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123 results for “credit”
Credit Score USP Example
<p>The granting of credit, in recent years, has become one of the main sources of<br> revenues for banks. In this area, a great difficulty encountered by the manager<br> (decision maker) is the establishment of criteria that allow to carry out a credit analysis<br> that aggregates greater security for the operation and, consequently, assist in reducing<br> the default of the sector. In order to minimize this difficulty found in credit analysis, the<br> study of case of this dissertation has as its basic proposal the elaboration of a model<br> that, according to perception of the granting manager himself, help him to evaluate the<br> performance of clients credit borrowers, in those criteria deemed important by the<br> manager. For construction model, the Multicriteria Decision Support Methodology<br> (MCDA-Construtivista) was considered the most appropriate, due to its ability to<br> integrate both objective and subjective elements, thus providing an excellent<br> opportunity for generating knowledge and promotion of understanding related to<br> problems of this nature. The utilization model proposed here, it will provide an<br> opportunity to assess the borrower's performance those criteria considered important<br> by the decision-maker, thus contributing to aggregate greater agility and security for<br> the act of granting credit.</p>
Credit for software creators: An adapted illustration from The Turing Way: Shared under CC-BY 4.0 for reuse
<p>Illustration adapted from <strong>The Turing Way Community, & Scriberia. (2023). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. <a href="https://doi.org/10.5281/zenodo.8169292">https://doi.org/10.5281/zenodo.8169292</a></strong>.</p> <p>The illustration has been adapted by Stephan Druskat (subsumed as co-author in <em>The Turing Way Community</em>):</p> <ul> <li>The speech bubble has been changed to read "More credit" instead of "More credit<strong>s</strong>". (One character removed and alignment adapted.)</li> <li>The inscription on the purse has been changed to read "Credit" instead of "Credit<strong>s</strong>". (One character removed and alignment adapted.)</li> </ul>
DITOs D4.5 Final Event Photographs Credit Erwan Reaud
<p>Photographs from the ECSA General Assembly 2/4/19 and the DITOs final event 3/4/19, if used you must credit Erwan Reaud</p>
DITOs D4.5 Final Event Photographs Credit Quentin Chevrier
<p>Photographs from the ECSA General Assembly 2/4/19 and the DITOs final event 3/4/19, if used you must credit Quentin Chevrier</p>
Original dataset for "A validation of co-authorship credit models with empirical data from the contributions of PhD candidates"
<p><strong>Publication reference:</strong><br> Donner, P. (2020). A validation of co-authorship credit models with empirical data from the contributions of PhD candidates. Quantitative Science Studies, v. 1, i. 2, p. 551-564. <a href="https://doi.org/10.1162/qss_a_00048">https://doi.org/10.1162/qss_a_00048</a>.</p> <p> </p> <p>The file contains one row per authorship contribution statement. Rows of publications and theses are grouped.</p> <p><strong>Description of columns:</strong></p> <p>dissertation_id - an integer identifying each dissertation thesis</p> <p>university - university at which the dissertation thesis was written and PhD degree conferred</p> <p>year - publication year of the dissertation thesis</p> <p>author - dissertation thesis author name</p> <p>title - dissertation thesis title</p> <p>subject - the field of research</p> <p>publication_id - an integer identifying each publication; publication associated with more than one thesis have the same id across theses</p> <p>reference - bibliographic reference for the publication associated with the thesis</p> <p>author_count - number of authors of the publication</p> <p>author_position - position in the author byline of the credited author</p> <p>credit - claimed credit of the author in percent</p> <p>corresponding_author - flag for whether the publication author of this row is a orresponding author</p>
Prediction of Churning Credit Card Customers
<p>A business manager of a consumer credit card portfolio is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off.</p> <p>We could construct a model to predict which customer might be churned and the manager could proactively provide them better services and turn customers' decisions in the opposite direction.</p>
Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy"
<p>Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy". In this research article we present the first cross-disciplinary descriptive analysis on the use of contribution statements. Our main objective is to obtain further insight on contributions by a variety of fields (Multidisciplinary, Health, Life, Physical and Social Sciences) from the largest dataset used up to now. We examine more than 700,000 articles published between 2017 and 2024 in Elsevier and PLOS journals, in combination with bibliometric data extracted from the Scopus database. The descriptive analysis of the dataset focuses on the overall coverage of the merged data, the distribution of authorship and disciplines at paper level, and the interactions between contribution statements, author order and disciplines. Our two main findings indicate that, on the one hand, looking at contributions and authorship order can enrich the way we understand science as a social endeavor. On the other hand, delving deeper into contributorship differences by field is key. We underscore the value of the CRediT taxonomy in unveiling nuanced research dynamics and offering a more equitable framework for evaluation.</p>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
Dataset: Dimensional Global Credit ETF (DGCB) 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: Credit Acceptance Corporation (CACC) 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: Credit Suisse X-Links Crude Oil Shares Covered Call ETNs (USOI) 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: Credit Suisse X-Links Silver Shares Covered Call ETN (SLVO) 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: FlexShares Credit-Scored US Corporate Bond Index Fund (SKOR) 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: OFS Credit Company, Inc. (OCCI) 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: OFS Credit Company, Inc. (OCCIN) 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: OFS Credit Company, Inc. (OCCIO) 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: Investcorp Credit Management BDC, Inc. (ICMB) 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.
Data for "Credit card risk behavior on college campuses: evidence from Brazil"
<p>This data set support the following research: College students frequently show they have little skill when it comes to using a credit card in a responsible manner. This article deals with this issue in an emerging market and in a pioneering manner. University students (<em>n</em> = 769) in São Paulo, Brazil's main financial center, replied to a questionnaire about their credit card use habits. Using Logit models, associations were discovered between personal characteristics and credit card use habits that involve financially risky behavior. The main results were: (a) a larger number of credit cards increases the probability of risky behavior; (b) students who alleged they knew what interest rates the card administrators were charging were less inclined to engage in risky behavior. The results are of interest to the financial industry, to university managers and to policy makers. This article points to the advisability, indeed necessity, of providing students with information about the use of financial products (notably credit cards) bearing in mind the high interest rates which their users are charged. The findings regarding student behavior in the use of credit cards in emerging economies are both significant and relevant. Furthermore, financial literature, while recognizing the importance of the topic, has not significantly examined the phenomenon in emerging economies.</p>
Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective
<p>The raw datasets provided here are intended for use in a Data in Brief article. These comprehensive files, sourced from the Freddie Mac website, offer quarterly snapshots of mortgage loans that have been originated in the USA since 1999, along with details of their subsequent repayment behaviours. This data remains current and is updated every three months. Specifically, the loan origination data present here encompasses amortized fixed-rate mortgage loans from 1999 up to June 2022. In contrast, the performance data is presented on a monthly basis, detailing loan repayment profiles from 1999 until September 30, 2022. Both the origination and performance datasets feature a unique loan ID, which can be utilized to integrate the data on loan originations with that of loan repayments.</p>
Replication package for: Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets
<p>Alessandro Gavazza and Andrea Lanteri, Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets, Review of Economic Studies</p> <p>The package includes four folders: data, stata_code, matlab_code, and figures. Please see the readme.pdf file for details.</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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.