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374 results for “Power Data”

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zenodo40/100

Figures and data: Combining wake redirection and derating strategies in a load-constrained wind farm power maximization

Figures from the publication Combining wake redirection and derating strategies in a load-constrained wind farm power maximization.   *.fig files can be opened in <code>Matlab</code> *.csv files can be opened through a standard text editor (e.g.,<code> Notepad++</code>), imported and visualized in <code>Matlab</code> through the functions <code>>>readmatrix()</code> and <code>>>readtable()</code>

opencc-by-4.0Apr 2024View details →

zenodo40/100

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Mar 2024View details →

zenodo40/100

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Mar 2024View details →

zenodo40/100

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Mar 2024View details →

zenodo40/100

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Mar 2024View details →

zenodo40/100

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.

opencc-by-4.0Mar 2024View details →

zenodo40/100

Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power »

This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022). It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries. Five separate datasets are included: Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022). Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input. Folder (3) provides the corresponding shapefiles. Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size. Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening). Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022). Countries/territories included in the dataset:  Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Congo Republic Democratic Republic of the Congo Djibouti Egypt Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guiné-Bissau Côte d'Ivoire Kenya Lesotho Liberia Libya Madagascar Malawi Mali Mauritania Morocco Mozambique Namibia Niger Nigeria Rwanda Senegal Sierra Leone Somalia South Africa South Sudan Sudan Togo Tunisia Uganda Tanzania Zambia Zimbabwe   References Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a> See also Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10650822" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10650822</a>

opencc-by-4.0Apr 2022View details →

zenodo40/100

Data Set for Publication "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications"

This is dataset for paper published in CPEM2020 Conference Proceedings: Crotti G., Delle Femine A., Gallo D., Giordano D., Landi C., Letizia P.,S.,  Luiso M., "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications". https://doi.org/10.5281/zenodo.4153819 Excel file provides data for Figure 2 and following evaluation

opencc-by-4.0Nov 2021View details →

zenodo40/100

UK Power Station Transformer Dissolved Gas Analysis Data (2010-2015)

This dataset includes dissolved gas analysis records from coolant oil in 13 UK power station transformers for various timespans between 2010-2015. They form the basis of the paper "Assessing the impact of weak and moderate geomagnetic storms on UK power station transformers" submitted to the AGU "Space Weather" Journal by Z.M. Lewis, J.A. Wild and M. Allcock in December 2021. Please cite Lewis et al. if using these data. The authors thank D. Barker, EDF Energy Nuclear Generation, for providing these data.   The data are presented in comma separated variable format files, as described in the readme.txt file.

opencc-by-4.0Dec 2021View details →

zenodo40/100

Patient-specific processed data, code and visualisations for "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions"

Processed data and code for reproducing the main results and figures of the paper "Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions". We analysed publicly available data from subjects with drug-resistant focal epilepsy. A total of 2656 hours of long-term intracranial electroencephalography (iEEG) from 18 subjects was obtained using the "The SWEC-ETHZ iEEG Database and Algorithms" (available at <a href="http://ieeg-swez.ethz.ch">http://ieeg-swez.ethz.ch</a>) (Burrello et al., 2019). Reference A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, ‘‘Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms’’ in proceedings of the ACM/IEEE Design, Automation, and Test in Europe Conference (DATE), Florence, Italy, March 25-29, 2019.

opencc-by-4.0Jan 2022View details →

zenodo40/100

Data set for "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2"

Data sets for the publication "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2", doi:10.1021/acsnano.1c07065

opencc-by-4.0Feb 2022View details →

zenodo40/100

Data - Coherent combining of low-power optical signals based on optically amplified error feedback

This dataset contains measurement data and processing code for the results published in "Coherent combining of low-power optical signals based on optically amplified error feedback". Code for the Micro-controllers used in the work is also attached. This work was funded by the Swedish Research Council (grant VR-2015-00535).

opencc-by-4.0Apr 2022View details →

zenodo40/100

Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power

Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability. The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps. <ul> <li>R/ <ul> <li>eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data.</li> <li>gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download.</li> <li>fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript.</li> <li>combine_data.R combines solar, hydropower, and SWE/P data</li> <li>analysis.Rmd: primary script in which analyses are conducted</li> </ul> </li> <li>data/ <ul> <li>annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study</li> <li>monthly_hydro.csv: output from eia923_860.R</li> <li>monthly_solar.csv: output from eia923_860.R</li> <li>combined_variables.csv: combines variables above in one CSV</li> <li>watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.</li> </ul> </li> </ul>

opencc-by-4.0Apr 2022View details →

zenodo40/100

data for "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems"

These data were used in article "On the tuning and performance of Stand-Alone Large-Power PV irrigation systems" ( <a href="https://doi.org/10.1016/j.ecmx.2021.100175">https://doi.org/10.1016/j.ecmx.2021.100175</a>)

opencc-by-4.0Jan 2022View details →

dryad40/100

Data from: Pitcher geometry facilitates extrinsically powered 'springboard trapping' in carnivorous Nepenthes gracilis pitcher plants

<div> Carnivorous pitcher plants capture insects in cup-shaped leaves that function as motionless pitfall traps. Nepenthes gracilis, evolved a unique 'springboard' trapping mechanism that exploits the impact energy of falling raindrops to actuate a fast pivoting motion of the canopy-like pitcher lid. We superimposed multiple computerized micro-tomography images of the same pitcher to reveal distinct deformation patterns in lid-trapping N. gracilis and closely related pitfall-trapping N. rafflesiana. We found prominent differences between downward and upward lid displacement in N. gracilis only. Downward displacement was characterised by bending in two distinct deformation zones while upward displacement was accomplished by evenly distributed straightening of the entire upper rear section of the pitcher. This suggests an anisotropic impact response, which may help to maximize initial jerk forces for prey capture, as well as the subsequent damping of the oscillation. Our results point to a key role of pitcher geometry for effective 'springboard' trapping in N. gracilis. </div>

opencc-zeroSep 2022View details →

zenodo40/100

Data used for the Walther et al (2017) Lya forest power spectrum measurement

The Files "PS_dataset_*.hdf5" contain the masked datasets used in the Walther et al (2017) power spectrum analysis with or without metal masking. They also contain MCMC chains for performing the masking corrections. The README file gives basic usage instructions.

opencc-by-4.0Nov 2017View details →

zenodo40/100

Open-source quality control routine and multi-year power generation data of 175 PV systems

Description The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution. PV power measurements Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in unfiltered_pv_power_measurements.csv. Filtered power measurements are included as filtered_pv_power_measurements_sc.csv and filtered_pv_power_measurements_ac.csv. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in metadata.csv. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary). Quality control routine An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (qcpv.py). Sample code to call and run the functions in the qcpv package is available as example.py. Objective By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid. By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine.  Data usage If you use the data and/or python package in a published work please cite: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Units Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00). Power measurements are in Watt. Installed capacities (DC and AC) are in Watt-peak. Additional information A detailed discussion of the data and qcpv package is presented in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Acknowledgements  This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign.

opencc-by-4.0Dec 2021View details →

zenodo40/100

Role of bark beetle disturbance and fuel types on fire radiative power and burn severity in the Bohemian-Saxon Switzerland - Data and Material.

This data repository includes different datasets for fuel types, burn severity, fire radiative power and burned area, which were analysed and used in our paper on the Role of bark beetle disturbance and fuel types on fire radiative power and burn severity in the Bohemian-Saxon Switzerland. Study area: National Park Bohemian and Saxon Switzerland and conservation areas, Germany and Czech Republic. Burn severity: dnbr_fire22.nc – Burn severity data covering the burned area, which has been calculated with the Difference Normalized Burn Index (dNBR) using Sentinel-2 and Landsat 8, 9 images. Remote sensing images were reprojected and resampled to 10 m to ensure harmonization before index calculation. cbi.csv – Burn severity surveyed in the field in autumn 2022 as validation data for the dNBR. Contains: ID, coordinates, CBI, CBI values separated for different strata (A to E) and individual strata variables, forest type and species for intermediate trees (strata D) and tall trees (strata E), and the dNBR value that covered the plot extent. Burned area: burned_area.shp – Burned area was mapped by rangers in the Saxon Switzerland National Park and was taken from the dataset provided by the Copernicus Emergency Management Service (EMS) for the Bohemian Switzerland National Park. FRP: frp.nc - Fire Radiative Power gridded to 300 m and clipped to the burned area. FRP during the main fire spread 24/07/22 - 29/07/22.  Fuel: fueltype_bohemiansaxonswitzerland.nc/fueltype_bohemiansaxonswitzerland_postfire.nc – Raster datasets (10m spatial resolution, EPSG:32633) of fuels present in the area before and after the fire. The fuel classification system can be found in the fuel_classification.xlsx. fuel_classification.xlsx – The fuel type classification system for the study area. Fuel type ID's as seen in fueltype_bohemiansaxonswitzerland.nc (pre- and postfire).

opencc-by-4.0May 2024View details →

dryad40/100

Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data

We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift. We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight bacterial species that are particularly influential in driving our model's predictions for IBD.

opencc-zeroJun 2024View details →

zenodo40/100

High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data

<div> Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".   </div>

opencc-by-4.0Dec 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record