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20 results for “sector analysis”
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)
<p>Data for <strong>Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)</strong></p> <p>Rooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting its diffusion. There is a gap, however, in the knowledge of non-residential adoption determinants, as less attention has been given to this sector compared to the residential sector. As a result of this gap, there is an absence of comparative analysis across sectors. As determinants of adoption cannot be assumed to be the same in both sectors, the objective of this research is threefold. First, to analyze whether the residential and non-residential sectors share key determinants of rooftop solar investment; second, to compare the sectoral differences in these determinants; and third, to assess the policy implications of the results obtained to further promote distributed solar photovoltaic energy. For this purpose, a regional case study in the Basque Country (Spain) was conducted, applying key theoretical frameworks to both sectors in a way that maximized the comparability of the results obtained across them. The results showed that adoption determinants are very different across sectors and, therefore, sector-specific policy actions need to be taken in each sector to efficiently promote rooftop solar. For the residential sector, policy actions could build upon behavioral aspects; for the non-residential sector, economic incentives are expected to be more successful, especially among medium size businesses, which are identified as the most promising segment.</p> <p> </p>
Analysis of policy measures on designing a renewable fuel supply chain in the transport sector- Supplementary materials
<p><strong>This repository contains supporting data for: "Analysis of policy measures on designing a renewable fuel supply chain in the transport sector"</strong></p> <ol> <li> <p><strong>Supplementary Materials</strong>: This collection encompasses the model input data, alongside a detailed formulation of the objective functions.</p> </li> <li> <p><strong>Pareto_Table</strong>: This file contains the Pareto frontier results. </p> </li> <li><strong>DoE_Results_Table</strong>: This file presents the results of various solution scenarios, each characterized by differing levels of policy measures.</li> </ol>
Data for Deciphering-the-CO2-emissions-and-emission-intensity-of-cement-sector-in-China-through-decomposition-analysis
<p>The dataset contains data for Figure 7-13 in our article "<em>Deciphering the CO<sub>2</sub> emissions and emission intensity of cement sector in China through decomposition analysis</em>", and data for part of<em> China Cement Industry Dataset (CCID)</em>. </p>
SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
<p><span>This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.</span></p>
Green taxes as ecosystem conservation: an analysis of the industrial sector's view in Peru
<p><strong>Background:</strong> Environmental problems are becoming more and more recurrent nowadays, which is why many countries have acted on the matter, through different forms, laws and taxes that can contribute and reduce the polluting impact of companies at the time of manufacturing their products. One of the reforms has been environmental taxes, which are not only aimed at raising money, but also at taking corrective action on the behavior of companies that damage both the environment and the health of the population.</p> <p><strong>Objective:</strong> The research is interested in finding out the opinion and interest of managers of different companies in the industrial sector on environmental taxes, also known as green taxes, and whether they consider it necessary to add green taxes to the current tax system.</p> <p><strong>Method:</strong> For the data collection of the research, 120 managers of small and medium-sized enterprises in the industrial sector were questioned about whether green taxes could have an influence on ecosystem conservation and several other questions.</p> <p><strong>Results: </strong>63.3% of the managers surveyed agree that the application of an environmental tax is necessary.</p>
Economics Analysis of Small and Large Farm Size Honey Bee Sub-Sector in Chitwan District, Nepal
<p>This is an SPSS file that can be used for calculating various descriptive statistics and performing statistical tests such as t-tests and chi-square tests. Ranking of scale can also be carried out from these datasets.</p>
Replication files for "The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data"
<p>The ZIP file contains all data and code to replicate the analyses reported in the following paper.</p> <p>Reber, U., Ingold, K., & Fischer, M. (2023). The role of actors' issue and sector specialization for policy integration in the parliamentary arena: An analysis of Swiss biodiversity policy using text as data. <em>Policy Sciences</em>. <a href="https://doi.org/10.1007/s11077-022-09490-2">https://doi.org/10.1007/s11077-022-09490-2</a></p> <p>If you use any of the material included in this repository, please refer to the paper.</p>
Circular Economy – current view by the Construction sector – analysis of published definitions.
<p>Data set (spreadsheet) for the article in the title.</p>
A COMPREHENSIVE STUDY OF MACHINE LEARNING APPROACHES FOR CUSTOMER SENTIMENT ANALYSIS IN BANKING SECTOR
<p>This study explores the application of sentiment analysis in the banking sector, focusing on customer feedback to enhance service quality and customer experiences. We collected a comprehensive dataset of approximately 100,000 entries from diverse sources, including customer satisfaction surveys, social media platforms, and direct feedback. A robust preprocessing pipeline was employed to address challenges associated with unstructured data, informal language, and mixed sentiments. We evaluated several machine learning and natural language processing models, including Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, Long Short-Term Memory (LSTM), and BERT (Bidirectional Encoder Representations from Transformers), using metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and training time. The results revealed that advanced models, particularly BERT, achieved superior performance with an accuracy of 88% and an F1 score of 0.86, demonstrating an exceptional ability to capture nuanced sentiments. This study underscores the importance of employing sophisticated sentiment analysis techniques in banking to derive actionable insights from customer feedback. The findings suggest that leveraging advanced models can significantly improve service quality and customer satisfaction, while also presenting avenues for future research into real-time sentiment analysis and its integration with customer relationship management systems.</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree STEM</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gender</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gap</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree rights</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Empirical conceptual map</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Theoretical concept map</p>
Dataset for Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems
<p>Dataset for preprocessing Balmorel data in this Danish case study.</p>
CONTENT ANALYSIS OF APPEALS RECEIVED AT THE VIRTUAL AND PUBLIC RECEPTION CENTRES OF THE PRESIDENT OF THE REPUBLIC OF UZBEKISTAN RELATED TO THE HEALTH SECTOR
Open the record for dataset details and reuse information.
ANALYSIS OF THE STATE OF INNOVATIVE DEVELOPMENT OF THE AGRARIAN SECTOR IN THE REGION
<p><em><span>This article analyzes the state of innovative development of the agricultural sector at the regional level and concludes that innovations include not only technical or technological developments, but also any changes in all areas of scientific and production activities that ensure a qualitative increase in the efficiency of processes or products. As a result of the ongoing reforms in the field of science, such innovative technologies as organic agriculture, precision agriculture, large-scale livestock farming, arable farming, loose livestock keeping, drip irrigation, integrated pest control, urbanized agriculture, automation and computerization, waste-free agriculture, etc. are also being consistently introduced into practice. It should be noted that the most common innovations in agriculture are new varieties and hybrids of plants, animal breeds. However, as practice shows, their implementation is quite slow.</span></em></p>
Life Cycle Cost Analysis for the automotive sector
<p>LCC analysis carried out considering data from automotive sector partners in the ECOBULK consortium to evaluate the profitability of the circular solutions studied.</p>
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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.