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8,233 results for “Radiation”
Bonanza Creek LTER: Hourly Net Radiation, Up and Down Shortwave/Longwave Radiation from 1992 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
A collection of radiation data taken in Caribou-Poker Creeks Research Watershed. This set includes PAR, Net Radiation, and Shortwave/Longwave Radiation (both up and down). Winter measurements may be inaccurate due to snow cover.
Radiation measurements at Key Largo Ranger Station, South Florida (FCE) for July 2001
Basic radiation data, including Infra-red canopy temperatures, collected as 1 minute averages from a 5 m tower at Key Largo Ranger Station, South Florida, near Everglades National Park.
Hubbard Brook Experimental Forest: 15 Minute Solar Radiation Measurements, 2014 - present
Beginning in 2014, solar radiation sensors were implemented at the Hubbard Brook Experimental Forest to measure solar radiation at 15-minute intervals. Two collocated LiCor sensors were installed at Station 1 in July 2014. The 15-minute record for Headquarters begins in 2018 with a single sensor. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
Alignments used in "The evolution of the phenylpropanoid pathway entailed pronounced radiations and divergences of enzyme families"
<p>Alignments used in de Vries et al. (2021) "The evolution of the phenylpropanoid pathway entailed pronounced radiations and divergences of enzyme families" published as</p> <p>(1) a pre-print: https://doi.org/10.1101/2021.05.27.445924</p> <p>(2) in Plant Journal (in press)</p>
Single column 1D radiative transfer simulations during PS106 including low-level-stratus clouds in the central Arctic
<p>The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG).</p><p>The data set contains simulations for the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing data which were processed with the Cloudnet algorithm to derive cloud macro - and microphyiscal products. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
Reproduction package for the paper "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"
<p>Research Data Management package for "Bottling the Champagne: Dynamics and Radiation Trapping of Wind-Driven Bubbles around Massive Stars"</p> <p>Authors: Sam Geen & Alex de Koter</p> <p>Status: Accepted by MNRAS<br> This package aims to provide a full data reproduction pipeline. Please see Readme.md for more information.</p>
The genetic population structure of Lake Tanganyika's Lates species flock, an endemic radiation of pelagic top predators
<p>Data associated with the manuscript "The genetic population structure of Lake Tanganyika’s Lates species flock, an endemic radiation of pelagic top predators," where we investigate the genetic population structure of the four endemic <em>Lates </em>species in Lake Tanganyika.</p> <p><strong>Abstract</strong>: Life history traits are important in shaping gene flow within species and can thus determine whether a species exhibits genetic homogeneity or population structure across its range. Understanding genetic connectivity plays a crucial role in species conservation decisions, and genetic connectivity is an important component of modern fisheries management in fishes exploited for human consumption. In this study, we investigated the population genetics of four endemic <em>Lates</em> species of Lake Tanganyika (<em>Lates stappersii</em>, <em>L. microlepis</em>, <em>L. mariae</em> and <em>L. angustifrons</em>), using reduced-representation genomic sequencing methods. We find the four species to be strongly differentiated from one another, with no evidence for contemporary admixture. We also find evidence for high levels of genetic structure within <em>L. mariae</em>, with the majority of individuals from the most southern sampling site forming a genetic group distinct from the individuals at other sampling sites<em>.</em> We find evidence for much weaker structure within the other three species, <em>L. stappersii,</em> <em>L. microlepis</em>, and <em>L. angustifrons</em>, although small and unbalanced sample sizes and imprecise geographic sampling locations may hinder our ability to detect weak population structure. We call for further research into the origins of the genetic differentiation that we observe in these four species, particularly that of <em>L. mariae</em>, which may be important for the conservation and management of this species.</p> <p>Code associated with the analysis of these data can be found on GitHub at <a href="https://github.com/jessicarick/lates-popgen">https://github.com/jessicarick/lates-popgen</a>.</p>
Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly
<p>Dataset for Cruz-Laufer et al. (2021) Explosive networking: the role of adaptive host radiations and ecological opportunity in a species-rich host-parasite assembly.</p> <p><strong>Abstract: </strong>Many species-rich ecological communities emerge from adaptive radiation events. The effects of this explosive speciation on community assembly remain poorly understood. Here, we explore the well-documented radiations of African cichlid fishes and their interactions with the flatworm gill parasites <em>Cichlidogyrus </em>spp., including 10529 reported infections and 477 different host-parasite combinations collected through a survey of peer-reviewed literature. We assess how evolutionary, ecological, and morphological parameters determine host-parasite meta-communities affected by adaptive radiation events through network metrics, host repertoire measures, and network link prediction. The hosts’ evolutionary history mostly determined host repertoires of the parasites. Ecological and evolutionary parameters determined host-parasite interactions. Generally, ecological opportunity and fitting have shaped cichlid-<em>Cichlidogyrus</em> meta-communities suggesting an invasive potential for hosts used in aquaculture. Meta-communities affected by adaptive radiations are increasingly specialised with higher environmental stability. These trends should be verified across other systems to infer generalities in the evolution of species-rich host-parasite networks.</p>
Database of Ultrasonic Transducer Radiation Characteristics
<p>Database of measurements of different properties of ultrasonic transducers of different types.</p> <ul> <li>On-axis frequency response measurements of multiple copies of the same transducer model, measured between 20 kHz and 160 kHz in an anechoic space. </li> <li>Directivity patterns of multiple copies of the same transducer model, measured for every degree in an anechoic space. Calculated for every 10 Hz between 20 kHz and 160 kHz.</li> <li>Frequency response measurements for every degree for a single transducer of each type, measured between 20 kHz and 160 kHz in an anechoic space.</li> </ul> <p>The datasets are stored in HDF5 files created using h5py. An example Jupyter notebook is included to show how the plots were created.</p> <p> </p> <p>The transducers measured are:</p> <ul> <li>Murata MA40S4S</li> <li>Camdenboss CDT40K1007T</li> <li>Multicomp MCUST10P40B07RO</li> <li>Multicomp 400PT16P</li> <li>Multicomp MCUSD14A40S09RS</li> <li>Multicomp MCUSD14A48S09RS</li> <li>Multicomp MCUSD14A58S09RS</li> </ul>
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 250 m monthly for period 2014-2019 based on COPERNICUS land products
<p>Long-term monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) median value at 250 m based on the time-series of <a href="https://land.copernicus.eu/global/products/fapar">COPERNICUS FAPAR</a>. Derived using the data.table package and quantile function in R. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/Copernicus_vito"><strong>here</strong></a>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>fapar = Fraction of Absorbed Photosynthetically Active Radiation,</li> <li>proba.v.oct = determination method: PROBA-V products, month October,</li> <li>d = median value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014..2019 = time reference: from 2014 to 2019,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Data set: Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes
<h1>Repository for "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes"</h1> <p>---</p> <p>These data scripts were used to perform analyses included in the research paper "Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes" </p> <p>Main questions for the study:</p> <p>1. What are the main axes of morphological variation?<br>2. Does variation in morphology among species correlate with their current environments? <br>3. Are lineages that occupy ecologically similar habitats morphologically convergent? <br>4. Is speciation predominantly allopatric or sympatric? <br>5. Do sister species have greater morphological and ecological niche overlap than expected relative to non-sister species pairs?</p> <h2>## Data structure</h2> <p>Contents in the data folder is archived as a zip and can be downloaded from Zenodo (for all versions see https://zenodo.org/doi/10.5281/zenodo.10397830). Once you unzip the zipped files, you will see three folders and some files that are no in any folders. </p> <p>/data/ - files that were manually created and the phylogeny</p> <p>/data/script_generated_data/ - A combination of processed data needed to run the analyses </p> <p>/data/dorsal/ - photographs of the head from the dorsal view. These photos were used for digitising landmarks and semilandmarks. </p> <p>/data/worldclim2_30s/ - cropped and merged annual temperature from WorldClim2 (Fick and Hijmans 2017), soil bulk density from <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData.html">Soil and Landscape Grid of Australia</a>, and Global Aridity Index from Zomer et al. (2022). <br><br>/DREaD/ - contains some files required to replicate DREaD analysis</p> <h2>## Code/Software</h2> <p>All scripts can be run using open source software. Scripts should be run in order to create necessary files that will be saved in /data/script_generated_data/ for further scripts. R is required to run R scripts (.R).</p> <h3>### /Code</h3> <p> - utility/*.R - scripts for custom functions. These are sourced in other scripts.<br> - DREaD/*.R - scripts associated with DREaD analyses<br> - 00_linear_measurement_shaperatio.R - script used to account for sexual dimorphism and calculate conventional PCA. Addresses Q1.<br> - 01_model_fitting.R - script used to address Q2 and plot visualisations.<br> - 02_convergence.R - this script calculates Ct1-4 and C5 scores. Addresses Q3.<br> - 02_convergence_model_fitting.R - this script evaluates fit of different evolutionary models to traits. Addresses Q3.<br> - 02_convergence_test_simulations.R - simulation studies to show that our phylogeny has sufficient power to detect convergence.<br> - 03_niche_enmtools_bias_account.R - calculates ecological niche models (ENMs) for each species using MAXENT. Runs Age-Overlap Correlation tests for geography and ENMs. Partially addresses Q4.<br> - 03_DREaD_Blindsnakes_AS.R - script to run DREaD analysis. <br> - 03_morpho_niche_overlap_plots.R - Runs Age-Overlap Correlation tests for body shape and snout shape. Plots AOCs. Partially addresses Q4. <br> - 04_pairwise_distance_test.R - Binomial tests between sister and non-sister pairs for ENMs and Geographic Range. Partially addresses Q5<br> - 04_morpho_pairwise.R - Binomial tests between sister and non-sister pairs for body shape and snout shape. Partially addresses Q5</p> <h2>## Contact</h2> <p>Should you have questions about these scripts or would like to request raw data, please do not hesitate to contact Sarin Tiatragul (contact information can be found in the paper) or on Github (https://github.com/stiatragul/blindsnakemorphoevo)</p> <h2>## References</h2> <p><a name="ref-fickWorldClim2017"></a>Fick, S. E., and R. J. Hijmans. 2017. <a href="https://doi.org/10.1002/joc.5086">WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas</a>. International Journal of Climatology 37:4302–4315.</p> <p><a name="ref-zomerVersion2022"></a>Zomer, R. J., J. Xu, and A. Trabucco. 2022. <a href="https://doi.org/10.1038/s41597-022-01493-1">Version 3 of the global aridity index and potential evapotranspiration database</a>. Scientific Data 9:409.</p>
Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs)
<p><span>These <a name="OLE_LINK1"></a>gridded surface cloud fraction radiative kernels (GCF-CRKs) are created by integrating refined downwelling surface shortwave radiation (DSSR) estimates and a high-precision cloud fraction (CF). The DSSR is corrected by a CF-dependent model, which leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. </span></p> <p><span><span> </span>There are five individual files. “SFC_SW_Kernel_Arc.nc” is for CRKs of all clouds, “SFC_SW_lowcloud_Kernel_Arc.nc” is for CRKs of low-level clouds, “SFC_SW_midlowcloud_Kernel_Arc.nc” is for CRKs of mid-low-level clouds, “SFC_SW_midhighcloud_Kernel_Arc.nc” is for CRKs of mid-high-level clouds, and “SFC_SW_highcloud_Kernel_Arc.nc” is for CRKs of high-level clouds. The four cloud layers are derived from four pressure layers (surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low, middle-low, middle-high, and high clouds, respectively) based on the CERES-SYN stratification standard.</span></p> <p><span> </span></p> <p><span>The file format is netcdf4, and was created by Matlab. To read these files, any software supporting netcdf4 can be used. These files only involved sunlit months from Apr to Sep during 2000-2020, with the longitude from -180°~180° and the latitude from 60°N~90°N.</span></p>
Simulation results: Radiative cooling induced coherent maser emission in relativistic plasmas
<p>This repository contains some of the simulation data presented in the recent article titled <em>"Radiative cooling induced coherent maser emission in relativistic plasmas"</em> (<a href="https://arxiv.org/abs/2409.18955" target="_new" rel="noopener">https://arxiv.org/abs/2409.18955</a>). The data available are from 2D particle-in-cell (PIC) simulations, which investigate the effects of radiative cooling in relativistic plasmas and its role in inducing coherent maser emission. The simulations were performed using OSIRIS, a massively parallel and fully-relativistic PIC code.</p> <p>The electric field data in the third direction (E3) included here has been spatially averaged by a factor of 8 in both directions, resulting in a dataset that reflects a resolution 64 times lower than the actual simulation. Additionally, the raw data includes only one two-thousandth of the simulated electron macro-particles. Also included is the phase space data in the x2, p2, and p3 dimensions.</p> <p>These datasets represent key aspects of the simulation results discussed in the paper, where the focus is on understanding the interplay between radiative losses and coherent emission mechanisms.</p> <p>More details on the simulations and the analysis of these results can be found in the corresponding article.</p>
Spectral transmittance of solar radiation by screens and nets used in horticulture and agriculture
<p>We present a dataset of measurement of the spectral transmittance of 197 horticultural nets and screens from five companies. These materials span a range of uses from shading and reducing the heat load on plants to blocking pests such as birds and insects. Routinely, these materials are used in greenhouses and polytunnels to reduce the sunlight received by plants, however their spectral transmittance is not routinely measured. The spectral irradiance that plants receive can affect plant growth and photomorphogenesis, hence this information is of value when selecting the most appropriate material for a given purpose. The spectral transmittance of the materials was measured outdoors close to solar noon using an array spectrometer calibrated for the range 290-900 nm and compared directly with the ambient solar spectral irradiance. The measured spectrum encompasses those regions perceived by plants through known photoreceptors and used by plants in photosynthesis: ultraviolet (UV); photosynthetically active radiation (PAR), and near infra red (far red – FR).</p> <p>The solar spectral photon irradiance (μmol m<sup>-2</sup> s<sup>-1</sup>) transmitted by screens and nets from several manufacturers was measured with an array spectroradiometer. Our measurements and analyses are focused on the differences in spectral irradiance, created when employing these screens and nets, in order to address the lack of detailed studies of these light environments, rather than the physiochemical properties of materials or their cost-effectiveness. The measurements of spectral irradiance under climate screens, and shade and insect nets, were made on clear days in sunny conditions close to solar noon (between 10 a.m. to 2 p.m local time) at NC State University campus (35.78°N, -78.67°W) in late July and early August 2017, and in Viikki Field Plots at the University of Helsinki (60.22°N, 25.01°E, 55 m asl) in July and August 2018. The methods for measurements at North Carolina State University follow the protocol described below and published in <a href="https://doi.org/10.1371/journal.pone.0199628">Kotilainen et al., (2018)</a>, where a comprehensive assessment of the results of this subset of screens/nets and their meaning is also given.</p> <p>The measurements were performed in an open field with no surrounding structures or buildings within 20 m. Repeated measurements of each different sample were made in a randomised order, thus ensuring comparability among measurements. Measurements were made on a tripod 0.7 m above the ground and the sample was secured to a wooden plate 3 cm above the diffusor. A test, comparing four larger (1 x 1 m) samples against those of the standard dimensions that we used, found that the area of screen/net measured did not affect the results at this distance between the screen/net and diffusor. Thus, there was no evidence that unfiltered diffuse or scattered radiation interfered with measurements despite the relatively small dimensions of the sample.</p> <p>Measurements under each screen/net sample in 2017 (Svensson 13 x 19 cm, Mallas Textiles 8 x 10 cm) were made twice to account for any possible effect of sample placement over the cosine diffuser and change in the sun angle during a set of measurements. Given that no significant differences were evidence, the 2018 screen/net samples (Criado y Lopez 8 x 12 cm, Howitec 15 x 25 cm, Huachang yarns 25 x 30 cm, and Jiangsu Huachang Yarns and Fabrics 8 x 12 cm) were only measurement once. A recording of spectral irradiance without the screen/net of filtered sunlight was made directly before and after each filter measurement (called “Open”).</p> <p>The spectrometer used had been calibrated for measurements of UV and visible solar radiation (Maya2000 Pro Ocean Optics, Dunedin, FL, USA; D7-H-SMA cosine diffuser, Bentham Instruments Ltd, Reading, UK - see <a href="https://doi.org/10.1002/ece3.4496">Hartikainen et al., 2018</a> for details of the measurement protocol). Briefly, each measurement of irradiance transmitted beneath a screen or net was followed by sequence of measurements in the dark and with a polycarbonate filter attenuating all UV radiation. These controls accounted for the dark noise and stray light in the UV waveband. Both a correction for the shape of the slit function and for stray light were included in the post-processing of the spectra (<a href="http://uv4plants.org/methods/how-to-check-an-array-spectrometer/">Aphalo et al., 2016</a>). Bracketing was performed by taking a measurement of the UV region and splicing this together this the entire spectrum. All measurements were processed using the Photobiology packages in R.</p> <p>Measurements of solar spectral irradiance in the wavelength range from 290 nm to 900 nm were processed in R, using the <em>photobiology</em> packages developed for spectral analysis (<a href="https://doi.org/10.19232/uv4pb.2015.1.14">Aphalo, 2015</a>). We present spectral photon irradiance (μmol m<sup>-2</sup> s<sup>-1</sup>) and spectral energy irradiance (W m<sup>-2</sup>). Plants absorbs photons producing a chemical change (Grotthus Law) thus photon irradiance is more easily applicable understanding to biological processes in plants. The spectral transmittance of the screens/nets are the most useful data presented. Essentially the patterns of spectral attenuation will be consistent, irrespective of whether spectra are expressed as photon or energy irradiance.</p> <p>Utilizing predefined functions available in the <em>photobiology</em> packages, we calculated the integrals and photon ratios of these integrals as follows: UVB:PAR 280–315 nm/400-700 nm, UVA:PAR 315–400 nm/400-700 nm, blue:green (B:G) 420–490 nm/500-570 nm, blue:red (B:R) 420–490 nm/620-680 nm. Red and far-red for the calculation of R:FR ratio are 655–665 nm and 725–735 nm, respectively. UVB radiation and UVA radiation are defined according to ISO, blue, green and red according to <a href="https://doi.org/10.1104/pp.110.160820">Sellaro et al. (2010)</a>, and R:FR according to <a href="https://doi.org/10.1146/annurev.pp.33.060182.002405">Smith(1982)</a>.</p> <p>The same definitions of the UV-waveband are maintained for both spectral integrals and their ratios throughout, i.e. according to ISO, (<a href="http://doi:%2010.21273/HORTTECH03648-16">Both et al., 2017</a>). This is because the UVB and UVA wavebands of solar radiation follow distinct daily patterns of variation; UVB irradiance is highest during the four hours around solar noon, whereas the UVA region of solar radiation remains a similar proportion of total irradiance throughout the day. These differences also imply that UVA and UVB radiation follow different diurnal and seasonal patterns of variation (<a href="https://doi.org/10.1111/j.1751-1097.2007.00216.x">Seckmeyer et al., 2007</a>).</p> <p><strong>Data Files Available</strong></p> <p><strong>DataBaseScreensNets.zip</strong></p> <p>Graphs (.jpg files) of actual measured (1) spectral energy irradiance, (2) spectral photon irradiance, and (3) proportion transmittance of solar radiation, for each screen and net. (1) Energy Irradiance figures (suffix _EI.) and (2) Photon Irradiance figures (suffix _PI.) are plot of the measured values of irradiance under the filter (screen/net) and corresponding measurements without the screen or net (“open” measurement) for comparison (290-898 nm wavelength range). The proportion transmittance under each screen or net is calculated from comparison of the open and measured spectrum (suffix _Trans). The low-wavelength tail end of the spectrum is trimmed (<310 nm) in each plots since % transmittance are inflated by low signal to noise ratio in the UV-B region where irradiance values are very low.</p> <p>The database screens and net are identified by the name of the company “_” name of the screen/net for all 197 materials.</p> <p>These figures can be reproduced from the file “ScreensNets_irrad_trans.txt” using the R code “Plotting_DataBaseScreensNets.r”</p> <p><strong>ImagesScreensNets.zip</strong></p> <p>Image files (.jpg files) from photos and scans of each of the measured screens and nets. One image from each of the 197 filter materials (screens/nets) measured is stored in folders arranged according to the company for each filter type. The companies are: Criado y Lopez; HowiTech; Huanchang yarns; Jiangsu Huachang Yarns & Fabrics; Mallas_Textiles and Svensson.</p> <p><strong>ScreensNets_irrad_trans.txt</strong></p> <p>This is the main database file containing the measurements of spectral irradiance beneath each filter material (screen/net) from 290 nm – 898 nm and corresponding open reading, and calculated spectral transmittance.</p> <p>Data are in columns as follows: (A) Company – the Company name; (B) FilterName – the filter name as given by the company; (C) Serial - a serial number, effectively equivalent to the order in which the materials were measured; (D) wavelength – at intervals recorded by the array spectrometer running for each spectrum from 290.02 nm to 897.73 nm; (D) FilterEI - energy irradiance of transmitted solar radiation measured 3 cm beneath the filter material (screen/net) at each wavelength of the spectrum; (E) FilterPI – photon irradiance equivalent to the energy irradiance; (F) OpenEI – energy irradiance of solar radiation at the same location without the filter material (screen/net) (G) OpenPI – photon irradiance equivalent to the energy irradiance; (H) FilterFactor – the proportion of radiation transmitted by the filter material (screen/net) at each wavelength measured, a value between 0.0 and 1.0 (values out of range at low wavelengths in the UV-B region are replaced with 0.0 or 0.1).</p> <p>Processed spectra are given: processing of raw spectra was done with <em>Photobiology</em> packages in R. Full spectra were recorded with an integration time set manually to give maximum counts of just less than 60 000 at the wavelength corresponding to peak spectral irradiance. Bracketing was performed by recording a second spectrum (long spectrum) with ten-times longer integration time than this, to achieve greater accuracy of measurement in the UV region (< 400 nm). These two spectra were spliced together. Each filter measurement was accompanied by a dark measurement (to estimate dark noise) and a measurement under a polycarbonate filter (PC) to correct for stray light. In 2018, these two readings were performed immediately after the filter material (screen/net) was measured; both within 10 s total of the filter material measurement for both the full spectrum, and long spectrum.</p> <p><strong>ScreensNets_irrad_trans.xlsx</strong></p> <p>This Excel file contains the same information in columns as the file ScreensNets_irrad_trans.txt but with a second worksheet showing the trimming calculations for out-of-range readings at low UV-B wavelength and with an addition final column, the irradiance spectrum open29_irrad (described below).</p> <p><strong>Open29_irrad.txt</strong></p> <p>In order to obtain standardised BSWF files to comparison with each other, the calculated proportion spectral transmittance results for each filter material (screen/net) were applied to a “standard” solar-noon open-spectrum from Helsinki recorded on a date close to midsummer (Open29_irrad.txt). This spectrum was measured as described above.</p> <p>This spectrum was measured at Viikki Fields, Helsinki on Wed June 27<sup>th</sup> 2018 at 13:15:33 EEST (Integration Time, 110000 μsec; bracketting x10) in a completely open area.</p> <p>To apply the transmittance data to their own locations, database users should substitute the spectrum from their own location for Open29_irrad.txt to obtain spectral irradiance data for the effects of the filter materials (screens/net) at their site using the R code Calculating_Spectral_Integrals.r</p> <p><strong>ScreensNets_spectral_integrals.txt</strong></p> <p>The file gives a matrix of spectral integrals and ratios calculated with the <em>Photobiology</em> packages in R for each of the spectra presented in ScreensNets_irrad_trans.txt. Column headings are the filter material ID, made up from the “Company name” “_” “filter name”. The first column contains row names identifying spectral integrals and ratios calculated – first as energy irradiance then as photon irradiance and finally as photon ratios. Calculations are made using the BSWF (<strong>Spectral_Integrals_Function.r</strong>) as follows: PAR_e; UVB_e; UVA_e; UVb350_e; UVa350_e; Blue_e; Green_e; Red_e; Far_red_e; GEN_G_e; GEN_T_e; PG_e; DNA_N_e; CIE_e; FLAV_e; Infra_red_e; PAR_q; UVB_q; UVA_q; UVb350_q; UVa350_q; Blue_q; Green_q; Red_q; Far_red_q; GEN_G_q; GEN_T_q; PG_q; DNA_N_q; CIE_q; FLAV_q; Infra_red_q; UVB_UVA; UVB_PAR; UVA_PAR; R_FR_Sellaro; R_FR_Smith10; R_FR_Smith20; B_G; B_R; PhyEqi.</p> <p><strong>ScreensNets_spectral_integrals.xlsx</strong></p> <p>This files contains the same data as ScreensNets_spectral_integrals.txt and shows on individual worksheets, processing of original, smoothed (in Photobiology package to improve the signal to noise in the UV-B tail of the spectr), and corrected (with values of transmittance greater than 1.0 or less than 0.0 replaced in the UV-B tail) data; and comparisons of the Original vs. Corrected, and Original vs. Smoothed data. The same BSWF calculations for the example open spectrum open29_irrad (used for standardisation) are given on their own worksheet, as is the corresponding “FilterFactor” (proportion spectral transmittance) for each spectral integral and spectral photon ratio. The final worksheet “Type” lists the filters and their expected function (i.e. shade, pest net, hale net, ground cover etc.).</p> <p>This “FilterFactor” information could be of practical use in situations where the spectral irradiance is unavailable for a given location, and comparisons among filters need to be made from only partial data (e.g. PAR PPDF). These FilterFactors can be applied to the PAR PPDF for instance to calculate the daily light integral through the day for horticultural proposes. Please note that differences in the shape of the solar spectrum at different locations will cause (small) deviations in the transmitted PAR PPFD calculated from the spectral integral compared with the more precise calculation from the spectral irradiance. Although for the purposes of comparison between filters these are likely to be of minor importance. </p> <p><strong>Plotting_DataBaseScreensNets.r</strong></p> <p>This file gives the R code for plotting the graphs in DataBaseScreensNets.zip from the source file ScreensNets_irrad_trans.txt. Make sure that the required packages are loaded. The code was run in R version 3.4.3.</p> <p><strong>Calculating_Spectral_Integrals.r</strong></p> <p>The file gives the R code to calculate spectral integrals and to include an open measurement for standardisation (Open29_irrad) from the source file ScreensNets_irrad_trans.txt (as described above). The spectra in ScreensNets_irrad_trans.txt are converted to source.spct for use in the Photobiology packages.</p> <p><strong>Spectral_Integrals_Function.r</strong></p> <p>The file is a function requiring the Photobiology packages in R to run. It is needed to calculate the spectral integrals described above and can be amended to obtain whichever spectral integrals and photon ratios from the Photobiology packages are desired.</p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
Dataset for Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator
<p>The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG). </p> <p>The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"</p> <p>In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged. </p> <p> </p> <p>[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023</p> <p>[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023</p>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface
<p>Experimental Record of a Longwave-Evaporation experiment. The record to be referenced in a forthcoming scientific paper.</p>
Lake Tahoe UV Radiation Profile data
UV and PAR profiles taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
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.