complete dataset for

USAGE SUMMARY

The phrase "complete dataset for" is correct and usable in written English.
It can be used when referring to a dataset that is fully compiled or comprehensive for a specific purpose or analysis. Example: "The complete dataset for the study was collected over a period of six months, ensuring all relevant data points were included."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

37 human-written examples

This process provided a total of 119,380 individual segments from the complete dataset for classification.

Urban Rail Transit

This subsection will also present how to create the complete dataset for finding the efficient combinations.

Journal of Industrial Engineering International

Consequently, field studies on full-scale WSPs are required to obtain a complete dataset for validation purposes.

Journal of Cleaner Production

A rather complete dataset for Egypt is available for 2011 the year of the national input output accounts used to the estimate of the interregional input output database.

Journal of Economic Structures

Applying these criteria resulted in a complete dataset for 124 subjects (n), representing approximately two thirds (66%) of the student population.

Evolution: Education and Outreach

Although, CARE database could present some limits due to the different standards used in the past for collecting road accident data, it remains the main available and complete dataset for these types of analyses.

European Transport Research Review

Human-verified similar examples from authoritative sources

Similar Expressions

23 human-written examples

In contrast to this Lutz model, the above mentioned 'straightforward methods' need complete datasets for any combination of elevation angles, azimuth angle separations and environments to achieve the same variability.

EURASIP Journal on Wireless Communications and Networking

The complete datasets for both 17-nucleotide and 20-nucleotide signatures are available for download from ftp://137.187.144.38/freed.

Plosone

Complete datasets for COMPASS were available for 317 patients.

Annals of the Rheumatic Diseases

Genuinely missing items were excluded for factor and reliability analysis, leaving 79 complete datasets for analysis.

Health and Quality of Life Outcomes

The intention is to use multiple imputation to generate complete datasets for analysis.

BMC Public Health

Expert writing Tips

Best practice

When using "complete dataset for", ensure that the dataset truly encompasses all necessary variables and data points relevant to the intended analysis or purpose. Clearly define the scope of completeness.

Common error

Avoid claiming a dataset is "complete" if there are known limitations or missing data. Instead, acknowledge any gaps or biases to maintain transparency and accuracy in your analysis.

Linguistic Context

The phrase "complete dataset for" functions as a noun phrase, typically acting as a subject or object within a sentence. It is used to describe a collection of data that is considered to be comprehensive and all-inclusive for a given purpose. The examples from Ludwig confirm this usage.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Encyclopedias

0%

Ludwig's WRAP-UP

The phrase "complete dataset for" is a grammatically correct and commonly used expression, primarily in scientific and academic writing. It signifies that a dataset is comprehensive and contains all necessary information for a specific analysis or purpose. Ludwig examples highlight its frequent use in research papers and scientific articles. While alternatives like "comprehensive dataset for" or "full dataset for" exist, the core meaning remains consistent. When using this phrase, ensure the dataset truly encompasses all relevant information and acknowledge any limitations. Overall, "complete dataset for" is a reliable and effective way to describe a thorough collection of data.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

comprehensive data set for

Replaces "complete" with "comprehensive", emphasizing the thoroughness of the data.

full data set for

Uses "full" instead of "complete", indicating that all data points are included.

entire data set for

Substitutes "complete" with "entire", stressing the inclusion of the whole data collection.

exhaustive data set for

Replaces "complete" with "exhaustive", highlighting the comprehensiveness and detail of the data.

thorough data set for

Uses "thorough" to emphasize the meticulous and detailed nature of the data.

inclusive data set for

Replaces "complete" with "inclusive", indicating that all relevant data has been incorporated.

holistic data set for

Substitutes "complete" with "holistic", focusing on the integration of all aspects of the data.

unabridged data set for

Uses "unabridged" to highlight that the data set is full and has not been shortened.

total data set for

Replaces "complete" with "total" emphasizing the aggregate amount of data.

integrated data set for

Replaces "complete" with "integrated", focusing on combining the parts into a functional whole.

FAQs

How can I use "complete dataset for" in a sentence?

You can use "complete dataset for" to describe a dataset that includes all the necessary information for a specific purpose. For example, "The "complete dataset for" RNAseq analysis is available as supplementary material".

What are some alternatives to "complete dataset for"?

Alternatives include "comprehensive dataset for", "full dataset for", or "entire dataset for", depending on the specific nuance you want to convey.

What does it mean for a dataset to be "complete"?

A "complete" dataset typically means that all relevant data points and variables required for a specific analysis or purpose are included, with minimal or no missing values that would significantly impact the results.

Is it always necessary to have a "complete dataset for" analysis?

While a "complete dataset for" is ideal, it's not always essential. Depending on the analysis, statistical methods can handle missing data. However, it's crucial to acknowledge and address any limitations caused by incomplete data.