the main sources for gathering them are presented.</p>
Updated Projections of Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL). Compared to the first version of the dataset (<a href="https://zenodo.org/record/79880387">https://zenodo.org/record/79880387</a>), this updated dataset is based on model runs where the Inflation Reduction Act (IRA) are implemented in the model scenarios. In addition to the queried and post-processed key output variables related to residential energy sector in .csv tables, we also upload the full model output databases in this repository, so that users can query their desired model outputs.</p> <p>GCAM-USA operates within the Global Change Analysis Model (GCAM), which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a>.</p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of September 24, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2050 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the full model output databases and key output variables related to the residential energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2050 (Casper et al., 2023)</li> <li>residential energy consumption <em>per capita</em> by service, fuel, state, and income group, 2015-2050</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita </em>by service, fuel, state, and income group, 2015-2050</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2050</li> <li>estimated satiation gap (Eq.2), by service, state, and income group, 2015-2050</li> <li>residential heating service inequality (Eq.3), by state, 2015-2050</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies (including IRA)</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZ (Net-Zero by 2050)</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZ_climate</td> <td> <p>In addition to BAU, two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_{i,k} = \dfrac{\sum_j (service\ output_{i,j,k} * service\ cost_{j,k})}{GDP_{i,k}}\)</span></p> <p>for income group <em>i </em>and state <em>k</em>, that sums over all residential energy services <em>j</em>.</p> <p> </p> <p><strong>Eq. 2</strong></p> <p><span class="math-tex">\(Satiation\ Gap_{i,j,k} = \dfrac{satiation\ level_{j,k} - service\ output_{i,j,k}} {satiation\ level_{j,k}}\)</span></p> <p>for service <em>j</em>, income group <em>i</em>, and state <em>k</em>. Note that the satiation level and service output are per unit of floorspace.</p> <p> </p> <p><strong>Eq. 3</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality_j = \dfrac{S_j^{d10}}{(S_j^{d1} +S_j^{d2} + S_j^{d3} + S_j^{d4})}\)</span></strong></p> <p>for service <em>j </em>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality. Among the key output variables in this repository, we provide the residential <em>heating</em> service inequality output table as an example.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, K. C., Narayan, K. B., O'Neill, B. C., Waldhoff, S. T., Zhang, Y., & Wejnert-Depue, C. (2023). Non-parametric projections of the net-income distribution for all U.S. states for the shared socioeconomic pathways. <em>Environmental Research Letters</em>. http://iopscience.iop.org/article/10.1088/1748-9326/acf9b8.</p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"
<p>Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".</p><p>Includes:</p><ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>
Effect of Insulin Detemir on Use of Energy in Type 1 Diabetes
ClinicalTrials.gov study NCT00509925. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A meta-analysis investigating the effects of energy infrastructure proximity on grouse demography and space use
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Soft tissue can absorb surprising amounts of energy during knee exoskeleton use
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Prey-driven behavioral habitat use in a low-energy ambush predator
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Data from: A method of separating linear internal wave and vortical mode energies using shipboard ADCP velocity measurements
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Impacts of Pleistocene extinctions on the biomass and energy use of local mammal assemblages around the world
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Measures of urban form and mobility energy use indices for each census tract in the United States
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Does pre-sorting by colour using visible and high-energy violet light improve the detection of plant species in honey bee pollen baskets?
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Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems
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Snow modulates winter energy use and cold exposure across an elevation gradient in a montane ectotherm
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Energy Use Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Dataset of Synchrotron Low Energy XRF and STXM files used in a manuscript on "Compressive Sensing for Dynamic XRF Scanning"
<p>Synchrotron Low Energy XRF and STXM Dataset used in a research manuscript on "Compressive Sensing for Dynamic XRF Scanning". This dataset includes HDF5 files with XRF (/dante) and STXM (/andor) maps and metadata such as XRF lifetime and sample stage positions (/sample_motors). The dataset also includes as TIFF images various outputs such as the sparse maps, the masked areas and the results of in-painting methods. In the DAT file, there is the output of the fitted XRF data as ASCII from PyMCA. In HTML there is included the relevant part of the electronic logbook (DonkiLOG). These data were acquired during the beamtime experiments 20180178 and 20192072 in the <a href="http://www.elettra.eu/elettra-beamlines/twinmic.html">TwinMic</a> soft X-ray microscopy beamline of Elettra Sincrotrone Trieste.</p> <p> </p> <p> </p>
Dataset for Metabolic Cost Calculations of Gait using Musculoskeletal Energy Models, a Comparison Study
<p>This data set contains raw and processed data of gait analysis experiments of level and inclined walking at two speeds for 12 participants. The slopes were uphill and downhill with 8% incline. The raw data contains the output of the force plates and marker data, as well as raw measurements from an K4B2 system. Mat files are processed data: measured metabolic rate, and measured and calculated metabolic cost, as well as kinetic and kinematic data of an averaged gait cycle: joint angles, velocities and moments, ground reaction forces, muscle activation, contractile element length and stimulation, and the duration of the gait cycle.</p>
ScienceDex guides
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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.