Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
858
datasets available to search
ShareScore release 0.9.0
Dataset results
858 results for “energy data”
Trout Lake USGS Water, Energy, and Biogeochemical Budgets (WEBB) Stream Data 1975-2013
This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
Benchmark Data for AI Safety for High Energy Physics
<p><strong>Datasets for the paper "AI Safety for High Energy Physics" by Ben Nachman and Chase Shimmin (<a href="https://arxiv.org/abs/1910.08606">arXiv:1910.08606</a>)</strong></p> <p>This record contains two files: particles_jj.npz and particles_yz.npz, which contain simulated events of dijet and Z+photon production, respectively, from proton-proton collisions at sqrt(s)=13 TeV.</p> <p>The parton-level events are generated with MadGraph5 aMC@NLO, which are then passed to Pythia 8 for parton showering and hardonization, and then finally to Delphes3 for ATLAS-like detector simulation. Reconstructed calorimeter towers are clustered using the anti-kT algorithm with radius parameter R=1.0. The highest-pT jet from each event is selected, and only events with jet pT > 300 GeV are saved.</p> <p>The Npz files contain three dictionary keys:</p> <ul> <li><strong>jets</strong><strong>:</strong> (N, 4)-shape array containing the pT, eta, phi, and mass of the leading R=1.0 jet for each event</li> <li><strong>constituents:</strong> (N, 128, 3)-shape array containing the pT, eta, phi of up to 128 highest-pT constituent momenta from the leading jet cluster. Jets with fewer than 128 constituents are padded with zero values.</li> <li><strong>photons:</strong> (N, 3)-shape array containing the pT, eta, phi of the leading reconstructed photon (if any) of the event. Events with no photon are filled with zeros.</li> </ul> <p>pT and mass values are stored in units of TeV.</p>
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</p>
Energy System Time Series Suite (ESTSS) - Data Archive
<h2>Energy System Time Series Suite - Data Archive</h2> <p> </p> <p>This archive contains variously sized sets of declustered time series within the context of energy systems. These series demonstrate low discrepancy and high heterogeneity in feature space, resulting in a roughly uniform distribution within this space.</p> <p>For detailed information, please refer to the corresponding GitHub project:<br><a href="https://github.com/s-guenther/estss/">https://github.com/s-guenther/estss/</a></p> <p>For associated research, see<br><a href="https://doi.org/10.1186/s42162-024-00304-8">https://doi.org/10.1186/s42162-024-00304-8</a></p> <p>Data is provided in .csv format. The GitHub project includes a Python function to load this data as a dictionary of pandas data frames.</p> <p>Should you utilize this data, kindly also cite the associated research paper. For any queries, please feel free to reach out to us through GitHub or the contact details provided at the end of this readme file.</p> <p> </p> <h3>Folder Content</h3> <ul> <li>`ts_*.csv`: Contains declustered load profile time series in tabular format. <ul> <li>Size: `(n+1) x (m+1)`, with `n` representing time steps (1000 per series) and `m` the number of series.</li> <li>Includes a header row and index column. Headers indicate series id, and the index column numbers each time step, starting from `0`.</li> <li>The first half of the series `(m/2)` consistently display a constant sign (negative). They are sequentially numbered from 0.</li> <li>The second half `(m/2)` display varying signs. Numbering starts from `1,000,000`.</li> </ul> </li> <li>`features_*.csv`: Tabulates features corresponding to the time series. <ul> <li>Size: `(m+1) x (f+1)`, where `m` is the number of time series and `f` is the number of features</li> <li>Includes a header row and index column. Indexes represent time series id (matching `ts_*.csv` headers), and headers name the features.</li> </ul> </li> <li>`norm_space_*.csv`: Shows feature vectors in normalized feature space where time series are declustered. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x (g+1)`, where `m` is the number of timer series and `g` is the number of selected features space features. (a subset of `f` from `features_*.csv`).</li> <li>Format matches `features_*.csv`.</li> </ul> </li> <li>`info_*.csv`: Maps declustered datasets to the manifolded dataset. Provided for completeness; typically not needed by users. <ul> <li>Size: `(m+1) x 2`, with `m` as series count. Columns contain manifolded set time series ids.</li> <li>Includes an index column and a header. The index holds the remapped id of declustered series. Header `0` is non-significant.</li> </ul> </li> </ul> <p>Each `ts_*.csv`, `features_*.csv`, `norm_space_*.csv`, and `info_*.csv` file comes in four versions to accommodate various set sizes:</p> <ul> <li>`*_4096.csv`</li> <li>`*_1024.csv`</li> <li>`*_256.csv`</li> <li>`*_64.csv`</li> </ul> <p>These represent sets with 4096, 1024, 256, and 64 time series, respectively,offering different densities in feature space population. The objective is to balance computational load and resolution for individual research needs.</p> <p> </p> <h3>Contact</h3> <p>ESTSS - Energy System Time Series Suite<br>Copyright (C) 2023<br>Sebastian Günther<br>sebastian.guenther@ifes.uni-hannover.de</p> <p>Leibniz Universität Hannover<br>Institut für Elektrische Energiesysteme<br>Fachgebiet für Elektrische Energiespeichersysteme</p> <p>Leibniz University Hannover<br>Institute of Electric Power Systems<br>Electric Energy Storage Systems Section</p> <p><a href="https://www.ifes.uni-hannover.de/ees.html">https://www.ifes.uni-hannover.de/ees.html</a></p>
Agrisolar Food, Energy, and Water and economic Lifecycle Scenario (FEWLS) Tool Data
<p>Input data and baseline outputs for the Agrisolar Food, Energy, Water, and economic Lifecycle Scenario (FEWLS) Tool. Note that corresponding code is linked in the attached Github doi (https://doi.org/10.5281/zenodo.10023281). </p> <p>The FEWLS tool was developed and used in the recently submitted research article, <em>Food-energy-water and economic outcomes of agrisolar co-location in irrigated regions</em>. In general, this code takes in a ground-mounted solar PV shape file (with some auxiliary information) and generates user set lifespan predictions for food (Calorie), energy (GWh), water (m3), and economic (USD) effects due to offsetting agricultural land with solar PV energy generation. </p>
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
Peru Energy Data (Part I)
<p>The data illustrate the evolution of the Peruvian Energy System from 2005 to 2022. This information has been compiled by consulting the National Energy Balances for each respective year. It is important to note that data for primary energy, transformation, and secondary energy for the years 2010 and 2011 is either missing or incomplete. Additionally, final consumption by source includes data dating back to 1985.</p>
Data on the Swiss energy system and electric vehicles
<p>This repository gathers the data used in the paper:</p> <p>Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Giacomo Pareschi, Philipp Heer and Giovanni Sansavini. <strong>The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland. </strong><em>Energies.</em> 2021; 14(16):4812. <a href="https://doi.org/10.3390/en14164812">https://doi.org/10.3390/en14164812</a>.</p> <p>The linked code can be found <a href="https://gitlab.nccr-automation.ch/loris.dinatale/v2g-in-switzerland">here</a>.</p> <p>Small description of the different files:</p> <ul> <li><em>Car_trips.csv:</em> List of trips from different cars in Switzerland.<br> Data provided by Giacomo Pareschi and based on the result of the 2015 edition of MZMV (Bundesamt für Statistik / Bundesamt für Raumentwicklung, Verkehrsverhalten der Bevölkerung, Ergebnisse des Mikrozensus Mobilität und Verkehr 2015, Neuchâtel und Bern (2017), <a href="https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html">https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html</a> ). Each weekly profile is not representative and any result obtained with less than 50 profiles should be interpreted with extreme caution.</li> <li><em>ch.bfe.ladestellen-elektromobilitaet.json:</em> Data on the charging stations in Switzerland.<br> Online data from the Swiss Federal Office of Energy.</li> <li><em>cs_power_Home.csv</em> and<em> cs_power_Work.csv: </em>Own data on the charging powers of charging stations located at home or at work.</li> <li><em>energy_system_model_empa_results_sc_1.csv: </em>Swiss Energy System model used in our work to generate the fixed hydropower output profile.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>EVs_cap.csv:</em> Data on different EV brands, from own research.</li> <li><em>gCO2_eq_kWh_techs.csv: </em>CO2-equivalent greenhouse gas emission factors for different technologies, from own research.</li> <li><em>Heat_BEV_demand_2018.csv:</em> Electricity demand for heating and EVs in Switzerland.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>inflows_Beer.csv: </em>Data on the water inflows in the Swiss dams over the year.<br> Data provided by Michael Beer, from Beer, M. Abschätzung des Potenzials der Schweizer Speicherseen zur Lastdeckung bei Importrestriktionen. Z. Energiewirtschaft <strong>2018</strong>, 42, 1–12.</li> <li><em>MeteoSchweiz_pop_weight_2018.csv:</em> Temperature data in Switzerland taken from MeteoSwiss and population-weighted.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>Scenarios.csv </em>and <em>Scenarios+.csv: </em>Different scenarios for electricity production and consumption, generated in-house based on <ul> <li>the Energy Strategy 2050 (Kirchner, A.; Bredow, D.; Ess, F.; Grebel, T.; Hofer, P.; Kemmler, A.; Ley, A.; Piégsa, A.; Schütz, N.; Strassburg, S.; et al. Energy Perspectives, Die Energieperspektiven für die Schweiz bis 2050; Prognos AG: Basel, Switzerland, 2012), respectively</li> <li>the Energy Strategy 2050+ (Prognos AG and INFRAS AG and TEP Energy GmbH and Ecoplan AG. ENERGIEPERSPEKTIVEN 2050+ Kurzbericht. 2020).</li> </ul> </li> <li><em>transfer_15min_2018.csv: </em>Swiss power system model of electricity production and consumption in 2018.<br> Data provided by Dr. Martin Rüdisüli.</li> </ul> <p> </p>
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.
Data for - The environmental footprint of transport by car using renewable energy
<p>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.</p>
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.
Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic (data).
<p>Data for the "Eddy Kinetic Energy in the Arctic Ocean from a High-resolution Global Simulation with 1-km Arctic".</p>
Data Analysis for "Laser Cooling of a Nanomechanical Oscillator to Its Zero-Point Energy"
<p>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.</p>
Data from: "Inventory of Earth's Ice Loss and Associated Energy Uptake from 1979 to 2017"
<p>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.</p> <p>In this HDF5 dataset, we provide cumulative annual estimates of energy uptake for three components of the cryosphere in Zetajoules (10<sup>21</sup> Joules):</p> <p>1) Antarctica<br> 2) Greenland<br> 2) Glaciers<br> 3) Sea Ice</p> <p>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.</p> <p>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></p>
Underlying data of publication D. Garcia-Sanchez et al., Applied Energy, 259 (2020) 114210, DOI: 10.1016/j.apenergy.2019.114210.
<p>This data set contains the underlying data of the publication D. Garcia-Sanchez et al., Applied Energy, 259 (2020)</p> <p>The provided files contain data shown in Fig. 2, 3, 4, 5, 6, 7, and 8.</p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Associated Data: RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features
<p>Additional digital data to "RASPD+: Fast protein-ligand binding free energy prediction using simplified physicochemical features" (ChemRxiv preprint:<a href="https://doi.org/10.26434/chemrxiv.12636704.v1">https://doi.org/10.26434/chemrxiv.12636704</a>).</p> <p>Associated code can be found at: <a href="https://github.com/HITS-MCM/RASPDplus">https://github.com/HITS-MCM/RASPDplus</a></p> <p>Files:</p> <ul> <li>weights.tar.gz: contains the model weights of one random dataset split and its associated crossvalidation folds. Used for standard RASPD+ evaluation.</li> <li>additional_model_replicates.tar.gz: contains the remaining models trained on the full set of descriptors.</li> <li>external_test_sets.tar.gz: contains the descriptor tables for all external test sets used</li> <li>dude.tar.gz: contains the descriptor tables for and several identifier lists for evaluation on the Directory of Useful Decoys - Enhanced (DUD-E)</li> <li>run_outputs.tar.gz: Performance metric data and predicted values created during the model training and evaluation runs. Basis for the figures and metrics in the manuscript.</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.