Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “LEND”
Dataset: Carlyle Secured Lending, Inc. 8.20% Notes due 2028 (CGBDL) 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: Carlyle Secured Lending, Inc. (CGBD) 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: Oaktree Specialty Lending Corporation (OCSL) 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: Open Lending Corporation (LPRO) 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.
Interpersonal lending network
<p>The dataset of this paper originated from quantitative surveys from May 2014 to June 2014. The data collection was embedded in a Participatory Action Research (PAR) which aim was to investigate the financial survival strategies of low-income households, in a Hungarian village in a disadvantaged region. The directed networks of lending and borrowing households is a unique dataset that empirically captures a hidden and informal finical activity between households. The network contains 158 households and 281 ties between them. A unique feature of the networks is that it indicates the social relations and the spatial distance between the households and also their financial situations. Therefore, the data describes not only the informal, interpersonal credit relations between the households but also the socioeconomic factors of the actors as well.</p>
Lending Club loan dataset for granting models
<p>Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on <a href="https://www.kaggle.com/datasets/wordsforthewise/lending-club">Kaggle</a> and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).</p> <p><strong>TARGET VARIABLE</strong></p> <p>The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.</p> <p><strong>EXPLANATORY VARIABLES</strong></p> <p>The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.</p> <h1><strong>FULL LIST OF VARIABLES</strong></h1> <p><strong>Loan identification variables:</strong></p> <ul> <li> <p>id: Loan id (unique identifier). </p> </li> <li> <p>issue_d: Month and year in which the loan was approved.</p> </li> </ul> <p><strong>Quantitative variables:</strong></p> <ul> <li> <p>revenue: Borrower's self-declared annual income during registration. </p> </li> <li> <p>dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.</p> </li> <li> <p>loan_amnt: Amount of credit requested by the borrower. </p> </li> <li> <p>fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.</p> </li> <li> <p>experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.</p> </li> </ul> <p><strong>Categorical variables:</strong></p> <ul> <li> <p>emp_length: Categorical variable with the employment length of the borrower (includes the no information category) </p> </li> <li> <p>purpose: Credit purpose category for the loan request. </p> </li> <li> <p>home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.</p> </li> <li> <p>addr_state: Borrower's residence state from the USA. </p> </li> <li> <p>zip_code: Zip code of the borrower's residence.</p> </li> </ul> <p><strong>Textual variables</strong></p> <ul> <li> <p>title: Title of the credit request description provided by the borrower.</p> </li> <li> <p>desc: Description of the credit request provided by the borrower.</p> </li> </ul> <p>We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&amp;” was substituted by “&”, “&lt;” was substituted by “<”, etc.).</p> <h1><strong>RELATED WORKS</strong></h1> <p>This dataset has been used in the following academic articles:</p> <ul> <li>Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. <a href="https://doi.org/10.48550/arXiv.2401.16458">https://doi.org/10.48550/arXiv.2401.16458</a></li> <li>Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. <a href="https://doi.org/10.1109/ACCESS.2020.2984412">https://doi.org/10.1109/ACCESS.2020.2984412</a></li> </ul>
« Lend your Money, Lose your Friend? » - Chinese Official Lending and Bilateral Political Alignment: The Case of Africa
<p>This database provides a set of 45 variables related to UNGA voting affinity vis-à-vis China, official lending, and other bilateral economic and political indicators for China and 43 African countries over the period 2000-2020. The indicators are grouped into four categories: voting data, loan and debt, economic indicators, and political indicators. </p><p>This dataset was compiled in order to conduct research and econometric work for a journal article entitled: </p><p><strong>« Lend your Money, Lose your Friend? » - Chinese Official Lending and Bilateral Political Alignment: The Case of Africa</strong></p><p>, written by Clément Durif, Junior Resarch Fellow at the Asia Centre <a href="mailto:clement.durif@sciencespo.fr">clement.durif@sciencespo.fr</a><br><br>The status of this journal article is pending submittal and acceptation from a Journal Publication</p>
European Investment Bank: Lending to Sub-Saharan Africa (2000-2022)
<p>The provided dataset traces the financial trajectory of the European Investment Bank (EIB) from 2000 to 2022, and has been derived from the EIB's annual financial reports. These reports offer a comprehensive account of the EIB's lending activities globally and specifically to countries outside of the European Union (non-EU or extra-EU), including and excluding the UK in the aftermath of Brexit. All numbers are in 2015 euros.</p> <p>However, the EIB's annual reports do not explicitly present an aggregate view of its lending activities in Sub-Saharan Africa (SSA). Therefore, this dataset is particularly valuable as it combines the country-level data from the reports to provide an overview of the EIB's total lending to SSA over this period.</p> <p> </p>
E-book Lending Services in Galician Public Libraries
<p>This dataset includes responses to the questionnaire sent to the staff of the Galician Public Library Network, lending data for 2021 from the two platforms that comprise GaliciaLe (eBiblio Galicia and the Galician Catalogue), and the socio-demographic characteristics of the populations served by each library, obtained from the Spanish National Statistics Institute (INE) and the Galician Regional Statistics Institute (IGE). Emails and municipality names have been removed to protect the privacy of the respondents.</p>
E-book Lending Services in Galician Public Libraries
<p>This dataset includes responses to the questionnaire sent to the staff of the Galician Public Library Network, lending data for 2021 from the two platforms that comprise GaliciaLe (eBiblio Galicia and the Galician Catalogue), and the socio-demographic characteristics of the populations served by each library, obtained from the Spanish National Statistics Institute (INE) and the Galician Regional Statistics Institute (IGE). Emails and municipality names have been removed to protect the privacy of the respondents.</p>
Data from: Parents lend a helping hand to their offspring in plant defence
Plants under attack by pathogens and pests can mount a range of inducible defences, encompassing both chemical and structural changes. Although few reports exist, it appears that plants responding to pathogen or herbivore attack, or chemical defence elicitors, may produce progeny which are better able to defend themselves against attack, compared to progeny from unthreatened or untreated plants. To date, all research on transgenerational effects of biotic stress have been conducted on dicotyledenous plants. We examined the possibility that resistance induced by application of chemical defence elicitors to the monocot plant barley, could be passed on to the progeny. Plants were treated with acibenzolar-S-methyl (ASM) or saccharin, and grain harvested at maturity. Germination was unaffected in seed collected from plants treated with saccharin, while germination was reduced significantly in seed collected from ASM-treated plants. The subsequent growth of the seedlings was not significantly different in any of the treatments. However, plants from parents treated with ASM or saccharin both exhibited significantly enhanced resistance to infection by Rhynchosporium commune, despite not being treated with elicitor themselves. These data hint at the possibility of producing disease-resistant plants by exposing parent plants to chemical elicitors.
Replication package for: Negative Nominal Interest Rates and The Bank Lending Channel
<p>Package contains replication kit for "Negative Nominal Interest Rates and the Bank Lending Channel", including both replication of the empirical results and the theoretical simulations.</p>
Data from: Parents lend a helping hand to their offspring in plant defence
Open the record for dataset details and reuse information.
Lending a Hand to Our Future: PTSD in Refugee Children and Youth
ClinicalTrials.gov study NCT02334566. IPD Sharing: NO. Countries: 0. Publications: 0.
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.