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54 results for “Energy Transition”
Characterization of investments profiles on the energy transition for european citizens
<ul> <li><strong>Name</strong>: Characterization of investments profiles on the energy transition for european citizens</li> <li><strong>Summary</strong>: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 15/10/2023</li> <li><strong>DOI</strong>: 10.5281/zenodo.4455198</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li><strong>Description:</strong> The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li><strong>Section 1 - Scenarios for energy transition.</strong> <ul> <li><strong>ID90.</strong> Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li><strong>ID36, ID38, ID43, ID44, ID72. </strong>Percentage of money people are willing to spend/save out of their income per scenario</li> <li><strong>ID191, ID192</strong>.. Amount of money people would spend based on an assumed case.</li> <li><strong>ID191, ID192. </strong>Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li><strong>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]</strong>. Priority service provision in case of <em>Intermittent energy service</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251</strong>]. Priority service provision in case of <em>full </em><em>black-outs</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID141, ID5, ID147</strong>]. Used for statements that best represent survey responder</li> </ul> </li> <li><strong>Section 2 - Determinants (factors).</strong> Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li><strong>ID100</strong> Risk profile</li> <li><strong>ID101</strong> Added value</li> <li><strong>ID102</strong> Self-Satisfaction</li> <li><strong>ID103</strong> Technical Fit</li> <li><strong>ID104</strong> Own competence</li> <li><strong>ID105</strong> Knowledge</li> <li><strong>ID106</strong> Cost-Efficiency</li> <li><strong>ID107</strong> Safety</li> <li><strong>ID108</strong> Trust</li> <li><strong>ID109</strong> Autarky</li> <li><strong>ID110</strong> Legal</li> <li><strong>ID111</strong> Climate Protection</li> <li><strong>ID112</strong> Wellbeing</li> <li><strong>ID113</strong> Coziness</li> <li><strong>ID114</strong> Rights and Duties</li> <li><strong>ID115</strong> Peer-Pressure</li> <li><strong>ID116</strong> Socialising</li> <li><strong>ID117</strong> Support</li> <li><strong>ID118</strong> Agreement</li> <li><strong>ID119</strong> Brag</li> <li><strong>ID120</strong> Fun</li> <li><strong>ID121</strong> Novelty</li> <li><strong>ID122</strong> Trends</li> <li><strong>ID123</strong> Authority</li> <li><strong>ID124</strong> Own Significance</li> <li><strong>ID125</strong> Poseur</li> <li><strong>ID2</strong> Frugality</li> <li><strong>ID3</strong> Environmental concerns</li> <li><strong>ID31</strong> Adherence</li> <li><strong>ID52</strong> Commitment</li> <li><strong>ID97</strong> Profits</li> <li><strong>ID99</strong> Credit Score</li> </ul> </li> <li><strong>Section 3 - “Socio-economic” description. </strong>Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li><strong>ID164</strong> Understanding of questions</li> <li><strong>ID300</strong> Country of residence</li> <li><strong>ID137</strong> Age</li> <li><strong>ID178</strong> Highest level of education</li> <li><strong>ID136</strong> Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li><strong>Section 4 - Investment decision</strong>. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li><strong>ID42</strong> Affordable cost of a Regular refrigerator</li> <li><strong>ID45</strong> Energy efficient refrigerator costs</li> <li><strong>ID50</strong> Willingness to purchase an energy efficient refrigerator <ul> <li><strong>ID65</strong> Why no</li> <li><strong>ID66</strong> affordable cost of an energy efficient option</li> <li><strong>ID67</strong> Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li><strong>ID47</strong> Affordable cost of updating to a state of the art insulation on the facade</li> <li><strong>ID56</strong> Willingness for paying/invest <ul> <li><strong>ID74</strong> Why no?</li> <li><strong>ID20</strong> affordable cost of an energy efficient option</li> <li><strong>ID34</strong> Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li><strong>ID68</strong> Affordable cost of a solar photovoltaic system</li> <li><strong>ID76</strong> Willingness for paying/invest <ul> <li><strong>ID84</strong> Why no?</li> <li><strong>ID132</strong> Affordable cost of a photovoltaic system</li> <li><strong>ID138</strong> Years that amortize a photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li><strong>ID142</strong> Affordable cost of an energy storage system</li> <li><strong>ID146</strong> Willingness for paying/invest <ul> <li><strong>ID181</strong> Why no? </li> <li><strong>ID182</strong> Affordable cost of an energy storage system </li> <li><strong>ID183</strong> Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li><strong>ID140</strong> Affordable cost of a gas boiler</li> <li><strong>ID209</strong> Affordable cost of an energy efficient heating system</li> <li><strong>ID217</strong> Willingness for paying/invest <ul> <li><strong>ID238</strong> Why no?</li> <li><strong>ID239</strong> Affordable cost of a energy efficient option</li> <li><strong>ID241</strong> Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li><strong>ID41</strong> Average kilometers traveled a typical day</li> <li><strong>ID51</strong> Usual travel option</li> <li><strong>ID264</strong> Affordable cost of a diesel or gasoline mid-range brand new car</li> <li><strong>ID265</strong> Affordable cost of a mid-range brand new electric car</li> <li><strong>ID281</strong> Willingness to buy an electric car <ul> <li><strong>ID289</strong> Why no?</li> <li><strong>ID290</strong> Affordable price of an electric car</li> <li><strong>ID291</strong> Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Section 5 - Household characterization</strong> <ul> <li><strong>ID127</strong> Selecting an asked value</li> <li><strong>ID189</strong> Type of living area</li> <li><strong>ID202</strong> Gender identity</li> <li><strong>ID1</strong> Those living in the house</li> <li><strong>ID32</strong> Number of inhabitants</li> <li><strong>ID220</strong> Average neat yearly income</li> <li><strong>ID229</strong> Average monthly saving</li> <li><strong>ID240</strong> Type of housing</li> <li><strong>ID249</strong> Owner / co-owner</li> <li><strong>ID255</strong> Usable area of the property (m²)</li> <li><strong>ID263</strong> Insulation level</li> <li><strong>ID270</strong> Climate zone</li> <li><strong>ID86</strong> Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li><strong>ID87</strong> Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li><strong>ID88</strong> Level of self-awareness about energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li><strong>ID89</strong> Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li><strong>ID190</strong> feedback about survey</li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> anonymization, data fusion, imputation of gaps.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned</li> <li><strong>Ethics and legal aspects:</strong> Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
gEneSys Project - Systematic Literature on the Nexus between Gender and Energy Transition Database
<p>The present Dataset containes the data collected for the gEneSys Systematic Literature Review on the nexus between gender and energy transition. Data have been collected from 152 papers published between 2000 and 2023. The publications have been identified through an hoc research query and retrieved from the Web of Science Database.</p> <p>The dataset inscludes the following variables:</p> <ol> <li>Title of the publication, category of the categorization of Bell et al., 2020 (Political, Economic, Socio-Ecological, Technological).</li> <li>Cluster in which the publication has been included.</li> <li>Parts of the publication’s results about the nexus between gender and energy.</li> <li>Parts of the publication’s text about the gender gap assessed by the publication.</li> <li>Parts of the publication’s text about the gender gap identified to be bridged by future research.</li> <li>The type of the gender issue/s addressed by the publication. </li> <li>The type of the gender issue/s addressed by the publication. </li> <li>Technology/ies mentioned in the publication.</li> <li>The name of the country or countries studied by the publication.</li> <li>World Bank classification of the level of income of the country or countries studied by the publication.</li> <li>World Bank classification of the region of the country or countries studied by the publication.</li> <li>Spatial Context (e.g. international, national, inner-country, peri-urban, rural) of the country or countries studied by the publication.</li> <li>Research method employed in the publication (qualitative, quantitative, mixed).</li> <li>Specific qualitative, quantitative or mixed method or methods employed in the publication.</li> <li>Number of observations for the methods used.</li> <li>Parts of the publication’s text about the policy recommendations elaborated in the publication.</li> <li>If the publication mentions a pathway.</li> <li>Year of publication.</li> <li>Author/s surname and name initial. </li> <li>Author/s full surnames and names. </li> <li>Keywords chosen by the author/s. </li> <li>Abstract of the publication. </li> <li>Name of the source or journal. </li> <li>Type of publication. </li> <li>Category/ies identified by Web of Science. </li> <li>Publication’s language. </li> <li>Keywords identified by Web of Science. </li> <li>Number of references cited by the publication. </li> <li>Number of times the publication has been cited in Web of Science Core Database. </li> <li>Number of times the publication has been cited in Web of Science All Databases. </li> <li>Name of the publisher. </li> <li>Digital Object Identifier. </li> <li>Digital Object Identifier link. </li> <li>Publication’s number of pages. </li> <li>Web of Science citation index. </li> <li>Research area or areas of the publication. </li> <li>Web of Science Unique Identifier.</li> </ol>
Free energy simulations of receptor-binding domain opening in the SARS-CoV-2 spike indicate a barrierless transition with slow conformational motions
<p>This online data set accompanies the manuscript entitled "Free energy<br> simulations of receptor-binding domain opening in the SARS-CoV-2 spike<br> indicate a barrierless transition with slow conformational motions."</p> <p>The dataset is composed of the following files:</p> <p>* pmf0-now.dcd -- pmf63-now.dcd : molecular dynamics trajectory frames in<br> each of the 64 umbrella sampling windows, from which water has been<br> removed to save space</p> <p>* s1am_0-now.pdb -- s1am_63-now.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, from which water has been removed,<br> corresponding to the trajectory data above</p> <p>* view -- Visual Molecular Dynamics command script to load a trajectory, <br> e.g., in Linux, use "vmd -e view"</p> <p>* s1am_0-cg.dcd -- s1am_63-cg.dcd : molecular dynamics<br> trajectory frames in each of the 64 umbrella sampling windows, coarse-grained to<br> 1 bead per residue.</p> <p>* s1am_0-cg.pdb -- s1am_63-cg.pdb : initial coordinates in each of the 64<br> umbrella sampling windows, corresponding to the coarse-grained trajectory<br> data above.</p> <p>* viewcg -- Visual Molecular Dynamics command script to load a<br> coarse-grained trajectory, e.g., in Linux, use "vmd -e viewcg"</p> <p>* 0readme -- brief instructions on how to view the trajectories</p> <p>* colors.vmd -- utility script for VMD</p> <p>* covmacros.vmd -- VMD script to define coronavirus spike subdomains</p> <p>* fe.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the free energy profiles</p> <p>* diff.zip -- ZIP archive that contains data and Matlab analysis files to<br> reproduce the diffusion and mean first passage times calculations</p> <p>* pca-qha.zip -- ZIP archive that contains the data and Matlab analysis files<br> to compute the autocorrelation functions of trajectory displacements<br> along principal/quasiharmonic modes</p> <p>Each ZIP archive contains a "0readme" file with brief instructions, and also the <br> results of the calculations<br> </p>
Annual Conference in Global Energy Transition Law and Policy
<p>The Environment Energy and Natural Resources (EENR) Center in association with the Center for U.S. and Mexican Law of University of Houston Law Center will be hosting a virtual symposium on Friday, April 17<sup>th</sup>, 2020, 9:00 a.m.- 12:30 p.m. (CDT), by way of our 1st Annual Conference in Global Energy Transition Law and Policy.</p> <p><strong>Topic</strong>: <strong>THE ENERGY TRANSITION IN A CLIMATE CONSTRAINED WORLD. AN INTEGRATIVE APPROACH IN GLOBAL ENERGY LAW AND POLICY ISSUES?</strong></p> <p><strong>Date</strong>: Friday, April 17<sup>th</sup>, 2020, from 9:00 to a.m.-12:30 p.m. (CDT)</p> <p>The conference, which is designed for all (policy-makers, researchers, professionals, students, etc.), will feature an outstanding faculty roster who will address the current energy transition issues.</p> <p><strong>Highlights</strong>:</p> <p>· Recent Developments in Energy Transition Law and Policy;</p> <p>· Energy Policy in Citizens’ Climate Assemblies;</p> <p>· Energy Communities in the European Union;</p> <p>· Europeanisation of the Development of Renewable Energy in Transition;</p> <p>· Finance and Risk Policy for The Just Transition to a Low-Carbon Economy;</p> <p>· A Sustainable and Prosperous Future: The Role of Climate Clubs and International Trade;</p> <p>· Decarbonization Options for Gas and Electricity Systems: Power-to-Gas and Carbon Capture Utilization and Storage;</p> <p>· Incorporation of DMDU decision-making under deep uncertainty) Framework into Energy Policy;</p> <p>· COVID-19 provides Warning about the Transition from Fossil Fuels. </p>
Results for the paper "The impact of temporal hydrogen regulation on hydrogen exporters and their domestic energy transition"
<p>As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for<br>export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic<br>energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change<br>mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen<br>exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated.<br>Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can<br>steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance<br>among actors.</p>
Code for data and figures published in "Solar energy as an early just transition opportunity for coal-bearing states in India"
<p>The following code and data were used to generate the figures in the article "Solar energy as an early just transition opportunity for coal-bearing states in India". The article was published in Environmental Research Letters (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/ac5194">https://iopscience.iop.org/article/10.1088/1748-9326/ac5194</a>)</p> <p>The code is written in R. Before running the Rmd file, create a folder called "Data" and store all the files there, except the Rmd file.</p>
Scenario data for article: Effects of the energy transition on environmental impacts of cobalt supply: A prospective Life Cycle Assessment study on future supply of cobalt
<p>This dataset contains the background data for the paper '<a href="https://onlinelibrary.wiley.com/doi/10.1111/jiec.13258">Effects of the energy transition on environmental impacts of the cobalt supply: A prospective Life Cycle Assessment study on the future cobalt supply</a>' as published in the Journal of Industrial Ecology.</p> <p><strong>Please note that an easier to use version of this data for LCA is available through the Premise (<a href="https://www.sciencedirect.com/science/article/pii/S136403212200226X">Sacchi et al. 2022</a>) Community Scenarios <a href="https://github.com/premise-community-scenarios/cobalt-perspective-2050">here</a>.</strong> This version is slightly adapted to fit into the Premise architecture and is compatible with ecoinvent v3.8 cutoff.</p> <p>This repository contains:</p> <ul> <li>Python code + readme to model the variables, generate presamples packages and generate LCA results based on those. (code folder)</li> <li>Input and output data for Variables 1-3 (files 1&2)</li> <li>Presamples excel sheets for each variable/scenario combination (file 3)</li> <li>Summarized LCA results (the full results can be generated through running the code provided) (file 4)</li> <li>Full LCA results used for the contribution analysis (file 5)</li> <li>Underlying data for each of the figures (file 6)</li> </ul> <p>We refer to the paper (linked above) for more information on the study.<br> </p> <p><strong>License: </strong>The metal supply scenario data is licensed under the CC-BY 4.0 license.</p> <p><strong>Access: </strong>Open access</p> <p> </p> <p>[Changelog]</p> <p>2023-03-23 - 1.3.1: Add link to Premise Community scenario page.<br> 2022-05-18 - 1.3.0: Fix minor error in data files '4 - LCA results' and '6 - Figure data' in demand amounts for total impacts.<br> 2022-04-06 - 1.2.1: Included link to article after publication<br> 2022-03-30 - 1.2.0: Included underlying figure data<br> 2022-01-24 - 1.1.1: Opened repository after paper acceptance<br> 2021-11-26 - 1.1.0: Update of code to comply with peer-review<br> 2021-07-12 - 1.0.0: Set-up of repository</p>
Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition
<ul> <li><strong>Name</strong>: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li><strong>Summary</strong>: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 01/06/2024</li> <li><strong>DOI</strong>: 10.5281/zenodo.11234441</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to build a set of causal diagrams of the .</li> <li><strong>Description:</strong> <br> <ul> <li><strong>Scenarios: </strong>This dataset contains the description of 20 different scenarios used in this research activity. </li> <li><strong>File 1 - individual reasons to be coded<br></strong>This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets:<br> <ul> <li><strong>Methodology</strong>: Methodology followed by the coders.</li> <li><strong>Help</strong>: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li><strong>Glossary</strong>: Glossary of terms build by the experts coding the answers. </li> <li><strong>Appliances/Flexibility/Buildings/Mobility</strong>: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li><strong>Summary</strong>: Assesment of the results.</li> </ul> </li> <li><strong>File 2 - individual microdata to sort determinants into causal threads from experts</strong>This dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li><strong>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts</strong> <p>This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears.</p> <ul> <li> <p><strong>Stage 1</strong>: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list:</p> <ul> <li> <p><strong>European panels</strong>: Group – F (8), Group–A (8). Group–FF (8), Group–M (4)</p> </li> <li> <p><strong>Latin America panels</strong>: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7)</p> </li> </ul> </li> </ul> <ul> <li> <p><strong>Stage 2</strong>: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist.</p> </li> <li> <p><strong>Stage3</strong>: In the "<em>Archetypes - Consensus Results</em>" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change).</p> </li> <li> <p><strong>Stage 4</strong>. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables:</p> <ul> <li> <p><em>Table (s)</em>: Difference and Agreements between both context: European & Latin American Archetypes. The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages.</p> </li> <li> <p><em>Table (t)</em>: This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes.</p> </li> <li> <p><em>Table (t.1)</em>: This table shows just the <strong>agreements</strong> (X) between both context: European & Latin American Archetypes.</p> </li> <li> <p><em>Table (t.2)</em>: Show the difference between both context: European (E) & Latin American Archetypes (L).</p> </li> <li> <p><em>Table (t.3)</em>: This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences.</p> </li> </ul> </li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> Data transcription from written documents and oral discussions.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned.</li> <li><strong>Ethics and legal aspects:</strong> Names of the persons involved have been removed. </li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
Dataset: Sprott Energy Transition Materials ETF (SETM) 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.
Dataset: Nabors Energy Transition Corp. II (NETDU) 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.
Dataset: Nabors Energy Transition Corp. II (NETDW) 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.
Dataset: Nabors Energy Transition Corp. II (NETD) 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.
Energy Transition Strategy Based on Bioenergy Potential from Empty Fruit Bunches to Support Indonesia's New Capital in East Kalimantan
<p><strong><span>Data Source</span></strong></p> <p><span>The study of bioenergy potential of Eastern Kalimantan, INDONESIA </span></p> <p><strong><span> </span></strong></p> <p><strong><span>Journal Title</span></strong><span><span> </span>: <span> </span></span><span>Energy Transition Strategy Based on Bioenergy Potential from Empty Fruit Bunches to Support Indonesia's New Capital in East Kalimantan</span></p>
Analysis of a solar-based energy transition in Greece
<p>These datasets contain the underlying data for the following publication: <strong>Barriers to and consequences of a solar-based energy transition in Greece, Environmental Innovation and Societal Transitions, https://doi.org/10.1016/j.eist.2018.12.004.</strong></p>
Survey questionnaire and results on user needs for energy models for the European energy transition, related to Süsser et al. (2021)
<p>The online survey was designed and conducted in the framework of the EU H2020 project SENTINEL in collaboration with the project openENTRANCE. The aim of the survey was to identify needs by modellers and model result users across Europe for energy modelling. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s and energy industry. </p> <p>The study by Süsser <em>et al.</em> (2021) investigates the differences between energy model improvements and adjustments as perceived by modellers, and the actual needs of users of model results. If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Süsser, D., Gaschnig, H., Ceglarz, A., Stavrakas, V., Flamos, A. & Lilliestam, J. (under review). Better suited or just more complex? </em><em>On the fit between user needs and modeller-driven improvements of energy system models. Energy.</em></p>
Safeguarding the energy transition against political backlash to carbon markets - figure raw data
<p>Figure raw data for the article "Safeguarding the energy transition against political backlash to carbon markets" (published in Nature Energy).</p>
Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts
<p>Supporting data for <strong>Free Energy Differences from Molecular Simulations: Exact Confidence Intervals from Transition Counts</strong></p> <p>Molecular simulations make it possible to predict equilibrium constants and corresponding free energy differences. For a system that exists in two states A and B, the equilibrium constant K can be predicted as K = t_B / t_A, where<br> t_B and t_A are times spent in states B and A, respectively. The free energy can be calculated as Delta G = -kT log(K). Here we propose a new method for calculation of confidence intervals for K and Delta G. The ratio of the true<br> value of K and estimated K follows the F-distribution with degrees of freedom df1 = number of B to A transitions and df2 = number of A to B transitions. This makes it possible to calculated the confidence interval of K solely from<br> the number of transitions.</p> <p>The code in the directory errors was used to calculate Table 1 of the article. The code in the directory type1error was used to generate 10000 first time passage times for a transition from A to B and B to A as random numbers with<br> exponential distribution. This was done for different combinations of number of transition and values of K. Number of confidence intervals not spanning the predefined value of K (type 1 errors) was expected to be 5 % for 95-% confidence intervals. This was in agreement with the result.</p> <p>The code in the directory type1errorodd was used to run similar experiment as type1error, but with number of A to B transitions higher than B to A by one. The code in the directory threestates was used to run similar experiment as<br> type1error and type1errorodd but for a system with three states A, B and C. The directory glycerol contains a trajectory, evolution of values of torsion angles and the code for analysis of the simulation of glycerol in water.</p> <p>The directory ffmp contains evolution of values of RMSD from the native structure, manual assignments of folded and unfolded states and the code for analysis of simulations of fast folding miniproteins (original data from Lindorf-Larsen et al. Science 2011, 334(6055) 517-520).</p> <p>The directory se contains the code for calculation of standard errors numerically and by the method presented in the article.</p> <p>The directory parallel presents the code for calculations supporting our method to calculate rate and equilibrium constants in parallel simulations.</p> <p>Codes written in R were executed using R version 3.4.4 by running:<br> <em>$ R –no-save < code.R > code.log</em></p> <p>File md5sums contains md5sum codes for all files.</p> <p> </p>
Characterizing the Folding Transition State Ensembles in the Energy Landscape of an RNA Tetraloop - available data.
<p>Trajectory file and scripts necessary to generate an ELViM [Oliveira, A. B.; Yang, H.; Whitford, P. C.; Leite, V. B. P. JCTC, 2019, 15, p.6482] projection of the conformational space for the GCAA tetraloop.</p>
Data and code for "Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr3 and MAPbI3"
<p>This record contains a neuroevolution potential (NEP) model (<code>nep-MAPI-SCAN.txt</code> ) for MAPbI3 used in the linked publication. The model can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.<br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP model included here. The <code>demo-for-using-structures-and-model.py</code> script illustrates how to access the structures and model.</p>
Energy transition matrix
<p>This table accompanies the SAPEA evidence review report published with the DOI 10.26356/energytransition. It is Table 5 of that report. For contextual information and accompanying descriptions, see the report.</p> <p>This table is based on a literature review.</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.