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607 results for “wind data”
Data for Nicolas & Boos, "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections"
<p><span>This directory contains all data used in producing the plots in Nicolas & Boos (2024), "Sensitivity of tropical orographic precipitation to wind speed with implications for future projections". It is divided in four subdirectories:</span></p> <p><span> - wrfData contains processed simulation output needed to reproduced figures 1, 2, and SI figure 1.</span></p> <p><span> - regionsData and globalData contain processed observational (APHRODITE and IMERG) and ERA5 data necessary to reproduce figure 3 and SI figures 2 and 3.</span></p> <p><span> - cmipData contains processed CMIP surface wind data necessary to reproduce figure 4.</span></p> <p><span>Code used in producing these figures will be made available and linked to this dataset once any needed revisions are complete.</span></p>
Wind radial obsevations: sample data
<p>Remote sensing sample data for developing and testing the wind retrievals codes. The prefix indicates the instrument source.</p> <p>Prefix:</p> <p>wc: WindCube 200s lidar</p> <p>rpg: Ka band RPG cloud radar</p>
Data for "Enhanced "wind-evaporation effect" drove the "deep-tropical contraction" in the early Eocene"
<p>This repository includes the data for the paper "Enhanced “wind-evaporation effect” drove the “deep-tropical contraction” in the early Eocene" (Ren et al., 2024). It contains the coupled model data we conducted.</p>
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
A wave detection procedure for electromagnetic cyclotron waves AND results (data) in case of the solar wind
<p>A detection procedure (produced via IDL) for electromagnetic cyclotron waves is presented,which is used to find low frequency waves in the solar wind over a period of 7 years. The procedure and the relevant results (data) are described in a manuscript entitled "Statistical study of low frequency electromagnetic cyclotron waves in the solar wind at 1 AU", which has been submitted to Journal of Geophysical Research - Space Physics for publication. More information about the procedure and data can be accessed by writing to the following address: zgqisp@163.com.</p>
raw 3D wind data at 30cm above the nebkha surface
<p>The raw wind data over a nebkha collected 2018. The other dataset the results of the quadrant analysis.</p>
Data in Effects of wind velocity and nebkha geometry on shadow dune formation
<p>File in this dataset include all the data in this manuscript, such as the flow data numerical simulation, flow and sand deposition data in wind tunnel simulation, and some other remote sense data.</p>
Data in "Effects of Wind Velocity and Nebkha Geometry on Shadow Dune Formation"
<p>This file includes the data for wind flow, sand deposition and relevant materials in manuscript "<strong>Effects of Wind Velocity and Nebkha Geometry on Shadow Dune Formation" </strong></p>
Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar (data and code)
<p>The archive contains supplemental material for the article "Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar" by Kelberlau and Mann:</p> <p>- Windcube RAW and 10-min averaged data</p> <p>- Ultrasonic anemometer data from the meteorological mast in Høvsøre</p> <p>- Monin-Obukhov length data</p> <p>- windsimu input files, a windsimu executable for unix systems to create turbulence boxes and a windsimu manual</p> <p>- Matlab scripts for data processing and visualization</p>
Windthrows Inventory Data Base (WInD)
<p>All detected windthrows (3170) were organized, structured, and stored in our Windthrow Inventory Database, Version 1.0 (WInD V.1). The WInD V.1 includes a complete catalog of windthrows detected in the 33 years we studied, and a detailed summary of their attributes (e.g., area, windthrow severity and the spatial distribution of pixels). The data in other formats, such as shapefile or CSV, can be made available upon request to the following email address: jurquiza@bgc-jena.mpg.de </p>
What Can Generative Modelling Do for Interpolation of Extremely Sparse Wind Farm Seismic Data
<p>2024 Global energy transition abstract about diffusion model data interpolation. </p>
Data for the publication "Wind farm layout optimization with alignment constraints", submitted to Wind Energy Science, 2024
<p>The data in pickle format contains the optimal layouts corresponding to the numerical applications of the paper "Wind farm layout optimization with alignment constraints" submitted in Wind Energy Science, 2024.</p>
Monthly RACMO2.4p1 data for Greenland (11 km) and Antarctica (27 km) for SMB, SEB, near-surface temperature and wind speed (2006-2015)
<p>Version 2: Updated missing months in the Antarctic data set.</p> <p>Monthly-accumulated (named monthlyS) and monthly-averaged (named monthlyA) data for RACMO2.4p1 for Greenland (GRN) and Antarctica (ANT) on a 11 km and 27 km horizontal resolution grid, respectively, are presented in this data set and are available for 2006 until 2015. The data include the surface mass balance (SMB), snow melt (mltgl), refreezing (rfrzgl), precipitation (pr), runoff (totrunoff), drifting snow erosion (sndiv), sublimation (sublgl) and sublimation due to blowing snow (sublsd), all in kg m-2 mo-1. For the surface energy balance (SEB): the downward shortwave radiation (rsds), shortwave upward radiation (rsus), downward longwave radiation (rlds), upward longwave radiation (rlus), sensible heat flux (hfss) and latent heat flux (hfls) are available. The SEB components are in J m-2. To convert to W m-2, divide by the amount of seconds in a month. In addition, the near-surface temperature (tas), in K, and near-surface wind speed (sfcwind), in m s-1, are included.</p> <p><br>This data set does not represent new surface mass balance and climate products for Greenland and Antarctica. This will follow in later publications, where RACMO2.4 simulations are presented covering the full historical time period of ERA5 with higher horizontal resolution. </p>
Input data and code related to "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe"
<p>Datasets and code for the submitted article: "Utilizing curtailed wind and solar power to scale up electrolytic hydrogen production in Europe" </p> <p>by Alissa Ganter<sup>1,2</sup>, Tyler H. Ruggles<sup>2</sup>, Paolo Gabrielli<sup>1</sup>, Giovanni Sansavini<sup>1,*</sup>, Ken Caldeira<sup>2</sup></p> <p><sup>1</sup> Institute of Energy and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland</p> <p><sup>2</sup> Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA</p> <p><sup>*</sup> Corresponding author: email - sansavig@ethz.ch</p> <p>All rights lie with the authors. Refer to the README.docx for a description of the datasets and their usage in the article.</p>
Data from: Meerkat close calling patterns are linked to sex, social category, season and wind, but not fecal glucocorticoid metabolite concentrations
It is well established that animal vocalizations can encode information regarding a sender's identity, sex, age, body size, social rank and group membership. However, the association between physiological parameters, particularly stress hormone levels, and vocal behavior is still not well understood. The cooperatively breeding African meerkats (Suricata suricatta) live in family groups with despotic social hierarchies. During foraging, individuals emit close calls that help maintain group cohesion. These contact calls are acoustically distinctive and variable in rate across individuals, yet, information on which factors influence close calling behavior is missing. The aim of this study was to identify proximate factors that influence variation in call rate and acoustic structure of meerkat close calls. Specifically, we investigated whether close calling behavior is associated with sex, age and rank, or stress hormone output (i.e., measured as fecal glucocorticoid metabolite (fGCM) concentrations) as individual traits of the caller, as well as with environmental conditions (weather) and reproductive seasonality. To disentangle the effects of these factors on vocal behavior, we analyzed sound recordings and assessed fGCM concentrations in 64 wild but habituated meerkats from 9 groups during the reproductive and non-reproductive seasons. Dominant females and one-year old males called at significantly higher rates compared to other social categories during the reproductive season. Additionally, dominant females produced close calls with the lowest mean fundamental frequencies (F0) and the longest mean pulse durations. Windy conditions were associated with significantly higher call rates during the non-reproductive season. FGCM concentrations were unrelated to close calling behavior. Our findings suggest that meerkat close calling behavior conveys information regarding the sex and social category of the caller, but shows no association with fGCM concentrations. The change in call rate in response to variation in the social and ecological environments individuals experience indicates some degree of flexibility in vocal production.
Large eddy simulation input data for publication "Pressure fields in the airflow over wind-generated surface waves" by Funke et al.
<p>Input files allow for the simulation of a turbulent flow over a sinusoidal surface using the PALM LES model, version 6.0, revision 4901.</p>
Intermediate data belonging to "Process-based climate change assessment for European winds using EURO-CORDEX and global models"
<p>This dataset contains the intermediate results of Wohland (2022) that are needed to redo the analysis und produce the figures. It allows to bypass those steps that rely on access to the supercomputers at the German Climate Computing Centre (DKRZ). When using this data in academic work, please reference</p> <blockquote> <p>Jan Wohland, Process-based climate change assessment for European winds using EURO-CORDEX and global models, Environmental Research Letters (provisionally accepted on 28/11/2022), 2022</p> </blockquote> <p><strong>Using this data to reproduce results</strong></p> <p>The data can be used together with the code provided in https://github.com/jwohland/kliwist_modelchain</p> <p>In the above mentioned github repository, there is a `run_all.py` script that repeats the analysis presented in Wohland (2022). After downloading and extracting this data, you can ignore the steps under "calculations", and begin with "plots".</p> <p><strong>Underlying data</strong></p> <p>The dataset draws on output from the CMIP5, CMIP6 and EURO-CORDEX initiatives. I thank the climate modeling groups for making their data openly available. In particular, I acknowledge the World Climate Research Programme’s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. I also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO-ESSP). I also acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6.</p> <p><strong>Funding</strong></p> <p>This work is part of the project "The influence of climate change on wind energy site assessments – KliWiSt" funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).</p> <p><strong>References to raw data journal articles</strong></p> <blockquote> <p>Jacob, D. <em>et al.</em> EURO-CORDEX: new high-resolution climate change projections for European impact research. <em>Reg Environ Change</em> <strong>14</strong>, 563–578 (2014).</p> </blockquote> <blockquote> <p>Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An Overview of CMIP5 and the Experiment Design. <em>Bull. Amer. Meteor. Soc.</em> <strong>93</strong>, 485–498 (2012).</p> </blockquote> <blockquote> <p>Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> <strong>109</strong>, 117–161 (2011).</p> </blockquote>
Pre-processed radial wind: sample data
<p>Sample data for testing and developing wind retrievals. Each file contains one hour of pre-processed (merged) WindCube observations.</p>
Data for: A neural circuit for wind-guided olfactory navigation
<p>To navigate towards a food source, animals must frequently combine odor cues that tell them what sources are useful with wind direction cues that tell them where the source can be found. Where and how these two cues are integrated to support navigation is unclear. Here we identify a pathway to the Drosophila fan-shaped body (FB) that encodes attractive odor and promotes upwind navigation. We show that neurons throughout this pathway encode odor, but not wind direction. Using connectomics, we identify FB local neurons called h∆C that receive input from this odor pathway and a previously described wind pathway. We show that h∆C neurons exhibit odor-gated, wind direction-tuned activity, that sparse activation of h∆C neurons promotes navigation in a reproducible direction, and that h∆C activity is required for persistent upwind orientation during odor. Based on connectome data, we develop a computational model showing how h∆C activity can promote navigation towards a goal such as an upwind odor source. Our results suggest that odor and wind cues are processed by separate pathways and integrated within the FB to support goal-directed navigation.</p>
Code Modifications and Experiment Data for Wind Perturbation Experiments in the Barents Sea
<p>This dataset contains data for the Publication: <strong>Impact of Cyclonic Wind Anomalies Caused by Massive Winter Sea Ice Retreat in the Barents Sea on Atlantic Water Transport towards the Arctic (Heukamp et al., JGR Oceans)</strong></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.