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1,596 results for “solar”
Solar radiation data from benchmark stations at the HJ Andrews Experimental Forest, 1973 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET), and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. In 2006, an additional Secondary Station was added at Watershed 7 (WS7MET) These solar radiation parameters were previously part of database code MS001, but were separated out into their own database in 2024 and the entities and attributes were reorganized in 2025 into shortwave, longwave, net radiation and PAR. The shortwave and longwave entities include both incoming and outgoing radiation where measured (see probe_code and method_code).
Hubbard Brook Experimental Forest: Daily Solar Radiation Measurements, 1959 - present
Daily solar radiation has been measured at Hubbard Brook Experimental Forest Headquarters since 1959, using several different sensors. Radiation was recorded continuously first by a Belfort pyranograph and later by a Weather Measure pyranograph. The pyranograph has been at its current Headquarter location since 1960 and before that at weather station 1, near weir 1. With the installation of the automatic weather station in 1981, a LiCor pyranometer was collocated with the pyranograph and used as the primary radiation sensor. In April, 2018 the LiCor sensor was exchanged for an Apogee SP230 light sensor. Data checking was done on the entire record and values flagged when changes were made to previously posted, erroneous or missing data. These data are gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Dataset for "An Alternative Chlorine-Assisted Optimization of CdS/Sb2Se3 Solar Cells: Towards Understanding of Chlorine Incorporation Mechanism"
<p>The current strategies in the development of Sb2Se3 thin film solar cells involve fabrication and optimization of<br>superstrate and substrate device architectures, with the preferable choice for TiO2 and CdS heterojunction layers.<br>For CdS-based superstrate cells, several studies reported the necessity to apply CdCl2 or other metal halide-based<br>post-deposition treatment (PDT), highlighting improvement of CdS/Sb2Se3 device efficiency. However, the need,<br>effect, and mechanism of such PDT are very often not described. Additionally, the fact that many groups have not<br>succeeded in demonstrating its benefits suggests that this strategy is not straightforward, requiring a deeper<br>understanding towards a more unified concept. The present study proposes an alternative approach to the<br>challenging CdCl2 PDT of CdS in CdS/Sb2Se3 device, involving controllable Cl incorporation in CdS films by<br>systematically varying the concentration of NH4Cl in the CBD precursor solution from 1 to 8 mM. Structural and<br>electrical characterizations are correlated with advanced measurements of Scanning Kelvin Probe, surface<br>photovoltage, and atomic force microscopy to understand the impact of Cl incorporation on the properties of CdS<br>films and CdS/Sb2Se3 devices. The validity of Cl incorporation in the CdS lattice and interdiffusion processes at<br>the CdS-Sb2Se3 interface is confirmed by secondary ion mass spectrometry analysis. It is demonstrated that<br>incorporation of 1 mM of NH4Cl, as a Cl source in CBD CdS, can boost the PCE of CdS/Sb2Se3 by ~20 %. With this<br>approach, we offer new perspectives on the optimization methodology for Cl-based CdS/Sb2Se3 device processing<br>and complementary understanding of the physiochemistry behind these processes.</p>
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
<p>This spatio-temporal dataset contains capacity factors timeseries for locations on a grid with 50km edge length in Europe. The data is resolved in one hour timesteps and comprises the years 2000--2016. It has been generated using <a href="https://www.renewables.ninja">Renewables.ninja</a> and is based on MERRA-2 reanalysis data. For each of the ~2700 onshore location, it contains one time series for onshore wind turbines and five time series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops. For each of the ~2800 offshore location there is one timeseries for offshore wind turbines.</p> <p>Two GeoTIFF files contain spatial information of onshore and offshore locations. For each of the three technologies -- onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDF files are linked through unique IDs for all locations.</p> <p>This data serves as input data to euro-calliope, a model of the European electricity system.</p> <p>The following parameters have been used to generate the timeseries:</p> <pre><code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code></pre> <p>CHANGELOG:</p> <p>Version 3 (2022-05-18)</p> <p>* Update spatial scope to include Iceland and its offshore EEZ.<br> * Update temporal scope to include 2017 and 2018.</p> <p>Effect of increasing spatial scope is a slight change in the spatial position of the data points.</p> <p>Version 2 (2020-06-18)</p> <p>* Add time series for rooftop PV with different orientations.</p>
Figure 2a - Rossi et al. Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells RRL Solar (2022)
<p>The Data set is related to the<strong> figure 2a</strong> of the paper </p> <p>Design of Highly Efficient Semitransparent Perovskite/Organic Tandem Solar Cells by Daniele Rossi,Karen Forberich,Fabio Matteocci,Matthias Auf der Maur,Hans-Joachim Egelhaaf,Christoph J. Brabec,Aldo Di Carlo, Rapid Research Letter (2022) https://doi.org/10.1002/solr.202200242</p>
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
Steady-state operation dataset of an experimental Wet Cooling Tower pilot plant located at Plataforma Solar de Almería
<p>Repository that contains experimental data obtained from a Wet Cooling Tower (WCT) plant located at <a href="https://www.psa.es/es/index.php">Plataforma Solar de Almería</a>.</p> <p>For the article "Wet cooling tower performance prediction in CSP plants: A comparison between artificial neural networks and Poppe’s model", three experimental campaigns were used, quoting from the article:</p> <blockquote> <p>A total of 132 steady-state experimental points have been obtained thanks to the thorough experimentation conducted. These data cover a large variety of ambient conditions (different seasons, days and nights) and thermal loads (from 27 kW to 207 kW). </p> </blockquote> <p> </p> <p>See <code>README.md</code> for a more detailed description and instructions on how to use the data.</p> <h2><br>License</h2> <p><a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0. Attribution 4.0 International</a></p> <p>If the data is used as part of a scientific publication, please cite the source publication:</p> <div> <div>Serrano, Juan Miguel, Pedro Navarro, Javier Ruiz, Patricia Palenzuela, Manuel Lucas, and Lidia Roca. “Wet Cooling Tower Performance Prediction in CSP Plants: A Comparison between Artificial Neural Networks and Poppe’s Model.” <em>Energy</em> 303 (September 15, 2024): 131844. <a href="https://doi.org/10.1016/j.energy.2024.131844">https://doi.org/10.1016/j.energy.2024.131844</a>.</div> </div>
Discrete water temperature, flow, solar radiation, chlorophyll-a and inundation, Sacramento-San Joaquin Delta, CA, 1999-2019
The objective of our study is to better understand the factors affecting chlorophyll-a production within a floodplain and its transport downstream to determine how lateral connectivity influences longitudinal connectivity. The Yolo Bypass is an engineered floodplain of the Sacramento River that inundates during periods of high outflow via overtopping weirs. Water traveling through the Yolo Bypass flows parallel to the Sacramento River and re-connects to the mainstem at the southern extent of the floodplain. Several monitoring programs in the Sacramento San-Joaquin Delta and Yolo Bypass collect discrete and continuous water quality data, including chlorophyll measurements. For this study, we synthesized available flow, water temperature, chlorophyll and inundation data between March 1999 to December 2019 and modeled the effects of environmental variables and inundation on chlorophyll-a production in the floodplain, the mainstem, and downstream of the floodplain/mainstem.
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.
Historical Weather, Load, Wind, and Solar Data for the Salt River Project
<p>We created and curated a dataset of historical (1980-2019) hourly meteorology, load, wind, and solar data for the Salt River Project (SRP) region. The data was created by PNNL's <a href="https://godeeep.pnnl.gov/">GODEEEP</a> project. Each row in the dataset is a single hour and each column is a variable. All meteorological variables are spatially-averaged over the SRP service territory. The variables and their units are as follows:</p><ol><li>"Time_UTC"; Coordinated Universal Time (UTC); Time of day.</li><li>"T2"; Fahrenheit; 2-m air temperature.</li><li>"Q2"; kg/kg; 2-m water vapor mixing ratio.</li><li>"SWDOWN"; W/m^2; Downwelling shortwave radiative flux at the surface.</li><li>"GLW"; W/m^2; Downwelling longwave radiative flux at the surface.</li><li>"WSPD"; m/s; 10-m wind speed.</li><li>"Scaled_2019_Load"; MWh; Simulated hourly demand for electricity that is scaled to 2019 levels of annual energy. This load estimate does not account for historical changes in population and economics within the SRP service territory. It is included to make it easier to isolate weather impacts on load without having to consider long-term changes.</li><li>"Load"; MWh; Simulated hourly demand for electricity.</li><li>"Agua_Fria_Solar_Capacity"; N/A; Solar capacity factor for the SRP Agua Fria project with plant configurations taken from the EIA-860 database.</li><li>"Phoenix_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Solar_Capacity"; N/A; Solar capacity factor for hypothetical solar plants derived using the grid cell nearest to Flagstaff, AZ.</li><li>"Phoenix_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Phoenix, AZ.</li><li>"Flagstaff_Wind_Capacity"; N/A; Wind capacity factor for hypothetical 80-m plants derived using the grid cell nearest to Flagstaff, AZ.</li></ol>
A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models
<p>Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure.</p> <p><strong>***Please note that the paper is under review and has not been accepted yet.***</strong></p>
Solar spectral irradiance measurements above and in-canopy (SLOCS and CloudRoots Amazonia, 2022)
<p> </p> <p><strong>Shedding Light On CloudRoots</strong></p> <p>Solar spectral irradiance measurements made with the sensors produced within the Shedding Light On Cloud Shadows (SLOCS) project, deployed at the CloudRoots Amazonia 2022 campaign. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Level 0 (raw): the raw data as it comes from the instruments</li> <li>Level 1 (L1): data in NetCDF format with metadata, quality control, homogenized factory calibration (counts bin-1 dt-1)</li> <li>Level 2 (L2): calibrated L1 data in W m-2 nm-1</li> <li>extras: this folder includes reference calibration spectra and data quality quicklooks</li> </ul> <p>Data is available at 1 Hz (resampled) and 10 Hz (native) resolution. 10 Hz resolution is compressed using NetCDF compression with gzip level 5 (uncompressed is 1.13 GB per date).</p> <p><strong>Data quality and uncertainty<br></strong></p> <p>Please note this dataset is in version 0.1.0, meaning you should use the dataset with caution. Not all unphysical data may have been flagged as such, and spectral calibration is an estimate based on a simple modelled spectrum. This modelled spectrum is a standard tropical atmosphere without aerosols, and is not run with observed profiles except an ERA5 estimate of total column water vapour. Please refer to 'extras' for technical validation of the spectral calibration method, and LibRadtran input/output files.</p> <p>A production (1.0) version will be released as soon data is fully validated.</p> <p>Lower-end uncertainty can be estimated by looking at the sensor to sensor spread at wavelength level during the calibration measurements. In the calibration phase, all sensors were co-located and homogenized at wavelength level. The 13:50 to 14:10 UTC time on August 7 is the reference frame for spectral calibration. </p> <p>Other sources of uncertainty are difficult to quantify due to measurements taking place in a very heteregeneous forest. These uncertainties relate primarily to the less-than-perfect placement of sensors on the towers in comparison to the reference calibration phase. </p> <p>Sensor 18 is only available in raw data or calibrated data. Precalibration (homogenizing) is not possible given its deviating spectral filter set compared to the others (sensor version 3b vs. 3a). </p> <p><strong>Technical information</strong></p> <ul> <li>The NetCDF files comply with CF1.7 where applicable.</li> <li>Metadata include sensor location (altitude relative to ground and sea level, lat, lon). </li> <li>Code for processing raw data to NetCDF available at <a href="../records/10159129">https://zenodo.org/records/10159129</a></li> <li>Calibration of raw sensor units to spectral irradiance is done using a reference clear-sky spectrum simulated with LibRadtran. Settings and output is included in "extras".</li> </ul> <p><strong>More information</strong></p> <ul> <li><a href="https://chiel.ghost.io/slocs">SLOCS project homepage</a></li> <li><a href="https://cloudroots.wur.nl/">CloudRoots project homepage</a></li> <li>2022 campaign reference paper is in preparation</li> <li>See 'related works' for the instrument reference paper </li> </ul>
TOMCAT CTM simulated ozone profiles using NRL2, SATIRE and SORCE solar fluxes
<p>Individual file contain TOMCAT CTM simulated ozone profiles from five model simulations analysed in the following publication. Briefly, </p> <p>vmro3_T2Mz_TOMCAT_A_NRL2_2005-2020.nc contain ozone profiles from the control simulation that uses ERA5 dynamical forcing fields and NRL V2 solar fluxes</p> <p>vmro3_T2Mz_TOMCAT_B_SATIRE_2005-2020.nc and vmro3_T2Mz_TOMCAT_C_SORCE_2005-2020.nc contain ozone profiles from a simulations that are similar to the control simulation but with SATIRE and SORCE solar fluxes</p> <p>vmro3_T2Mz_TOMCAT_D_SFix_2005-2020.nc has ozone profiles from simulation that is similar to the control simulation but with fixed solar fluxes, whereas vmro3_T2Mz_TOMCAT_E_DFix_2005-2020.nc also contain ozone profiles from a simulation where model uses annually repeating dynamical fields.</p> <p> </p> <p>Dhomse, S. S., Chipperfield, M. P., Feng, W., Hossaini, R., Mann, G. W., Santee, M. L., and Weber, M.: A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-663, in review, 2021.</p>
The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America
<p><strong>Title: </strong>The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America.</p> <p><strong>Authors:</strong> Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz.</p> <p><strong>Contact:</strong> Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>27 Jan 2022 - MANVI v2 was released!</strong> All data were reprocessed and improved. It is advised to re-download the whole series instead of combining v1 and v2. The dataset now covers years 2000-2021.</p> <p><strong>23 May 2019 - MANVI v1 was released.</strong> It covers years 2000-2018.</p> <p> </p> <p><strong>Data:</strong> MODIS (MAIAC) EVI and NDVI indices</p> <p><strong>Scale factor</strong>: 10000</p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in 2000, Julian day 64)</p> <p><strong>Spatial resolution:</strong> 1 km</p> <p><strong>Temporal resolution:</strong> 16 days</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details:</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product</li> <li>The daily data were aggregated into 16-day composites by the pixel’s median. The 16-day composites always start from Day Of Year (DOY) 016 and end with DOY 352. Therefore, the remaining days from 352 to 365/366 were not used. This procedure was used to facilitate inter-annual comparisons</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files for EVI and NDVI - one per year: <ul> <li>Inside them there are raster files with ".tif" format, one per 16-day window. The filename syntax is "maiac_southamerica_DATA_YYYYDOY.tif", where YYYY is the year (e.g. 2000), and the DOY is the Julian day of the last day of the composite window, i.e. YYYYDOY for January 2005 for DOY from 001 to 016 is 2005016, from DOY 017 to 032 is 2005032, etc.</li> </ul> </li> <li>Csv files with the YYYYDOY and "real" dates for the time period</li> </ul> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p><strong>Acknowledgements:</strong> This work was funded by São Paulo Research Foundation – FAPESP, Brazil, grant 2015/22987-7. We thank NASA, and especially Yujie Wang and Alexei Lyapustin, for providing the freely available MODIS (MAIAC) data.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of the first author's PhD work and lots of hours of coding and patience. It is free to use, but if you use this dataset in your work, please make sure to properly cite the repository. We also welcome users to invite us for collaboration.</p> <p> </p> <p><strong>For use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Wagner, Fabien Hubert; Galvão, Lênio Soares; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America". (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3159487">https://doi.org/10.5281/zenodo.3159487</a></p> <p> </p> <p><strong>More information: </strong>contact Ricardo Dalagnol (ricds@hotmail.com). We also have the MODIS (MAIAC) BRDF-corrected bands 1-8, EVI, NDVI at 1 km with 16-day and monthly aggregation composites.</p>
Atlas of the Solar Intensity Spectrum and its Center-to-Limb Variation
<p>The atlas of the Third Solar Spectrum (SS3) represents the ratio between the intensity spectrum at different distances from the solar limb and the intensity spectrum at disk center (µ = 1.0), both in units of the intensity of the local continuum level. The observed positions of the measurements cover 9 different µ values along the solar axis ranging from 0.1 to 0.9 in step of 0.1, where µ=cosθ is the cosine of the heliocentric angle θ. The current version of the atlas covers the range 4384- 6610 Å.</p> <p>In the PDF file, the first plot represents the spectrum at the center of the solar disk, recorded at IRSOL. The next 9 plots represent the 3rd solar spectrum for µ=0.1, µ=0.2, µ=0.3, …, µ=0.9</p> <p>Columns of the CSV file:</p> <table> <tbody> <tr> <td><strong>WL:</strong></td> <td>Wavelength, 4384-6610 Å</td> </tr> <tr> <td><strong>IC:</strong></td> <td>I/I<sub>c</sub> at disc center</td> </tr> <tr> <td><strong>RMU01:</strong></td> <td>limb / disk-center ratio at µ=0.1</td> </tr> <tr> <td><strong>RMU02:</strong></td> <td>limb / disk-center ratio at µ=0.2</td> </tr> <tr> <td><strong>…</strong></td> <td>…</td> </tr> <tr> <td><strong>RMU09:</strong></td> <td>limb / disk-center ratio at µ=0.9</td> </tr> </tbody> </table>
Data for the article "Typhon: a polar stream from the outer halo raining through the Solar neighborhood"
<p>This data contains the stellar parameters of Typhon stream stars in the context of the "Typhon: a polar stream from the outer halo raining through the Solar neighborhood" (Tenachi et al. 2022) paper, in two formats:.csv and .fits (which also contains a short description of each column).</p> <p>This data includes:</p> <ul> <li>Stellar coordinates and parameters from Gaia DR3 (Gaia Collaboration 2022) with extinction-corrected magnitudes using the (Schlafly and Finkbeiner 2011) corrections to the (Schlegel et al. 1998) extinction maps, assuming the extinction ratios A<sub>G</sub>/A<sub>V</sub> = 0.86117, A<sub>GBP</sub>/A<sub>V</sub> = 1.06126 and A<sub>GRP</sub> /A<sub>V</sub> = 0.64753, as listed on the web interface to the PARSEC isochrones (Bressan et al. 2012) and assuming a solar position (x,y,z) = (−8.2240, 0, 0.0028) kpc (Bovy 2020, Widmark et al. 2021) and a solar velocity (vx,vy,vz) = (11.10, 7.20, 7.25) km/s with a circular velocity = 243 km/s (Schonrich et al. 2010, Bovy 2020).</li> <li>Added dynamical parameters (actions, energy, apocenters and pericenters values) derived in a (McMillan et al 2017) potential.</li> <li>Metallicity parameters from LAMOST DR8 PASTEL column (Wang et al 2022).</li> <li>Independent measurements from the "Chemical Abundances of the Typhon Stellar Stream" follow-up paper (Ji et al 2022).</li> </ul>
Database of optical parameters for the simulation of perovskite/silicon solar cells
<p>This dataset contains a set of representative optical parameters, i.e. the wavelength dependent complex refractive index (n+ik), for common materials used in perovskite-silicon tandems. In particular: SnO2, Spiro-MEOTAD, CH3NH3PbI2, a-Si:H, undoped c-Si, MgF.<br> The data are stored in the ASCII file “<em>nk_MaterialParameters.txt</em>”, where for each material, we report three data columns: wavelength (in µm), refractive index <em>n</em> and extinction coefficient <em>k</em>.</p>
Kodaikanal Solar Observatory (KoSO) White-Light Sunspot Regions Masks (1904-2017)
<p>Regular observations at the Kodaikanal Solar Observatory (KoSO) began in 1904 using a white-light telescope with a 10-cm aperture lens and an f/15 light beam. Between 1912 and 1917, the objective lens was changed several times. In 1918, a 15-cm achromatic lens was installed. This new configuration produced a 20.4 cm size image of the Sun in the image plane. Photographic plates were used to capture the image. The same telescope has been used since 1918 up until 2017 to take regular white-light observations of the Sun. This data set provides the sunspot mask in HDF5 format for all the White Light Observations acquired at KoSO. Each HDF5 file contains the sunspot mask for all the observations for that year. The sunspot masks are provided in two different coordinate systems: (i) Full Disk as observed and (ii) Carrington heliographic coordinate, which is transformed from full disk using near point interpolation. Each data set also contains metadata in the form of HDF5 attributes. The Carrington co-ordinate data if full Sun map, hence the near-side of the Sun is the region where values in the mask in non-zero, where as sunspot regions are filled with value 2. </p> <p>A <strong>Python package (KoSOpy), which can be located on <a href="https://github.com/Kodaikanal-Solar-Observatory/kosopy" target="_blank" rel="noopener">GitHub</a>,</strong> is being developed which can be used to navigate through these data sets.</p>
Parker Solar Probe Filtered Ion Scale Wave Activity for Encounters 8 to 16
<p>The following datasets are the result of filtering algorithm applied to a wave analysis of Parker Solar Probe data from Encounters 8 to 16. The wave analysis was conducted by Kristoff Paulson using a Short-Time Fourier Transform (STFT) approach based on polarization techniques derived by Means, 1972 (DOI: <a href="http://doi.org/10.1029/JA077i028p05551">10.1029/JA077i028p055511135</a>). Included is a jupyter notebook containing the filtering algorithm, the results of the filtering, and a demonstration of how to best open the files. The dataset for each encounter contains 9 columns that correspond with:</p> <ol> <li>Date in CDF epoch</li> <li>Left-handed (LH) Integrated Wave Power (nT^2) where integration is over frequency space (0-32 Hz) of filtered activity</li> <li>Right-handed (RH) Integrated Wave Power (nT^2)</li> <li>LH median ellipticity where median is over frequency space</li> <li>RH median ellipticity</li> <li>LH median coherency</li> <li>RH median coherency</li> <li>LH median wave normal angle (deg)</li> <li>RH median wave normal angle (deg)</li> </ol> <p>In all cases, ellipticity is measured in the Parker Solar Probe spacecraft frame. Ellipticity measures the ellipticity of the polarization ellipse and takes on values between -1 and 1. Values of 1 correspond with RH circular polarization and -1 with LH circular polarization. Coherency takes on values between 0 and 1. It measures how interrelated fluctuations are where 0 represents noise and 1 represents coherent fluctuations. The wave normal angle is the angle between the wave vector, k, and the local mean magnetic field, B. Since there are inherent ambiguities in the direction of the wave vector for single spacecraft measurements, the wave normal angle is calculated such that it takes on angles from 0 to 90 degrees. The filtering algorithm selects activity in which coherency is above 0.8, absolute value of ellipticity is above 0.5, and wave normal angle is below 45 degrees such that coherent, circularly polarized, near parallel propagating wave activity on ion scales is selected. <strong>If wave power for a given time has value of 0.0, then no fluctuations in the magnetic field data passed the required filters at that time.</strong></p> <p>:</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.