Find research datasets worth reusing

Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction

This Zip file contains the cartesian coordinates of optimized stationary points of the OH(2Π) + C2H4 potential energy surface published in our article “OH(2Π) + C2H4 Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (Phys. Chem. A 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>. All calculations have been performed with  Gaussian 09, Revision D.01. All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.

Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction

This Zip file contains the cartesian coordinates of optimized stationary points of the O(3P, 1D) + HCCCN(X1Σ+) potential energy surface published in our article “Reactions O(3P, 1D) + HCCCN(X1Σ+) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (J. Phys. Chem. A 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>. All calculations have been performed with  Gaussian 09, Revision D.01. All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.

Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles

This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments.

Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation

This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the  CH3OH˙+ cation. The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙+: an experimental and computational study” (Phys. Chem. Chem. Phys., 2022, 24, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F . All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.

opencc-by-4.0Nov 2022View details →

Characterization of investments profiles on the energy transition for european citizens

<ul> <li>Name: Characterization of investments profiles on the energy transition for european citizens</li> <li>Summary: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 15/10/2023</li> <li>DOI: 10.5281/zenodo.4455198</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li>Description: The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li>Section 1 - Scenarios for energy transition. <ul> <li>ID90. Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li>ID36, ID38, ID43, ID44, ID72. Percentage of money people are willing to spend/save out of their income per scenario</li> <li>ID191, ID192.. Amount of money people would spend based on an assumed case.</li> <li>ID191, ID192. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]. Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251]. Priority service provision in case of full black-outs. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[ID141, ID5, ID147]. Used for statements that best represent survey responder</li> </ul> </li> <li>Section 2 - Determinants (factors). Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li>ID100 Risk profile</li> <li>ID101 Added value</li> <li>ID102 Self-Satisfaction</li> <li>ID103 Technical Fit</li> <li>ID104 Own competence</li> <li>ID105 Knowledge</li> <li>ID106 Cost-Efficiency</li> <li>ID107 Safety</li> <li>ID108 Trust</li> <li>ID109 Autarky</li> <li>ID110 Legal</li> <li>ID111 Climate Protection</li> <li>ID112 Wellbeing</li> <li>ID113 Coziness</li> <li>ID114 Rights and Duties</li> <li>ID115 Peer-Pressure</li> <li>ID116 Socialising</li> <li>ID117 Support</li> <li>ID118 Agreement</li> <li>ID119 Brag</li> <li>ID120 Fun</li> <li>ID121 Novelty</li> <li>ID122 Trends</li> <li>ID123 Authority</li> <li>ID124 Own Significance</li> <li>ID125 Poseur</li> <li>ID2 Frugality</li> <li>ID3 Environmental concerns</li> <li>ID31 Adherence</li> <li>ID52 Commitment</li> <li>ID97 Profits</li> <li>ID99 Credit Score</li> </ul> </li> <li>Section 3 - “Socio-economic” description. Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li>ID164 Understanding of questions</li> <li>ID300 Country of residence</li> <li>ID137 Age</li> <li>ID178 Highest level of education</li> <li>ID136 Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li>Section 4 - Investment decision. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li>ID42 Affordable cost of a Regular refrigerator</li> <li>ID45 Energy efficient refrigerator costs</li> <li>ID50 Willingness to purchase an energy efficient refrigerator <ul> <li>ID65 Why no</li> <li>ID66 affordable cost of an energy efficient option</li> <li>ID67 Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li>ID47 Affordable cost of updating to a state of the art insulation on the facade</li> <li>ID56 Willingness for paying/invest <ul> <li>ID74 Why no?</li> <li>ID20 affordable cost of an energy efficient option</li> <li>ID34 Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li>ID68 Affordable cost of a solar photovoltaic system</li> <li>ID76 Willingness for paying/invest <ul> <li>ID84 Why no?</li> <li>ID132 Affordable cost of a photovoltaic system</li> <li>ID138 Years that amortize a  photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li>ID142 Affordable cost of an energy storage system</li> <li>ID146 Willingness for paying/invest <ul> <li>ID181 Why no? </li> <li>ID182 Affordable cost of an energy storage system </li> <li>ID183 Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li>ID140 Affordable cost of a gas boiler</li> <li>ID209 Affordable cost of an energy efficient heating system</li> <li>ID217 Willingness for paying/invest <ul> <li>ID238 Why no?</li> <li>ID239 Affordable cost of a energy efficient option</li> <li>ID241 Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li>ID41 Average kilometers traveled a typical day</li> <li>ID51 Usual travel option</li> <li>ID264 Affordable cost of a diesel or gasoline mid-range brand new car</li> <li>ID265 Affordable cost of a mid-range brand new electric car</li> <li>ID281 Willingness to buy an electric car <ul> <li>ID289 Why no?</li> <li>ID290 Affordable price of an electric car</li> <li>ID291 Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li>Section 5 - Household characterization <ul> <li>ID127 Selecting an asked value</li> <li>ID189 Type of living area</li> <li>ID202 Gender identity</li> <li>ID1 Those living in the house</li> <li>ID32 Number of inhabitants</li> <li>ID220 Average neat yearly income</li> <li>ID229 Average monthly saving</li> <li>ID240 Type of housing</li> <li>ID249 Owner / co-owner</li> <li>ID255 Usable area of the property (m²)</li> <li>ID263 Insulation level</li> <li>ID270 Climate zone</li> <li>ID86 Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li>ID87 Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li>ID88 Level of self-awareness about  energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li>ID89 Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li>ID190 feedback about survey</li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: anonymization, data fusion, imputation of gaps.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned</li> <li>Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li>Technical aspects: </li> <li>Other:</li> </ul>

opencc-by-sa-4.0Nov 2023View details →

Minute-timescale free-energy calculations reveal a pseudo-active state in the adenosine A2A receptor activation mechanism

Dataset of the paper "Minute-timescale free-energy calculations reveal a pseudo-active state in the adenosine A2A receptor activation mechanism" accepted for publication on ACS Chem journal.

edi48/100

Surface carbon, water and energy fluxes measured by eddy covariance at 3 sites within the Alaska Peatlands Experiment and Bonanza Creek Experimental Forest

These data are simultaneous and continuous measurements of carbon, water and energy fluxes of the terrestrial landscape. These fluxes are major regulatory drivers of the boreal climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. At the APEX project site, within Bonanza Creek Experimental Forest, this monitoring is repeated across a chronosequence of permafrost degradation; the Black Spruce site is an area of stable permafrost with intact black spruce forest (APEX gamma site), the Thermokarst site is an active thermokarst zone with considerable tree mortality (APEX betaSW site), the Fen site is within a stable treeless fen with deep active layer depth (APEX apexcon,low, and ele sites). The main variables being monitored are the instananeous fluxes of CO2, water vapor and surface energy (shortwave, longwave and net radiation), secondary variables included photosynthetically active radiation (PAR), air and soil temperatures, rainfall, snow depth, soil moisture content, wind direction and speed, and average atmospheric concentrations of CO2 and H2O through the year.

openOpenJan 2013View details →

edi48/100

Atlantic sea scallop energy budget data on the Northeast U.S. Shelf, monthly in 2010 and 2012

This dataset includes monthly Atlantic sea scallop energy budget data from Georges Bank to the Mid-Atlantic Bight based on Scope For Growth (SFG) model results in 2010 and 2012. Results were supported in part by Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER). For more details please see: Zang, Z., et al. (2022) Modeling Atlantic sea scallop (Placopecten magellanicus) scope for growth on the Northeast U.S. Shelf. Fisheries Oceanography, https://doi.org/10.1111/fog.12577.

openCC (other)May 2022View details →

opencc-by-4.0Jan 2020View details →

PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)

As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.

opencc-by-4.0Jan 2020View details →

Data for - The environmental footprint of transport by car using renewable energy

Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.

opencc-zeroFeb 2020View details →

EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database

EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.

opencc-by-4.0Mar 2020View details →

Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption

Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis.

opencc-by-4.0Apr 2020View details →

Energy transfers and reflexion of infragravity waves at a dissipative beach under storm waves.

%%% Author:     Xavier Bertin (xbertin@univ-lr.fr)                                                                  %%% %%% Date:         15/04/2020                                                                                                  %%%     %%% Purpose:    This repository provides the field observations and XBeach model input  %%% %%%                required to reproduce the results presented in paper referred below.           %%% %%% Reference:    Bertin, X., Martins, K., de Bakker, A., Guérin, T., Chataigner, T.,            %%% %%%                Coulombier, T. et de Viron, O., 2020. Energy transfers and reflexion of        %%% %%%                infragravity waves at a dissipative beach under storm waves. In press        %%% %%%                to Journal of Geophysical Research-Ocean.                                                 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% *The directory Obs includes:     -The wave bulk parameters computed as explained in the paper for the 10 sensores used in this     study: the offshore ADCP1, the intertidal PT1, PT2, ADCP2/PT3, PT4, PT5, ADV/PT6, PT7/Altus, PT8     and PT9. Each file has the same format and includes: the date (YYYY MM DD), the time (HH MM SS),      the mean water depth, the spectral significant wave height Hm0, mean wave periods Tm01 and Tm02,      the discrete and continuous peak periods, the energetic wave period Tm0,-2 and the spectral     significant height of IG waves Hm0,IG.      -The spectral significant height Hm0,IG+ and mean wave period Tm02,IG+ of incoming IG waves     separated at the ADCP2 and ADV using the method of Guza et al. (1984). The two files have the same      format and includes the date (YYYY MM DD), the time (HH MM SS), Hm0,IG+ and Tm02,IG+.     -The position of each sensore measured with a geodetic GNSS and provided in the same datum as the      bathymetry used in the model (Lambert93 and mean sea level).       *The directory XBeach includes all the necessary files required to reproduce the simulations presented  in this study     -The bathymetry interpolated over a rectilinear grid, with X and Y given in Lambert93 coordinates (files     X_L93.grd and Y_L93.grd) and Z referred with respect to mean sea level (Z_L93.grd).     -The water level fluctuations measured at ADCP1 (WLevel_ADCP_201702.dat).     -The XBeach input file (params.txt) and a file providing the list of directional wave spectra     provided in the directory "Spectra_WWIII". These spectra were computed from a regional application of      WaveWatchIII over the North Atlantic Ocean and forced with CFSR wind fields but they were converted     in the format of SWAN, readable by XBeach.

opencc-by-4.0Apr 2020View details →

Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).

Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".

opencc-by-4.0Apr 2020View details →

Data Analysis for "Laser Cooling of a Nanomechanical Oscillator to Its Zero-Point Energy"

Data Analysis for the paper "Laser Cooling of a Nanomechanical Oscillator to Its Zero-Point Energy". All the original data and analysis codes in Matlab are provided. In addition, we provide a python notebook with detailed description of the data analysis.

opengpl-2.0-or-laterJul 2020View details →

Block-wise sparse matrix-vector product dataset and convolutional neural nets for estimating the run time and energy consumption of the sparse matrix-vector product

Introduction SpMV-CNN is a set of Convolutional Neural Networks (CNNs) that provide accurate estimations of the performance and energy consumption of the SpMV kernel. The proposed CNN-based models use a block-wise approach to make the CNN architecture independent of the matrix size. These models cat be trained to estimate run time as well as total, package and DRAM energy consumption at different processor frequencies. Prerequisites SpMV-CNN requires Python3 with the following packages: <pre><code>keras==2.1.6 tensorflow==1.8.0 h5py==2.7.1 matplotlib==2.1.1 scikit-learn==0.19.1 </code></pre> Obtaining the dataset The execution time and energy consumption data corresponding to the SpMV operation on a set of sparse matrices from the SuiteSparse Matrix Collection have been obtained on an Intel Xeon E5-2630 core running at frequencies 1.2, 1.6, 2.0, 2.4 GHz. The energy consumption measurements are obtained via the Intel RAPL interface and gathered at three different levels (total, package and DRAM, where total = package + DRAM) for this specific processor. The <code>spmv-cnn-dataset.tgz</code> archive contains the whole dataset, including the following HDF5 files: <pre><code>$ tree . |-- test | |-- f_1200000_b250 | | |-- output_2cubes_sphere_1200000.h5 | | |-- output_apache2_1200000.h5 | | |-- output_bcsstk36_1200000.h5 | | |-- output_cfd1_1200000.h5 | | |-- output_cfd2_1200000.h5 | | |-- output_ct20stif_1200000.h5 | | |-- output_denormal_1200000.h5 | | |-- output_Dubcova2_1200000.h5 | | |-- output_Dubcova3_1200000.h5 | | |-- output_ecology2_1200000.h5 | | |-- output_gyro_1200000.h5 | | |-- output_gyro_k_1200000.h5 | | |-- output_msc10848_1200000.h5 | | |-- output_msc23052_1200000.h5 | | |-- output_nasasrb_1200000.h5 | | |-- output_nd3k_1200000.h5 | | |-- output_offshore_1200000.h5 | | |-- output_oilpan_1200000.h5 | | |-- output_olafu_1200000.h5 | | |-- output_parabolic_fem_1200000.h5 | | |-- output_qa8fm_1200000.h5 | | |-- output_raefsky4_1200000.h5 | | |-- output_s3dkq4m2_1200000.h5 | | |-- output_s3dkt3m2_1200000.h5 | | |-- output_ship_001_1200000.h5 | | |-- output_ship_003_1200000.h5 | | |-- output_shipsec1_1200000.h5 | | |-- output_shipsec5_1200000.h5 | | |-- output_shipsec8_1200000.h5 | | |-- output_smt_1200000.h5 | | |-- output_thermomech_dM_1200000.h5 | | |-- output_thread_1200000.h5 | | `-- output_vanbody_1200000.h5 | |-- f_1600000_b250 | | |-- output_2cubes_sphere_1600000.h5 | | |—- ... | | `-- output_vanbody_1600000.h5 | |-- f_2000000_b250 | | |-- output_2cubes_sphere_2000000.h5 | | |—- ... | | `-- output_vanbody_2000000.h5 | `-- f_2400000_b250 | |-- output_2cubes_sphere_2400000.h5 | |—- ... | `-- output_vanbody_2400000.h5 |-- test_pagerank | |-- f_1200000_b250 | | |-- output_adaptive_1200000.h5 | | |-- output_cit-HepPh_1200000.h5 | | |-- output_delaunay_n22_1200000.h5 | | |-- output_email-Enron_1200000.h5 | | |-- output_email-EuAll_1200000.h5 | | |-- output_europe_osm_1200000.h5 | | |-- output_hugebubbles-00020_1200000.h5 | | |-- output_rgg_n_2_24_s0_1200000.h5 | | |-- output_road_usa_1200000.h5 | | |-- output_Stanford_1200000.h5 | | |-- output_wb-edu_1200000.h5 | | |-- output_web-BerkStan_1200000.h5 | | |-- output_web-Google_1200000.h5 | | |-- output_web-NotreDame_1200000.h5 | | |-- output_wiki-Talk_1200000.h5 | | `-- output_wiki-Vote_1200000.h5 | |-- f_1600000_b250 | | |-- output_adaptive_1600000.h5 | | |—- ... | | `-- output_wiki-Vote_1600000.h5 | |-- f_2000000_b250 | | |-- output_adaptive_2000000.h5 | | |—- ... | | `-- output_wiki-Vote_2000000.h5 | `-- f_2400000_b250 | |-- output_adaptive_2400000.h5 | |—- ... | `-- output_wiki-Vote_2400000.h5 `-- train |-- merged_energy_train_shuffle_f1200000_250.h5 |-- merged_energy_train_shuffle_f1600000_250.h5 |-- merged_energy_train_shuffle_f2000000_250.h5 `-- merged_energy_train_shuffle_f2400000_250.h5 </code></pre> The matrices contained in the merged training files (<code>merged_energy_train_shuffle_fXX00000_250.h5</code>) are the following: <pre><code>$ tree . |-- output_af_0_k101_1200000.h5 |-- output_af_1_k101_1200000.h5 |-- output_af_2_k101_1200000.h5 |-- output_af_3_k101_1200000.h5 |-- output_af_4_k101_1200000.h5 |-- output_af_5_k101_1200000.h5 |-- output_af_shell10_1200000.h5 |-- output_af_shell1_1200000.h5 |-- output_af_shell2_1200000.h5 |-- output_af_shell3_1200000.h5 |-- output_af_shell4_1200000.h5 |-- output_af_shell5_1200000.h5 |-- output_af_shell6_1200000.h5 |-- output_af_shell7_1200000.h5 |-- output_af_shell8_1200000.h5 |-- output_af_shell9_1200000.h5 |-- output_atmosmodd_1200000.h5 |-- output_atmosmodj_1200000.h5 |-- output_atmosmodl_1200000.h5 |-- output_audikw_1_1200000.h5 |-- output_BenElechi1_1200000.h5 |-- output_bmw3_2_1200000.h5 |-- output_bmw7st_1_1200000.h5 |-- output_bmwcra_1_1200000.h5 |-- output_bone010_1200000.h5 |-- output_boneS01_1200000.h5 |-- output_boneS10_1200000.h5 |-- output_bundle_adj_1200000.h5 |-- output_cage14_1200000.h5 |-- output_cage15_1200000.h5 |-- output_circuit5M_1200000.h5 |-- output_circuit5M_dc_1200000.h5 |-- output_CO_1200000.h5 |-- output_consph_1200000.h5 |-- output_CoupCons3D_1200000.h5 |-- output_crankseg_1_1200000.h5 |-- output_crankseg_2_1200000.h5 |-- output_CurlCurl_2_1200000.h5 |-- output_CurlCurl_3_1200000.h5 |-- output_CurlCurl_4_1200000.h5 |-- output_dielFilterV2real_1200000.h5 |-- output_dielFilterV3real_1200000.h5 |-- output_Emilia_923_1200000.h5 |-- output_ESOC_1200000.h5 |-- output_F1_1200000.h5 |-- output_F2_1200000.h5 |-- output_Fault_639_1200000.h5 |-- output_Freescale1_1200000.h5 |-- output_Freescale2_1200000.h5 |-- output_FullChip_1200000.h5 |-- output_G3_circuit_1200000.h5 |-- output_Ga10As10H30_1200000.h5 |-- output_Ga19As19H42_1200000.h5 |-- output_Ga3As3H12_1200000.h5 |-- output_Ga41As41H72_1200000.h5 |-- output_Ge87H76_1200000.h5 |-- output_Ge99H100_1200000.h5 |-- output_Geo_1438_1200000.h5 |-- output_gsm_106857_1200000.h5 |-- output_Hardesty3_1200000.h5 |-- output_hood_1200000.h5 |-- output_Hook_1498_1200000.h5 |-- output_human_gene1_1200000.h5 |-- output_human_gene2_1200000.h5 |-- output_inline_1_1200000.h5 |-- output_JP_1200000.h5 |-- output_kkt_power_1200000.h5 |-- output_ldoor_1200000.h5 |-- output_Long_Coup_dt0_1200000.h5 |-- output_Long_Coup_dt6_1200000.h5 |-- output_mat_104_10000_1200000.h5 |-- output_mat_104_1000_1200000.h5 |-- output_mat_104_5000_1200000.h5 |-- output_mat_112_10000_1200000.h5 |-- output_mat_112_1000_1200000.h5 |-- output_mat_112_5000_1200000.h5 |-- output_mat_120_10000_1200000.h5 |-- output_mat_120_1000_1200000.h5 |-- output_mat_120_5000_1200000.h5 |-- output_mat_128_10000_1200000.h5 |-- output_mat_128_1000_1200000.h5 |-- output_mat_128_5000_1200000.h5 |-- output_mat_16_10000_1200000.h5 |-- output_mat_16_1000_1200000.h5 |-- output_mat_16_5000_1200000.h5 |-- output_mat_24_10000_1200000.h5 |-- output_mat_24_1000_1200000.h5 |-- output_mat_24_5000_1200000.h5 |-- output_mat_32_10000_1200000.h5 |-- output_mat_32_1000_1200000.h5 |-- output_mat_32_5000_1200000.h5 |-- output_mat_40_10000_1200000.h5 |-- output_mat_40_1000_1200000.h5 |-- output_mat_40_5000_1200000.h5 |-- output_mat_48_10000_1200000.h5 |-- output_mat_48_1000_1200000.h5 |-- output_mat_48_5000_1200000.h5 |-- output_mat_56_10000_1200000.h5 |-- output_mat_56_1000_1200000.h5 |-- output_mat_56_5000_1200000.h5 |-- output_mat_64_10000_1200000.h5 |-- output_mat_64_1000_1200000.h5 |-- output_mat_64_5000_1200000.h5 |-- output_mat_72_10000_1200000.h5 |-- output_mat_72_1000_1200000.h5 |-- output_mat_72_5000_1200000.h5 |-- output_mat_80_10000_1200000.h5 |-- output_mat_80_1000_1200000.h5 |-- output_mat_80_5000_1200000.h5 |-- output_mat_8_10000_1200000.h5 |-- output_mat_8_1000_1200000.h5 |-- output_mat_8_5000_1200000.h5 |-- output_mat_88_10000_1200000.h5 |-- output_mat_88_1000_1200000.h5 |-- output_mat_88_5000_1200000.h5 |-- output_mat_96_10000_1200000.h5 |-- output_mat_96_1000_1200000.h5 |-- output_mat_96_5000_1200000.h5 |-- output_memchip_1200000.h5 |-- output_ML_Laplace_1200000.h5 |-- output_mouse_gene_1200000.h5 |-- output_msdoor_1200000.h5 |-- output_m_t1_1200000.h5 |-- output_nd12k_1200000.h5 |-- output_nd24k_1200000.h5 |-- output_nd6k_1200000.h5 |-- output_nlpkkt120_1200000.h5 |-- output_nlpkkt80_1200000.h5 |-- output_PFlow_742_1200000.h5 |-- output_pwtk_1200000.h5 |-- output_rajat31_1200000.h5 |-- output_RM07R_1200000.h5 |-- output_Rucci1_1200000.h5 |-- output_Serena_1200000.h5 |-- output_Si34H36_1200000.h5 |-- output_Si41Ge41H72_1200000.h5 |-- output_Si87H76_1200000.h5 |-- output_SiO2_1200000.h5 |-- output_sls_1200000.h5 |-- output_StocF-1465_1200000.h5 |-- output_TEM152078_1200000.h5 |-- output_TEM181302_1200000.h5 |-- output_thermal2_1200000.h5 |-- output_tmt_sym_1200000.h5 |-- output_torso1_1200000.h5 |-- output_Transport_1200000.h5 |-- output_TSOPF_FS_b300_c2_1200000.h5 |-- output_TSOPF_FS_b300_c3_1200000.h5 |-- output_TSOPF_RS_b2383_1200000.h5 |-- output_TSOPF_RS_b2383_c1_1200000.h5 |-- output_TSOPF_RS_b678_c2_1200000.h5 `-- output_x104_1200000.h5 f_1600000_b250 |-- output_af_0_k101_1600000.h5 |—- ... `-- output_x104_1600000.h5 f_2000000_b250 |-- output_af_0_k101_2000000.h5 |—- ... `-- output_x104_2000000.h5 f_2400000_b250 |-- output_af_0_k101_2400000.h5 |—- ... `-- output_x104_2400000.h5 </code></pre> Creating your own dataset If you wish to create your own training/testing dataset on a different target architecture you need to take the following steps: <ol> <li> Build the SpMV driver: <ol> <li> Go to <code>cd SpMV-driver/src</code> </li> <li> Edit makefile and set the PAPI and HDF5 install prefixes. </li> <li> Build the driver via <code>make.</code> </li> </ol> </li> <li> Run the SpMV driver:  <code>./driver <arg0> <arg1> ...</code> List of driver arguments: <pre><code>matrix = audikw_1.rb # Input matrix in rb format reps = 10000 # Number of repetitions of the operation to avoid overhead block_size_ini = 250 # Minimum block size block_size_end = 1000 # Maximum block size increment = 250 # Increment between block sizes base = 0 # Starting nnz of the matrix freq = [2400000, 2000000, 1600000, 1200000] # Operating frequency sym = 1 # If 1 the matrix is symmetric. If 0 the matrix is no-symmetric.</code></pre> Example: <code>numactl --membind 0 taskset -c 0 ./src/driver audikw_1.rb 10000 250 1000 250 0 2400000</code> Note that <code>numactl</code> and <code>taskset</code> utilities are used to guarantee both NUMA and process-to-core affinity. </li> <li> Generating the dataset: <ol> <li> Edit the <code>SpMV-driver/run_all.sh</code> and uncomment the line <code>matrices =</code> in order to launch the driver for Train_symmetric / Train_noSymmetric / Test_symmetric / Test_noSymmetric matrices. </li> <li> Edit the 3rd parameter in the command SpMV-driver/run_driver.sh: 1 for symmetric matrices, 2 for unsymmetric matrices. </li> <li> Edit the command in SpMV-driver/run_driver.sh to select the input parameters of the driver as explained before. </li> <li> Run <code>SpMV-driver/run_all.sh</code> to obtain <code>hdf5</code> files that will create the dataset. </li> </ol> </li> <li> Merging the dataset: Run the script <code>python3 SpMV-driver/merge_train_matrices.py /path/to/hdf5/matrix/files /output/path</code> to obtain a single <code>hdf5</code> file containing all data from individual <code>hdf5</code> files obtained in the previous step. This merged file is the training dataset. </li> </ol> Hyperparameter search The script <code>spmv_cnn_hyperas.py</code> performs the hyperparameter search via the Hyperas tool. This script requires the hdf5 file dataset in the directory <code>dataset/train/</code> and produces both a <code>best_model_*.json</code> and <code>best_run_*.json </code>files in the <code>results/models/</code> directory containing the model structure and hyperparameters of the best performing configuration. This script can be invoked in the following way: <code>python3 spmv_cnn_hyper.py 2400000 Time</code> where <code>2400000</code> is the operating processor frequency (2.4 GHz) at which the dataset was generated and <code>Time</code> the modeled metric. According to the labels in the dataset, the hyperparameter search can also be performed with the <code>Energy</code>, <code>EPKG</code> and <code>EDRAM</code> metrics, corresponding to the energy measured by the Intel RAPL counters from our Intel Xeon Haswell core. In our case, however, we only search hyperparameters for the <code>Time</code> and <code>Energy</code> metrics at 2.4 GHz. Other metrics and frequencies inherit the best performing model and settings from the previous configuration. Training The script <code>spmv_cnn_train.py</code> performs the training on the best performing models obtained on the previous step. For that, it uses both the <code>best_model_*.json</code> and <code>best_run_*.json</code> files obtained in the hyperparameter search. This script can be invoked in the following way: <code>python3 spmv_cnn_train.py 2400000 Time</code> where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The training should be performed per metric and frequency. The training produces a file that contains the trained weights, so the model is ready for performing inference (testing). Testing The script <code>spmv_cnn_test.py</code> performs the test on the set of testing matrices involved in the SpMV operation. This script can be invoked in the following way: <code>python3 spmv_cnn_test.py 2400000 Time</code> where <code>2400000</code> is the operating processor frequency (2.4 GHz) and <code>Time</code> the modeled metric. The test should be performed per metric and frequency. The training produces two files in the <code>results/tests/</code> directory: <ul> <li><code>Pred_*.txt</code>: This file contains the real measurements and the predictions obtained by the CNN for the individual vpos blocks of the testing matrices.</li> <li><code>Test_*.txt</code>: This file summarizes the information of <code>Pred_*.txt</code> file, showing the average relative error among the blocks of each test matrix and the total relative error, which is computed by summing up the real measurements and the predictions for all the blocks of a same matrix and computing the relative error upon those values.</li> </ul> Note that this testing step and the two previous steps (hyperparameter search and training) can be performed at once using the <code>run.sh</code> script. References Publications describing SpMV-CNN-Model: <ul> <li>Barreda, M., Dolz, M.F., Castaño, M.A. et al. Performance modeling of the sparse matrix–vector product via convolutional neural networks. J Supercomputing (2020). <a href="https://doi.org/10.1007/s11227-020-03186-1">https://doi.org/10.1007/s11227-020-03186-1</a></li> </ul> Acknowledgments The SpMV-CNN-Model research has been partially supported by: <ul> <li> Project TIN2017-82972-R “Agorithmic Techniques for Energy-Aware and Error-Resilient High Performance Computing” funded by the Spanish Ministry of Economy and Competitiveness (2018-2020). </li> <li> Project CDEIGENT/2017/04 “High Performance Computing for Neural Networks” funded by the Valencian Government. </li> <li> Project UJI-A2019-11 “Energy-Aware High Performance Computing for Deep Neural Networks” funded by the Universitat Jaume I. </li> </ul>

opencc-by-4.0Sep 2020View details →

Energy metabolic pattern of China and EU28 between 2000-2016 from subsectors to average societal levels.

This repository contains the data needed to reproduce the results in: Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., Giampietro, M., 2020. A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns. Energy Strategy Reviews. 32, 100562. https://doi.org/10.1016/j.esr.2020.100562

openmit-licenseOct 2020View details →

Data from: "Inventory of Earth's Ice Loss and Associated Energy Uptake from 1979 to 2017"

Earth’s cryosphere is a buffer to the warming of the planet and its loss must be accounted for in planetary energy budgets. Yet, even as melting ice is an evident manifestation of climate change, inventories of its energy uptake are largely lacking, based on inconsistent methods, or limited to the fraction that contributes to sea level rise. By combining recent syntheses, we undertake a systematic estimate of ice loss to show that Earth lost 40700 ± 5800 Gt of ice with a corresponding energy uptake of 13.8 ± 2.0 ZJ, from 1979 to 2017, larger than previous estimates and equivalent to the energy uptake by the deep ocean, the land and the atmosphere. The total loss is due to approximately equal contributions from Arctic sea-ice, the Antarctic and Greenland ice sheets, and glaciers. Only half of it contributed to sea level rise. From the 1980s to the 2010s, the rate of ice loss has almost tripled. In this HDF5 dataset, we provide cumulative annual estimates of energy uptake for three components of the cryosphere in Zetajoules (1021 Joules): 1) Antarctica 2) Greenland 2) Glaciers 3) Sea Ice For 1–3, we separate energy uptake contributions for the grounded and floating components. We also provide a Matlab file with code to read the fields in the dataset. Python code to read the data is available at: <a href="https://github.com/sioglaciology/energy_imbalance_cryosphere">https://github.com/sioglaciology/energy_imbalance_cryosphere</a>

opencc-by-4.0Nov 2020View details →

Underlying data of publication D. Garcia-Sanchez et al., Applied Energy, 259 (2020) 114210, DOI: 10.1016/j.apenergy.2019.114210.

This data set contains the underlying data of the publication D. Garcia-Sanchez et al., Applied Energy, 259 (2020) The provided files contain data shown in Fig. 2, 3, 4, 5, 6, 7, and 8.