data quality control

USAGE SUMMARY

The phrase "data quality control" is correct and usable in written English.
It can be used in contexts related to ensuring the accuracy, consistency, and reliability of data within various fields, such as research, data analysis, and information management. Example: "Implementing robust data quality control measures is essential for maintaining the integrity of our research findings."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Following data control quality checks (see Data quality control below), 19 children (13 males) were excluded.

Cerebral Cortex

S.M. implemented the air-sea flux parameter and worked on the data quality control.

Nature

Extended Data Fig. 1 Project Score CRISPR Cas9 screening pipeline, data quality control and analysis set.

Nature

These templates and associated rules form the initial component of ImmPort data quality control.

Nature

The data quality control and processing use objective, statistically robust techniques.

The New York Times

His mother also works for Yeshiva University, where she is the director of technical training and data quality control for the fund-raising and support staff.

The New York Times

In this paper, we present a basis for data quality control for cancer survival.

Cancer Epidemiology

The database cooperatives have set standards for data quality control and for taxonomic nomenclature and synonymy.

Encyclopedia of Quaternary Science

There has been heavy focus on performing raw data quality control.

Genomics

The spectroradiometer systems at Lauder are maintained and calibrated by Michael Kotkamp, NIWA Lauder, who also assists with data quality control at the other sites.

Nature

The WTCCC2 DNA, genotyping, data quality control and informatics group (S.D., S.E., E. Gray, S.E.H. and C.L). executed GWAS sample handling, genotyping and quality control.

Nature

Expert writing Tips

Best practice

When writing about "data quality control", clearly define the specific metrics and processes used to ensure data accuracy and reliability in your context.

Common error

Avoid vague statements about "data quality control" without specifying the methods employed. Always provide concrete examples of the checks and validations performed.

Linguistic Context

The phrase "data quality control" functions as a noun phrase, identifying a set of procedures aimed at ensuring the reliability and accuracy of data. Ludwig AI indicates its common usage in various contexts, especially in scientific and technical domains.

Expression frequency: Very common

Frequent in

Science

62%

Academia

22%

News & Media

16%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "data quality control" is a noun phrase that refers to the processes used to ensure the accuracy and reliability of data. Ludwig AI confirms that this phrase is grammatically correct and commonly used across various domains, including science, academia, and news media. When discussing this concept, it's important to provide specific details about the methods and metrics employed, rather than making broad generalizations. Alternatives such as "data validation process" or "data integrity assurance" can be used depending on the specific context.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data validation process

Focuses more on the act of confirming data meets certain criteria.

data integrity assurance

Emphasizes the protection of data from unauthorized changes.

quality data management

Highlights the organizational aspect of maintaining data quality.

data accuracy verification

Centers on confirming the correctness of data.

data cleansing procedures

Specifically refers to correcting or removing inaccurate data.

information quality control

Broader term that encompasses all forms of information, not just data.

database integrity checks

Applies specifically to databases and their maintenance.

data reliability testing

Highlights the process of evaluating data for consistency and dependability.

data governance practices

Refers to the overall framework for managing data within an organization.

data error prevention

Focuses on proactive measures to avoid data inaccuracies.

FAQs

How is "data quality control" implemented in research?

In research, "data quality control" involves various steps such as data validation, error detection, and correction, often using statistical methods and domain-specific knowledge. These steps ensure the integrity and reliability of research findings.

What are some alternatives to saying "data quality control"?

You can use alternatives like "data validation process", "data integrity assurance", or "quality data management" depending on the specific context.

Why is "data quality control" important?

"Data quality control" is crucial because it ensures that data is accurate, consistent, and reliable. This leads to better decision-making, more effective processes, and more credible research outcomes.

What are the key steps in "data quality control"?

Key steps in "data quality control" include defining data quality metrics, implementing validation rules, monitoring data for errors, correcting inaccuracies, and regularly auditing data quality processes.