This data table provides the detailed data quality assessment scores for the Curtailment dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

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).

This data table provides the detailed data quality assessment scores for the Technical Limits dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

The TCVQ system determines the SSN of an individual whose check has been returned to a local field office. The FO is able to request this information via the TCVQ located on the DXQM. SSNs were removed from the SSA checks in early 2004.

Commercial Checks Collected through the Federal Reserve, Commercial Checks Returned through the Federal Reserve, Government Checks Processed by the Federal Reserve, and Postal Money Orders Processed by the Federal Reserve

The Customer Data Quality Check consists of the Person Checker, Address Checker, Phone Checker and Email Checker as standard. All personal data, addresses, telephone numbers and email addresses within your file are validated, cleaned, corrected and supplemented. Optionally, we can also provide other data, such as company data or, for example, indicate whether your customer database contains deceased persons, whether relocations have taken place and whether it contains organizations that are bankrupt.

Benefits: - An accurate customer base - Always reach the right (potential) customers - Reconnect with dormant accounts - Increase your reach and thus the conversion - Prevents costs for returns - Prevents image damage

This data table provides the detailed data quality assessment scores for the Historic Faults dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

🇺🇸 미국 English Commercial Checks Collected through the Federal Reserve, Commercial Checks Returned through the Federal Reserve, Government Checks Processed by the Federal Reserve, and Postal Money Orders Processed by the Federal Reserve

Taiwan Check Cleared: Number data was reported at 9,137,317.000 Unit in Oct 2018. This records an increase from the previous number of 5,603,591.000 Unit for Sep 2018. Taiwan Check Cleared: Number data is updated monthly, averaging 9,067,794.500 Unit from Jan 1972 (Median) to Oct 2018, with 562 observations. The data reached an all-time high of 17,872,180.000 Unit in Dec 1997 and a record low of 1,996,832.000 Unit in Apr 1972. Taiwan Check Cleared: Number 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.KA026: Bank Clearings and Dishonored Checks.

Details of processing accuracy rates

United States - Travelers Checks was 1.70000 Bil. of $ in December of 2018, according to the United States Federal Reserve. Historically, United States - Travelers Checks reached a record high of 9.20000 in April of 1995 and a record low of 0.30000 in February of 1959. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Travelers Checks - last updated from the United States Federal Reserve on June of 2025.

Welcome to the County of Sonoma Auditor-Controller's website for uncashed County checks.

If your check has been lost/destroyed or stale-dated, please fill out this form and follow the included instructions to obtain a replacement check:

Affidavit to Obtain Duplicate Of Lost/Destroyed Check.

Contained here is the inventory of all Sonoma County checks (excluding those checks that are considered private, such as welfare payments, child support, and employee benefits and payroll, which are exempt from disclosure under Welfare & Institutions Code §10850 and §11478.1 and Government Code §7927.700 and §7927.705) that have been issued and mailed, but which remain uncashed 6 months after their issue date ("stale" checks). Please note the County does not maintain a list of reissued checks, this listing could include checks that were subsequently reissued and cashed and are therefore no longer eligible for reissue.

In accordance with Government Code §29802, uncashed County checks are void and become stale after 6 months. Uncashed County checks that are lost or destroyed may be reissued up to 2 years from the stale date (2 years and 6 months from the date of issuance) when a valid Affidavit is filed certifying the loss or destruction, and after a review of records indicates the check was not previously reissued. An uncashed, stale County check that is more than 2 years and 6 months old may be reissued only upon receipt of the original check, and if a review of records indicates the check was not previously reissued.

This information is refreshed daily and contains 2.5 years of data. Checks are stale dated on a monthly basis, the most recent set of stale dated checks will be reflected in this listing by the 15th of the month.

The dataset includes locations in NYC that offer free blood pressure checks at self-serve blood pressure kiosks or by pharmacy staff. Blood pressure check data collected to promote access to free blood pressure checks throughout NYC. Data collected so users can visit the NYC HealthMap online to find locations nearest to them to check their blood pressure at a self-serve kiosk or by pharmacy staff. Data collected manually. Each record represents a location that offers free blood pressure checks. Data can be used by general public seeking places to check their blood pressure. Data may change as sites are added and/or sites inform us of updates (i.e. address changes, pharmacy closures)

This data table provides the detailed data quality assessment scores for the Long Term Development Statement dataset. The quality assessment was carried out on 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality; to demonstrate our progress we conduct annual assessments of our data quality in line with the dataset refresh rate. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Brazil Checks Cleared: Number data was reported at 34.996 Unit mn in Nov 2018. This records a decrease from the previous number of 38.360 Unit mn for Oct 2018. Brazil Checks Cleared: Number data is updated monthly, averaging 65.513 Unit mn from Jan 2009 (Median) to Nov 2018, with 119 observations. The data reached an all-time high of 112.124 Unit mn in Mar 2009 and a record low of 33.279 Unit mn in Sep 2018. Brazil Checks Cleared: Number data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAA022: Credit Card Statistics.

This data table provides the detailed data quality assessment scores for the Embedded Capacity Register dataset. The quality assessment was carried out on 30th April. Please note, this assessment only covers 1MW and above data. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

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.

Check NBA Data

## Overview

Check NBA Data is a dataset for object detection tasks - it contains Check NBA Data annotations for 2,000 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 [MIT license](https://creativecommons.org/licenses/MIT).

The National Instant Criminal Background Check System (NICS) is a national system that provides the timely and accurate determination of a person's eligibility to possess firearms and/or explosives in accordance with the Brady Handgun Violence Prevention

Verification and validation (V&V) has been identified as a critical phase in fielding systems with Integrated Systems Health Management (ISHM) solutions to ensure that the results produced are robust, reliable, and can confidently inform about vehicle and system health status and to support operational and maintenance decisions. Prognostics is a key constituent within ISHM. It faces unique challenges for V&V since it informs about the future behavior of a component or subsystem. In this paper, we present a detailed review of identified barriers and solutions to prognostics V&V, and a novel methodological way for the organization and application of this knowledge. We discuss these issues within the context of a prognostics application for the ground support equipment of space vehicle propellant loading, and identify the significant barriers and adopted solution for this application.