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4,230 results for “Energie”
The Open Review–Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High–Energy Physics
<p>We introduce the new comprehensive Open Review–Based dataset (ORB); it includes a curated list of more than 62,000 scientific papers, publications and submitted preprints with their more than 157,000 reviews and final decisions. We gather this information from three peer-reviewed sources: the OpenReview.net, SciPost.org and PeerJ.com websites.</p>
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
Energy and Emissions Impacts of Atlanta's Reversible Express Toll Lanes and High-Occupancy Toll Lanes
<p>This dataset is the MOVES-Matrix emission rates for the NCST project of Energy and Emissions Impacts of Atlanta’s Reversible Express Toll Lanes and High-Occupancy Toll Lanes, developed by our research team at Georgia Institute of Technology.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p>This report summarizes the impact on corridor-level energy use and emissions associated with the 2018 opening of the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA. The research team tracked changes in vehicle throughput on the managed lane corridors (extracted from GDOT’s Georgia NaviGAtor machine vision system after comprehensive QA/QC) and performed a difference-in-difference analysis to exclude regional changes, pairing test sites vs. control sites not influenced by the openings. The results show a large increase in overall peak-period vehicle throughput on the NWC, especially on I-575, due to the congestion decrease (20 mph speed increases at some locations). The increase in corridor-level energy use and emissions was smaller than vehicle throughput, but still significant. Predicted downwind maximum CO concentrations only increased from 1.81 ppm to 1.93 ppm(which remains extremely low). The increase in morning peak activity on the corridor likely resulted from diversion of some traffic into the peak from the shoulder periods, diversion of some traffic from other nearby freeway corridors, and diversion of local road traffic into the corridor. Unfortunately, without overall control volume totals and/or pre-and-post travel behavior surveys for the alternative commute routes, it is not possible to quantify the likely reductions in traffic flow and emissions that occurred along the other corridors that likely resulted from morning commute shifts. Hence, the team cannot draw reliable conclusions related to net regional or sub-regional impacts associated with the new managed lane corridors. The impact observed on the I-85 corridor was much smaller than on the NWC, especially at Indian Trail/Lilburn Road (far from the Express Lane Extension). After the Express Lanes opened, energy use and emission rates at Old Peachtree Road increased slightly (as uncongested vehicle speeds increased), but this increase may be short-lived as traffic on the corridor changes over time.</p> <p> </p>
Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters
<p>Dataset for the article titled "Energy recovery by an unbiased gas phase photofuel cell with a nickel foam supported WO3 photoanode decorated with plasmonic gold clusters".</p> <p>This research was conducted at Antwerp Engineering, Photoelectrochemistry and Sensing (A-PECS) group, University of Antwerp, Belgium.</p>
Unclean but affordable solid fuels effectively sustained household energy equity
<p>This dataset contains the data used in preparation for the paper "Unclean but affordable solid fuels effectively sustained household energy equity" by Ke Jiang et al, describing the inequity of household energy consumption, cost and burden in mainland China in 2017.</p>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Data in: Reduced predation and energy flux in soil food webs by introduced tree species
<p>The introduction of non-native tree species has become a global concern and may disruptnative communities and related ecosystem functions. Soil food webs regulate organic matter decomposition and nutrient cycling in forests with their feeding activities, butevaluating consequences of tree species introduction on soil invertebrates is challengingdue to the complex trophic structure and wide range in body size of soil invertebrates. Here, we employed an energetic food web approach, and estimated the energy flux in soil food webs using a four-node model including soil meso- and macrofauna decomposers and predators. We examined pure and mixed stands of native European beech (<em>Fagus sylvatica</em>), introduced Douglas fir (<em>Pseudotsuga menziesii</em>) and native range-expanding Norway spruce (<em>Picea abies</em>) across site conditions. Compared to native forests, introduced tree species reduced total mass of macrofauna predators by 92% at sandy sites but not that of decomposers, suggesting trophic downgrading in soil food webs by Douglas fir. The energy flux in mixed forests was intermediate between respective monocultures, suggesting that tree mixtures mitigate potential negative impacts of introduced tree species on food web functioning. Across size classes, soil macrofauna responded more sensitively to changes in environmental conditions than soil mesofauna. Despite the lower total mass, the energy flux through mesofauna outweighed that through macrofauna when consideringenergy loss to predators, highlighting the importance of mesofauna for decomposition processes in forest soil food webs. Additionally, total energy flux positively correlated with species richness, pointing to the significance of soil biodiversity for trophic functionality. Overall, the study emphasizes the critical role of tree species composition, site conditionsand soil biodiversity in driving energy flux through soil food webs and maintaining forest ecosystem functions.</p>
Peru Energy Data (Part I)
<p>The data illustrate the evolution of the Peruvian Energy System from 2005 to 2022. This information has been compiled by consulting the National Energy Balances for each respective year. It is important to note that data for primary energy, transformation, and secondary energy for the years 2010 and 2011 is either missing or incomplete. Additionally, final consumption by source includes data dating back to 1985.</p>
SolarSMART Social Energy Atlas 2020 Georgia Interviews
<p>The SolarSMART Social Energy Atlas 2020 Georgia Interview dataset provides results from interviews with 2018 residents of the State of Georgia above the age of 18 on their perceptions of photovoltaic solar energy. The interview includes the following: demographic information for each respondent, their location in the state (by ZIP Code); the type of house they reside within; whether they have or have not adopted solar energy (and if so, how did they adopt solar energy); audio recordings of a semi-structured interview between the informants and representatives from the project who were often agents from the UGA Cooperative Extension Service; and the outputs from that interview that also included draw-a-map responses to where they perceived people adopted solar technologies in the United States and the State of Georgia. Respondents were from 25 of the 159 counties of the State of Georgia, with representation of both urban and rural residents. In total, the dataset includes 25 variables currently coded (with over 170 identified. Transcriptions of the interviews are also provided in the dataset alongside graphics for each informant that can be used in presentations and other derivatives involving this dataset in the future. This dataset inspired the SolarSMART 2020 Georgia Survey dataset (DOI: 10.5281/zenodo.4540819). </p>
Data on the Swiss energy system and electric vehicles
<p>This repository gathers the data used in the paper:</p> <p>Loris Di Natale, Luca Funk, Martin Rüdisüli, Bratislav Svetozarevic, Giacomo Pareschi, Philipp Heer and Giovanni Sansavini. <strong>The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland. </strong><em>Energies.</em> 2021; 14(16):4812. <a href="https://doi.org/10.3390/en14164812">https://doi.org/10.3390/en14164812</a>.</p> <p>The linked code can be found <a href="https://gitlab.nccr-automation.ch/loris.dinatale/v2g-in-switzerland">here</a>.</p> <p>Small description of the different files:</p> <ul> <li><em>Car_trips.csv:</em> List of trips from different cars in Switzerland.<br> Data provided by Giacomo Pareschi and based on the result of the 2015 edition of MZMV (Bundesamt für Statistik / Bundesamt für Raumentwicklung, Verkehrsverhalten der Bevölkerung, Ergebnisse des Mikrozensus Mobilität und Verkehr 2015, Neuchâtel und Bern (2017), <a href="https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html">https://www.are.admin.ch/are/de/home/mobilitaet/grundlagen-und-daten/mzmv.html</a> ). Each weekly profile is not representative and any result obtained with less than 50 profiles should be interpreted with extreme caution.</li> <li><em>ch.bfe.ladestellen-elektromobilitaet.json:</em> Data on the charging stations in Switzerland.<br> Online data from the Swiss Federal Office of Energy.</li> <li><em>cs_power_Home.csv</em> and<em> cs_power_Work.csv: </em>Own data on the charging powers of charging stations located at home or at work.</li> <li><em>energy_system_model_empa_results_sc_1.csv: </em>Swiss Energy System model used in our work to generate the fixed hydropower output profile.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>EVs_cap.csv:</em> Data on different EV brands, from own research.</li> <li><em>gCO2_eq_kWh_techs.csv: </em>CO2-equivalent greenhouse gas emission factors for different technologies, from own research.</li> <li><em>Heat_BEV_demand_2018.csv:</em> Electricity demand for heating and EVs in Switzerland.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>inflows_Beer.csv: </em>Data on the water inflows in the Swiss dams over the year.<br> Data provided by Michael Beer, from Beer, M. Abschätzung des Potenzials der Schweizer Speicherseen zur Lastdeckung bei Importrestriktionen. Z. Energiewirtschaft <strong>2018</strong>, 42, 1–12.</li> <li><em>MeteoSchweiz_pop_weight_2018.csv:</em> Temperature data in Switzerland taken from MeteoSwiss and population-weighted.<br> Data provided by Dr. Martin Rüdisüli.</li> <li><em>Scenarios.csv </em>and <em>Scenarios+.csv: </em>Different scenarios for electricity production and consumption, generated in-house based on <ul> <li>the Energy Strategy 2050 (Kirchner, A.; Bredow, D.; Ess, F.; Grebel, T.; Hofer, P.; Kemmler, A.; Ley, A.; Piégsa, A.; Schütz, N.; Strassburg, S.; et al. Energy Perspectives, Die Energieperspektiven für die Schweiz bis 2050; Prognos AG: Basel, Switzerland, 2012), respectively</li> <li>the Energy Strategy 2050+ (Prognos AG and INFRAS AG and TEP Energy GmbH and Ecoplan AG. ENERGIEPERSPEKTIVEN 2050+ Kurzbericht. 2020).</li> </ul> </li> <li><em>transfer_15min_2018.csv: </em>Swiss power system model of electricity production and consumption in 2018.<br> Data provided by Dr. Martin Rüdisüli.</li> </ul> <p> </p>
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>
SEEtheSkills resources database from the Interregional research on the status of energy skills
<p>This dataset includes a list of resources identified during the interregional research on the status of energy skills, done in the frame of SEEtheSkills project. The comprehensive overview of the information created in the area of Energy Efficiency (EE) and Renewable Energy Systems (RES), goes both wide, by trying to identify as many different examples as possible, and deep, by digging into the examples themselves. The key areas the research focused on: skills defined in national roadmaps; skills developed as part of previous BUS projects; developed training schemes; the number of trained workers and professionals; companies that design and produce EE materials; status of Recognition of Previous Learning (RPL); status of demand for energy skills; level of awareness of energy skills; available certification schemes; legal obligations promoting the use of energy skills and their timelines, predictions for future development of energy skills. The survey covers mainly the five countries participating in the project Slovenia, Spain, Netherlands, Slovakia and North Macedonia, but also beyond their geographical coverage.</p>
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Wind energy production in forests conflicts with tree - roosting bats
<p>Many countries are investing heavily in wind power generation,<sup>1</sup> triggering a high demand for suitable land. As a result, wind energy facilities are increasingly being installed in forests,<sup>2,3</sup> despite the fact that forests are crucial for the protection of terrestrial biodiversity.<sup>4</sup> This green-green dilemma is particularly evident for bats, as most species at risk of colliding with wind turbines roost in trees.<sup>2</sup> With some of these species reported to be declining,<sup>5-8</sup> we see an urgent need to understand how bats respond to wind turbines in forested areas, especially in Europe where all bat species are legally protected. We used miniaturized global positioning system (GPS) units to study how European common noctule bats (<em>Nyctalus noctula</em>), a species that is highly vulnerable at turbines,<sup>9</sup> respond to wind turbines in forests. Data from 60 tagged common noctules yielded a total of 8129 positions, of which 2.3% were recorded at distances <100 m from the nearest turbine. Bats were particularly active at turbines <500 m near roosts, which may require such turbines to be shut down more frequently at times of high bat activity to reduce collision risk. Beyond roosts, bats avoided turbines over several kilometers, supporting earlier findings on habitat loss for forest-associated bats.<sup>10</sup> This habitat loss should be compensated by developing parts of the forest as refugia for bats. Our study highlights that it can be particularly challenging to generate wind energy in forested areas in an ecologically sustainable manner with minimal impact on forests and the wildlife that inhabit them.</p>
Thermal site energy fluctuations in photosystem I: New insights from MD/QM/MM calculations
<p>We calculated the site energies of all 96 chlorophylls in a fully atomistic, trimeric and membrane-embedded model of cyanobacterial photosystem I (<a href="https://www.rcsb.org/structure/1JB0">PDB Code: 1JB0</a>). The resulting data set accounts for </p> <ul> <li> <p>thermal fluctuations within the entire photosystem by sampling 40 conformational snapshots from molecular dynamics trajectories.</p> </li> <li> <p>the electrostatic effects of the natural environment on the chlorophyll excitation by means of QM/MM calculations.</p> </li> <li> <p>the multireference nature of chlorophyll excitations by using the high-level DFT/MRCI method in the QM region.</p> </li> </ul> <p>Analysis of the data set reveals thermal fluctuations which prevent the formation of a single prominent energy funnel in the antenna complex. We conclude that energy transfer pathways may form only transiently at physiological temperatures, as thermal fluctuations overcome energy barriers.</p> <p><strong>v1.1.0</strong></p> <p>Added more snapshots for reaction center chlorophylls, results from statistical analysis and excitonic couplings in the point dipole approximation.</p> <p><strong>v1.0.0</strong></p> <p>Site energies, site energy shifts and absorption spectra of each chlorophyll in each snapshot, computed with and without environmental point charges. Structures and newly derived or modified force field parameters are also provided.</p> <p>For a detailed overview of each file, please refer to the README.</p>
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></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>
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>
Dataset for Hydropower Expansion in Eco-Sensitive River Basins under Global Energy-Economic Change
<p>The data presented in this repository can be fed into the codes provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a> to reproduce the results of the following paper:</p> <p> </p> <p>Chowdhury, A.F.M.K., Wild, T., Zhang, Y. <em>et al.</em> Hydropower expansion in eco-sensitive river basins under global energy-economic change. <em>Nat Sustain</em> <strong>7</strong>, 213–222 (2024). <a href="https://doi.org/10.1038/s41893-023-01260-z">https://doi.org/10.1038/s41893-023-01260-z</a></p> <p> </p> <p><strong>Summary</strong></p> <p>In this study, we investigate how rapid economic growth and transition to low-carbon energy may impact hydropower development, with potential countervailing effects of increasingly cost-competitive variable renewable energy (VRE). We explore the effects of these forces on hydropower expansion in the world's 20 most eco-sensitive river basins, that have substantial untapped hydropower potential and ecological richness. Our investigation is based on the Global Change Analysis Model (GCAM), an integrated model of global energy-water-economy dynamics. The GCAM outputs and other data provided in this repository, in combination with the Jupyter Notebooks provided in <a href="https://github.com/kamal0013/chowdhury-etal_2023_hydropower">this GitHub repository</a>, can be used to conduct our key analysis, and reproduce the relevant results.</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.