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4,230 results for “Energie”
Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts
<p>Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts</p>
German Renewable Energy Unit Register
<p>The UnitRegister dataset is a comprehensive compilation of all renewable energy units connected to the grid in Germany. It combines data from two primary sources: the <a href="https://www.marktstammdatenregister.de/MaStR/Datendownload">Marktstammdatenregister</a> published by Germany's Federal Network Agency (BNetzA), and individual publications from the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Anlagenstammdaten">webpage</a>.<br> The dataset encompasses active, decommissioned, and planned units, resulting from the merging and cleaning of these two unit registers. The Marktstammdatenregister dataset is regularly updated and was last downloaded in December 2022. The TSOs' unit register is updated annually around August, with a one-year lag, and the last data extraction took place in 2022, providing information until 2021.<br> While both datasets are similar, the main difference lies in the reporting format. The TSOs report units as bundles of installations under a common payment code (Unit_ID), whereas BNetzA publishes each installation as a separate unit with an individual identification code (BNetzA_Unit_ID). For example, a wind park with multiple turbines is reported as a single unit by the TSOs, whereas BNetzA provides information on each turbine separately.<br> The main dataset, "UnitRegister.csv," is located in the "3Output" folder. It comprises more than 2.4 million units categorized into ten technologies, including Geothermal, Hydropower, Gas_landfill, Gas_mine, Gas_sewage, Biomass, Wind_on, Wind_off, Solar_GM, and Solar_RM. This dataset consists of 13 columns/variables, including "Unit_ID," "BNetzA_EEG_ID," "BNetzA_Unit_ID," "Technology," "Capacity_kW," "Commissioning," "Decommissioning," "State," "PLZ," "Source," "RefYield," "Depth," and "Coast_dist."<br> In addition to the main dataset, this publication provides separate files for different stages of data processing. For example, "UnitRegister_TSOs.csv" and "UnitRegister_BNetzA.csv" offer clean datasets for the TSOs and BNetzA sources individually. The file "UnitRegister_BNetzA_Raw.csv" contains the compiled BNetzA register in its raw form, comprising 82 columns/variables. Detailed information on these variables can be found in the BNetzA user manual, which is included in the "Dokumentation MaStR Gesamtdatenexport" folder within the BNetzA directory.</p> <p>Notes:</p> <p>The dataset has undergone merging and cleaning processes aligned with the research objectives of the Chair of Energy Economics at BTU. It is important to note that these objectives may differ from other research purposes. Specifically, our research focused on analyzing payments associated with renewable energy projects, giving priority to the TSO data. The BNetzA data set was used to supplement the unit stock and fill in data gaps for TSO units whenever possible.<br> Users should exercise caution when selecting the data source that best aligns with their research goals. To assist in this decision-making process, we have included the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 22), users can run the code. However, it is crucial to verify that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past) and update the file reading sections in the code accordingly.</p> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. To gain a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:<br> • Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p>
Database for "Estimation of the Energy Recovery and Emission Potential of Typically Incinerated Norwegian Waste Classes"
<p>A great challenge for waste-to-energy power plants is their uncertain and variable feedstock, which<br> can lead to the power plants not being run as efficiently as possible, leading to reduced energy<br> output and control of emissions. A way to describe the feedstock is to use surrogates. This is a<br> method where the hundreds or thousands of different species of a feedstock are modelled using a few<br> surrogate species, enabling the feedstock’s modelling. The surrogates also provide an estimation<br> of the HHV and the fraction of biomass, oil-based waste and inorganics.<br> This thesis formulated surrogates for waste classes typically incinerated, using a linear least-square<br> solution between available surrogate species and experimental values. Most of the species used<br> were from two existing models in the literature, but three new species were created to improve the<br> representation of some waste classes containing fossil-originated wastes, rubber and PET. These<br> were made by creating reactions based on experimental data from the literature and then testing<br> these reactions under pyrolysis conditions in a stochastic reactor model.<br> The surrogates for the waste classes were formulated by first dividing the waste into components<br> and then finding the surrogate formulation for each component. There were found surrogates<br> for 41 components, which were used to create the surrogate formulation for 30 waste classes. It<br> was found that most of the surrogates modelled the elemental composition accurately compared<br> to experimental values. A statistical overview of the experimental and model data for the waste<br> classes was also created. This overview is relevant for stakeholders in waste management and for<br> other research, such as life-cycle analysis.</p>
German Renewable Energy Tariff Register
<p>The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">webpage</a>. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs.</p> <p>Notes:</p> <p>The TariffRegister.csv can be matched with the <a href="https://zenodo.org/record/8010410">PaymentRegister</a> dataset to identify the payment types received by renewable energy units.</p> <p>The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors.</p> <p>Users can update the dataset by following the outlined steps:</p> <ol> <li>Update the “Original files” by downloading the latest published data (<a href="http://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">here</a>)</li> <li>Clean the files from any row that is not including a Tariff_ID</li> <li>Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs.</li> <li>Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files.</li> <li>Run the provided R code after modifying the top-level directory.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <ul> <li>Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></li> <li>Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></li> </ul>
German Renewable Energy Payment Register
<p>The PaymentRegister dataset offers a comprehensive compilation of subsidies and tariffs paid to renewable energy units installed in Germany between 2000 and 2021. The dataset is constructed based on the yearly statements provided by the four German Transmission System Operators (TSOs) on their conjoint reporting webpage. These statements are updated annually around August, with a one-year lag, and the dataset encompasses yearly statements from 2007 to 2021.<br> Containing over 53 million observations, the dataset comprises 12 variables/descriptors. It includes a unit identifier (Unit_ID) that can be matched with the <a href="https://zenodo.org/deposit/7945029">UnitRegister</a> dataset, along with information on generation (G_kWh) and associated payments (P_Euro). Additionally, descriptors related to the associated tariff are provided, such as the Tariff identification code (Tariff_ID) and the regulated tariff value (Tariff). The dataset also includes the technology of the unit, a tariff categorization (Ref1, Ref2) based on the payment type (e.g., full feed-in tariff, market premium, bonus payment, reduced payment due to sanction), the year of payment (Year), and the name of the TSO under which the unit is registered (TSO). Notably, two additional descriptors, "Tariff1" and "P_Euro1," are included to highlight any discrepancies between reported tariff values and actual payments.</p> <p>Notes:</p> <p>The PaymentRegister.csv dataset was compiled to meet the research needs of the Chair of Energy Economics at BTU. Payments were categorized based on their tariffs, resulting in a more specific first categorization process with 29 categories, followed by a second categorization level grouping the tariffs into 17 categories. However, these categorizations are solely descriptive and do not impact the original data, allowing users to choose whether to utilize the categorization descriptors or not.</p> <p>To assist users in utilizing the dataset, we have provided the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 13), users can run the code. To update the dataset, the following steps are recommended:</p> <ol> <li>Update the TariffRegister following this publication.</li> <li>Download the new year statements from the TSOs into each TSO folder and run the individual TSO data analysis R-codes. Please modify the top-level directory for these codes as well and update the sections for the new data. Ensure that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past).</li> <li>Execute the main code to bind all the files together.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/deposit/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8013071">TariffRegister</a> and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <p>• Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></p> <p>• Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></p> <p> </p>
Supporting dataset for "Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon"
<p>Supporting dataset for the manuscript “Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon", currently under review in the Journal of Geophysical Research: Biogeosciences.</p>
TAT: Timing Analysis Toolkit for high-energy pulsar astrophysics
<p>The TAT-pulsar (Timing Analysis Toolkit for Pulsars) package is a specialized toolkit designed for handling the scientific intricacies of pulsar timing. It provides a suite of Python-based utilities and scripts that facilitate the analysis, processing, and visualization of pulsar data. By leveraging observational data from pulsars, along with the associated physical processes and statistical characteristics, TAT-pulsar integrates a series of useful tools and data analysis scripts specifically developed for both isolated pulsars and binary systems. This enables swift analysis and the detailed presentation of timing properties in the high-energy pulsar field. Developed and implemented completely independently from other pulsar timing software such as Stingray (<a href="https://ascl.net/1608.001">ascl:1608.001</a>) and PINT (<a href="https://ascl.net/1902.007">ascl:1902.007</a>), TAT-pulsar serves as a valuable cross-checking and supplementary tool for data analysis.</p>
Baseline data for Citizen Energy Responsible Behaviour in 27 Member States
<p>This dataset contains all the information on the levels that each country in Europe should have in the labelling system to assess citizens energy behaviours fully described in the document AURORA D1.1 Near-Zero Emissions Citizens Label https://doi.org/10.5281/zenodo.7594879</p> <p>The zip file contains one document for each member state. Reference values are extracted from the same datasources individuated in D1.1 for the 5 selected countries fully described in the document.</p>
Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector. Energy Policy (2023). Supplementary Information on Assumptions and Results
<p>This dataset supplements the article with the title "Deep decarbonisation pathways of the energy system in times of unprecedented uncertainty in the energy sector", published in Energy Policy. </p> <p>The dataset contains the following:</p> <ul> <li>The Latin Hypercube Sample of the multipliers that are applied to the key input parameters of ETSAP-TIAM in order to generate 1000 different states of the world regarding economic and demographic growth, energy resources potentials, energy technology costs, climate sensitivity and radiative forcing, LULUCF CO<sub>2</sub> sink potential, CO<sub>2</sub> sequestration potential, and decoupling between energy consumption and economic development. The multipliers are sampled from the underlying probability distributions described in the article. </li> <li>The results (at the global scale) from four scenario families for each one of the 1000 wofld states. These scenario families are: <ul> <li>BASE_SSP2: Describes the development of the global energy system consistent with recent trends and policies.</li> <li>2C_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 2 °C as the maximum post-industrial temperature change from 2020 to 2100.</li> <li>2C_SSP2_DA30: Delayed climate action. The climate change mitigation policies of 2C_SSP2 start in 2030. </li> <li>1p5c_OS_SSP2: Introduces to the BASE_SSP2 scenario a global constraint of 1.5 °C as the maximum post-industrial temperature change from 2020 onwards to 2100</li> </ul> </li> </ul> <p>Key results included in the dataset are: Temperature change, Radiative Forcing, GHG concentrations, CO2 emissions, Marginal abatement cost, Electricity Supply Primary Energy Consumption, and Annual Total Global Energy System Cost.</p> <p>The dataset also includes sectoral results regarding energy consumption and use, such as shares of different electric uses, shares of hydrogen consumption in end-use sectors, hydrogen supply, Demand electrification by sector, Renewable energy consumption by sector, Alternative fuels consumption in transport, and Total final energy consumption by sector. </p>
Results of linear ocean model experiment for: Dual wave energy sources for the Atlantic Niño events identified by wave energy flux in case studies
<p>These are sensitivity experiments designed by manipulating the wind forcing that drives the linear ocean model to investigate the difference in equatorial waves in 1999, 2019, and 2021. </p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Empirical primary studies where gamification is used to study energy behavior
<p>The data set is used to conduct a literature review on primary studies where gamification is used to study energy behavior; it contains original search results and after-filter results. The data set also includes a Bib file for all reviewed studies.</p>
Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases
<p>Dataset associated to the article: Rodríguez-Álvarez, J.Urban Building Energy Modelling for the Renovation Wave: A Bespoke Approach Based on EPC Databases. <em>Buildings </em><strong>2023</strong>, <em>13</em>, x.</p> <p>It contains filtered EPC datasets as xls and csv and shapefiles with the buildings' geometry and estimated energy loads</p>
Accurately Measuring Energy Consumption of Large Cosmological Simulations
<p><a href="https://event.pasc23-conference.org/session/sess138">https://event.pasc23-conference.org/session/sess138</a></p> <p>Minisymposium</p> <p>MS6G - Green Computing Architectures and Tools for Scientific Computing</p>
[DATASET 9] - PLANT-ROBOT INTERFACES FOR ENERGY HARVESTING
<p>In the framework of GrowBot project, task 7.2 aims at developing bio-hybrid energy harvesting systems based on the triboelectric effect.<br> The energy conversion occurs at plant leaves level during mechanical stimulation (i.e., wind, rain, etc.).<br> Two main components have been developed in GrowBot:<br> - Flexible artificial “leaves” (flexible electrodes covered with tailored materials) to enhance mechanical impacts with the plant leaves and further enhance power output.<br> - Minimal-invasive electrodes that establish electrical contact between GrowBots and real plants.</p> <p>DS9 aims at collecting all the experimental data gathered during these activities.</p>
Dataset of paper "Removal of diclofenac by UV-B and UV-C light-emitting diodes (LEDs) driven advanced oxidation processes (AOPs): Wavelength dependence, kinetic modelling and energy consumption"
<p>Dataset of paper "Removal of diclofenac by UV-B and UV-C light-emitting diodes (LEDs) driven advanced oxidation processes (AOPs): Wavelength dependence, kinetic modelling and energy consumption"</p> <ul> <li>Molar absorption coefficient of the DCF (pH 7.2), FC (pH 8.5), and H<sub>2</sub>O<sub>2</sub> (pH 6.5) in the wavelength range of 200-400 nm. </li> <li>Time-based and UV fluence-based kinetic constant and synergy factor for the DCF degradation.</li> <li>Diclofenac degradation fitted by the proposed models (UV/H<sub>2</sub>O<sub>2</sub> and UV/FC).</li> <li>Oxidant degradation fitted by the proposed models (UV/H<sub>2</sub>O<sub>2</sub> and UV/FC).</li> </ul>
Double-Fourier engineering of Josephson energy-phase relationships applied to diodes
<p>Code used to produce the numerical results presented in "Double-Fourier engineering of Josephson energy-phase relationships applied to diodes" manuscript.</p>
Frequency Tunable Electromagnetic Vibration Energy Harvester Utilizing Piecewise Linear Nonlinearity Dataset
<p>This is the data gathered during the experiments referenced in the manuscript. All files are .mat and native to Matlab. The variable "time' is the time array gathered during operation corresponding to the 'data' array of the same length. 'data' contains 2-3 columns depending on the file. The first column is always the base displacement voltage output, the second is the mass displacement voltage output, and if a third column is included then this is the measured voltage at the load applied to the harvester.</p>
Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space
<p>Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of <em>a priori</em> knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.</p>
Modelling results for the paper "The impact of methane leakage on the role of natural gas in the European energy transition"
<p>This data file represents the modelling results of the paper "The impact of methane leakage on the role of natural gas in the European energy transition" that is accepted to Nature Communications.</p>
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.