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78 results for “Wind farms”
Data from: Wind farms affect the occurrence, abundance and population trends of small passerine birds: the case of the Dupont's lark
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Data from: Avian vulnerability to wind farm collision through the year: insights from lesser black-backed gulls (Larus fuscus) tracked from multiple breeding colonies
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Data from: A global review of Procellariiform flight height, flight speed and nocturnal activity: Implications for offshore wind farm collision risk
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Data from: Factors affecting carcass detection at wind farms using dogs and human searchers
<p>1. The use of detection dogs to effectively monitor bird and bat fatalities at wind farms is becoming increasingly popular. All studies to date agree that dogs outperform human searchers at finding bird and bat carcasses around wind turbines; however, it remains unclear how particular conditions during the search may influence carcass detection.</p> <p>2. We investigate the effect of carcass size, habitat characteristics and weather conditions on carcass detection probability, for both dogs and humans, using data from the monitoring program of a wind farm in Spain.</p> <p>3. A generalized linear model reveals a high performance of dogs (~80% detection probability), with no clear influence of any of the variables analysed. Humans, on the contrary, were markedly affected by the size of the carcass and to some extent, by the vegetation structure. Humans performed poorly at detecting small carcasses (~20% detection probability), more so in dense vegetation.</p> <p>4. Synthesis and applications. Our results provide evidence that dogs perform at a high level under a wide range of environmental conditions. They are particularly well-suited for the monitoring of fatalities of small, rare or inconspicuous species in cluttered environments. Humans, by contrast, are very poor at detecting all but the largest carcasses.16-Jun-2020</p>
Data Used for Article: Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout
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Environmental and seasonal correlates of capercaillie movement traits in a Swedish wind farm
<p>Animals continuously interact with their environment through behavioural decisions, rendering the appropriate choice of movement speed and directionality an important phenotypic trait. Anthropogenic activities may alter animal behaviour, including movement. A detailed understanding of movement decisions is therefore of great relevance for science and conservation alike. The study of movement decisions in relation to environmental and seasonal cues requires continuous observation of movement behaviour, recently made possible by high-resolution telemetry. We studied movement traits of 13 capercaillie (Tetrao urogallus), a mainly ground-moving forest bird species of conservation interest, over two summer seasons in a Swedish windfarm using high-resolution GPS tracking data (5-minute sampling interval). We filtered and removed unreliable movement steps using accelerometer data and step characteristics. We explored variation in movement speed and directionality in relation to environmental and seasonal covariates using Generalized Additive Mixed Models (GAMMs). We found evidence for clear daily and seasonal variation in speed and directionality of movement that reflected behavioural adjustments to biological and environmental seasonality. Capercaillie moved slower when more turbines were visible and faster close to turbine access roads. Movement speed and directionality were highest on open bogs, lowest on recent clear-cuts (<5 y.o.) and intermediate in all types of forest. Our results provide novel insights into the seasonal and environmental correlates of capercaillie movement patterns and supplement previous behavioural observations on lekking behaviour and wind turbine avoidance with a more mechanistic understanding.</p>
Effect of wind farms on wintering ducks at an important wintering ground in China along the East Asian-Australasian Flyway
<p>Wind farms offer a cleaner alternative to fossil fuels and can mitigate their negative effects on climate change. However, wind farms may have negative impacts on birds. The East China Coast forms a key part of the East Asian-Australasian Flyway and it is a crucial region for wind energy development in China. However, despite ducks being the dominant animal taxon along the East China Coast in winter and considered as particularly vulnerable to the effects of wind farms, the potential negative impacts of wind farms on duck populations remain unclear. We therefore assessed the effects of wind farms on duck abundance, distribution, and habitat use at Chongming Dongtan, which is a major wintering site for ducks along the East Asian-Australasian Flyway, using field surveys and satellite tracking. We conducted seven paired field surveys of ducks inside (IWF) and outside wind farm (OWF) sites in artificial brackish marsh, paddy fields, and aquaculture ponds. Duck abundance was significantly higher in OWF compared with IWF sites, and significantly higher in artificial brackish marsh than in aquaculture ponds and paddy fields. Based on 1,918 high-resolution satellite tracking records, the main habitat types of ducks during the day and at night were artificial brackish marsh and paddy fields, respectively. Furthermore, grid-based analysis showed overlaps between ducks and wind farms, with greater overlap at night than during the day. According to resource selection functions, habitat use by wintering ducks was impacted by distance to water, land cover, human activity, and wind-farm effects, and the variables predicted to have significant impacts on duck habitat use differed between day and night. Our study suggests that wintering ducks tend to avoid wind turbines at Chongming Dongtan, and landscape of paddy fields and artificial wetlands adjoining natural wetlands is crucial for wintering ducks</p>
Inland-Offshore Wind Farm Dataset2
<p>The wind turbine data in these two datasets include observations during the first four years of the turbines' operations. They are 10-minute data. The inland turbine data are from 2008 to 2011, whereas the offshore data are from 2007 to 2010. The measurements for the inland wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Inland Wind Farm Dataset1</a> and those for the offshore wind farm include the same x's as in the <a href="https://zenodo.org/record/5516552">Offshore Wind Farm Dataset1</a>. Most of the environmental measurements are taken from the met mast closest to the turbine, with the exception of wind speed and turbulence intensity which are measured on the wind turbine. The mast measurements are used either because some variables are only measured at the mast (such as air pressure and ambient temperature, which are used to calculate air density) or because the mast measurements are considered more reliable (such as wind direction).</p>
Inland-Offshore Wind Farm Dataset1
<p>Data included in these two datasets are 10-minute data generated from six wind turbines and three met masts and are arranged in six files, each of which is associated with a turbine. The six turbines are named WT1 through WT6, respectively. The layout of the turbines and the met masts is shown in Fig. 5.6 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. On the offshore wind farm, all seven environmental variables as mentioned above are available, namely x =(V, D, rho, H, I, Sa, Sb), whereas on the inland wind farm, the humidity measurements are not available, nor is the above-hub wind shear, meaning that x =(V, D, rho, I, Sb). Variables in x were measured by sensors on the met mast, whereas y was measured at the wind turbines. Each met mast has two wind turbines associated with it, meaning that the x's measured at a met mast are paired with the y's of two associated turbines. For WT1 and WT2, the data were collected from July 30, 2010 through July 31, 2011 and for WT3 and WT4, the data were collected from April 29, 2010 through April 30, 2011. For WT5 and WT6, the data were collected from January 1, 2009 through December 31, 2009.</p> <p>Meaning of variables; V: wind speed; D: wind direction; rho: air density; H: humidity; I: turbulence intensity; S: vertical wind shear; Sa: above-hub height wind shear, Sb: below-hub height wind shear.</p>
A nocturnal jet flows over a wind farm in complex terrain
<p>A nocturnal jet simulated with the Weather Research and Forecasting model flows over topography where a wind farm is located. It produces downstream flow acceleration that enhances the performances of turbines in the back rows. This simulation is related to the case 3 (transect 1) of the paper <em>Nocturnal jets over wind farms in complex terrain </em>that will be submitted to <em>Applied Energy.</em></p>
Figures: The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
<p>Python figures in pickle format</p>
Effect of wind farms on wintering ducks at an important wintering ground in China along the East Asian-Australasian Flyway
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Data from: Factors affecting carcass detection at wind farms using dogs and human searchers
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Environmental and seasonal correlates of capercaillie movement traits in a Swedish wind farm
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Multi-scale simulation of flow through a wind farm during a frontal passage event
<p>Animation from a multi-scale simulation of flow through a wind farm showing hub-height wind speed during a frontal passage event. The simulation was performed using the Weather Research and Forecasting (WRF) model using an embedded generalized actuator disk (GAD) wind turbine model. This animation is included as supplementary material for the manuscript "Multi-scale simulation of wind farm performance during a frontal passage" published in<em> <a href="https://www.mdpi.com/2073-4433/11/3/245#">Atmosphere</a></em><a href="https://www.mdpi.com/2073-4433/11/3/245#"> 2020 11(3), 245</a>, Special Issue "Modeling of Atmospheric Boundary Layers at Turbulence-Resolving Grid Spacings." This work was prepared by LLNL under Contract DE-AC52-07NA27344.</p>
Towed chain datasets and input files for simulations used in the manuscript "Increased mixing and turbulence in the wake of offshore wind farm foundations"
<p><strong>Contents</strong></p> <p>1. File S01_S12 Input files for simulations (precursor runs and main runs)</p> <p>2. File S13 Topography file for simulations with monopile</p> <p>2. Data sets ds01 to ds06 (towed chain data collected in May 25, 2015)</p> <p>3. Data sets ds07 to ds14 (towed chain data collected in July 19, 2017)</p> <p>4. Data sets d15 to ds16 (ADCP data collected in May 25, 2015 and July 19, 2017)</p> <p><strong>Introduction </strong></p> <p>This package contains the input parameters used in each of the precursor (S01 - S04) and main runs (S05 - S12) presented in the manuscript “Increased mixing and turbulence in the wake of offshore wind farm foundations”. These input files are found in the PDF file "S01_S12".</p> <p>The main runs, in which the wake of a monopile was simulated (S05, S07, S09, S11), require a topography file, which is a NETCDF-file that has been uploaded separately (S13). All simulations were run using the Parallelized Large-Eddy Simulation Model for atmospheric and oceanic flows (PALM, version 4.0, revision 2504).</p> <p>Further, this package contains the data sets collected using the towed chain in 2015 (ds01-ds06, ds15) and 2017 (ds07-ds14, ds16), which have been uploaded as separate NETCDF-files.</p>
Open Data sets from Cold Climate Wind Farms in Finland, Pori
<p>This dataset includes 6 years of meteorological mast data and operational data from one turbine located in Pori, Western Finland. Dataset also includes simultaneous and longer-term monthly icing time series from WIceAtlas database for reference. Site can be described as an easy site both in terms of icing (IEA Ice Class 2) and terrain complexity. </p> <p>The work was funded by an EU IRPWIND project.</p>
Open Data sets from Cold Climate Wind Farms in Finland, Olostunturi
<p>This dataset includes 6 years of open access meteorological mast data and operational data of multiple turbines from Olos wind farm in Finland, and simultaneous and longer-term monthly icing time series from WIceAtlas database for reference. Olos is a complex terrain site with severe icing conditions during winter (IEA Ice Class 4).</p> <p>The work was funded by an EU IRPWIND project.</p>
ScienceDex guides
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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.