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.
607
datasets available to search
ShareScore release 0.9.0
Dataset results
607 results for “wind data”
Data from: Active anemosensing hypothesis: How flying insects could estimate ambient wind direction
Open the record for dataset details and reuse information.
Data from: Geographic source of bats killed at wind-energy facilities in the eastern United States
Open the record for dataset details and reuse information.
Data from: Patterns in lek persistence and attendance by lesser prairie chicken <em>Tympanuchus pallidicinctus</em> near a wind energy facility in southern Kansas
Open the record for dataset details and reuse information.
Data from: Influence of topography and the underlying surface of the Bohai Sea on wind and gust forecasts
Open the record for dataset details and reuse information.
Data from: An ecological vulnerability index to assess impacts of offshore wind facilities on migratory song-birds
Open the record for dataset details and reuse information.
Data for: Multi-LEO satellite stereo winds
Open the record for dataset details and reuse information.
Data from: Multi-fluid MHD study of the disappearing solar wind event observed by MAVEN: Effects of solar wind density
Open the record for dataset details and reuse information.
Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems
Open the record for dataset details and reuse information.
Data from: The effect of initial vortex asymmetric structure on tropical cyclone intensity change in response to an imposed environmental vertical wind shear
Open the record for dataset details and reuse information.
Data of 'Estimating Wind Speed and Direction Using Wave Spectra'
<p>This set contains data of a Spotter wave buoy deployment and of the RMSE of wind speed and direction estimates as described in 'Estimating Wind Speed and Direction Using Wave Spectra', submitted for review to Journal of Geophysical Research.</p>
Data for: Wind drives temporal variation in pollinator visitation in a fragmented tropical forest
<p>Data for "Wind drives temporal variation in pollinator visitation in a fragmented tropical forest." Files include:</p> <p>1. Data on orchid bee abundance and species identification.</p> <p>2. Data for collection sessions, including forest cover at each site, as well as wind data for the specific time period of the collection, and overall abundance counts, etc.</p> <p>3. R scripts to recreate statistical presented in the paper, using the two datasets described above.</p>
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>
Politecnico di Milano - Wind tunnel test data on high-rise building
<p>High-resolution pressure data recorded in wind tunnel tests performed at the Politecnico di Milano wind tunnel on a generic prismatic high-rise building.<br>If you use these data, please cite:<br>Lamberti, G., Amerio, L., Pomaranzi, G., Zasso, A., & Gorlé, C. (2020). Comparison of high resolution pressure measurements on a high-rise building in a closed and open-section wind tunnel. Journal of wind engineering and industrial aerodynamics, 204, 104247. DOI: 10.1016/j.jweia.2020.104247</p>
Data of tangential wind speed of Typhoon Trami (2018) derived by Tsujino et al. (2021)
<p>Time series data of tangential wind speed in the eye and eyewall of Typhoon Trami (2018) from 0000 UTC 25 to 0600 UTC 27 September 2018, derived by Tsujino et al. (2021) using the data observed by Himawari-8 satellite. More information is also available at <a href="http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html">http://wwwoa.ees.hokudai.ac.jp/people/horinouchi-lab/TC/data_en.html</a>.</p>
Data from: Contrasting vulnerability of monospecific and species-diverse forests to wind and bark beetle disturbance: The role of management
<p>The published dataset contains the results of the paper titled <strong>Contrasting vulnerability of monospecific and species-diverse forests to wind and bark beetle disturbance: The role of management, in Ecology and Evolution.</strong></p> <p>Results are based on the output of the model iLand<strong> (</strong>http://iland.boku.ac.at/startpage).</p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p>
Data for the effect of optic flow cues on honeybee flight control in wind
<p><span>To minimise the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee <i>Apis mellifera</i>, which is known to rely on the pattern of visual motion generated across the eye – known as optic flow – to maintain constant ground speeds and heights. We find that, when provided with optic flow cues in both the longitudinal and transverse directions of flight, honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimised. This change in height with wind does not appear to result in a constant rate of optic flow in the ventral visual field, suggesting that honeybees may rely on a combination of mechanosensory and visual information when controlling flight in wind. We also find that, when the transverse component of optic flow is minimised, or when all optic flow cues are minimised, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.</span></p>
Measurements of the solar wind propagation delay for L1 to Earth based on ACE and ground-based magnetometer data
<p>This database is the basis for the analysis described in the manuscript</p> <p>'Timing of the solar wind propagation delay between L1 and Earth based on machine learning'</p> <p>published in Journal of Space Weather and Space Climate.</p> <p> </p> <p><a href="https://doi.org/10.1051/swsc/2021026">https://doi.org/10.1051/swsc/2021026</a></p> <p> </p> <p>The database contains the times of 380 interplanetary shocks detected at ACE (T_ACE) which also caused a sudden impulse (T_SI) in the magnetosphere based on ground-based magnetometer data. This information can be found in 'measurement_SW_propagation.txt'.</p> <p>final_learningset_SWdelay.pickle contains the corresponding ACE data (solar wind speed, ACE position) at time T_ACE for each of the 380 cases.</p> <p>The datafile can be loaded with Python as follows:</p> <p>import pickle<br> with open('final_learningset_SWdelay.pickle', 'rb') as f:<br> [learnvector_o,learnvector_m,learnvector_s,learnvector,timevector]=pickle.load(f)</p> <p> </p> <p>The content is described as follows:</p> <p>learnvector_o - contains a list of ACE data in its original form, ordering ['rx','ry','rz','vx','vy','vz']</p> <p>learnvector_m - median of each feature</p> <p>learnvector_s - standard deviation of each feature</p> <p>learnvector - contains an array of the standardized data, which have been used to train the ML models.</p> <p>timevector - contains an array with the vector delay in seconds(first column), flat delay in seconds (second column), and measured solar wind propagation delay in seconds (third column)</p>
Data from: Automated curtailment of wind turbines reduces eagle fatalities
<ol> <li>Collision-caused fatalities of animals at wind power facilities create a 'green versus green' conflict between wildlife conservation and renewable energy. These fatalities can be mitigated via informed curtailment whereby turbines are slowed or stopped when wildlife are considered at increased risk of collision. Automated monitoring systems could improve efficacy of informed curtailment, yet such technology is undertested.</li> <li>We test the efficacy of an automated curtailment system—a camera system that detects flying objects, classifies them, and decides whether to curtail individual turbines to avoid potential collision—in reducing counts of fatalities of eagles, at Top of the World Windpower Facility (hereafter, the treatment site) in Wyoming, USA. We perform a before-after-control-impact study, comparing the number of eagle fatalities observed at the treatment site with those at a nearby (15 km) control site without automated curtailment, both before and after the implementation of automated curtailment at the treatment site.</li> <li>After correcting for carcass detection probability and scaling fatality estimates to turbine-years, we estimate that the number of fatalities at the treatment site declined by 63% (95% CI = 59% – 66%) between before and after periods while increasing at the control site by 113% (51% – 218%). In total, there was an 82% (75% – 89%) reduction in the fatality rate at the treatment site relative to the control site.</li> <li> <i>Synthesis and applications.</i> Automated curtailment of wind turbine operation substantially reduced eagle fatalities. This technology therefore has the potential to lessen the conflict between wind energy and raptor conservation. Although automated curtailment reduced fatalities, they were not fully eliminated. Therefore, automated curtailment, as implemented here, is not a panacea and its efficacy could be improved if considered in conjunction with other mitigation actions.</li> </ol>
Data from: Radar wind profilers and avian migration - a qualitative and quantitative assessment verified by thermal imaging and moon watching
1. Radars of various types have been used in ornithological research for about 70 years. However, the potential of radar wind profiler (RWP) as a tool for biological purposes remains poorly understood. The aim of this study is to assess the suitability of RWP for ornithological research questions. 2. A 1290 MHz RWP at the southeastern coast of the Bay of Biscay has been known to exhibit seasonally occurring nocturnal signals attributed to migrating birds. As a first step to verify the origin of these seasonal patterns, historical radar data from 2010-2012 were analysed, and both bird patterns and temporal occurrence were identified in RWP data at different levels of the signal processing. A thermal-imaging (TI) camera in conjunction with moon watching was used as verification systems at the radar site to confirm the ornithological origin of the radar echoes. The simultaneous data on spring migration served as a basis for the identification of biological signatures (qualitative parameters) on time series level (raw data) and to derive quantitative migration parameters (flight altitude, migration traffic rates) thereof. Finally the quantitative measurements of the TI camera and the radar were compared considering meteorological conditions. 3. The approach allowed identifying reproducible criteria based on time series to calculate migration traffic rates and altitudinal flight distribution. General flight directions were only available in the final wind data. In clear weather conditions the calibration methods coincided well with the wind profiler data. 4. Findings show that wind profiler raw data offers reliable information on migration intensity, flight altitudes and flight directions in a variety of meteorological conditions. The method presented can be applied as a complement to present efforts to use weather radars for large-scale bird monitoring. Furthermore it is also interesting for the meteorological community to refine signal-processing methods.
Data from: Tropical trees in a wind-exposed island ecosystem: height-diameter allometry and size at onset of maturity
1. Tropical tree species adapted to high wind environments might be expected to differ systematically in terms of stem allometry and life-history patterns, as compared with species found in less windy forests. We quantified height-diameter (H-D) allometries and relative size at onset of maturity (RSOM) for rainforest tree and tree fern species native to Dominica, West Indies, an island that experiences some of the highest average wind speeds pantropically. 2. H-D allometries for 17 Dominican angiosperm tree species were strongly concave on a log-log scale with asymptotic heights ranging from 9-32 m among species, averaging 25 m for canopy trees. H-D allometries for species-pooled data deviated strongly from recorded patterns for other tropical forest trees: asymptotic heights for trees in Dominica were 30-116% lower than those recorded for continental rainforest trees in Australia, South America, Africa, and Southeast Asia. In a subset of canopy trees sampled in steep, sheltered valleys, heights were 12-26% larger at a given diameter, and approached those observed in other tropical regions, suggesting large phenotypic responses of H-D allometries to wind conditions. 3. RSOM (quantified as the ratio of height at onset of reproduction to asymptotic maximum height) for Dominican angiosperm species was highly variable, ranging from 0.23-0.89 (mean 0.54), similar to patterns observed in Malaysia and Panama; very low RSOM values were estimated for two tree fern species. Pooling data from Dominica with published values from other tropical forests, we observed a significant negative correlation between RSOM and wood density. 4. Synthesis: Our data suggest that wind regimes are a critical determinant of height-diameter (H-D) allometries of tropical trees at both the local and global scale. Although we found no evidence for a systematic differences in reproductive onset related to wind regime, RSOM was negatively correlated with species' wood density, suggesting that more shade-tolerant tree species show a longer period of gradually increasing reproductive allocation through ontogeny.
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.