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99 results for “Renewable Energy”
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
Historical and modelled renewable energy production for India
<p>This archive contains all the datasets produced for the paper:<br><br><span>Hunt, K. M. R.</span>, & <span>Bloomfield, H. C.</span> (<span>2024</span>). <span>Quantifying renewable energy potential and realized capacity in India: Opportunities and challenges</span>. <em>Meteorological Applications</em>, <span>31</span>(<span>3</span>), e2196. <a href="https://doi.org/10.1002/met.2196">https://doi.org/10.1002/met.2196</a></p> <p> </p> <table style="border-collapse: collapse; width: 99.9642%;"><colgroup><col style="width: 31.0476%;"><col style="width: 17.1785%;"><col style="width: 37.7477%;"><col style="width: 14.0133%;"></colgroup> <tbody> <tr> <td><strong>Data Description </strong></td> <td><strong>Figure/Table</strong></td> <td><strong> File Name</strong></td> <td><strong>Dates Valid</strong></td> </tr> <tr> <td>Installed capacity by type in each state</td> <td>Table 1</td> <td>installed-by-state-oct2022.csv</td> <td>Oct 2022</td> </tr> <tr> <td>All-India installed capacity by type</td> <td>Figure 2</td> <td>tabulated-installed-by-date.csv</td> <td>2017–2023</td> </tr> <tr> <td>Hourly wind capacity factor</td> <td>Figure 4</td> <td>wind capacity factor.zip</td> <td>1979–2022</td> </tr> <tr> <td>Hourly solar capacity factor</td> <td>Figure 6</td> <td>solar capacity factor.zip </td> <td>1979–2022</td> </tr> <tr> <td>Present-day installation locations</td> <td>Figure 11</td> <td>OSM[hydropower,wind_turbine,solar]_ installations.geojson</td> <td>Mar 2022</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind/solar capacity</td> <td>Figure 12a/13a</td> <td>CEA_1x1_gridded_installed_[wind,solar]_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed wind capacity</td> <td>Figure 12b</td> <td>TWP_1x1_gridded_installed_wind_cap.nc</td> <td>May 2021</td> </tr> <tr> <td>Gridded 1°×1° estimate of installed solar capacity</td> <td>Figure 13b</td> <td>K21_1x1_gridded installed solar cap.nc</td> <td>Sep 2018</td> </tr> <tr> <td>Reported daily wind/solar/hydro production</td> <td>Figure 14/S3</td> <td>POSOCO_reported_[wind,solar,hydro]_MU_ daily.csv</td> <td>2012–2023</td> </tr> <tr> <td>Modelled ‘historical’ production</td> <td>Figure 14/16/S4a/b</td> <td>modelled-historical-[daily,hourly]-renewable output.nc</td> <td>1979–2022</td> </tr> <tr> <td>Recommended locations for new wind/solar installations</td> <td>Figure 17</td> <td>areas-for-exploration.nc</td> <td>--</td> </tr> </tbody> </table> <p> </p> <p> </p>
Hybridization of Fossil- and CO2-Based Routes for Ethylene Production using Renewable Energy
<p>Dataset associated with the publication "Hybridization of Fossil- and CO<sub>2</sub>-Based Routes for Ethylene Production using Renewable Energy" by Iasonas Ioannou, Sebastiano C. D'Angelo, Antonio J. Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1002/cssc.202001312">https://doi.org/10.1002/cssc.202001312</a>. The dataset includes the numeric data associated with most of the scenarios described in the main manuscript and in the Supporting Information (SI), as well as the tables presented in the main manuscript and in the SI converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>MS-Results</strong>: numerical values associated with the economic and environmental results included in both the main manuscript and the SI, for all the considered scenarios. The results include the total price for the assessed scenarios, with and without externalities, with uncertainty ranges, as well as the environmental results for human health, ecosystems, resources, and global warming potential (GWP).</li> <li><strong>MS-Tables</strong>: table reported in the main manuscript associated with the price and breakeven point of four assessed scenarios dependent on different CO<sub>2</sub> source assumptions.</li> <li><strong>SI-Tables-Economics</strong>: tables reported in the SI associated with the economic assessment of all the scenarios.</li> <li><strong>SI-Tables-LCI</strong>: tables reported in the SI associated with the environmental assessment of all the scenarios.</li> <li><strong>SI-Tables-AdditionalResults</strong>: tables reported in the SI associated with additional results presented in the work.</li> </ul>
PROSEU Collective Renewable Energy Prosumers Stakeholders Database (Template)
<p>As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.</p>
Data for - The environmental footprint of transport by car using renewable energy
<p>Replacing fossil fuels in the transport sector by renewable energy will help combat climate change. However, lowering greenhouse gas emissions by switching to alternative fuels or electricity can come at the expense of land and water resources. To understand the scale of this possible tradeoff we compare and contrast carbon, land and water footprints per driven km in midsize cars utilizing conventional gasoline, biofuels, bioelectricity, solar electricity and solar-based hydrogen. Results show that solar-powered electric cars have the smallest environmental footprints per km, followed by solar-based hydrogen cars, and that biofuel-driven cars have the largest footprints.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Hourly LC impacts - Primary Non-renewable energy - current mix and future scenarios, average demand
<p>Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Primary Non-renewable Energy, average demand perspective.</p> <p>Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".</p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
Dataset: Import options for chemical energy carriers from renewable sources to Germany
<p>This dataset contains results and additional data related to the publication "Import options for chemical energy carriers from renewable sources to Germany".</p> <p>Files containing major results / important cost input data:</p> <ul> <li><strong>results.csv</strong>: Contains major model results for all scenarios as CSV file (seperator is ';', all fields are quotes using double quotation marks '"'). Can be explored using standard software like Excel/Libre Office or other tools.</li> <li><strong>costs.zip</strong>: Technology specific input cost assumption for 2030, 2040 and 2050.</li> </ul> <p>The dataset further contains the following archives related to the model structure as contained in the software repository (GitHub):</p> <ul> <li><strong>config.zip</strong>: File contents of the <em>config/</em> folder of the model directory. Configuration files for running the model used by the publication.</li> <li><strong>data.zip</strong>: File contents of the <em>data/</em> folder of the model directory. Includes distance specifications, conversion efficiencies, details on shipping transport. Also contains (with this version) the cost data (same as in <em>costs.zip</em>).</li> <li><strong>resources.zip</strong>: Some file contents of the <em>resources/</em> folder of the model directory. Most files in this folder are automatically recreated if the <em>Snakemake</em> workflow is executed. The files in this archive are the files created by GlobalEnergyGIS (RES supply time-series and demand data for investigated regions) which is difficult to setup and are thus provided here as an optional dataset for download.</li> <li><strong>results.zip</strong>: Optimised energy system models (<a href="https://pypsa.readthedocs.io/en/latest/">PyPSA</a> networks, for PyPSA version v0.19.3) for all scenarios (default 10% WACC, optimistic 5% WACC, scenarios for sensitivity analysis), energy supply chains (ESCs) and exporting countries. For each network an additional results.csv exists containing a number of key results extracted from each network. Also contains the combined <em>results.csv</em> file as <em>results/results.csv</em> for all scenario runs.</li> </ul>
Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen (Dataset)
<p>This dataset contains central input assumptions and results related to the publication "Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen".</p> <p>Result files are contained in the <strong> results.zip</strong> archive file. The file contains for each scenario, as indicated by the folder structure, the following files:</p> <ul> <li><strong>results.csv</strong>: Central scenario results exported as <em>character separated value</em> <em>(csv)</em> file, with a semicolon (<strong>;</strong>) as field separator. All fields are quoted using double quotation marks <strong>"..."</strong>. Can be explored using standard office software like Microsoft Excel/Libre Office or other tools.</li> <li><strong>network.nc</strong>: PyPSA network file containing the optimized scenario with all input and unprocessed outputs (results). Can be explored using the <a href="https://pypsa.readthedocs.io">PyPSA software package</a>.</li> <li><strong>lcoes.csv</strong>: Levelised Cost of Electricity used to construct the renewable energy source (RES) based supply curve for each scenario.</li> </ul> <p>The dataset further contains the following files which represent central input assumptions to the model and scenarios, both as <em>CSV</em> files:</p> <ul> <li><strong>efficiencies.csv</strong>: Technology process and conversion efficiencies<em> </em>including more details on the assumptions and information on which references the assumptions are based.</li> <li><strong>costs_2030.csv</strong>: Technology cost assumptions for 2030 including more details on the assumptions and information on which references the assumptions are based. This data is based on this <a href="https://github.com/pypsa/technology-data">Technology Data repository</a> on GitHub.</li> </ul>
Plummeting costs of renewables - Are energy scenarios lagging?
<p>Raw data for the working paper "Plummeting costs of renewables - Are energy scenarios lagging?"</p>
Tool for Renewable Energy Potentials - Database
<p>Database for scenarios of "Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets"</p> <p>The used datasets and applied methodology can be found in the paper <a href="https://doi.org/10.3390/en15155536">Potentials of Renewable Energy Sources in Germany and the Influence of Land Use Datasets</a><br> . Please cite the paper if you utilize the dataset. Applied datasets among others:</p> <ul> <li>Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Digitales Basis-Landschaftsmodell (Ebenen) (Basis-DLM)’. 2021. (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>Geobasisdaten: © GeoBasis-DE / BKG (2021), ‘Amtliche Hausumringe Deutschland (HU-DE)’. 2021. (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>Geobasisdaten: ©GeoBasis-DE / BKG (2021), 3D-Gebäudemodelle LoD2 Deutschland (LoD2-DE) (2021). (Conditions of use: <a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>)</li> <li>UNEP-WCMC, IUCN, ‘The world database on protected areas’. 2016. Accessed: Oct. 22, 2021. [Online]. Available: https://www.protectedplanet.net/</li> </ul> <p>Please be aware of the conditions of use for parts of the applied datasets (<a href="https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf">https://sg.geodatenzentrum.de/web_public/nutzungsbedingungen.pdf</a>) if you utilize the data.</p>
Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members' active contributions
<p>This dataset was used in the case study of the following publication:</p> <p> - Luis Gomes, Zita Vale, "Costless renewable energy distribution model based on cooperative game theory for energy communities considering its members’ active contributions," Sustainable Cities and Society, Volume 101, 2024, 105060, ISSN 2210-6707, <a href="https://doi.org/10.1016/j.scs.2023.105060">https://doi.org/10.1016/j.scs.2023.105060</a> </p> <p><em>(if you used this dataset in your publications, please send us your information so we can add your publication to the list above)</em></p> <p> </p> <p>The dataset is composed by energy generation, consumption, and forecast (for generation, and for consumption) expressed in Wh. The data considers an energy community of 10 prosumers in 30 days.</p> <p>The dataset also has energy prices that have been collected from MIBEL (Iberian Electricity Market).</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Papers and KPIs for the Evaluation of Renewable Energy Communities' Performance
<p>This database contains the methodology used to explore and identify the papers that containes KPIs related to the evaluation of RECs performance. This methodology is divided into three phases: </p> <p>1.1) <em>Papers Exploration – </em>Comprehensive search of papers in the field of RECs using Scopus and Web Of Science databases;</p> <p>1.2) <em>Papers Screening</em> – Initial screening of collected literature based on research domain and accessibility;</p> <p>1.3) <em>Papers Eligibility</em> – Further filtering papers by extracting those that explicitly define KPIs through mathematical formulations in the context of the RECs.</p> <p> In the <em>Papers Exploration</em> step, the authors conducted a systematic review of the state-of-the-art of literature on performance metrics in the context of the renewable energy community. The search was conducted in March 2024 using the search engines Scopus and Web Of Science (the used queries are detailed explain in thte database). The output of this phase is a large database of the most recent and relevant studies, cataloged by the following information: authors, article title, abstract, author keywords, index keywords, and year of publication. At this stage, only journal articles and research works published after 2010 were considered. In the <em>Papers Screening</em> phase, the articles are further filtered by the authors screening manually all papers based on keywords, titles, and abstracts, removing articles not relevant to the context of the RECs. In addition, articles for which it was not possible to access the full text are excluded. In the <em>Papers Eligibility</em> phase, the articles are entirely read to identify those articles that directly address the use of performance metrics. The eligibility criterion used by the reviewers’ team refers to the explicit definition of KPIs through mathematical formulas combined with their direct usage to evaluate RECs’ performances. The main objective of this phase is therefore to identify those articles that explicitly define and use KPIs, so that they can later be collected and labeled, based on their definition and usage.<br><br></p> <p>In additions, KPIs are extracted from the papers deemed elegible generating Tables A1, A2, A3 and A4. In these tables, similar KPIs are aggregated together in one single mathematical definition based on the methodology described in <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4991932">Key Performance Indicators for Renewable Energy Communities: A Comprehensive Review by Lorenzo Giannuzzo, Minuto Francesco Demetrio, Daniele Salvatore Schiera, Samuele Branchetti, Carlo Petrovich, Angelo Frascella, Nicola Gessa, Andrea Lanzini :: SSRN</a></p>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
Climate Change and 2030 Cooling Demand in Ahmedabad, India: Opportunities for Expansion of Renewable Energy and Cool Roofs (Supplemental Information)
<p>Supplemental information and analysis files for article, "Climate change and 2030 cooling demand in Ahmedabad, India: opportunities for expansion of renewable energy and cool roofs" (Original article available at: https://doi.org/10.1007/s11027-022-10019-4)</p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Dataset: Enlight Renewable Energy Ltd (ENLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.