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374 results for “Power Data”
Pathways to national-scale adoption of enhanced geothermal power through experience-driven cost reductions: Supplementary Data
<p>Dataset containing inputs, results, and code referenced in "Pathways to national-scale adoption of enhanced geothermal power through experience-driven cost reductions."</p> <ul> <li>"Central_Cases.zip", "NoEGS.zip", "Policy_Sensitivities.zip", "EGS_Sensitivities_Learning.zip", "EGS_Sensitivities_Other.zip", "OtherTech_Sensitivities.zip", and "IRA_Repeal_Cases.zip" contain the full set of capacity expansion model results referenced in the paper</li> <li>"GenX_EGS_HalfTimeSeries.zip" contains the source code for the modified version of the GenX electricity system capacity expansion model used in this work</li> <li>"GenX_Input_Files.zip" contains the full set of GenX inputs used in this work, including customized run files that apply inter-planning-period linkages, conditional policies, and endogenous learning-by-doing, which may be used alongside the model source code to replicate the results</li> <li>"PowerGenome_Inputs_and_Processing.zip" contains the settings files used to create GenX inputs in the PowerGenome tool, as well as post-processing scripts used to modify certain technologies and implement 2-hourly resolution</li> <li>"Costing_and_Supply_Curves.zip" contains the source code for the EGS cost model used in this work, as well as input temperature-at-depth data from Aljubran and Horne (2024). Temperature-at-depth data from Blackwell et al. (2011) is available for purchase from the SMU Geothermal Laboratory.</li> <li>"EGS_GenX_Inputs.zip" contains other EGS performance data and scripts used to build final GenX inputs</li> </ul> <p> </p>
HPCG Power Usage Data Set
<p>Node-level power samples for the HPCG benchmark workload running on 96 nodes of the Mutrino HPC system at Sandia.</p>
Data Repository for the study of Groundwater Depletion, Food Security, and Power Utility
<p>The present article is based on exploratory and qualitative research methodology for understanding irrigation system with reference to water-energy-food nexus. This article is based on both primary and secondary data. The primary data has been collected through semi-structured in-depth interviews by picking some of the samples from the field. The primary data includes data from the field which is compiled into different sections of the data management plan as follows.</p><ol><li>The socio-economic and demographic profile of the 2 Villages based on the <strong>PRA</strong> (Participatory Rural Appraisal).</li><li>The qualitative data from all types of farmers to understand the common issues and problems of the topic under research (owners as well as non-owners with all socio-economic categories) through <strong>FGD</strong> (Focused Group Discussion).</li><li>The qualitative data from <strong>Cases and Case Studies</strong> to get the deeper understanding of the research of farmers / incidence / processes from the villages to provide some unique insights to the research.</li><li>The qualitative data through <strong>Semi-Structured Interviews</strong> from other stakeholders to understand the issues and problems of the topic under research from their point of view.</li></ol>
Pre-Processed Data Sets for Marine Spatial Planning of a Wave-Powered Aquaculture Farm in the Northeast U.S.
<p>These data sets are intended for marine spatial planning applications including the modeling of wave-powered aquaculture farms. They work in tandem with the Python model developed by the SEA Lab to evaluate potential sites for this development in the Northeastern U.S. The code for this model is available on GitHub at <a href=" https://github.com/symbiotic-engineering/aquaculture">https://github.com/symbiotic-engineering/aquaculture</a>, and details about the data and model are discussed in several related publications.</p>
Robust CO2-abatement from early end-use electrification under uncertain power transition speed in China's netzero transition - Data and Plotting script
Open the record for dataset details and reuse information.
Training data for the shared task Ideology and Power Identification in Parliamentary Debates (2024)
<p>This dataset contains a selection of speeches from <a href="https://www.clarin.eu/parlamint">ParlaMint</a> corpora (version 4.0) as the training set for the shared task on "<a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">Ideology and Power Identification in Parliamentary Debates</a>" in <a href="https://clef2024.imag.fr/">CLEF 2024</a>.</p> <p>All files are tab-separated text files with the following fields:</p> <ul> <li>"<em>id</em>" is a unique (arbitrary) ID for each text.</li> <li>"<em>speaker</em>" is a unique (arbitrary) ID for each speaker. There may be multiple speeches from the same speaker.</li> <li>"<em>sex</em>" is the (binary/biological) sex of the speaker. This information is collected from varying sources (typically data published by the respective parliament), and in some cases it may be unspecified or unknown.</li> <li>"<em>text</em>" is the transcribed text of the parliamentary speech. Real examples may include line breaks, and other special sequences escaped or quoted.</li> <li>"<em>text_en</em>" is an automatic English translation of the corresponding text. This field may be empty (obviously) for speeches in English, but the translations may be missing for a small number of non-English speeches as well.</li> <li>"<em>label</em>" is the binary/numeric label. For political orientation, 0 is left and 1 is right. For power identification 0 indicates coalition (or governing party) and 1 indicates opposition.</li> </ul> <p>File names indicate the task and the parliament. We provide data from the following national and regional parliaments.</p> <ul> <li>Austria (at)</li> <li>Bosnia and Herzegovina (ba)</li> <li>Belgium (be)</li> <li>Bulgaria (bg)</li> <li>Czechia (cz)</li> <li>Denmark (dk)</li> <li>Estonia (ee) [only political orientation]</li> <li>Spain (es)</li> <li>Catalonia (es-ct)</li> <li>Galicia (es-ga)</li> <li>Basque Country (es-pv) [only power]</li> <li>Finland (fi)</li> <li>France (fr)</li> <li>Great Britain (gb)</li> <li>Greece (gr)</li> <li>Croatia (hr)</li> <li>Hungary (hu)</li> <li>Iceland (is) [only political orientation]</li> <li>Italy (it)</li> <li>Latvia (lv)</li> <li>The Netherlands (nl)</li> <li>Norway (no) [only political orientation]</li> <li>Poland (pl)</li> <li>Portugal (pt)</li> <li>Serbia (rs)</li> <li>Sweden (se) [only political orientation]</li> <li>Slovenia (si)</li> <li>Turkey (tr)</li> <li>Ukraine (ua)</li> </ul> <p>The number of training instances and the class imbalance differs for each training set. We do not provide a fixed validation split. Please see the <a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">shared task website</a> for further description of the data set and the sampling process.</p>
Unleashing the power of data through organization: Structure and connections for meaning, learning, and discovery
<p><span>Knowledge organization is needed everywhere. Its importance is marked by its pervasiveness. This paper will show many areas, tasks, and functions where proper use of Knowledge Organization, construed as broadly as the term implies, provides support for learning and understanding, for sensemaking and meaning making, for inference, and for discovery by people and computer programs and thereby will make the world a better place. The paper focuses not on metadata but rather on structuring and representing the actual data or knowledge itself and argues for more communication between the largely separated KO, Ontology, Data Modeling, and Semantic Web communities to address the many problems that need better solutions. In particular, the paper discusses the application of knowledge organization in Knowledge bases for question answering and cognitive systems; Knowledge bases for information extraction from text or multimedia; Linked data; Big data and data analytics; Electronic health records as one example; Influence diagrams (causal maps), dynamic system models, process diagrams, concept maps, and other node-link diagrams; Information systems in organizations; Knowledge organization for understanding and learning; and Knowledge transfer between domains. The paper argues for moving beyond triples to a more powerful representation using entities and multi-way relationships but not attributes.</span></p>
Data set for A Novel VNS-based Algorithm for SVC Allocation in the Brazilian Interconnected Power System
<p>This release includes the 107-bus version of the Brazilian Interconnected Power System (available <a href="https://www.sistemas-teste.com.br/">here</a>). The system consists of 107 buses, 104 lines, and 67 transformers distributed across three areas: South, Southeast, and Mato Grosso. This test system provides extensive applications for problems related to steady-state analysis.</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
Data and code for 'Global disparity in synergy of solar power and vegetation growth'
<p>The 'stepwisefit' requires Statistics and Machine Learning Toolbox installed in the MATLAB to run the code. The import data are attached.</p>
A Data Set for State and Parameter Estimation in Power Systems
<p>This data set consists of data from three power system models of different scales (IEEE 14, IEEE 118 and <a href="https://doi.org/10.5281/zenodo.2642175">PanTaGruEl</a>). For each of these systems, 5 different cases are provided, they are sorted from the least to the most "advanced" system operations.</p> <p>Data are stored in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> format (as H5T_NATIVE_FLOAT) which can be read by (mostly) any language (e.g. Python, Matlab or Julia).</p> <p><strong>Description of the different cases:</strong></p> <ul> <li><em>Case 1#</em> consists of 2000 samples. Each sample is obtained by: firstly, defining total active and reactive loads in the system which are then distributing to the buses and, secondly, dispatching generation (this is performed by running an OPF (Optimal Power Flow) with <a href="https://matpower.org/">Matpower</a>). The same distribution factors were used for every samples.</li> <li><em>Case 2#</em> is similar to <em>case 1#</em> with the addition of independent white noises to each bus load.</li> <li><em>Case 3#</em> differs from <em>case 1#</em> in that independent active and reactive bus loads are randomly drawn.</li> <li><em>Case 4# </em>is similar to <em>case 3#</em>, but some generators are randomly drawn to be in maintenance. This set of generators is independently generated for each sample.</li> <li><em>Case 5#</em> is similar to <em>case 4#</em>, plus the generation cost of each generator is randomly drawn from a predefined range. Costs are independently generated for each sample.</li> </ul> <p><strong>General Description:</strong></p> <p>Each data set case file contains the following elements:</p> <ul> <li>V (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage magnitudes,</li> <li>theta (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): Voltage phases,</li> <li>P (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Active</a> power injections (i.e. = generation - load),</li> <li>Q (<span class="math-tex">\(N_{\rm bus} \times N_{\rm sample}\)</span> matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Reactive</a> power injections,</li> <li>idgen (<span class="math-tex">\(N_{\rm gen}\)</span> vector): index of generator buses,</li> <li>id_slack: index of the bus used as <a href="https://en.wikipedia.org/wiki/Slack_bus">slack bus</a>,</li> <li>epsilon (<span class="math-tex">\(N_{\rm line} \times 2\)</span> matrix): list of the lines in the system (Each row corresponds to a line. Entries are buses’ indices.),</li> <li>b (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">susceptances</a>,</li> <li>g (<span class="math-tex">\(N_{\rm line}\)</span> vector): line <a href="https://en.wikipedia.org/wiki/Admittance">conductances</a>,</li> <li>bsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt susceptances,</li> <li>gsh (<span class="math-tex">\(N_{\rm bus}\)</span> vector): shunt conductances.</li> </ul> <p><strong>Visualization:</strong></p> <p>The data set also includes bus coordinates.</p> <p><strong>Some theory:</strong></p> <p>The <a href="https://en.wikipedia.org/wiki/Incidence_matrix">incidence matrix</a> B is defined as</p> <p><span class="math-tex">\(B_{ij} = \left\{\begin{array}{l}-1,\; \text{if line $j$ starts at bus $i$,}\\1,\; \text{if line $j$ ends at bus $i$,}\\ 0,\; \text{otherwise.} \end{array}\right.\)</span></p> <p>(“Ends” and “starts” are purely conventional, but they have to be assigned to account for the direction power flows in the system. We use the first column of epsilon as "starts" and the second one as "ends".)</p> <p>The <a href="https://en.wikipedia.org/wiki/Nodal_admittance_matrix">admittance matrix</a> Y is obtained by</p> <p><span class="math-tex">\(y = g + ib,\\ y_{\rm sh} = g_{\rm sh} + ib_{\rm sh},\\ Y = B\,{\rm diag}(y)\,B^\top + {\rm diag}(y_{\rm sh}) .\)</span></p> <p>Defining the <a href="https://en.wikipedia.org/wiki/AC_power">complex</a> power injections and voltages, respectively, as</p> <p><span class="math-tex">\(S = P + iQ,\\ \underline{V} = V \cdot e^{i \theta}, \)</span></p> <p>where <span class="math-tex">\(\cdot\)</span> denotes the element-wise product. One has the following relation</p> <p><span class="math-tex">\(S = \underline{V} \cdot {\rm conj}(Y\, \underline{V}).\)</span></p> <p>This relation is equivalent to the <a href="https://en.wikipedia.org/wiki/Power-flow_study">power flow equations</a>.</p> <ul> </ul> <p> </p>
Data from: A generalized distribution interpolated between the exponential and power law distributions and applied to pill bug (Armadillidium vulgare) walking data
<p>The walking pattern of an organism is typically designated as either a Lévy walk or a Brownian walk based on whether the frequency distribution of its linear step lengths follows a power law distribution or an exponential distribution. However, there are many cases where actual data cannot be classified into either of these categories. In this paper, we propose a general distribution that includes the power law and exponential distributions as special cases. This distribution has two parameters: one parameter represents the exponent, similar to the power law and exponential distributions, and the other is a shape parameter representing the shape of the distribution. By introducing this distribution, an intermediate distribution model can be interpolated between the power law and exponential distributions. In this study, the proposed distribution was fitted to the frequency distribution of the step length calculated from the walking data of pill bugs. The autocorrelation coefficients were also calculated from the time-series data of the step length, and the relationship between the shape parameter and time dependency was investigated. The results indicate that individuals whose step length frequency distributions are closer to the power law distribution have stronger time dependence.</p> <p>C++ program for parameter estimation of generalized distributions and source code for statistical analysis using R.</p>
Data and code from: A large-scale experiment demonstrates line marking reduces power line collision mortality for large terrestrial birds, but not bustards, in the Karoo, South Africa
<p>Line markers are widely used to mitigate bird collisions with power lines, but few studies have robustly tested their efficacy. Power line collisions are an escalating problem for several threatened bird species endemic to southern Africa, so it is critical to know whether or not marking works to adequately manage this problem. Over 8 years, a large-scale experiment was set up on 72 of 117 km of monitored transmission power lines in the eastern Karoo, South Africa, to assess whether line markers reduce bird collision mortality, particularly for Blue Cranes <i>Grus paradisea</i> and Ludwig's Bustards <i>Neotis ludwigii</i>. We tested the two marking devices commonly used in South Africa: bird flappers and static bird flight diverters. Using a before-after-control-impact design, we show that line marking reduced collision rates for Blue Cranes by 92% (95% CI 77-97%) and all large birds by 51% (95% CI 23-68%), but had no effect on bustards. Both marker types appeared similarly effective. Given that monitoring at this site also confirmed high levels of mortality of a range of species of conservation concern, we recommend that marking be widely installed on new power lines. However, other options need to be explored urgently to reduce collision mortality of bustards. Five bustard species were in the top ten list of most frequently found carcasses, and high collision rates of Ludwig's Bustards (0.68 birds·km<sup>-1</sup>·year<sup>-1</sup> uncorrected for survey biases) add to wider concerns about population level effects for this range-restricted and Endangered species. </p> <p>This dataset includes the data and R code for this journal paper.</p>
Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets
<p>Supplemental data and code for Improved air quality in China can enhance solar power performance and accelerate carbon neutrality targets</p>
A decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident
<p>This site shares a decade of cumulative radiocesium testing data for foodstuffs throughout Japan after the 2011 Fukushima Daiichi Nuclear Power Plant accident.</p> <p>The unexpected accident at the Fukushima Daiichi Nuclear Power Station in Japan, which occurred on March 11th, 2011, after the Great East Japan Earthquake and tsunami struck the north-eastern coast of Japan, released radionuclides into the environment. Today, because of the amounts of radionuclides released and their relatively long half-life, the levels of radiocesium contaminating foodstuffs remain a significant food safety concern. Foodstuffs in Japan have been sampled and monitored for <sup>134,137</sup>Cs since the accident. More than 2.5 million samples of foodstuffs have been examined with the results reported monthly during each Japanese fiscal year (FY, from April 1<sup>st</sup> to March 31<sup>st</sup>) from 2012 to 2021. A total of 5,695 samples of foodstuffs within the “general foodstuffs” category collected during this whole period and 13 foodstuffs within the “drinking water including soft drinks containing tea as a raw material” category sampled in FY 2012 were found to exceed the Japanese maximum permitted level (JML) set at 100 and 10 Bq/kg, respectively. No samples from the “milk and infant foodstuffs” category exceeded the JML (50 Bq/kg). The annual proportions of foodstuffs exceeding the JML in the “general foodstuffs” category varied between 0.37% and 2.57%, and were highest in FY 2012. The <sup>134,137</sup>Cs concentration for more than 99% of the foodstuffs monitored and reported has been low and not exceeding the JML in recent years, except for those foodstuffs that are difficult to cultivate, feed or manage, such as wild mushrooms, plants, animals and fish. The monitoring data for foodstuffs show the current status of food safety risks from <sup>134,137</sup>Cs contamination, particularly for cultured and aquaculture foodstuffs on the market in Japan.The unexpected accident at the Fukushima Daiichi Nuclear Power Station in Japan, which occurred on March 11th, 2011, after the Great East Japan Earthquake and tsunami struck the north-eastern coast of Japan, released radionuclides into the environment. Today, because of the amounts of radionuclides released and their relatively long half-life, the levels of radiocesium contaminating foodstuffs remain a significant food safety concern. Foodstuffs in Japan have been sampled and monitored for <sup>134,137</sup>Cs since the accident. More than 2.5 million samples of foodstuffs have been examined with the results reported monthly during each Japanese fiscal year (FY, from April 1<sup>st</sup> to March 31<sup>st</sup>) from 2012 to 2021. A total of 5,695 samples of foodstuffs within the “general foodstuffs” category collected during this whole period and 13 foodstuffs within the “drinking water including soft drinks containing tea as a raw material” category sampled in FY 2012 were found to exceed the Japanese maximum permitted level (JML) set at 100 and 10 Bq/kg, respectively. No samples from the “milk and infant foodstuffs” category exceeded the JML (50 Bq/kg). The annual proportions of foodstuffs exceeding the JML in the “general foodstuffs” category varied between 0.37% and 2.57%, and were highest in FY 2012. The <sup>134,137</sup>Cs concentration for more than 99% of the foodstuffs monitored and reported has been low and not exceeding the JML in recent years, except for those foodstuffs that are difficult to cultivate, feed or manage, such as wild mushrooms, plants, animals and fish. The monitoring data for foodstuffs show the current status of food safety risks from <sup>134,137</sup>Cs contamination, particularly for cultured and aquaculture foodstuffs on the market in Japan.</p>
Supporting data for "KOCL: Power Self-awareness for Arbitrary FPGA-SoC-accelerated OpenCL Applications"
<p>Supporting data for "KOCL: Power Self-awareness for Arbitrary FPGA-SoC-accelerated OpenCL Applications"</p>
Test data for the shared task Ideology and Power Identification in Parliamentary Debates 2024
<p>This dataset contains a selection of speeches from <a href="https://www.clarin.eu/parlamint">ParlaMint</a> corpora (version 4.0) as the test set for the shared task on "<a href="https://touche.webis.de/clef24/touche24-web/ideology-and-power-identification-in-parliamentary-debates.html">Ideology and Power Identification in Parliamentary Debates</a>" in <a href="https://clef2024.imag.fr/">CLEF 2024</a>. The format of the files are similar to the <a title="training set" href="10450640">training set</a>, with the exception that the labels are not provided in this data set.</p>
data and codes for paper "Understanding power-law photoluminescence decays and bimolecular recombination in lead-halide perovskites"
<p>These are the data and Matlab codes used in the paper "Understanding power-law photoluminescence decays and bimolecular recombination in lead-halide perovskites".</p>
Source research data for the article titled "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)".
<p>The source research data set developed and utilized while working on the article "A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)" for Sensors SI. The data set includes simulation results from OMNeT++ and the data to evaluate the parameters of the algorithms.</p>
DATA:Signal-Power Dependent Features Causing Non-Gaussian Noise in Natural Electromagnetic Signals Revealed by Magnetotelluric Arrays
<p>The data set mainly consists of three parts, which are respectively the longitude and latitude of all stations, signal power and noise power, and residual error obtained by different methods. Each part is divided into three folders by region.</p>
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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.