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.
15
datasets available to search
ShareScore release 0.9.0
Dataset results
15 results for “offshore energy”
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>
Source data for "Halving the North Sea's offshore wind energy carbon footprint"
<p>This dataset provides source data for the paper "Halving the North Sea’s offshore wind energy carbon footprint". It contains basic geographical factors, including wind speed, water depth, and distance from shore, and environmental impact intensities, including steel, Cu, and Al use, climate change, marine ecotoxicity, and marine eutrophication impacts. For more details, please refer to https://pubs.acs.org/doi/full/10.1021/acs.est.2c02183 and https://www.sciencedirect.com/science/article/pii/S1364032122004993. </p>
Exploring Gaussian processes for short-term forecasting in offshore energy systems: Supplementary material
<p>Two supplementary videos are provided. The first video analyses the performance of wave excitation force forecasting across different horizons in a noise-free case. The second video examines the impact of noise on the forecast. Both videos include results from a Gaussian-based forecaster, an AR forecaster, and show the uncertainty bounds provided by the Gaussian forecaster. The variable analysed and forecasted in these videos is the wave excitation force.</p>
Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design
<p>Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087.</p> <p>Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. </p> <p>Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a></p>
Wind shadows from U.S. east coast offshore wind energy lease areas.
<p>Georeferenced data layers describing whole wind farm wakes (wind shadows) for use in planning and development along the U.S. east coast based on WRF simulations performed using the accompanying namelist. Full details of the analysis are provided in: Pryor and Barthelmie: Wind shadows impact planning of large offshore wind farms</p> <p> </p> <p>This work is supported by the U.S. Department of Energy (DoE) (DE-SC0016605). The research used computing resources from the National Science Foundation: Extreme Science and Engineering Discovery Environment (XSEDE) (allocation award to SCP is TG-ATM170024) and National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Data for Potential feeding sites for seabirds and marine mammals reveal large conflicting areas with offshore wind energy development worldwide
<p>This dataset contains;</p> <p>1) The dataset (sample_point_data.Rdata) to develop the Structural Equation model </p> <p>2) The spatial tiff bivariate maps for small-ranged seabird and marine mammals and fish and zooplankton biomass respectively</p> <p>3) The Potential Feeding Sites likelihood map in a tiff format</p> <p>4) The global power density at 200m </p> <p>5) Dataset of risk category and corresponding values and coordinates for spatial representation</p>
Datasets for the publication " Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil"
<p>This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil".</p> <ul> <li><strong>Bias correction</strong></li> <li>Technical data of existing farms (ABBEólica) </li> <li>Bias correction factors at the farm level</li> </ul> <p> </p> <ul> <li><strong>Demand</strong></li> <li>Simulated wind and solar power</li> <li>Biomass, nuclear, and small hydropower generation in 2019</li> <li>Raw demand data</li> <li>Updated demand </li> </ul> <p> </p> <ul> <li><strong>Hydropower time series</strong></li> <li>Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage</li> </ul> <p> </p> <ul> <li><strong>Offshore wind farms</strong></li> <li>Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity;</li> <li>Code to estimate offshore wind farm capex and opex.</li> </ul> <p> </p> <ul> <li><strong>Results of Calliope model </strong></li> <li>capacity</li> <li>carrier_prod (power generation)</li> <li>storage</li> <li>costs</li> <li>emissions</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Marine stepping-stones: Connectivity of Mytilus edulis populations between offshore energy installations
Recent papers postulate that epifaunal organisms use artificial structures as stepping-stones to spread to areas that are too distant to reach in a single generation. With thousands of artificial structures present in the North Sea, we test the hypothesis that these structures are connected by water currents and act as an interconnected reef. Population genetic structure of the Blue mussel, Mytilus edulis was expected to follow a pattern predicted by particle tracking models (PTM). Correlation between population genetic differentiation, based on microsatellite markers, and particle exchange was tested. Specimens of M. edulis were found at each location, although the PTM indicated that locations >85 km offshore were isolated from coastal sub-populations. Fixation coefficient FST correlated with the number of arrivals in the PTM. However, the number of effective migrants per generation as inferred from coalescent simulations did not show a strong correlation with the arriving particles. Isolation by distance analysis showed no increase in isolation with increasing distance and we did not find clear structure among the populations. The marine stepping-stone effect is obviously important for the distribution of M. edulis in the North Sea and it may influence ecologically comparable species in a similar way. In the absence of artificial shallow hard substrates, M. edulis would be unlikely to survive in offshore North Sea waters. Although we found an indication that FST was lower between connected locations, isolation by distance analysis showed no increase in isolation with increasing distance. Finally, we did not find clear structure among the populations.
European offshore wind farms and marine energy deployements
<p>Three distinct dataset used to forecast the development of marine energy in Europe in the upcoming three decades:</p> <p>- European offshore wind farms</p> <p>- tidal energy converter deployements in Europe</p> <p>- wave energy converter deployements in Europe</p>
Marine stepping-stones: Connectivity of Mytilus edulis populations between offshore energy installations
Open the record for dataset details and reuse information.
Analysis and Optimizing Solar Panels for Offshore Remote Wellhead Platforms as a Sustainable and Renewable Energy Source
<p>Data set</p>
Data for "Reducing operational costs of offshore HVDC energy export systems through optimized maintenance"
<p>The files in this record contain the scripts, input data and results for the maintenance model described in the above publication (will be added later).</p> <p>To use the scripts, you need the software MATLAB (min. version 2016b).</p> <p>Steps to run the model:</p> <p>1. run "ini.m"</p> <p>2. run "szenarien_~"</p> <p>3. save the results in the directory "Ergebnisse"</p> <p>4. run scripts located in the directory "Auswertungen"</p>
Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California: Data and Software
<p>These files contain the raw data and code used to analyzed wind resource and local load compatibility of offshore wind in Humboldt, California.</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.