raw data

USAGE SUMMARY

"raw data" is correct and usable in written English.
You can use it when referring to information that hasn't been analyzed or processed in any way. For example: "The scientists gathered raw data from the experiment to use in their analysis."

✓ Grammatically correct

Science

News & Media

Academia

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Raw data.

MIT Technology Review

See raw data.

The Guardian

The raw data are alarming.

The Economist

They analyzed the raw data.

Nature

Images shown are raw data.

Nature

Raw data were weighted.

Journal of Vascular Nursing

"Raw Data" Is an Oxymoron.

University of California, Berkeley

The raw data: on our datablog.

The Guardian

"Now we're asking for raw data".

The New York Times

"What we want is the raw data.

The New York Times

Well, first, raw data is important.

The New Yorker

Expert writing Tips

Best practice

Always preserve a copy of the "raw data" before performing any transformations or analyses. This safeguards against data loss and allows for verification of results.

Common error

Don't assume that "raw data" is free from bias. Collection methods, measurement tools, and even the definition of what constitutes 'data' can introduce subjective elements. Critically evaluate the data's origins and potential influences.

Linguistic Context

The phrase "raw data" primarily functions as a noun phrase, referring to data in its original, unprocessed form. Ludwig AI indicates the phrase is grammatically correct and commonly used.

Expression frequency: Very common

Frequent in

Science

39%

News & Media

35%

Academia

26%

Less common in

Ludwig's WRAP-UP

The phrase "raw data" is a grammatically correct and very common term used to describe information in its original, unprocessed form. As Ludwig AI confirms, its primary function is to designate data that has not been subjected to analysis or transformation. This phrase finds its main application in scientific, news media, and academic contexts, highlighting its relevance in formal and objective discussions. When using "raw data", remember to emphasize the source and potential limitations to ensure transparency. Moreover, maintaining a copy of the "raw data" is crucial for future verification and analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

unprocessed information

Emphasizes the lack of initial processing stages.

unanalyzed data

Highlights the absence of analytical review.

original data

Focuses on the data's state before any modifications.

source data

Indicates the data's origin and preliminary form.

primary data

Stresses the data's fundamental and unrefined status.

crude data

Suggests the data is in its most basic, unpolished form.

initial data

Highlights the data as it exists at the start of a process.

unrefined data

Stresses the lack of refinement or enhancement.

basic data

Focuses on the fundamental and simple nature of the data.

elementary data

Underscores the data's foundational and uncomplicated form.

FAQs

How do you properly cite "raw data" in a research paper?

When citing "raw data", provide the source (e.g., the database, organization, or individual who collected the data), the date of collection, and a persistent identifier (if available), such as a DOI. If the data is unpublished, follow guidelines for citing personal communications.

What is the difference between "raw data" and processed data?

"Raw data" is the original, unanalyzed information collected from a source. Processed data, on the other hand, has undergone transformations, such as cleaning, normalization, or statistical analysis, to make it more suitable for interpretation and reporting.

What are some alternatives to the phrase "raw data"?

You can use alternatives like "unprocessed information", "unanalyzed data", or "original data" depending on the context.

Why is it important to keep "raw data"?

Keeping "raw data" is essential for reproducibility, verification, and further analysis. It allows researchers to re-examine the original data, validate findings, and conduct new analyses that may not have been initially considered.