Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Rating Scale Dataset

## Overview

Rating Scale Dataset is a dataset for object detection tasks - it contains Scale annotations for 1,119 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Based on a 2022 analysis, the product display page (PDP) views experience the highest surge beyond the ***-star rating threshold. While products with an average rating from *** to **** generate the most traffic and receive the highest number of reviews, consumers remain hesitant when confronted with an average rating of *** stars.

Yearly data of Quality Review ratings from 2005 to 2017

An important indicator of the financial strength of governmental entity is its bond rating. The bond rating is similar in nature to the credit score of an individual – the higher the score, the better the ability to borrow money to finance purchases at a lower interest rate. Similarly, the higher the bond rating for a governmental entity, the more opportunities to borrow money for capital needs at lower interest rates. A high bond rating is in excellent indicator of the overall financial health of a government.This measure is obtained each year when the city seeks to issue bonds to finance its’ projects. As part of this process, bond ratings are always obtained from the rating agencies: Standard & Poor’s. Fitch Ratings and Moody's Investor Service.This page provides data for the Bond Rating performance measure.Bond ratings are a reflection of the financial strength of an entity. A high rating means an entity can issue bonds to finance capital projects at lower interest rates; lower rates result in less interest to be paid on the repayment of the bonds. Ultimately, this lowers the costs of our capital projects to our taxpayers.The performance measure dashboard is available at 5.04 Bond Rating.Additional InformationSource: Standard & Poors, Moody's Investor Service, and Fitch Ratings are the major bond rating agencies in the United States and are widely used by governmental and non-governmental entities throughout the country.Contact: Jerry HartContact E-Mail: Jerry_Hart@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

The State Review Framework is a primary means by which EPA conducts oversight of three core federal statutes: Clean Air Act, Clean Water Act, and Resource Conservation and Recovery Act. The routine, nationwide review provides a consistent process for evaluating the performance of state, local and EPA compliance and enforcement programs. The overarching goal of the reviews is to ensure fair and consistent enforcement necessary to protect human health and the environment.

This dataset provides estimated hourly dynamic line ratings for ~84,000 transmission lines across the contiguous United States from 2007-2013. The calculation methods are described in the presentation linked below, and the associated open-source Python code repository is linked in the Resources section below.

Abbreviations used in filenames and descriptions are: - SLR: static line ratings - ALR: ambient-temperature-adjusted line ratings - NLR: ambient-temperature- and day/night-irradiance-adjusted line ratings - CLR: ambient-temperature- and clear-sky-irradiance-adjusted line ratings - ILR: ambient-temperature- and measured-irradiance-adjusted line ratings - DLR: full dynamic line ratings (including air temperature/pressure, wind speed/direction, and measured irradiance)

Transmission lines are referenced by their ID in the Homeland Infrastructure Foundation-Level Data (HIFLD) on Transmission Lines (linked in Resources section). Time indices are in UTC. The data files contain ratios between modeled hourly ratings and modeled static ratings. Columns are indexed by HIFLD ID; rows are indexed by hourly timestamps from 2007-2013 (UTC). A data directory is also included in the Resources section.

The SLR files contain modeled static ratings (the denominator of the ratios in the files described above) in amps. As described in the presentation linked in the Resources section below, SLR calculations assume an ambient air temperature of 40 C, air pressure of 101 kPa, wind speed of 2 feet per second (0.61 m/s) perpendicular to the conductor, global horizontal irradiance of 1000 W/m^2, and conductor absorptivity and emissivity of 0.8. Conductor assumptions are Linnet for ~69 kV and below, Condor for ~115 kV, Martin for ~230 kV, and Cardinal for ~345 kV and above.

Caveats and Limitations

Results are sensitive to the weather data used. Validation studies on the WIND Toolkit and NSRDB are available at: - King, J. et al. "Validation of Power Output for the WIND Toolkit", 2014 (https://www.nrel.gov/docs/fy14osti/61714.pdf) - Draxl, C. et al. "Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit", 2015 (https://www.nrel.gov/docs/fy15osti/61740.pdf) - Sengupta, M. et al. "Validation of the National Solar Radiation Database (NSRDB) (2005-2012)", 2015 (https://www.nrel.gov/docs/fy15osti/64981.pdf) - Habte, A. et al. "Evaluation of the National Solar Radiation Database (NSRDB Version 2): 1998-2015", 2017 (https://www.nrel.gov/docs/fy17osti/67722.pdf)

More work is required to determine how well ratings calculated from NSRDB and WIND Toolkit data reflect the actual ratings observed by installed sensors (such as sag or tension monitors). In general, ratings calculated from modeled weather data are not a substitute for direct sensor data.

Assuming a single representative conductor type (ACSR of a single diameter) for each voltage level is an important simplification; reported line ratings at a given voltage level can vary widely.

HIFLD line routes are primarily based on imagery instead of exact construction data and may have errors.

We use historical weather data directly; calculated line ratings are thus more indicative of real-time ratings than forecasted ratings

ml-hub/flipkart-reviews-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset shows the bond ratings assigned to the City of Memphis by Moody’s Investors Service and Standard and Poor’s, the two largest rating agencies.

Gain credit ratings, risk analysis, and research for stocks, bonds, and government entities with Fitch Ratings, covering over 3,000 corporate entities globally.

Rating distributions of the reported datasets.

This dataset contains a stage-discharge relationship developed for the iUTAH GAMUT Network aquatic site on Red Butte Creek near Red Butte Gate Basic Aquatic Site (RB_RBG_BA). Discharge measurements were collected by a SonTek FlowTracker. Measured stage and discharge and the curve are contained in the Rating Curve file. Information on the site conditions and any issues with discharge measurements are documented in the README file. Files associated with each measurement (e.g., output by the FlowTracker instrument) are contained in the .zip directory. This rating curve was used to generate discharge data through 12/31/2015. New versions of these files may be loaded when new flow measurements are taken. Resulting discharge data is published in the iUTAH GAMUT operational databases and may be accessed via http://data.iutahepscor.org/tsa.

Yelp hotels and restaurants reviews ( spam and not spam) with sentiments ( positive, negative, and neutral) and review features. Please cite following our published works, when used this dataset. 1. Naveed Hussain, Hamid Turab Mirza, Faiza Iqbal, Ibrar Hussain, and Mohammad Kaleem. "Detecting Spam Product Reviews in Roman Urdu Script." The Computer Journal (2020).
2. Naveed Hussain, Hamid Turab Mirza, Abid Ali, Faiza Iqbal, Ibrar Hussain, and Mohammad Kaleem. " Spammer group detection and diversification of customers’ reviews ". PeerJ Computer Science 7:e472 https://doi.org/10.7717/peerj-cs.472 (2021).

The TV rating of the **** Academy Awards amounted to **** and, thus, was higher than the one recorded in the previous year. Viewership of the Academy Awards was the lowest yet in 2021 with a rating of just ****, down from *** in 2020.

1. Data Source

This dataset is a synthetic version inspired by the original Credit Risk dataset on Kaggle and enriched with additional variables based on Financial Risk for Loan Approval data. SMOTENC was used to simulate new data points to enlarge the instances. The dataset is structured for both categorical and continuous features.

2. Metadata

The dataset contains 45,000 records and 14 variables, each described below:

3. Data Usage

The dataset can be used for multiple purposes:

• Exploratory Data Analysis (EDA): Analyze key features, distribution patterns, and relationships to understand credit risk factors.
• Classification: Build predictive models to classify the loan_status variable (approved/not approved) for potential applicants.
• Regression: Develop regression models to predict the credit_score variable based on individual and loan-related attributes.

Mind the data issue from the original data, such as the instance > 100-year-old as age.

This dataset provides a rich basis for understanding financial risk factors and simulating predictive modeling processes for loan approval and credit scoring.

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

Previous scholarship has demonstrated that shaming by human rights organizations produces economic consequences. For example, human rights shaming by international organizations negatively affects FDI, decreases exports, and redirects foreign aid received. The relationship between shaming and sovereign credit, however, has yet to be studied. States greatly rely on their ability to access cheap international credit, as evidenced by the fact that newly issued sovereign debt outpaced new foreign investment by almost $3 trillion in 2017. Understanding the relationship between human rights-related advocacy efforts and a states’ access to this valuable source of capital is critical for recognizing the effects naming and shaming can have on creditors. We therefore ask: does naming and shaming by human rights organizations have a negative impact on the target state’s sovereign credit rating? We find that increased naming and shaming leads to a decrease in a state's sovereign credit rating

To assist consumers purchasing new vehicles or replacement tires, NHTSA has rated more than 2,400 lines of tires, including most used on passenger cars, minivans, SUVs and light pickup trucks using a grading system known as the Uniform Tire Quality Grading System (UTQGS). UTQGS allows consumers to compare tire tread wear, traction performance and temperature resistance.

To view the full data set please click the Export link above. This data set contains ratings data for State of Texas Issuers, including State agencies, Institutions of Higher Education and Conduit Borrowers. Excludes commercial paper issuances. The rating information includes rating agency, assigned rating, rating fee, bond insurance and credit enhancements.

The Credit Bureaus & Rating Agencies industry in Massachusetts is expected to grow an annualized x.x% to $x.x million over the five years to 2025, while the national industry will likely grow at x.x% during the same period. Industry establishments increased an annualized x.x% to xx locations. Industry employment has increased an annualized x.x% to xxx workers, while industry wages have increased an annualized x.x% to $x.x million.