Pursuant to the City of Chicago Municipal Code, certain banks are required to report, and the City of Chicago Comptroller is required to make public, information related to lending equity. The datasets in this series and additional information on the Department of Finance portion of the City Web site, make up that public sharing of the data. This dataset shows bank accounts at responding banks, aggregated by either ZIP Code or Census Tract. For further information applicable to all datasets in this series, please see the dataset description for Lending Equity - Residential Lending.

Using an online randomized experiment in the context of the 2019 European elections campaign in France, we study how fact-checking affects sharing of false news on social media. We exposed a random sample of French voting-age Facebook users to statements on the role of the European Union made by the far-right populist party Rassemblement National. A randomly selected subgroup of participants was also presented with fact-checking of these statements; another subgroup was offered a choice whether to view the fact-checking information. Then, all participants could choose whether to share the false statements on their Facebook pages. We show that (i) both imposed and voluntary fact-checking reduce sharing of false statements by about 45%; (ii) the size of the effect is similar between imposed and voluntary fact-checking; and (iii) each additional click required to share false statements substantially reduces sharing. These results inform the debate about policy proposals aimed at limiting propagation of false news on social media.

Bank Checks Dataset

## Overview

Bank Checks Dataset is a dataset for object detection tasks - it contains Bank Checks annotations for 2,384 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).

The global data validation services market size was valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period. Growing concerns over data inaccuracy and the increasing volume of data being generated by organizations are the key factors driving the market growth. Additionally, the adoption of cloud-based data validation solutions is expected to further fuel the market expansion. North America and Europe are the largest markets for data validation services, with a significant presence of large enterprises and stringent data regulations. The market is fragmented with several established players and a number of emerging vendors offering specialized solutions. Key market participants include TELUS Digital, Experian Data Quality, Flatworld Solutions Inc., Precisely, LDC, InfoCleanse, Level Data, Damco Solutions, Environmental Data Validation Inc., DataCaptive, Process Fusion, Ann Arbor Technical Services, Inc., and others. These companies are focusing on expanding their geographical reach, developing new products and features, and offering value-added services to gain a competitive edge in the market. The growing demand for data privacy and security solutions is also expected to drive the adoption of data validation services in the coming years.

Browse LSEG's World-Check Data for extensive risk intelligence data, aiding in compliance of regulation related to anti-bribery, corruption, and more.

Prediction Data of Base Models from AutoGluon on 71 classification datasets from the AutoML Benchmark for Balanced Accuracy and ROC AUC.

The files of this figshare item include data that was collected for the paper: CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure, Lennart Purucker, Joeran Beel, Second International Conference on Automated Machine Learning, 2023.

The data was stored and used with the assembled framework: https://github.com/ISG-Siegen/assembled.

In detail, the data contains the predictions of base models on validation and test as produced by running AutoGluon for 4 hours. Such prediction data is included for each model produced by AutoGluon on each fold of 10-fold cross-validation on the 71 classification datasets from the AutoML Benchmark. The data exists for two metrics (ROC AUC and Balanced Accuracy). More details can be found in the paper.

The data was collected by code created for the paper and is available in its reproducibility repository: https://doi.org/10.6084/m9.figshare.23609226.

Its usage is intended for but not limited to using assembled to evaluate post hoc ensembling methods for AutoML.

Details The link above points to a hosted server that facilitates the download. We opted for a hosted server, as we found no other suitable solution to share these large files (due to file size or storage limits) for a reasonable price. If you want to obtain the data in another way or know of a more suitable alternative, please contact Lennart Purucker.

The link resolves to a directory containing the following:

example_metatasks: contains an example metatask for test purposes before committing to downloading all files.
metatasks_roc_auc.zip: The Metatasks obtained by running AutoGluon for ROC AUC. metatasks_bacc.zip: The Metatasks obtained by running AutoGluon for Balanced Accuracy.

The size after unzipping is:

metatasks_roc_auc.zip: ~85GB metatasks_bacc.zip: ~100GB

The metatask .zip files contain 2 files per metatask. One .json file with metadata information and a .hdf file containing the prediction data. The details on how this should be read and used as a Metatask can be found in the assembled framework and the reproducibility repository. To obtain the data without Metataks, we advise looking at the file content and metadata individually or parsing them by using Metatasks first.

Taiwan Deposit Money: Checking Accounts data was reported at 360,522.000 NTD mn in Oct 2018. This records a decrease from the previous number of 402,392.000 NTD mn for Sep 2018. Taiwan Deposit Money: Checking Accounts data is updated monthly, averaging 271,160.000 NTD mn from Jan 1972 (Median) to Oct 2018, with 562 observations. The data reached an all-time high of 451,200.000 NTD mn in Sep 2015 and a record low of 12,081.000 NTD mn in May 1972. Taiwan Deposit Money: Checking Accounts data remains active status in CEIC and is reported by Central Bank of the Republic of China. The data is categorized under Global Database’s Taiwan – Table TW.KA001: Money Supply.

The Data Validation Services market is experiencing robust growth, driven by the increasing reliance on data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the rising volume and complexity of data, stringent regulatory compliance requirements (like GDPR and CCPA), and the growing need for data quality assurance to mitigate risks associated with inaccurate or incomplete data. Businesses are increasingly investing in data validation services to ensure data accuracy, consistency, and reliability, ultimately leading to improved operational efficiency, better business outcomes, and enhanced customer experience. The market is segmented by service type (data cleansing, data matching, data profiling, etc.), deployment model (cloud, on-premise), and industry vertical (healthcare, finance, retail, etc.). While the exact market size in 2025 is unavailable, a reasonable estimation, considering typical growth rates in the technology sector and the increasing demand for data validation solutions, could be placed in the range of $15-20 billion USD. This estimate assumes a conservative CAGR of 12-15% based on the overall IT services market growth and the specific needs for data quality assurance. The forecast period of 2025-2033 suggests continued strong expansion, primarily driven by the adoption of advanced technologies like AI and machine learning in data validation processes. Competitive dynamics within the Data Validation Services market are characterized by the presence of both established players and emerging niche providers. Established firms like TELUS Digital and Experian Data Quality leverage their extensive experience and existing customer bases to maintain a significant market share. However, specialized companies like InfoCleanse and Level Data are also gaining traction by offering innovative solutions tailored to specific industry needs. The market is witnessing increased mergers and acquisitions, reflecting the strategic importance of data validation capabilities for businesses aiming to enhance their data management strategies. Furthermore, the market is expected to see further consolidation as larger players acquire smaller firms with specialized expertise. Geographic expansion remains a key growth strategy, with companies targeting emerging markets with high growth potential in data-driven industries. This makes data validation a lucrative market for both established and emerging players.

Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

The codes and data for: “Fact-checking” fact-checkers: A data-driven approach

Taiwan Deposit Money: Monthly Average: Checking Accounts data was reported at 360,114.000 NTD mn in Jun 2018. This records an increase from the previous number of 356,046.000 NTD mn for May 2018. Taiwan Deposit Money: Monthly Average: Checking Accounts data is updated monthly, averaging 265,626.000 NTD mn from Jan 1982 (Median) to Jun 2018, with 438 observations. The data reached an all-time high of 390,285.000 NTD mn in Jan 2017 and a record low of 81,057.000 NTD mn in Apr 1982. Taiwan Deposit Money: Monthly Average: Checking Accounts data remains active status in CEIC and is reported by Central Bank of the Republic of China. The data is categorized under Global Database’s Taiwan – Table TW.KA001: Money Supply.

New websites and smartphone applications provide easy-click checking opportunities that can help consumers in many domains. However, this technology is not always used effectively. For example, many consumers skip checking “Terms and Conditions” links even when a quick evaluation of the terms can save money, but check their smartphone while driving even thought this behavior is illegal and dangerous. Four laboratory experiments clarify the significance of one contributor to such contradictory deviations from effective checking. Studies 1, 2, and 3 show that, like basic decisions from experience, checking decisions reflect underweighting of rare events, which in turn is a sufficient condition for the coexistence of insufficient and too much checking. Insufficient checking emerges when most checking efforts impair performance even if checking is effective on average. Too much checking emerges when most checking clicks are rewarding even if checking is counterproductive on average. This pattern can be captured with a model that assumes reliance on small samples of past checking decision experiences. Study 4 shows that when the goal is to increase checking, interventions which increase the probability that checking leads to the best possible outcome can be far more effective than efforts to reduce the cost of checking.

manishiitg/data-check-v2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Mexico Checking Accounts: Weighted Avg Rates Before Tax data was reported at 6.350 % pa in Mar 2025. This records an increase from the previous number of 6.260 % pa for Feb 2025. Mexico Checking Accounts: Weighted Avg Rates Before Tax data is updated monthly, averaging 3.360 % pa from Jul 1990 (Median) to Mar 2025, with 417 observations. The data reached an all-time high of 22.250 % pa in Dec 1995 and a record low of 1.390 % pa in Jan 2015. Mexico Checking Accounts: Weighted Avg Rates Before Tax data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.M005: Bank Instruments Interest Rates.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map feature extraction challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

This dataset includes the MIPS Data Validation Criteria. The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) streamlines a patchwork collection of programs with a single system where provider can be rewarded for better care. Providers will be able to practice as they always have, but they may receive higher Medicare payments based on their performance.

Venezuela All Banks: Liabilities: AP: Deposits: Checking Accounts data was reported at 15,625,642.585 VES th in Aug 2018. Venezuela All Banks: Liabilities: AP: Deposits: Checking Accounts data is updated monthly, averaging 15,625,642.585 VES th from Aug 2018 (Median) to Aug 2018, with 1 observations. Venezuela All Banks: Liabilities: AP: Deposits: Checking Accounts data remains active status in CEIC and is reported by Superintendency of Banking Sector Institutions. The data is categorized under Global Database’s Venezuela – Table VE.KB005: Balance Sheet: All Banks: Bolivar Soberano.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Check Casher locations without payday lending authority. The dataset contains locations and attributes of check cashers provided by the Department of Insurance, Securities, and Banking.

This dataset contains all uncashed checks that have been issued by the Town of Cary.The dataset is updated the 9th of every month following a bank information update.