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About 10,000 papers for “Data verification”

Data Verification

National Cancer Institute · Definitions · 2020

Ope n Pe e r Re v ie w on Qe ios

Cited by 0Open access

Data Verification

Jean Dumais, J. Heward Gough · The World Bank eBooks · 2012

Defines data verification as the process of ensuring that the data received from the various sources are free from error. Sources of error include miskeyed responses, omitted data, and errors in manipulating and merging data from different sources. Given that national assessments involve teams working on different aspects of data, sometimes over a considerable time period, the national assessment agency must have a record of all changes made to data. A ReadMe file should be created to record any changes made to the data file by the key operators during data entry and should help prevent confusion over the version of the data that should be analyzed. Consistency between files can be addressed by double punching so that two data sets can be checked against each other to find miskeyed cases; within-file consistency pertains to the checking processes to determine whether data are as accurate as possible.

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Data Verification

Bobby Curtis · Apress eBooks · 2016

This chapter discusses the options for verification of data replicated by Oracle GoldenGate. We discuss how to install Veridata and create and execute comparison jobs using Veridata. Should you not own a license for the Veridata product, we discuss other options for data verification that could be as simple as a row count comparison or the execution of the DBMS_COMPARE package that is part of the database.

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Computational Verification Data

Shang-Yu.Chen · OSF Preprints (OSF Preprints) · 2025

This component provides the computational and geometric validation of the Goldbach Decomposition System (GDS), focusing on verifying proportional formulas and prime symmetry structures based on modular arithmetic and spiral mappings.

Cited by 0Open access

Verification data summaries.

Upendo Mwingira (528289), Maria Chikawe (4248718), Wilfred Lazarus Mandara (4248721) et al. · Figshare · 2017

<p>Note that the reported lymphoedema-elephantiasis and hydrocoele rows include those who were reported to have both conditions. The report was considered to be correct if the patient was confirmed to have the reported condition, regardless of whether or not another LF-related condition was also reported or confirmed.</p>

Cited by 0Open access

Big Data Processing Verification

M. A. Poltavtseva, Alexey Podorov, D. P. Zegzhda · 2025

The Subject of the paper is the application of temporal logic to verify data processing processes in big data processing and storage systems, including large industrial systems of the digital economy. The purpose of verification is to identify errors and inconsistencies in the data processing and storage processes at the information processing logic level. In this paper, the authors analyze temporal logics and verification approaches. The choice of linear time logic and the TLA+ tool is justified, and a formalization of the big data processing lifecycle model using the selected mathematical apparatus is proposed. A test bench has been developed that performs automatic specification generation in the TLA+ language. Experimental testing was conducted using the example of a data processing process involving the sharing of SCADA data and data from other enterprise information systems.

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Data intensive applications verification

Daniel Arménio Silva Mendonça · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2017

The main challenges for a Data-Intensive Application are the data quantity, the data complexity and the speed at which the data changes. For these applications, there are two predominant data categories, Master Data and Transactional Data. Master Data represents the data that is not frequently modified and that is required for the system to operate. Transactional Data refers to the data that is produced in the system operations. The verification of this kind of applications require big investments in the development of tests and long test runs.Data-Intensive applications tests requires the pre-existence of master data. In the beginning of a test, the data that will be used is expected to be in a specific state, otherwise the test integrity cannot be assured. One of the possibilities to cope with these requirements is to generate the master data in the test set-up, which is the solution that Critical Manufacturing is currently using to test its' Manufacturing Execution System (MES).In this project, it was developed a decision support tool to identify sets of tests with resemblances in their set-ups through static code analysis. The tests in the same set may eventually share the same Master Data, instead of generating new data for each time it runs. Also through static code analysis, it was developed a tool that transforms conventional tests into data-driven tests. The tools were developed for the test-suite of Critical Manufacturing's MES.

Cited by 0Open access

Verification of DEG data.

Shanyi Chen (552080), Ting Ye (346594), Lu Hao (552081) et al. · Figshare · 2015

<p>(A) Four up-regulated and four down-regulated DEGs randomly selected were confirmed by qRT-PCR. <i>ACTIN2</i> gene was used as an internal control. Data are means and standard errors of three biological replicates. (B) Four DEGs randomly selected were confirmed by Northern blot. 18S RNA was used as a loading control.</p>

Cited by 0Open access