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186 results for “Willow”
WILLOW - Norther: data set for the full-scale validation of model-based virtual sensing methods for an operational offshore wind turbine
<h1><em><strong>1. General description </strong></em></h1> <p>This data set contains as-build design information, as well as full-scale vibration response measurements from an operational offshore wind-turbine. The turbine is part of the Norther wind farm which is located in the Belgian North Sea<em> </em>and includes a total of 44 Vestas V164 (8.4MW) wind turbines on monopile foundations, see <a href="../api/records/11093262/draft/files/Fig1_Norther_locaction.png/content" target="_blank" rel="noopener noreferrer">Fig1_Norther_locaction.png</a>. This data set is intended to verify and validate model-based virtual sensing algorithms, using data as well as modeling information from a real turbine. </p> <h2><em><strong>1.1 Summary of the shared structural information</strong></em></h2> <p>The included information entails a detailed description of the geometric properties of the monopile and transition piece, distributed and lumped structural masses . All information shared in this record is conform the as-designed documentation. An example of the lumped masses considered in the model input files is presented in "<a href="../api/records/11093262/draft/files/Fig2_Sensor_Network.png/content" target="_blank" rel="noopener">Fig2_Sensor_Network.png"</a></p> <h2><em><strong>1.2 Summary of the shared geotechnical information</strong></em></h2> <p>Monopiles are distinguished by the significant role of soil-structure interaction. Ground reaction is most typically included in the structural model as non-linear p-y curves. Different p-y curves are available for a certain number of soils in the standards applicable to offshore structures (API RP 2GEO, 2011, and ISO 19901-4:2016(E), 2016).</p> <p>The required soil properties to define p-y curves according to the API framework are given in the soil profile provided in a separate Excel. Rather than symbols, the name of the soil properties is generally used as column header (e.g., <em>Undrained shear strength</em>). Therefore, it is straightforward to identify each soil parameter. The only soil parameter that might lead to confusion is:</p> <ul> <li><em>"epsilon50 [-]" </em>represents the vertical strain at half the maximum principal stress difference in a static undrained triaxial compression test on an undisturbed soil sample.</li> </ul> <p>It's worthy to note that estimates for the small shear strain stiffness, referred to as Gmax, are also included. Despite not being required as an input to define the API p-y curves, this parameter remains a key input for other soil reaction frameworks than the API (e.g., PISA). </p> <h2><em><strong>1.3 Summary of the shared measurement data</strong></em></h2> <p>Two sets of measurement data have been curated for validation purposes; the first interval has been collected during parked conditions, whereas the second interval has been collected during rated operational conditions. Both records have a length of 2 hours, and are subdivided into 10-minute data sets. Furthermore 1Hz SCADA data has been made available for the selected intervals. All different data sources are time synchronized and have been subjected to several internal quality checks. </p> <p>The sensor network on NRT-WTG is illustrated in in <strong>Fig. 2, </strong>whereas a description of the sensor types is presented in <strong>Tab.1.</strong> The acceleration sensors are installed in the horizontal plane, and measure tangential (Y) and orthogonal (X) to the wall, where the positive Y direction is pointing clockwise and the positive X direction is pointing inwards. All strain sensors are installed vertically and are located on the inside of the wall.</p> <table> <tbody> <tr> <td><strong>Data type </strong></td> <td><strong>Sensor type</strong></td> <td><strong>Fs (Hz)</strong></td> <td> <p><strong>Level mLAT (m)</strong></p> </td> <td><strong>Description </strong></td> </tr> <tr> <td>Acceleration (g) </td> <td>Piezo-electric acc. sensor (<strong>ACC</strong>)</td> <td>30</td> <td>15, 69, 97 </td> <td>3 Bi-directional accelerometers at different levels. LAT 15 installed at 240 degree heading; LAT 69 and 97 at 60 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Resistive strain gauge (<strong>SG</strong>)</td> <td>30</td> <td>14</td> <td>6 SGs: equally spaced around the inner circumference of the can. Headings: 50, 110, 170, 230, 290, 350 degree.</td> </tr> <tr> <td>Strain (micro strain)</td> <td>Fiber-Bragg Grating strain gauge (<strong>FBG</strong>)</td> <td>100</td> <td>-17, -19</td> <td>2 FBGs per level at 165 and 255 degree respectively.</td> </tr> </tbody> </table> <p><strong>Table 1. Description of sensor types.</strong></p> <p>The FBG strain time series have been synchronized with the SG time series using using a cross-correlation based approach. Therefore the SG data has been used to genereate refrence strain time series at the headings of the FBG sensors; the FBG data is subsequently synchronized with regard to this reference time series. No synchronization of the acceleration data was needed, since these are collected using the same data aquisition system as the SG data. </p> <p>The SG strain time series have been calibrated and temperature compensated, whereas this is not the case for the FBG strain time series. The latter have a yet to be determined calibration offset. </p> <p>In conjunction to the sensor channels presented in <strong>Tab. 1</strong>, 1 Hz SCADA data is provided. A summary of the provided SCADA parameters, all sampled at 1Hz, is presented in <strong>Tab 2.</strong></p> <table> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Wind speed</td> <td>m/s</td> <td>Wind speed as recorded in the turbine SCADA</td> </tr> <tr> <td>Wind direction</td> <td>°</td> <td>Wind direction relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Yaw angle</td> <td>°</td> <td>Yaw orientation of the nacelle relative to North (0°) as recorded in the turbine SCADA</td> </tr> <tr> <td>Pitch angle</td> <td>°</td> <td>Rotor blade pitch as recorded in the turbine SCADA</td> </tr> <tr> <td>Rotor speed</td> <td>rpm</td> <td>Rotor speed in rotations per minute as recorded in the turbine SCADA</td> </tr> <tr> <td>Power</td> <td>kW</td> <td>Active power of the turbine as recorded in the turbine SCADA</td> </tr> </tbody> </table> <p><strong>Table 2. </strong>List of provided SCADA parameters</p> <p> </p> <p>A summary of the selected intervals and relevant corresponding scada parameters is given in <strong>Tab 3</strong>.</p> <table> <tbody> <tr> <td><strong>Scenario </strong></td> <td><strong>T1 (UTC)</strong></td> <td><strong>T2 (UTC) </strong></td> <td><strong>Windspeed</strong></td> <td><strong>RPM </strong></td> <td><strong>Pitch </strong></td> </tr> <tr> <td>Parked</td> <td> <p>03/07 01:30</p> </td> <td> <p>03/07 03:30</p> </td> <td>< 4.5 m/s</td> <td>~1</td> <td>~18 °</td> </tr> <tr> <td>Rated</td> <td> <p>05/07 22:30</p> </td> <td> <p>06/07 00:30 </p> </td> <td>~15 m/s</td> <td>10.5</td> <td>8.1°</td> </tr> </tbody> </table> <p><strong>Table 3. </strong>Selected data intervals and relevant scada parameters</p> <p> </p> <h1><em><strong>2. Included in this version </strong></em></h1> <h2><em><strong>2.1 Version - 0.1.0</strong></em></h2> <ul> <li>Relevant Design information can be found in: <ul> <li>Geometry data for NRT-WTG: "WILLOW-Geometry_v4.xlsx"</li> <li>Best estimate soil profile: "WILLOW-BE_soil_profile.xlsx"</li> </ul> </li> <li>Acceleration, strain and scada data can be found in the following parquet files: <ul> <li>Measurement data for the parked case: "NRT-WTG_Parked.parquet.gz"</li> <li>Measurement data for the rated case: "NRT-WTG_Rated.parquet.gz"</li> </ul> </li> </ul> <p> </p> <h1><em><strong>3. Importing parquet files </strong></em></h1> <p>To import the measurement data into Python it is recommended to use pandas:</p> <pre>import pandas as pd<br># Read Parquet file with Pandas: relative_file_path = '<a href="../api/records/11093262/draft/files/NRT-WTG_Parked.parquet.gz/content" target="_blank" rel="noopener noreferrer">NRT-WTG_Parked.parquet.gz</a>' data = pd.read_parquet(relative_file_path ) <br><br>Once the dataframe has been imported, the users can process/re-arrange the raw data according the their needs; it should be noted that the imported dataframe contains NAN values - these are caused by the different sampling rates of the provided signals. </pre>
Genotyping-by-sequencing (GBS) dataset for genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))
<p>These vcf-files constitute underlying raw data material for the manuscript "Genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))". For more detailed information please consult the README file in the repository.</p>
Potential and realized distribution at 30m for Goat willow (Salix caprea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the goat willow (<em>Salix caprea, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_salix.caprea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>salix.caprea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p> <p> </p>
The mycobiome of the gut of willow wood borer, Xiphydria prolongata (Hymenoptera: Xiphydriidae): a rich source of rare yeasts
<p>A high-throughput amplicon sequencing as a culture-independent approach was used to identify the gut mycobiome of the willow wood borer <em>Xiphydria prolongata</em>. The findings of this study are significance in terms of the insect-fungal interactions and indicate the unexpected richness of the mycobiome and the presence of many rare yeast species in the wood borer gut. A total of 40 fungal genera were found, and among them, only one endophytic fungi, <em>Daldinia</em> (Hypoxylaceae), has been previously reported in <em>Xiphydria.</em> <em>Zygosaccharomyces siamensis</em> is the most prevalent ascomycete species, while <em>Rhodosporidiobolus colostri</em> is the most abundant basidiomycetous yeast in <em>X. prolongata</em>. Some of the species identified in here was known as very rare fungus such as <em>Skoua fertilis</em>, <em>Chaetomium nepalense, R. colostri </em>and<em> Vustinia terrae</em>. This study is also the first report to <em>S. fertilis </em>and<em> V. terrae</em> in the insect gut flora. These funguses most likely aid in the digestion of lignocellulose in the gut of wood borer. Therefore, further researches are required to know the source of acquisition and functional role of these yeast and their industrial potential.</p>
Unpublished data on leaf rust infection and herbivory in willow plantations
<p>The data were collected during 2015 and 2018 within ~20 willow plantations in the Uppsala area, Sweden. I neither found the time nor research funding to use the data in a scientific publication or otherwise.</p> <p>The data from 2015 also offer canopy openness and vegetation cover within the willow short rotation plantations. The data from 2018 also included the establishment of plots for measuring stem diameter that were intended to be measures again.</p> <p>An associated data set (10.5281/zenodo.6995718) consist of similar estimation on willow bushes in Europe.</p>
Unpublished data on leaf rust infection, leaf galls, and herbivory on willow in Europe
<p>The data were collected between 2016 and 2022 in sites between southern Germany and Middle Sweden. I neither found the time nor research funding to use the data in a scientific publication or otherwise.</p> <p>In total 654 samples are available that record the occurrence of leaf rust, leaf galls, percent herbivory, and plant size.</p> <p>An associated data set (10.5281/zenodo.6992488) consist of similar estimation on willow bushes in willow plantations in the Uppsala area, Sweden.</p>
Unpublished data on predation on leaf beetle eggs in willow bushes
<p>The data were collected during field experiments in 2012 and 2014 in the Uppsala area, Sweden. I neither found the time nor research funding to use the data in a scientific publication or otherwise.</p> <p>In both field seasons the predation on leaf beetle eggs on willow bushes/plants was measured. Additionally, in 2012 the insect diversity on the bushes was sampled.</p>
Data from: Female-biased population sex ratios caused by genetic rather than ecological mechanisms in dwarf willow (Salix herbacea L.)
<p>Biased sex ratios among reproductive individuals are common in plants, but the underlying mechanisms, as well as the evolutionary consequences, are not well understood. The classical theory of Düsing and Fisher predicts an equal primary sex ratio at seed production, based on the selective advantage of the rare sex. Biased sex ratios among reproductive plants can arise from sexual dimorphism in survival and flowering. Sex ratio biases can also be present from the seed stage; in these cases, assumptions of Düsing's and Fisher's theory, for example, random mating or demographic equilibrium, are thought to be violated.</p> <p>We investigated mechanisms leading to female-biased sex ratios in the arctic-alpine dwarf willow <em>Salix herbacea</em> L. We studied sex ratios in three natural populations over three years as well as in 29 crosses (full-sib families) under controlled conditions over four growth periods. We tested whether sex ratio was associated with habitat parameters (elevation and snowmelt time), or with germination, survival or flowering, and whether females and males differed in size or flowering that may cause observation bias.</p> <p>We detected a strong and consistent female bias, both in natural populations (sex ratio [proportion of females]: 0.71-0.82) and in our controlled experiment (overall sex ratio: 0.70-0-72). Female bias became more pronounced with increasing elevation. Our data did not support sexual dimorphism in size or flowering. Family sex ratios varied largely (from 0.25 to 1), including many female-biased families, unbiased families and two male-biased families. Families with lower germination, seedling establishment, survival or flowering did not have stronger female bias, indicating that intrinsically higher survival or flowering in females does not explain overall female bias. </p> <p>Synthesis: Our results suggest that sex ratio bias in <em>S. herbacea</em> is already present in seeds and does not arise through intrinsic differences between sexes. Candidate mechanisms that can lead to both overall female bias and variation in sex ratio among families are meiotic drive or cyto-nuclear interactions. The pioneer habit of <em>Salix</em> may lead to non-equilibrium population dynamics that allow for the long-term persistence of variable genetic sex ratio distortion systems that arise from genetic conflict.</p>
Figs 17–24 in Aphid-Parasitoid (Hymenoptera, Braconidae, Aphidiinae) Associations On Willows And Poplars In Iran
Figs 17–24. Lateral aspect of genitalia (+): 17 = Ephedrus helleni, 18 = E. chaitophori, 19 = Lysiphlebus confusus, 20 = Adialytus salicaphis, 21 = Euaphidius cingulatus, 22 = Aphidius salicis, 23 = Binodoxys brevicornis, 24 = B. heraclei
Figs 25–30 in Aphid-Parasitoid (Hymenoptera, Braconidae, Aphidiinae) Associations On Willows And Poplars In Iran
Figs 25–30. Dorsal aspect of petiol (+): 25 = Lysiphlebus confusus, 26 = Adialytus salicaphis, 27 = Euaphidius cingulatus, 28 = Aphidius salicis, 29 = Binodoxys brevicornis, 30 = B. heraclei
Figs 9–16 in Aphid-Parasitoid (Hymenoptera, Braconidae, Aphidiinae) Associations On Willows And Poplars In Iran
Figs 9–16. Dorsal aspect of propodeum (+): 9 = Ephedrus helleni, 10 = E. chaitophori, 11 = Lysiphlebus confusus, 12 = Adialytus salicaphis, 13 = Euaphidius cingulatus, 14 = Aphidius salicis, 15 = Bino-
Figs 1–8 in Aphid-Parasitoid (Hymenoptera, Braconidae, Aphidiinae) Associations On Willows And Poplars In Iran
Figs 1–8. Forewing (+): 1 = Ephedrus helleni, 2 = E. chaitophori, 3 = Lysiphlebus confusus, 4 = Adialytus salicaphis, 5 = Euaphidius cingulatus, 6 = Aphidius salicis, 7 = Binodoxys brevicornis, 8 = B.
Figure 5 in Geographic variability in Calligrapha verrucosa (Suffrian 1858), a willow-feeding leaf beetle from western North America (Coleoptera: Chrysomelidae)
Figure 5. Dorsal view of two specimens identified as C. verrucosa in the Suffrian collection at the Martin-Luther- Universität in Halle, Germany. a) Specimen 31733 from "Illionis" [sic]. b) Specimen 27673 from "Nordamerika."
Figure 6 in Geographic variability in Calligrapha verrucosa (Suffrian 1858), a willow-feeding leaf beetle from western North America (Coleoptera: Chrysomelidae)
Figure 6. Distribution map of C. verrucosa based on material examined in connection with this study. Within the United States, each dot denotes a county record and may be representative of multiple sites within the county.
Figure 4 in Geographic variability in Calligrapha verrucosa (Suffrian 1858), a willow-feeding leaf beetle from western North America (Coleoptera: Chrysomelidae)
Figure 4. Ventral view of beetles showing coloration of thoracic sternum. a) Nevada specimen (dark form). b) Montana specimen (light form).
Figure 2 in Geographic variability in Calligrapha verrucosa (Suffrian 1858), a willow-feeding leaf beetle from western North America (Coleoptera: Chrysomelidae)
Figure 2. Dorsal habitus views showing variation among populations of C. verrucosa. a-b) Northwest Territories. c) British Columbia. d) Alaska. e) Washington. f-h) Montana. i-j) California. k-n) Nevada. o-r) Idaho. s) Wyoming. t) Nebraska.
Data from: Female-biased population sex ratios caused by genetic rather than ecological mechanisms in dwarf willow (Salix herbacea L.)
Open the record for dataset details and reuse information.
Vascular plant species list, by quadrat, for harvests of tussock , wet sedge and dry heath tundra and a toposequence which included "shrub/lupine," "riverside willow" and "footslope Equisetum" communities North Slope Alaska, Arctic LTER 1983-1996.
Vascular plant species list, by quadrat, for harvests of tussock tundra, wet sedge tundra, dry heath tundra, and a toposequence which also included "shrub/lupine," "riverside willow" and "footslope Equisetum" communities. Includes results of long-term nutrient enrichment, increased temperature, and shade houses in selected tundra types.
Photoprotective leaf pigments and chlorophyll fluorescence measurements during a greenhouse drought experiment: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
Phospholipid fatty acids (PFLA) on decomposed litter: An evolutionary perspective on functional diversity in co-occuring willow(salix) species
Thirteen willow (Salix) species occur in southeastern Minnesota and often co-occur within the same wetlands. This high local diversity is challenging to explain since closely related species are often functionally similar and density-dependent interactions such as competition and susceptibility to pests and pathogens should limit their co-occurrence. However, if willow species are partitioning resources, or if they are phylogenetically structured so that closely related species rarely co-occur, then the impact of these density-dependent processes could be reduced. In this study, I examined the role of niche partitioning in maintaining local willow diversity by comparing species physiology in a greenhouse.
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