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78 results for “Wind farms”
Documentation for "Modelling wind farm effects in HARMONIE-AROME"
<p>This archive provides the configuration files for the Weather Research and Forecasting (WRF) and the wind farm information for HARMONIE-AROME as well as the scripts to repoduce the simulations and analysis in "Modelling wind farm effects in HARMONIE-AROME".</p>
Data for : "Three months of combined high resolution rainfall and wind data collected on a wind farm"
<p>The data set corresponds the data presented in the data paper : “Three months of combined high resolution rainfall and wind data collected on a wind farm “ Earth System Science Data” (https://www.earth-system-science-data.net/).</p> <p>More details can be found in the Read_me_v1.txt file and in the paper.</p>
Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations
<p>Dataset used for results in paper "Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations"</p>
Wind Value: Second Conference 2024 End of Life Choices for Wind Farms, Video
<p>Video of Slides written by Dorcas Mikindani presented by Peter Deeney. The topic is the use of real options in wind farm valuation as a guide for decisions at end of life.</p>
Dataset and Code for "A case study of space-time performance comparison of wind turbines on a wind farm"
<p>This is the computer code and partial dataset for producing the results in the paper, Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” <em>Renewable Energy</em>, Vol. 171, pp. 735-746 .</p> <p>[<strong>Note 1</strong>: In the Reproducibility Report, The table numbers are off by one, namely that Table 2 should be Table 3, and Table 3 should be Table 4.]</p> <p>[<strong>Note 2:</strong> The results in Table 4 included in the paper were produced by the DSWE version 1.3.4. Those results are still reproducible if using the same version of DSWE. Since then, DSWE went through some changes and updates. When using DSWE 1.5.1 version (the latest version as of May 10, 2022) and setting the optimization method to 'L-BFGS-B' (because version 1.3.4’s default optimization setting was 'L-BFGS-B'), the results corresponding to the second row in Table 4 are somewhat different. For the specific results using DSWE 1.5.1, please see the note section at the end of the <a href="https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2022/05/J77_Reproducibility_Report_v2.pdf">updated Reproducibility Report</a>.]</p>
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
SMARTEOLE Wind Farm Control open dataset
<p><strong>Introduction</strong></p> <p>This dataset is issued from the third and final field campaign of the French national project SMARTEOLE. It consists in data from 7 wind turbines of a single wind farm (Sole du Moulin Vieux, located in France) for which Wind Farm Control field tests were performed to evaluate the performance of a wake steering strategy for improving the power production.</p> <p>The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW).</p> <p> </p> <p><strong>Description</strong></p> <p>The tests were realized between 17 February – 25 May 2020, with wake steering implemented on turbine SMV6. This dataset covers this full period, and it has been pre-processed to facilitate the analysis of the Wind Farm Control experiment. All timesteps when at least one turbine was stopped were removed, and SCADA nacelle position and wind direction signals have been corrected to remove any north alignment issues. Finally, the time resolution has been standardized at 1-min from the raw data recorded at higher frequencies from the different sensors. For more details about the development of the field campaign and the pre-processing steps followed in the data analysis, please consult the related publication : <a href="https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html">https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html</a>. Some information can also be found in the related <a href="https://ieawindtask44.tudelft.nl/index.php?title=SMARTEOLE_Field_Test_3">IEA task 44 wiki page</a>.</p> <p>The following files can be found in the dataset :</p> <ul> <li>SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines.</li> <li>SMARTEOLE_WakeSteering_ControlLog_1minData.csv : logs from the control system located on turbine SMV6, responsible for the application of the wake steering. The applied yaw offset on the turbine at each timestep can be found here.</li> <li>SMARTEOLE_WakeSteering_WindCube_1minData.csv : data from the ground based WindCube profiler lidar, located between SMV2 and SMV3. This can be used to assess the ambient environmental wind conditions at the farm.</li> <li>SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (<a href="https://epsg.io/4326">WGS84</a>) and XY metric system (<a href="https://epsg.io/2154">French Lambert 93</a>).</li> <li>SMARTEOLE_WakeSteering_Map.pdf : the map of the farm showing the location of wind turbines and WindCube. This is the exact same map as the one seen in the paper indicated above.</li> <li>SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv : the transfer function used in the paper to correct the wind speed measured by SMV6 to better match the freestream wind speed at 150m upstream (i.e. approximately 1.8 diameters), derived using WindCube nacelle lidar installed on top of the turbine.</li> <li>SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv : the transfer function used in the paper to derive and correct the reference power and wind speed signals —defined as the mean values of the power and wind speeds from SMV1, SMV2, SMV3, and SMV7— to remove biases from the values at SMV6 as a function of wind direction and wind speed. These corrected reference signals are used for quantifying the impact of the wake steering.</li> <li>SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine.</li> <li>SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables.</li> </ul> <p>Unfortunately, the WindCube nacelle lidar data on top of SMV6 could not be shared, instead the transfer functions derived thanks to this sensor can be used to correct the SCADA channels. The Wind Energy Science publication describes how these transfer functions were obtained.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The creation of this dataset was realized in the scope of French national project SMARTEOLE, supported by the <em>Agence Nationale de la Recherche</em> (grant no. ANR-14-CE05-0034).</p> <p>Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available.</p> <p> </p> <p><strong>How to cite this dataset</strong></p> <p>When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication :</p> <p>"The dataset used in this research has been obtained by ENGIE Green in the scope of French national project SMARTEOLE (grant no. ANR-14-CE05-0034)".</p> <p>When citing the dataset in the core text of a paper, the reference to <a href="https://wes.copernicus.org/articles/6/1427/2021/">Simley et al.</a> can simply be used.</p> <p> </p> <p><strong>Related datasets and publications</strong></p> <p>Several field test campaigns were realized in the scope of SMARTEOLE project. Although these data are not made publicly available by default, they can be shared in a per-project basis and under the protection of a dedicated NDA. Please refer to the following publications listed below to get an idea of the content of the different datasets.</p> <p><em>SMARTEOLE Field Test 1</em></p> <ul> <li>Ahmad T. et al., Field Implementation and Trial of Coordinated Control of WIND Farms, <em>IEEE Transactions on Sustainable Energy</em>, 9(3), 2018, 10.1109/TSTE.2017.2774508.</li> <li>Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017.</li> <li>Duc T. et al., Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, <em>Wind Energy Science</em>, 4(2), 2019, 10.5194/wes-4-287-2019.</li> <li>Torres Garcia E. et al., Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign, <em>Renewable Energy</em>, 130, 2019, 10.1016/j.renene.2018.06.030.</li> <li>Hegazy A. et al., LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models, <em>Renewable Energy</em>, 181, 2022, 10.1016/j.renene.2021.09.019.</li> </ul> <p><em>SMARTEOLE Field Test 2</em></p> <ul> <li>Tagliatti F., Investigation of Wind Turbine Fatigue Loads under Wind Farm Control: Analysis of Field Measurements, Master’s thesis, DTU Wind Energy-M-0302, 2019.</li> <li>Göçmen T. et al., FarmConners wind farm flow control benchmark – Part 1: Blind test results, <em>Wind Energy Science</em>, 7(5), 2022, 10.5194/wes-7-1791-2022.</li> </ul> <p><em>SMARTEOLE Field Test 3</em></p> <ul> <li>Simley E. et al., Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, <em>Wind Energy Science</em>, 6(6) 2021, 10.5194/wes-6-1427-2021.</li> </ul> <p> </p> <p><strong>Release Notes</strong></p> <ul> <li>v1.0 (2022-11-24) : first version of the dataset.</li> </ul>
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> <p>matplotlib version 3.5.1 </p>
Wind Farm Survey Ireland 2019
<p>A survey of windfarms in Ireland as of 2019 as part of my introductory period in my PhD research at the University of Limerick funded by Sustainable Energy Authority of Ireland.</p>
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>
Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective - Supplementary material
<p>This is the additional material provided with the journal article "Techno-economic evaluation and resource assessment of hydrogen production through offshore wind farms: A European perspective" published in Renewable and Sustainable Energy Reviews (<a href="https://doi.org/10.1016/j.rser.2023.113699">https://doi.org/10.1016/j.rser.2023.113699</a>).</p> <p>Datasets are provided as NetCDF files for European maps and CSVfor Economically Attractive Resource curves.</p> <p>European and National plots are provided as PDF files.</p>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
Fig. 1 in Research on nesting birds on the territory of Kaliakra Wind Farm
Fig. 1. Location of "Kaliakra" wind farm.
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
<p>The expanding use of wind farms as a source of renewable energy can impact bird populations due to collisions and other factors. Globally, seabirds are one of the avian taxonomic groups most threatened by anthropogenic disturbance; adequately assessing the potential impact of offshore wind farms (OWFs) is important for developing strategies to avoid or minimize harm to their populations. We estimated avoidance rates of OWFs — the degree to which birds show reduced utilization of OWF areas — by Sandwich Terns <em>Thalasseus sandvicensis</em> at two breeding colonies in western Europe: Scolt Head (United Kingdom) and De Putten (the Netherlands). We modeled GPS tracking data using integrated Step Selection Functions (iSSFs) to estimate the relative selection of habitats at the scale of time between successive GPS relocations – in our case 10 minutes, in which terns traveled ca. 2 km on average. The foraging ranges of birds from each colony overlapped with multiple OWFs. iSSFs considered distance from the colony and habitat characteristics (water depth and sediment grain size) and movement characteristics. Macro-avoidance rates, where 1 means complete avoidance, were estimated at 0.54 (95% CrI = 0.35, 0.7) for birds originating from Scolt Head and 0.41 (95% CrI = 0.21, 0.56) for those from De Putten. Estimates for individual OWFs also indicated avoidance but were associated with considerable uncertainty. Our results were inconclusive with regard to the behavioral response to the areas directly surrounding OWFs (within 1.5 km); estimates suggested indifference and avoidance and were associated with large uncertainty. Avoidance rate of OWFs significantly increased with turbine density, suggesting OWF design may help to reduce the impact of OWFs on Sandwich Terns. The partial avoidance of OWFs by Sandwich Terns implies that the species will experience risks of collision and habitat loss due to OWFs constructed within their foraging ranges.</p>
Avoidance of offshore wind farms by Sandwich Terns increases with turbine density
Open the record for dataset details and reuse information.
Wind River Experimental Forest site, station Skamania County, WA (FIPS 53059), study of number of farms in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Wind River Experimental Forest (WIN) contains number of farms measurements in number units and were aggregated to a yearly timescale.
Wind River Experimental Forest site, station Skamania County, WA (FIPS 53059), study of population employed at farms (percent of total) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Wind River Experimental Forest (WIN) contains population employed at farms (percent of total) measurements in percent units and were aggregated to a yearly timescale.
Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
Data from: Patterns of migrating soaring migrants indicate attraction to marine wind farms
Monitoring of bird migration at marine wind farms has a short history, and unsurprisingly most studies have focused on the potential for collisions. Risk for population impacts may exist to soaring migrants such as raptors with K-strategic life-history characteristics. Soaring migrants display strong dependence on thermals and updrafts and an affinity to land areas and islands during their migration, a behaviour that creates corridors where raptors move across narrow straits and sounds and are attracted to islands. Several migration corridors for soaring birds overlap with the development regions for marine wind farms in NW Europe. However, no empirical data have yet been available on avoidance or attraction rates and behavioural reactions of soaring migrants to marine wind farms. Based on a post-construction monitoring study, we show that all raptor species displayed a significant attraction behaviour towards a wind farm. The modified migratory behaviour was also significantly different from the behaviour at nearby reference sites. The attraction was inversely related to distance to the wind farm and was primarily recorded during periods of adverse wind conditions. The attraction behaviour suggests that migrating raptor species are far more at risk of colliding with wind turbines at sea than hitherto assessed.
Data from: Bird and bat species' global vulnerability to collision mortality at wind farms revealed through a trait-based assessment
Mitigation of anthropogenic climate change involves deployments of renewable energy worldwide, including wind farms, which can pose a significant collision risk to volant animals. Most studies into the collision risk between species and wind turbines, however, have taken place in industrialized countries. Potential effects for many locations and species therefore remain unclear. To redress this gap, we conducted a systematic literature review of recorded collisions between birds and bats and wind turbines within developed countries. We related collision rate to species-level traits and turbine characteristics to quantify the potential vulnerability of 9538 bird and 888 bat species globally. Avian collision rate was affected by migratory strategy, dispersal distance and habitat associations, and bat collision rates were influenced by dispersal distance. For birds and bats, larger turbine capacity (megawatts) increased collision rates; however, deploying a smaller number of large turbines with greater energy output reduced total collision risk per unit energy output, although bat mortality increased again with the largest turbines. Areas with high concentrations of vulnerable species were also identified, including migration corridors. Our results can therefore guide wind farm design and location to reduce the risk of large-scale animal mortality. This is the first quantitative global assessment of the relative collision vulnerability of species groups with wind turbines, providing valuable guidance for minimizing potentially serious negative impacts on biodiversity.
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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.
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.