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96 results for “Photovoltaic”
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
Raw data for Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications
<p>In the following the raw data lying the foundation of the paper “Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications” (Lopez-Garcia et al.) published in Solar Rapid Research Letters, DOI: 10.1002/solr.202100909 (2021), are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Mater-One (Refs. PID 2020-116719RB-C42 and PID 2020-116719RB-C41) and SCALED (Ref. PID 2019-109215RB-C4) projects funded by the Spanish MCIN/AEI/10.13039/5011000110033.</p> <p>UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0<sup>o</sup>)) and in reflectance mode (with an Integrating sphere) scanning from 300 to 800 nm.</p> <p>J–V measurements under illumination were carried out using a homemade setup consisting on a AAA solar simulator calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).</p>
Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities"
<p>Research data supporting "A validated model of a photovoltaic water pumping system for off-grid rural communities", Applied Energy, 2019</p>
Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics
<p>Dataset of the work entitled "Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics".</p>
Real operating data of a photovoltaic system installed at Area Science Park - Trieste - Italy
<p>Data collected from a monocrystalline silicon photovoltaic (PV) plant installed on building Q2 at Area Science Park in the Basovizza campus located in Trieste, Italy. The data represent almost 9 years of real operating conditions of the PV plant. Every 15 minutes the DC side electrical PV system working parameters were recorded, in addition also ambient temperature, irradiance in the plane of the modules and panel temperatures were recorded. Data are periodically downloaded using a control software.</p>
From Chalcogen Bonding to S–𝝅 Interactions in Hybrid Perovskite Photovoltaics
<p>Dataset for “From Chalcogen Bonding to S–𝝅 Interactions in Hybrid Perovskite Photovoltaics” (doi:10.1002/advs.202405622), including main and supporting figures</p>
Shaping photovoltaic array output to align with changing wholesale electricity price profiles
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles." Applied Energy 2019. <a href="http://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Some of the scripts and data are also used in the following working paper:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States electricity markets". Working Paper, MIT Center for Energy and Environmental Policy Research. 2019. <a href="http://ceepr.mit.edu/publications/working-papers/705">http://ceepr.mit.edu/publications/working-papers/705</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm.zip : Python module with functions for modeling PV generation, calculating PV revenues and capacity factors, and optimizing PV orientation.</li> <li>notebooks.zip : Jupyter notebooks, including: <ul> <li>pvvm-pvtos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-analysis.ipynb: Example scripts used to perform the calculations and reproduce some figures in reference [1]</li> <li>pvvm-pvtos-plots.ipynb: Scripts used to produce additional figures in reference [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the usage of the PV generation model and orientation optimization</li> </ul> </li> <li>html.zip : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data.zip : Day-ahead and real-time nodal locational marginal prices (LMPs) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will linked here with the complete dataset.</li> </ul> </li> <li>results.zip : Simulation results associated with reference [1] above, including modeled revenue, capacity factor, and optimized orientations for PV systems at all LMP nodes</li> </ul> <p><strong>Data terms and usage notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="http://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="http://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-pvtos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Code license and usage notes</strong></p> <ul> <li>Code (*.py and *.ipynb files) is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file.</li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_pvtos">https://github.com/patrickbrown4/pvvm_pvtos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing latitude, longitude floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul>
Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London - supporting data
<p>Supporting data for "Modelled temperature, mortality impact, and external benefits of cool roofs and rooftop photovoltaics in London"</p> <p>Included are outputs from the Weather Research and Forecast (WRF) model. All simulations cover London, United Kingdom over summer 2018. Scenarios include a "baseline" which represents the real urban climate of the region, and scenarios which model 100% coverage of rooftops with either high albedo materials or solar panels. Data are provided in netCDF format.</p> <ul> <li>The baseline simulation which models the current urban climate of the region WRF_Urb_BouLac_T2-V10-U10_20180525-20180831.nc</li> <li>The 100% rooftop-solar simulation WRF_Urb_BouLac_PV_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The 100% high-albedo roof simulation WRF_Urb_BouLac_ClRf_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The non-urban scenario is in WRF_NoUrb_BouLac_T2-V10-U10-PSFC-RAINNC-TH2-Q2_20180525-20180831.nc</li> <li>The power production estimates solarpv_prod_2018.nc</li> <li>wrf_popweighting-main.zip contains the analysis code. It is provided as-is with no guarantee of usability.</li> </ul> <p>T2 means air temperature at 2m height. V10 and U10 are windspeeds at 10m height. PSFC is surface level pressure. TH2 is potential temperature. Q2 is specific humidity at 2m height.</p> <p>More description of the simulations is given in the citing article.</p> <p> </p>
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
<p>A <a href="https://www.sciencedirect.com/topics/engineering/photovoltaics">photovoltaic</a> (PV) dataset from satellite and aerial imagery. The dataset includes three groups of PV samples collected at the spatial resolution of 0.8m, 0.3m and 0.1m, namely PV08 from Gaofen-2 and Beijing-2 imagery, PV03 from aerial photography, and PV01 from UAV orthophotos. PV08 contains rooftop and ground PV samples. Ground samples in PV03 are divided into five categories according to their background land use type: shrub land, grassland, cropland, saline-alkali, and water surface. Rooftop samples in PV01 are divided into three categories according to their background roof type: flat concrete, steel tile, and brick. Data document can refer to the preprint https://essd.copernicus.org/preprints/essd-2021-270/</p>
A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
<p><strong>Summary</strong></p> <p>Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, residential PV installations are deployed at an unprecedented pace, and their safe integration into the grid necessitates up-to-date, high-quality information. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata. We provide installation metadata for more than 28000 installations. We provide ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.</p> <p>This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", published in Scientific data. The article is accessible here : <a href="https://www.nature.com/articles/s41597-023-01951-4">https://www.nature.com/articles/s41597-023-01951-4</a> These complete records consist of:</p> <ol> <li>The complete training dataset containing RGB overhead imagery, segmentation masks and metadata of PV installations (folder <strong>bdappv</strong>),</li> <li>The raw crowdsourcing data, and the postprocessed data for replication and validation (folder <strong>data</strong>).</li> </ol> <p><strong>Data records</strong></p> <p>Folders are organized as follows:</p> <ul> <li><strong>bdappv/</strong> Root data folder <ul> <li><strong>google / ign:</strong> One folder for each campaign <ul> <li><strong>img/</strong>: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.</li> <li><strong>mask/</strong>: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.</li> </ul> </li> <li><em>metadata.csv</em> The <code>.csv</code> file with the installations' metadata.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>data/ </strong>Root data folder <ul> <li><strong>raw/</strong> Folder containing the raw crowdsourcing data and raw metadata; <ul> <li><em>input-google.json</em>: <code>.json </code>input data data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the first annotation campaign;</li> <li><em>input-ign.json</em>:<em> </em><code>.json </code>input data containing all information on images and raw annotators’ contributions for both phases (clicks and polygons) during the second annotation campaign;</li> <li><em>raw-metadata.json</em>: <code>.json </code>output containing the PV systems’ metadata extracted from the BDPV database before filtering. It can be used to replicate the association between the installations and the segmentation masks, as done in the notebook metadata.</li> </ul> </li> <li><strong>replication/</strong> Folder containing the compiled data used to generate the segmentation masks; <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis.json</em>: <code>.json </code>output on the click analysis, compiling raw input into a few best-guess locations for the PV arrays. This dataset enables the replication of our annotations,</li> <li><em>polygon-analysis.json</em>: <code>.json </code>output of polygon analysis, compiling raw input into a best-guess polygon for the PV arrays.</li> </ul> </li> </ul> </li> <li><strong>validation/</strong> Folder containing the compiled data used for technical validation. <ul> <li><strong>campaign-google/campaign-ign</strong>: One folder for each campaign <ul> <li><em>click-analysis-thres=1.0.json</em>: <code>.json </code>output of the click analysis with a lowered threshold to analyze the effect of the threshold on image classification, as done in the notebook annotation;</li> <li><em>polygon-analysis-thres=1.0.json</em>: <code>.json </code>output of polygon analysis, with a lowered threshold to analyze the effect of the threshold on polygon annotation, as done in the notebook annotations.</li> </ul> </li> <li><em>metadata.csv</em>: the <code>.csv </code>file of filtered installations' metadata.</li> </ul> </li> </ul> </li> </ul> <p><strong>License</strong></p> <p>We extracted the thumbnails contained in the <strong>google/img/</strong> folder using Google Earth Engine API and we generated the thumbnails contained in the <strong>ign/img</strong><strong>/</strong> folder from high resolution tiles downloaded from the online IGN portal accessible here: <a href="https://geoservices.ign.fr/bdortho">https://geoservices.ign.fr/bdortho</a>. Images provided by Google are subjet to Google's terms and conditions. Images provided by the IGN are subject to an open license 2.0.</p> <p>Access the terms and conditions of Google images at this URL: <a href="https://www.google.com/intl/en/help/legalnotices_maps/">https://www.google.com/intl/en/help/legalnotices_maps/</a></p> <p>Access the terms and conditions of IGN images at this URL: <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf">https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf</a></p>
Floating Photovoltaics Installed Capacity in Europe in 2020
<p>Collected data for existing Installed Capacity regarding Floating Photovoltaics Plants in Euroep in 2020. Data collected from public available sources.</p>
SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation
<p>The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation</p>
R SCRIPT: INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED
<p>The files describe the r-script code and data base used in the article titled: "INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED"</p>
Global 10-m spatial distribution of Water-surface photovoltaics (2019-2021)
<p>The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map, with overall accuracy exceeding 96%.</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
Open the record for dataset details and reuse information.
Economic assessment of photovoltaic heat pump systems
<p>The economic viability of photovoltaic heat pump systems is assessed for an industrial application, comparing Self-Consumption and Autonomous configurations. </p>
Data and results related to "Fattori et al. 2017 - High Solar Photovoltaic Penetration in the Absence of Substantial Wind Capacity: Storage Requirements and Effects on Capacity Adequacy - Energy"
<p>The file includes data used for the analysis and results coming from the study (which was focused on the Italian "Nord" bidding zone). In particular:</p> <p>(i) Series of hourly load data [MW], from 01.01.2006 to 31.12.2015. The data come from elaborations based on ENTSO-E (https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package) and Terna S.p.A. (http://www.terna.it/en-gb/sistemaelettrico/transparencyreport/load/actualload.aspx). All the elaborations are described in details on the paper.</p> <p>(ii) Data related to the penetration of PV. Installed capacity of PV is assumed to increase from zero up to the capacity needed so that the average annual PV generation (based on the years 1986-2015) potentially equals the average annual demand (based on the years 2006-2015).</p> <p>(iii) Synthesis of the results about: residual load (with and w/o storage), ramps (with and w/o storage), excess energy (with and w/o storage), storage requirements</p>
Triarylamine modulation for hybrid perovskite photovoltaics
<p>Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Triarylamine modulation for hybrid perovskite photovoltaics”, DOI:10.1002/admi.202301053<br>Figure_2_XRD.opju: Data described in Figure 2 (XRD patterns) as Origin (.opju) software file.<br>Figure_2_XPS.opju: Data described in Figure 2 (XPS spectra) as Origin (.opju) software file.<br>Figure_2_UV-vis.opju: Data described in Figure 2 (UV-vis spectra) as Origin (.opju) software file.<br>Figure_2_Tauc_plot.opju: Data described in Figure 2 (Tauc plot) as Origin (.opju) software file.<br>Figure_3_PV.opju: Data described in Figure 3 (Photovoltaics metrics) as Origin (.opju) software file.<br>Figure_4_TRPL.opju: Data described in Figure 4 (TRPL) as Origin (.opju) software files.<br>Figure_4_PL_no_spiro.opju: Data described in Figure 4 (steady state PL) as Origin (.opju) software files<br>Figure_4_PL_with_spiro.opju: Data described in Figure 4 (steady state PL with spiro-OMeTAD) as Origin (.opju) software files<br>Figure_5_stability.opju: Data described in Figure 5 (operational stability) as Origin (.opju) software files.<br>Figures_SI.zip: Data described in the Supporting Information Figures S2 (FTIR spectra data as Origin (.opju) software file); Figures S5a (JV curve for champion devices as Origin (.opju) software file); Figures S5b (IPCE as Origin (.opju) software file); Figures S6 (Supplementary photovoltaic metrics as Origin (.opju) software file).<br><br></p>
Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet
<p>Data Availability for <strong>Enhanced U-Net(E-UNET)</strong></p> <p>Thank you for your interest in our dataset.<br> <strong>Repository contents:</strong><br> <em>Sentinel-2 L2A product</em>: The tiles that we selected containing photovoltaic.<br> <em>ImageFusion_Result</em>: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results.<br> <em>Image</em>: The RGB images for the ROI containing photovoltaic.<br> <em>Label</em>: The manual annotations for the ROI containing photovoltaic.</p>
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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.