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858 results for “Energy Data”
Supplemental Data for "Energy minimization of paired composite fermion wave functions in the spherical geometry"
<p>Includes extra data for "Energy minimization of paired composite fermion wave functions in the spherical geometry".</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
US Department of Energy funded publications from Astrophysics Data System
<p>The John G. Wolbach Library, in collaboration with the NASA Astrophysics Data System (ADS), has compiled this bibliography of United States Department of Energy (DOE) funded publications. Grants were identified within the ADS by regular expression matching against article full text. There are over 70,000 articles in the bibliography, with bibliographic information for each publication. This bibliography is being released to explore its possible usage.</p> <p>Please note:</p> <p>Papers may include arXiv preprints (as non-refereed, with arXiv bibcode) and their subsequent journal articles (as refereed, with journal bibcode) as separate and unlinked records</p> <p>This compilation is a snapshot in time for DOE grants and ADS papers</p> <p>The method of data collection does not differentiate between grants credited with supporting projects and those supporting individuals contributing to projects.</p> <p>Results are based on available metadata which may be incomplete. This may include incomplete records of grant identifiers, due to limitations within the ADS. However, all articles in the bibliography acknowledge the support of the DOE in some way.</p> <p>Results have not been individually verified</p> <p>All abstracts and articles in the ADS are copyrighted by the publisher, and their use is free for personal use only. For more information, please read the Terms and Conditions regulating usage of resources.</p> <p>This dataset contains bibliographic information only, no information on the grants themselves is included. A potential application of the data could be to link the bibliographic information to grant information.</p> <p><br /> Links</p> <p>Harvard-Smithsonian Center for Astrophysics John G. Wolbach Library http://www.cfa.harvard.edu/lib/information/about.html</p> <p>NASA ADS http://labs.adsabs.harvard.edu/</p> <p>USA Spending: information on US government spending, including the Department of Energy: http://www.usaspending.gov</p> <p>Grants.gov, more information on US government grants: http://www.grants.gov</p> <p>Department of Energy: http://energy.gov/</p>
Supplementary underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes additional underlying data for the publication "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"</p> <p>Contents:</p> <p>Tukey Honest Significant Difference (HSD) results for solutes 1-47 across all seven parameter sets, as *.txt. These are pairwise comparisons of results between all possible parameter sets. Significant differences are treated as p < 0.05.</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Supporting Data for Figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide"
<p>Supporting data for figures in "Localized, tidal energy extraction in Puget Sound can adjust estuary resonance and friction, modifying barotropic tides system-wide" by Preston S. Spicer, Parker MacCready, and Zhaoqing Yang. The manuscript is being considered for publication in Journal of Geophysical Research: Oceans (2024). The article analyzes the effect of a tidal turbine farm on incident and reflected tidal energy fluxes in the Salish Sea. Files are in MATLAB data and .m format with some .txt and shape files. Files named figX.m create the corresponding Figure X using provided .mat and other files. Variable names and units correspond to graphed data of each figure in the journal article.</p>
Data and code in support of "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower"
<p>This dataset contains all the data and processing needed to produce results and figures reported in the manuscript "Rethinking energy planning to mitigate environmental and climatic impacts of future African hydropower".</p> <p> </p> <p>The README file guides through the material available to support replication of the results and figures.</p>
Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"
<p>This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation.</p> <p>The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".</p>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>hydroelectric</span> <span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1011</span></span><span><span> datapoints from </span></span><span><span>11</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span> Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>battery energy storag</span><span>e </span><span>systems</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Battery energy storage is the fastest growing form of power system </span><span>flexibility, and</span><span> will be critical to integrating large shares of variable renewable energy.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>671</span></span><span><span> datapoints from </span></span><span><span>18</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the </span><span>literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>wind</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>1506</span></span><span><span> datapoints from </span></span><span><span>28</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>gas</span><span>-fired power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Natural gas supplies 23% of global </span><span>electricity, but</span><span> must be rapidly phased down to meet global decarbonisation </span><span>objectives</span></span><span><span>.</span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>620</span></span><span><span> datapoints from </span></span><span><span>14</span></span><span><span> sources</span><span>.</span></span></p> <p> </p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span>Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span><span>coal-fired power </span><span>generation</span> <span>from</span><span> the open literature. </span><span>Coal supplies 35% of global </span><span>electricity, but</span><span> must be rapidly phased down to meet global decarbonisation </span><span>objectives</span><span>.</span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> <span>345</span><span> datapoints from </span><span>12</span><span> sources</span><span>.</span> <span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Codes and Data for "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy"
<p>This file contains the analysis code and a condensed version of data to reproduce figures in the paper "Vertically resolved analysis of the Madden-Julian Oscillation highlights the role of convective transport of moist static energy". </p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>solar</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>753</span></span><span><span> datapoints from </span></span><span><span>31</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Scripts and datas for "A unified energy-constrained mesoscale parameterisation for ocean climate models".
<p>Scripts and datasets used for creating the results of a submitted work :</p> <p><strong>R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak</strong>: <em>A unified energy-constrained mesoscale parameterisation for ocean climate models. </em>(submitted in JAMES).<em><br></em></p> <p>Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017").</p> <p>The reference EKE of <a href="https://doi.org/10.1029/2023gl104688">Torres et al. (2023)</a> is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. <a href="https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18">World Ocean Atlas 2018</a>, <a href="https://gmd.copernicus.org/articles/13/3643/2020/">Tsujino et al. (2020)</a> and <a href="https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/04c79ece-3186-349a-e063-6c86abc0158c/">RAPID</a>)</p> <p>IPython notebooks for computing and plotting metrics are provided :</p> <ul> <li><em>james-eke-heat_budget.ipynb</em> : plots for heat transport and global heat storage (section 4.1)</li> <li><em>james-eke-southern_ocean.ipynb</em> : plots for Southern Ocean (section 4.2) analysis</li> <li><em>james-eke-north_atlantic.ipynb</em> : plots for North Atlantic and Labrador Sea (section 4.3) analysis</li> <li><em>james-eke-timeseries.ipynb</em> : plot 0D metric timeseries for simulations (including spin-up)</li> </ul> <p>Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres@meteo.fr) for any help in installing and using this library.</p>
Solar and meteorological data collected from the Antsiranana station (Madagascar) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Ouani station (Comores) by the ENERGY-Lab at the University of La Reunion between December 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Antananarivo station (Madagascar) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></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.