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334 results for “club”
Figures 31-33. Laemophloeus insulatestudinorum Thomas, n in A review of New World Laemophloeus Dejean (Coleoptera: Laemophloeidae): 2. Neotropical species with antennal club of three antennomeres
Figures 31-33. Laemophloeus insulatestudinorum Thomas, n.sp. 31) Head and pronotum, male. 32) Aedeagus. 33) Male genitalia.
Figs. 196–212. Antennal clubs. 196. Plocamocera sesquipedalis. 197. P. similis. 198. P. coactilis. 199. P. aspera. 200. P. santa. 201. P. onorei. 202. P. bispina. 203. P. carnegei. 204. P. paris. 205. P. ambra. 206. P. bolivari. 207. P. auratilis. 208. P. insula. 209. P. selva. 210. P. cericellopsis. 211. P in Classification, Natural History, And Evolution Of The Epiphloeinae (Coleoptera: Cleridae). Part Ii. The Genera Chaetophloeus Opitz And Plocamocera Spinola
Figs. 196–212. Antennal clubs. 196. Plocamocera sesquipedalis. 197. P. similis. 198. P. coactilis. 199. P. aspera. 200. P. santa. 201. P. onorei. 202. P. bispina. 203. P. carnegei. 204. P. paris. 205. P. ambra. 206. P. bolivari. 207. P. auratilis. 208. P. insula. 209. P. selva. 210. P. cericellopsis. 211. P.
Data from Lamb et al.: "Hanging out at the club: breeding status and territoriality affect individual space use, multi-species overlap, and pathogen transmission risk at a seabird colony"
<p>This dataset consists of two files:</p> <p><strong>ams_sku_all provides</strong> GPS locations from tracked skuas.</p> <p><strong>Skua_GPS_Metadata</strong> provides information on tracked skuas. The file consists of two workseets, the data table ("skua_gps_metadata", and a key providing descriptions of the column names and values ("Key")</p>
아벤카지노▷railart.org/aven◁ Club ist ein Betreiber
<p>아벤카지노 ist ein Betreiber von Poker-Spielen, einschließlich Cash-Spiele und Turniere. Der erste Poker King Club-Raum wurde 2009 im StarWorld-Geschäft der Galaxy Entertainment Group Ltd in Macau eröffnet.</p> <p>Nach Donaco Freilassung verwaltet Poker King Club derzeit drei Pokersäle, nämlich im venezianischen Macao Casino Resort, im Cotai Viertel Macau, im Solaire Resort und 아벤카지노 Casino, in der philippinischen Hauptstadt Manila und im Maison Glad Hotel Jeju, auf der südkoreanischen Ferieninsel Jeju.</p> <p>Seit seinem Start hat Poker King Club "mehr als 40 Turniere" rund um Asien organisiert, sagte die Veröffentlichung. Es fügte hinzu, dass Poker King Club auch der Veranstalter der 아벤카지노 Triton Poker Serie von Veranstaltungen war, die "auf den wohlhabenden Super-High-Roller-Markt".</p>
TEXT-FIGURE I. Leptomydas omeri sp. n. (A) Third segment and terminal club of left antenna. (B) Lateral view of hypopygium. in A New Mydaid Fly from South Africa (Diptera : Mydaidre)
TEXT-FIGURE I. Leptomydas omeri sp. n. (A) Third segment and terminal club of left antenna. (B) Lateral view of hypopygium.
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>
Year 2011, January to September, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) January to September 2011 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year 2010, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) year 2010 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year 2009, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) year 2009 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Hach, OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2015
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2015. OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
Year 2008, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) year 2008 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year 2007, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) year 2007 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year, end of 2005 thru 2006, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for end of 2005 and all of 2006 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year 2011, September-December, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) September 2011 through December 2011 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2012, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2012 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2013, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2013 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2014, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2014 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2015, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2015 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Hach, OTT RLS measurements of water column depth at 15 minute intervals in the lower Plum Island Sound off the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2016
Measurements of water column depth at 15 minute intervals in Plum Island Sound at the Ipswich Bay Yacht Club, for year 2016. OTT radar level sensor (RLS) installed September 20, 2011 out of the water under the concrete pad on the Ipswich Bay Yacht Club pier, Ipswich, MA. RLS was mounted so that continuous year round measurements can be conducted without the concern of ice flows damaging the sensor.
Year 2016, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2016 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
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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.