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4,230 results for “Energie”
Net-zero 1.5 °C sectorial pathways for G20 countries: energy and emissions data to inform science-based decarbonization targets
<p><span>This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). </span></p> <p><span>The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data):</span></p> <p><span>1. </span><span>Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario.</span></p> <p><span>2. </span><span>Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed.</span></p> <p><span>3. </span><span>Sector folder (CSV): The data contained are the same as those described in point 2.</span></p> <p><span>4. </span><span>Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector.</span></p> <p><span>5. </span><span>Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. </span></p> <p><span>6. </span><span>Scope emissions folder (CSV): The data contained are the same as those described in point 5.</span></p>
Dataset and description of an EnergyPLAN model of the Italian energy system in 2021
<p>This document describes an EnergyPLAN model of the Italian energy systems for the year 2021.</p> <p>The dataset, consisting of the EnergyPLAN input files necessary to run the simulation, is also provided.</p> <p>See the EnergyPlan website (<a href="https://www.energyplan.eu/">https://www.energyplan.eu/</a>) for instructions.</p>
MCNP Results for Nanocomposite Shielding in High-Energy Proton Fields
<p>Monte Carlo radiation transport results for various physics schemes (neutron + proton, neutron + proton + delta ray, and neutron + proton + delta ray + light recoil ions) and nanocomposite structural models (bulk homogenous material, hollow carbon cylinders suspended in polymer matrix, and carbon spheres in nanotube structure suspended in polymer matrix) of a polymer-carbon-nanotube nanocomposite shielding material in high-energy proton beams of 63 MeV and 105 MeV. </p>
Oxydation of the chromophore group in Venus66azF. Structures at minima on the potential energy surface.
<p>Minima on the potential energy surface obtained at the QM(PBE0-D3/6-31G*)/MM(AMBER) level.</p> <p>Th reaction path is REAC ->INT1 -> INT2 -> PROD</p>
Supporting data for "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer"
<p>This repository contains the supporting data for the publication: "Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer".</p>
Dataset for "Disentangling mechanisms responsible for wind energy effects on European bats"
<p>Data used in the study "Disentangling mechanisms responsible for wind energy effects on European bats".</p> <p><strong>site </strong>= ID attributed to the studied site ; <strong>ID_Parc</strong> = ID attributed to the studied wind farm ; <strong>night </strong>= date of the sampling ; <strong>SM4 </strong>= ID attributed to the recorder ; <strong>NbWT_1500m</strong> = number of wind turbines in a 1500m buffer ; <strong>Dist_WT</strong> = distance to the nearest wind turbine ; <strong>Dist_water</strong> = distance to the nearest water body or water course ; <strong>Dist_forest</strong> = distance to the nearest forest ; <strong>avg_temp</strong> = average temperature of the night ; <strong>Prev_wind_mod1</strong> = prevailing wind direction of the night ; <strong>count_prev_wind22.5</strong> = number of hours during which the wind direction was close (± 22.5°) to the prevailing wind direction (mode) of the night ; <strong>angle </strong>= angle between the axis wind-turbine - detector and the north direction ; <strong>MES_year</strong> = year of commissioning of the nearest wind turbines ; <strong>Model </strong>= model of the nearest wind turbine ; <strong>Rotor_diameter</strong> = diameter of the rotor of the nearest wind turbine ; <strong>Hub_height</strong> = height of the hub of the nearest wind turbine ; <strong>ID_WT</strong> = ID attributed to the nearest wind turbine</p>
Data from: Lizard richness in mainland China is more strongly correlated with energy and climatic stability than with diversification rates
<p><strong><span>Aim</span></strong><span>: Contemporary environmental, historical, and evolutionary factors are increasingly used to decipher the drivers of spatial patterns of species richness. Evidence of such correlations for Chinese reptiles is scarce and poorly understood. We therefore explored the validity of the environmental capacity, historical climatic stability, and diversification rates hypotheses on Chinese lizard richness.</span></p> <p><span><strong>Location</strong>:</span><span> Mainland China</span></p> <p><span><strong>Taxon</strong>:</span><span> Squamata: Sauria</span></p> <p><strong><span>Methods</span></strong><span>: We mapped the distribution ranges of all 237 lizards in mainland China using a combination of different datasets. We used current environmental conditions (ambient energy, environmental productivity, and habitat heterogeneity), historical climate stability indices (long-term: since ~3.3 Ma and short-term: since the Last Glacial Maximum), and mean tip diversification rates to test whether current environmental conditions, historical climate change, and diversification rates drive contemporary richness patterns of lizards in China. We applied piecewise structural equation models (pSEM) to jointly evaluate our hypotheses, considering direct and indirect effects.</span></p> <p><strong><span>Results</span></strong><span>: Chinese lizards showed latitudinal diversity gradients. We found consistent support for contemporary climatic and environmental factors' relationships with richness. Richness was also positively correlated with short-term climatic stability, but less so with long-term stability. Diversification rates were only seldom found to be positively correlated with lizard richness.</span></p> <p><span><strong>Main conclusions</strong>:</span><span> Our results support the</span> <span>environmental capacity and historical climate hypotheses, which link high richness to highly productive warm and stable regions (and low richness to cold and unstable regions). We conclude that post-speciation dispersal and short-term climatic oscillations quickly swamp the long-term signal of diversification rates and climatic fluctuations, creating strong current climate-richness associations.</span></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>
Data for "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors"
<p>Data for publication "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors".All structures of complex and input files for FMO , FMO/SMD , FMO/PCM , and COSMO calculation are provided .</p>
Input files for binding energy calculations in "Coupling finite and boundary element methods to solve the Poisson--Boltzmann equation for electrostatics in molecular solvation"
<p>Input files (meshes, pqr, cavities) for binding energy calculations in the manuscript "Coupling finite and boundary element methods to solve the Poisson--Boltzmann equation for electrostatics in molecular solvation" (preprint at https://arxiv.org/abs/2305.11886). This data set of molecular structures was originally proposed by Harris, R.C., Boschtisch, A.H., and Fenley, M.O., JCTC 9 (8) (2013) (<a href="https://doi.org/10.1021/ct300765w">https://doi.org/10.1021/ct300765w</a>). Scripts to generate results are available in https://github.com/MichalBosy/FEM_BEM_coupling/.</p>
Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis
<p>This dataset was collected to understand how Norwegian households responded to the electricity price shock due to the European energy crisis. It consists of consumer characteristics and their self-reported responses to the extraordinarily high electricity prices which were collected by a survey of 4,446 consumers. The consumer characteristics contain information about socio-demographics such as income, age, education, number of residents, residence type, residence size, and how conscious the respondents are about their electricity consumption. Furthermore, major electricity-consuming appliances are identified, such as whether the residents have an electric vehicle and how they heat their homes, and if they have a variable electricity tariff. In addition, hourly metered electricity consumption data covering October 2020 to March 2022 from a subset of 1,136 residential consumers of the surveyed households and the total hourly residential electricity consumption per Norwegian bidding area from July 2019 to July 2022as well as the hourly day-ahead electricity prices are included in the dataset. These data are interesting to researchers that aim to gain insight into the electricity consumption behaviour of the residential sector and the impact of different socio-demographic variables.</p> <p>A detailed description is available as a data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923007667">Norwegian hourly residential electricity demand data with consumer characteristics during the European energy crisis - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Dataset for the publication: Flexible copper: exploring capacity-based energy demand flexibility in the industry
<p>This file contains the inputs for the study (currently under review) "Flexible copper: exploring capacity-based energy demand flexibility in the industry"</p>
Natural lignin modulators improve bagasse saccharification of sugarcane and energy cane in field trials
<p>The burgeoning cellulosic ethanol industry necessitates advancements in enzymatic saccharification, effective pretreatments for lignin removal, and the cultivation of crops more amenable to saccharification. Studies have demonstrated that natural inhibitors of lignin biosynthesis can enhance the saccharification of lignocellulose, even in tissues generated several months post-treatment. In this study, we applied daidzin (a competitive inhibitor of coniferaldehyde dehydrogenase), piperonylic acid (a <i>quasi</i>-irreversible inhibitor of cinnamate 4-hydroxylase), and methylenedioxy cinnamic acid (a competitive inhibitor of 4-coenzyme A ligase) to 60-day-old crops of two conventional Brazilian sugarcane cultivars and two energy cane clones, bred specifically for enhanced biomass production. The resultant biomasses were evaluated for lignin content and enzymatic saccharification efficiency without additional lignin-removal pretreatments. The treatments amplified the production of fermentable sugars in both the sugarcane cultivars and energy cane clones. The most successful results softened the most recalcitrant lignocellulose to the level of the least recalcitrant of the biomasses tested. Interestingly, the softest material became even more susceptible to saccharification.</p>
Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production
<p>Dataset associated with publication "Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production"</p>
Potential energy surfaces and rovibrational line lists for thioformyl cyanide
<p>Molpro restart files (ASCII) for the XSURF program of the potential energy and dipole moment surfaces of thioformyl cyanide (HCSCN) and its fully deuterated isotopologue. Rovibrational line list (ASCII) for HCSCN obtained from RVCI calculations. Data refer to the publication <em>Thioformyl cyanide, HC(S)CN, revisited: Accurate rovibrational simulations for a molecule observed in interstellar clouds </em>(http://dx.doi.org/10.1080/00268976.2023.2262059)<em>.</em></p>
Results dataset for research paper "Ultra-long-duration energy storage anywhere: methanol with carbon cycling"
<p>Results dataset for research paper <a href="https://doi.org/10.1016/j.joule.2023.10.001">Ultra-long-duration energy storage anywhere: methanol with carbon cycling</a>. The code is also available on <a href="https://github.com/PyPSA/methanol-uldes">GitHub</a>. Results include both the raw PyPSA NetCDF networks as well as summary files for each run. The main runs from the paper are in the directory "final-main". Runs for the other countries (Ireland, France, Sweden) are in "final-IEFRSE". Runs without wind power are in "final-nowind".</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>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
A complete energy community dataset with photovoltaic generation, battery energy storage systems and electric vehicles (v1.5)
<p>This dataset represents a complete European energy community based on actual data. In this scenario, a community of 250 households was built using real energy consumption and solar generation data obtained in homes throughout Europe. In total, 200 community members were assigned solar generation, while 150 were assigned a battery storage system. From the acquired sample, new profiles were created and randomly assigned to each end-user while also receiving two electric cars with information on their capacity, state-of-charge, and usage. Furthermore, it is provided the electric vehicle chargers’ information on their location, type, and cost of operation.</p> <p> </p> <p>Version 1.5 update: <span>on the Sheet EVs, lines 29 (Capacity kW), 30 (Charge kW), and 31 (Discharge kW) were updated to the correct values.</span></p> <p> </p> <p>This work has been published in Elsevier's Data in Brief journal:<br><em> Ricardo Faia, Calvin Goncalves, Luis Gomes, Zita Vale<br> Dataset of an energy community with prosumer consumption, photovoltaic generation, battery storage, and electric vehicles<br> Data in Brief, 2023, 109218, ISSN 2352-3409<br> <a href="https://doi.org/10.1016/j.dib.2023.109218.">https://doi.org/10.1016/j.dib.2023.109218</a><br> (<a href="https://www.sciencedirect.com/science/article/pii/S2352340923003372)">https://www.sciencedirect.com/science/article/pii/S2352340923003372)</a></em></p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.</p> <p> </p> <p>Reference data used to create this dataset:</p> <ul> <li>Filtered energy profiles and renewable energy production profiles: <a href="../record/6778401">https://zenodo.org/record/6778401</a></li> </ul> <ul> <li>Battery storage systems and electric vehicles: <a href="../record/4737293">https://zenodo.org/record/4737293</a></li> </ul>
2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics
<p> </p> <p>This dataset is the second release of data for the <a href="https://www.gecad.isep.ipp.pt/ERM-competitions/2025-energy-forecast/">2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics</a></p> <p>The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.</p> <h2>Competition Outline</h2> <p>Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.</p> <p> </p> <h2>Releases Details</h2> <ul> <li><strong>v1.0</strong>: one year of data from a smart building with readings taken every 5 minutes.</li> <li><strong>v2.0</strong>: 40 days of data from a smart building with readings taken every 5 minutes.</li> <li><strong>v3.x</strong>: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).</li> <li><strong>v4.x</strong>: a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period (from 14/07/2025 to 18/07/2025).</li> </ul> <p> </p> <h2>Dataset Description</h2> <p>All releases are composed of the following data:</p> <ul> <li>Time: in hours and minutes</li> <li>Power: in Watts</li> <li>Voltage: in Volts</li> <li>Current: in Ampers</li> <li>Generation power: in Watts</li> <li>Temperature: in ºC</li> </ul>
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.