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812 results for “renewables”
Result data related to "Tröndle et al (2019) -- Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe"
<p>The files include results to out study investigating the possibility for renewable electricity autarky in Europe. For each administrative unit on the continental, national, regional, and municipal levels these files include:</p> <ol> <li>Name, country, population, current electricity demand, land cover statistics, shared coast with exclusive economic zone</li> <li>Potential in terms of area [km2], installable capacity [MW], annual electricity yield [TWh]</li> </ol> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Pfenninger, S., & Lilliestam, J. (2019). Home-made or imported: on the possibility for renewable electricity autarky on all scales in Europe. <em>Energy Strategy Reviews</em>, <em>26</em>.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 3 (2021-07-19)</p> <p>* Fix ID of EEZ in shared-coast.csv files.</p> <p>Version 2 (2019-11-08)</p> <p>* Add land cover statistics for each unit.</p>
GENeSYS-MOD Germany: Technology, demand, and renewable data
<p>This dataset contains renewable potentials, timeseries, technology data, and additional data tables for the current implementation of GENeSYS-MOD Germany.</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>
Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households
<p><strong>Country greenhouse gas emissions from the non-renewable fraction of woodfuel used in households</strong></p> <p> </p> <p><strong>Data Structure</strong></p> <p>The data is structured as a tabular data with attributes: AreaName, ISO3, ItemName, ElementName, Year, Value, Unit.</p> <p><strong>Attributes (Columns)</strong></p> <p>Attributes in the data are defined as below:</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Descriptions</strong></p> </td> </tr> <tr> <td> <p><strong>AreaName</strong></p> </td> <td> <p>characterizes all countries including world and regional aggregates</p> </td> </tr> <tr> <td> <p><strong>ISO3</strong></p> </td> <td> <p>represents three letter ISO3 country codes (not all regional aggregates have ISO3 country codes)</p> </td> </tr> <tr> <td> <p><strong>ItemName</strong></p> </td> <td> <p>represents all items covered in the data</p> </td> </tr> <tr> <td> <p><strong>ElementName</strong></p> </td> <td> <p>represents all gases covered in the data</p> </td> </tr> <tr> <td> <p><strong>Year</strong></p> </td> <td> <p>period covered by the data</p> </td> </tr> <tr> <td> <p><strong>Value</strong></p> </td> <td> <p>represents the emissions value</p> </td> </tr> <tr> <td> <p><strong>Unit</strong></p> </td> <td> <p>Unit of measurement (in this data emissions are measured in kilotonnes)</p> </td> </tr> </tbody> </table> <p> </p>
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>
Integrated experimental and techno-economic modeling of renewable natural gas production from prairie biomass
This study coupled experimental and techno-economic modeling to evaluate the economic prospects of utilizing prairie biomass as a feedstock for anaerobic digestion. Anaerobic digestion experiments were performed using 15 lab-scale bioreactors under semi-continuous operation, designed based off a box-Behnken design with three factors. Response variables included biogas and biomethane yields, in addition to numerous digestate physico-chemical characteristics. Statistical models were developed from the experimental data to predict these responses and were subsequently incorporated into a techno-economic model developed in Python using BioSTEAM. In addition to optimizing key anaerobic digestion parameters, four scenarios were evaluated investigating liquid digestate recirculation, as well as methane recovery from the liquid digestate in a two-stage anaerobic digestion process.
Renewable and consumption data in DK and Italy
<p>The wind and solar power, and electricity consumption hourly data in Denmark and Italy for 2018 & 2030 scenarios. </p>
Plummeting costs of renewables - Are energy scenarios lagging?
<p>Raw data for the working paper "Plummeting costs of renewables - Are energy scenarios lagging?"</p>
Hélène Lepaumier - From Fossil to Renewable Fuels
<p>What is the link between neutral climate and renewable fuels? Are all clean fuels equally beneficial? Find out more about solar fuels & don't miss our interview with Hélène Lepaumier, research engineer at ENGIE Laborelec and a SUNRISE consortium member</p>
Policy brief on intergenerational renewal in EU-farming systems. What can policy do?
<p>For a resilient farming system, smooth and sufficient intergenerational renewal is crucial, and it has been defined as one of nine goals for the CAP post-2020. Before implementing specific policy measures and instruments, however, policy makers must determine the degree and nature of the generational renewal problem that needs to be addressed. Also, policies need to focus on increasing the attractiveness of farming as an occupation and lifestyle, as many non-entry decisions are made before measures aimed at the young farmer start to play a role. Further policy directions include increasing the mobility of land and labour, supporting the management of extreme calamities as they involve a great risk of exit and non-entry and facilitating the provision of personal and farm-specific advice and coaching. The power and responsibility of national and regional governments to address these issues is often underestimated and overlooked.</p>
Six questions to shifting the focus from more to more successful generational renewal in farming
<p>One important longer-term building-block of a farming system’s resilience is generational renewal. Vice versa, farming systems are only attractive for the younger generation to enter if they offer long-term prospects. Within the EU, regional farming systems differ enormously with regard to their organisational and demographic characteristics, their production systems, and their local natural, institutional, and infrastructural conditions. Accordingly, generational renewal is multifaceted and related to many different issues, such as whether and how farms should organise farm succession, how farms can ensure the availability of a sufficient labour force, how they can adapt to and prepare for generational and demographic changes, and how the younger generation can benefit from the training and employment opportunities in the farming sector. Fuelled by key findings of the SURE-Farm Project, the aim of this business brief is to raise awareness among farmers and stakeholders of opportunities and challenges related to the various aspects of generational renewal.</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </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>
FIGURE 1 in Review Renewed perspectives on the sedentary-pelagic last common bilaterian ancestor
FIGURE 1 The fossil records of the earliest sedentary and motile multicellular eukaryotes, metazoans and bilaterians. The first appearance of metazoan and bilaterian phyla (where records are present) is indicated by a genus name, a higher-level taxon acronym, and an age estimation (in millions of years). Several taxa with uncertain attribution are included to show diversity. Note that there is no evidence for active mobility of the muticellular eukaryotes over a very long period of time (see text for references and details). Abbrevations: Act = Acanthocephala; Ann = Annelida; Art = Arthropoda; Bil = Bilateria; Cha = Chaetognatha; Cte = Ctenophora; Cni = Cnidaria; Cho = Chordata; Deu = Deuterostomia; Ech = Echinodermata; Ecd = Ecdysozoa; Enp = Enteropneusta; Ent = Entoprocta; Gna = Gnathifera; Loph = Lophophorata; Lphz = Lophotrochozoa; Met = Metazoa; Mol = Mollusca; Pho = Phoronida; Por = Porifera; Pri = Priapulida; Pte = Pterobranchia; Tun = Tunicata. The controversial motile metazoans without evident bilaterian affinities are indicated by an asterisk. ARTWORK AND DESIGN BY TATIANA KORSHUNOVA
FIGURE 5 in Review Renewed perspectives on the sedentary-pelagic last common bilaterian ancestor
FIGURE 5 Reconstructions of the adult states of the sedentary/semi-sedentary stem groups (indicated by asterisks) with well-defined clonal reproduction in the major bilaterian lineages Deuterostomia, Lophotrochozoa and Ecdysozoa and the semi-sedentary/motile crown groups with reduced clonality (based on recent or fossil taxa, except for the reconstructed stem-chordata). The drawings and text from bottom to top are meant to illustrate the evolutionary changes along these lineages. The cephalic shield and potential derived structures (foot and mantle, see assessment of the potential homology in the text) are indicated by yellow, the oral lobes indicated by green, the stolon and tail by blue, the protoconch-like structures by violet, and the gut by red. ARTWORK BY TATIANA KORSHUNOVA
Experimental insights on biofouling growth in marine renewable structures
<p>This is version 2 of the files containing friction forces generated from scraping biofouling samples and the friction forces from subsequential scraping of certain samples. Data for all the samples that had subsequential scrapings has been aded. </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>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
#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>
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.