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101 results for “Solar Energy”
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>
Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy
<p>Dataset associated with the publication "Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy" by Sebastiano C. D'Angelo, Antonio J. Martín, Selene Cobo, Diego Freire-Ordóñez, Gonzalo Guillén-Gosálbez, and Javier Pérez-Ramírez, available at <a href="https://doi.org/10.1039/D2EE02683J">https://doi.org/10.1039/D2EE02683J</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript and in the Electronic Supplementary Information (ESI).</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>GeneralParameters</strong>: numerical values for the scaled functional unit used in the study, the world population value adopted, and the three voltage efficiencies assumed in different parts of the study.</li> <li><strong>AL_BaseCase_SensECE</strong>: numerical values associated with the results for the ammonia leaf scenarios adopting a voltage efficiency of 63% (base case) and a Faradaic efficiency varying from 1% to 100%; highest, average, and lowest capacity factors for the solar power production were here used. The ammonia leaf configuration here assessed is the one including solar panels, electrolyzer, and fuel cell as key components. The results report all the ReCiPe 2016 (hierarchical approach) midpoints and endpoints and the values for the assessed planetary boundaries; the levelised cost of ammonia (LCOA) is reported, as well.</li> <li><strong>AL_EtaV75_SensECE</strong>: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 75% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_Eta100_SensECE</strong>: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 100% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_NoFC_H2Vented_SensECE</strong>: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered vented to the air. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_NoFC_H2Subst_SensECE</strong>: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered substituting the production of an equivalent quantity from a water electrolyzer deployed in the same location as the ammonia leaf. The remaining assumptions do not deviate from the base case.</li> <li><strong>AL_BaseCase_SpatAnal_BreakFEff</strong>: numerical results for the ammonia leaf base case scenario stemming from the spatial analysis performed on a global grid of 1140 points. The yearly average capacity factors for the solar panels at each location are included, and the results portraying the breakeven Faradaic efficiency for the indicators climate change - CO<sub>2</sub> concentration, global warming, human health, and levelised cost of ammonia were included. The assumptions for the voltage efficiency and the other parameters correspond to the base case.</li> <li><strong>AL_BaseCase_SpatAnal_AbsValues</strong>: numerical results for the ammonia leaf scenarios using the base case state-of-the-art (34%) and 100% Faradaic efficiency, as well as the base case voltage efficiency of 63%. The same metrics as the previous sheet are reported. The structure of the sheet is the same as the previous one.</li> <li><strong>AL_BaseCase_Breakdowns</strong>: breakdown of the same four indicators as the previous sheet for the best and worst combination of Faradaic efficiency and solar panels capacity factors, i.e., 34% Faradaic efficiency and 6% capacity factor on one side and 100% Faradaic efficiency and 26% capacity factor on the other side. The breakdown is divided into solar panels, electrolyser, fuel cell, and other elements. A further breakdown of the levelised cost of ammonia (LCOA) into capital expenditure (CAPEX) and operating expenditure (OPEX) is provided, as well. The voltage efficiency is the same as the base case, as well as the other parameters.</li> <li><strong>AL_BaseCase_CAPEXSens</strong>: numerical results for the levelised cost of ammonia (LCOA) in dependence of the sensitivity on the capital expenditure (CAPEX) for the ammonia leaf configuration assessed in the base case. Two cases assuming state-of-the-art (34%) and 100% Faradaic efficiency were assumed, and lowest, average, and highest capacity factor are included. The remaining parameters do not deviate from the base case configuration.</li> <li><strong>AL_gHB_BestMap</strong>: numerical results to produce the map showing the best technology between ammonia leaf (AL) and green Haber-Bosch (gHB) in the category climate change - CO<sub>2</sub> concentration for all the assessed locations. column D shows the share of safe operating space (%SOS) for each location, while column E shows which technology was selected, where 1 is ammonia leaf and 2 is green HB.</li> <li><strong>AL_BaseCase_Sensitivity</strong>: percentual variation of the results obtained assuming the base configuration ammonia leaf for a state-of-the-art Faradaic efficiency and an average capacity factor for the solar panels. The varied parameters include the voltage efficiency (columns C-D-E), the levelised cost of electricity (columns G-H-I), the electrolyser cost (columns K-L-M), the fuel cell cost (columns O-P-Q), the electrolyser environmental impact (columns S-T-U), and the fuel cell environmental impact (columns W-X-Y).</li> <li><strong>CompTech_BaseCase</strong>: environmental and economic metrics characterizing the assessed Haber-Bosch scenarios (business as usual, BAU; blue Haber-Bosch; green Haber-Bosch for lowest, average, and highest solar panels capacity factor; BAU assuming natural gas spot prices in Europe in August 2022). The reported metrics are the ReCiPe 2016 (hierarchical approach) midpoints and endpoints, the planetary boundaries, and the levelised cost of ammonia (LCOA).</li> <li><strong>CompTech_EtaV75</strong>: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 10% stack efficiency improvement. The remaining parameters are the same.</li> <li><strong>CompTech_EtaV100</strong>: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 100% stack efficiency. The remaining parameters are the same.</li> <li><strong>CompValues_Fig1</strong>: numerical values for yearly global warming impacts of a selection of countries, as well as for the yearly human health impacts of selected diseases and catastrophic events.</li> </ul> <p> </p>
Supplementary material for the publication: J.D. Nixon, K. Bhargava and E. Gaura, Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps, 2020
<p>The dataset deposited here was prepared under the EPSRC-funded <a href="http://heed-refugee.coventry.ac.uk/">Humanitarian Engineering and Energy for Displacement</a> research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. </p> <p>As part of the project, we deployed a Standalone Solar System for a Community Hall in Nyabiheke camp, Rwanda, and a PV-battery Microgrid in Kigeme camp, Rwanda. The microgrid supplies power to a playground and two nursery buildings. It powers a total of 20 CPE (each with 3 LEDs) and 10 sockets. The standalone system at Hall powers 7 CPE (with 3 LEDs each) and 4 sockets. The aim of the study was to (a) understand the energy consumption behaviour, light usage and other enabled uses within the set location in each camp (b) create an evidence base on the value of energy and its benefits in displaced contexts (c) identify best practice in the construction, control and operation of the respective systems as a shared energy resource.</p> <p>The system data used for the performance analysis for this study (July 2019 and March 2020) is deposited here along with the metadata. The results from analysis are presented in a paper titled '<strong>Energy Performance Gap in Community-Based Solar Energy Interventions: Lessons from two Rwandan Refugee Camps</strong>' (currently under submission). The scripts for analysis can be found at our Github account <a href="https://github.com/cogent-computing">Cogent Labs</a> under HEED-Microgrid and HEED-Hall repositories.</p>
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning
<p><strong>TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW.</strong></p> <p>Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a></p> <p><a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a></p> <h1><strong>1. Dataset Description</strong></h1> <p>We publish six files.</p> <ul> <li><em>analysis_polygons.gpkg:</em> our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li><em>analysis_polygons.csv:</em> a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li><em>sources.csv</em>: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li><em>raw_polygons.gpkg:</em> the raw geometries used to compose analysis_polygons.gpkg.</li> <li><em>TZ Solar Asset Mapper Q1 2024.xlsx</em>: an Excel formatted version of the analysis_polygons.csv file.</li> <li><em>tz-sam_scientific_data.pdf</em>: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2><strong>1.1 Analysis-level datasets</strong></h2> <p>Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets.</p> <p>The geospatial dataset contains the following fields:</p> <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> <p>The CSV version replaces the Geometry column with:</p> <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2><strong>1.2 Raw datasets and sources</strong></h2> <p>The analysis-level datasets hide some complexity in the underlying data that we expose in the <em>raw_polygons</em> and <em>sources</em> file.</p> <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> <p>Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon.</p> <p>raw_polygons.gpkg contains the following fields:</p> <ul> <li>id: ID of the raw source polygon</li> <li>geometry<strong>: </strong>Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <p>Sources.csv contains the following fields:</p> <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2><strong>1.3 Caveats and limitations</strong></h2> <h3><strong>1.3.1 Capacity Estimates</strong></h3> <p>While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%.</p> <h3>1.3.2 Plant Shapes</h3> <p>Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out.</p> <h3>1.3.3 Capacity Updates</h3> <p>Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates.</p> <h3>1.3.4 Construction Date Estimates</h3> <p>We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound.</p> <p>We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates.</p> <p><strong>2. Attribution</strong></p> <p>TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats:</p> <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>
Evaluating F10.7 and F30 Radio Fluxes as Long-Term Solar Proxies of Energy Deposition in the Thermosphere
<p><span>We use model simulations and observations to examine how well the F10.7 and F30 solar radio fluxes represent solar forcing in the thermosphere during the last 60 years of weakening solar activity. We found that increased saturation of F10.7 during the last two extended solar minima leads to an overestimation of solar energy deposition, which manifests as a change in the linear relation between thermospheric parameters and F10.7. On the other hand, the linear relation between thermospheric parameters and F30 remains nearly the same throughout the whole studied period because of a recently found relative increase of F30 with respect to F10.7. Therefore, F30 is a more consistent proxy than F10.7 during the last 60 years. We note that continued evaluation is needed to see how well F10.7 and F30 will serve as solar proxies in the future when solar activity may start increasing toward the next grand maximum.</span></p>
Katrin Mueller - Solar Energy for a Carbon-Neutral Society
<p>Are we on the right track towards reaching negative net-zero CO2 emissions by 2050? Find out more about how solar power could help achieve a climate-neutral Europe & don't miss our interview with Katrin Mueller, sustainability engineer at SIEMENS AG and a SUNRISE consortium member.</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </p>
Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
<p>This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. </p> <p>The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). </p> <p>Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).</p> <p>Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.</p> <p>Folder (3) provides the corresponding shapefiles.</p> <p>Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size.</p> <p>Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).</p> <p>Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).</p> <p>Countries/territories included in the dataset: </p> <p>Argentina<br>Belize<br>Bolivia<br>Brazil<br>Chile<br>Colombia<br>Costa Rica<br>Cuba<br>Dominican Republic<br>Ecuador<br>El Salvador<br>French Guiana<br>Guatemala<br>Guyana<br>Haiti<br>Honduras<br>Jamaica<br>Nicaragua<br>Panama<br>Paraguay<br>Peru<br>Suriname<br>Uruguay<br>Venezuela</p> <p> </p> <p><strong>References</strong></p> <p>Sterl, S., Hussain, B., Miketa, A. <em>et al.</em> An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. <em>Sci Data</em> <strong>9</strong>, 664 (2022). <a href="https://doi.org/10.1038/s41597-022-01786-5">https://doi.org/10.1038/s41597-022-01786-5</a></p> <p>Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14870967">https://doi.org/10.5281/zenodo.14870967</a></p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>solar</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>753</span></span><span><span> datapoints from </span></span><span><span>31</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span><span> It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span><span> </span>Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Solar and meteorological data collected from the Antsiranana station (Madagascar) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Ouani station (Comores) by the ENERGY-Lab at the University of La Reunion between December 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Antananarivo station (Madagascar) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Hahaya station (Comores) by the ENERGY-lab at the University of La Reunion between December 2019 and November 2022
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Amitie station (Seychelles) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Reserve Francois Leguat station (Mauritius) by the ENERGY-Lab at the University of La Reunion between May 2017 and August 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Anse Boileau station (Seychelles) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Durban station (South Africa) by the ENERGY-lab at the University of La Reunion between August 2013 and October 2018
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Le Port Barbusse station (La Réunion) by the ENERGY-lab at the University of La Reunion between July 2010 and December 2012
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
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.