data were edited

USAGE SUMMARY

The phrase "data were edited" is correct and usable in written English.
It can be used in contexts where you are discussing the modification or alteration of data, typically in research or data analysis. Example: "After the initial collection, the data were edited to remove any inconsistencies and errors."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

37 human-written examples

Data were edited to remove RETT likely to have been affected by a recent drinking bout.

Journal of Dairy Science

Data were edited manually (VivoSense, Vivonoetics, San Diego, CA), and the average HR (in beats per minute [bpm]) was calculated for 2-min time intervals.

Sports Medicine - Open

Data were edited for artifacts by offline visual inspection as well.

Plosone

For each locus, sequence data were edited using CodonCode Aligner v.2.06.

Plosone

BAC DNA isolation, fingerprinting, contig assembly were same as [50] except that DNA fingerprinting raw data were edited using "GenoProfiler" [51] which is different from the one (ABI-to-FPC, unpublished) used for whole genome physical map of tetraploid cotton.

Plosone

SNP chip data were edited using a number of criteria.

Genetics, Selection, Evolution

Human-verified similar examples from authoritative sources

Similar Expressions

23 human-written examples

In the event that relevant data is edited or added, the cache is cleared.

Journal of Cheminformatics

MGF data are edited into files with a common data file format, archived on a data server, and made available to the science community.

Earth, Planets and Space

Changes of OSM data at most places occur mostly during day-time; before 8 a. m., only very little data are edited.

Open Geospatial Data, Software and Standards

These raw image data are edited by image processing, which translates them into local fiber directions at each pixel with the help of an eigenvalue analysis of the Hessian matrix of the second partial derivatives of the gray values, for more details consult [23].

Journal of Mathematics in Industry

All sequence data was edited by BioEdit 7.0.1 and aligned by Clustal X 1.8.

Plosone

Expert writing Tips

Best practice

When describing how "data were edited", be specific about the criteria and methods used for editing to ensure transparency and reproducibility.

Common error

Avoid using "data were edited" without specifying what kind of edits were performed. Provide details on the editing process, such as error correction, outlier removal, or data transformation, to give context to your work.

Linguistic Context

The phrase "data were edited" primarily functions as a passive voice construction indicating that data has undergone a process of modification or refinement. As Ludwig AI explains, this is a common phrase used to describe alterations made to raw information.

Expression frequency: Common

Frequent in

Science

97%

Formal & Business

2%

News & Media

1%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, the phrase "data were edited" is a grammatically correct and frequently used expression, particularly in scientific and academic contexts. It indicates that data has undergone a process of refinement, correction, or modification before being analyzed. Ludwig AI confirms its proper usage in the context. When using this phrase, clarity and specificity are crucial; detail the criteria and methods employed during the editing process to ensure transparency and reproducibility. Avoid generalizations, and always specify the type of edits performed, such as error correction, outlier removal, or data transformation. This ensures that readers understand the rigor and reliability of your data.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data were modified

Replaces "edited" with "modified", emphasizing a general change or alteration.

data were corrected

Replaces "edited" with "corrected", emphasizing the fixing of errors.

data were revised

Substitutes "edited" with "revised", indicating a more thorough review and correction.

data were cleaned

Replaces "edited" with "cleaned", highlighting the removal of errors and inconsistencies.

data were processed

Replaces "edited" with "processed", focusing on the systematic handling and transformation of data.

data were adjusted

Replaces "edited" with "adjusted", indicating modifications made to align data with specific standards.

data were refined

Substitutes "edited" with "refined", suggesting an improvement in accuracy and clarity.

data were transformed

Substitutes "edited" with "transformed", emphasizing the conversion of data into a different format.

data underwent editing

Changes the structure to use "underwent editing" instead of the passive voice.

data were curated

Replaces "edited" with "curated", implying careful selection and presentation of data.

FAQs

How is "data were edited" typically used in research papers?

In research papers, "data were edited" is used to describe the process of refining raw data by removing errors, inconsistencies, or irrelevant information to improve its quality and suitability for analysis. This process ensures the accuracy and reliability of the research findings.

What's the difference between "data were edited" and "data were analyzed"?

"Data were edited" refers to the process of cleaning, correcting, or modifying raw data before analysis. "Data were analyzed", on the other hand, refers to the application of statistical or computational techniques to extract meaningful insights and draw conclusions from the processed data. Editing is a preparatory step, while analysis is the core process of interpretation.

Which is correct: "data was edited" or "data were edited"?

The correct phrase is "data were edited". "Data" is a plural noun, so it requires the plural form of the verb "to be" (i.e., "were"). Using "data was edited" is grammatically incorrect.

What are common tools used when "data were edited"?

Common tools used when "data were edited" include statistical software packages like SPSS and SAS, programming languages like R and Python, and specialized data editing software. These tools help in identifying and correcting errors, handling missing values, and transforming data to a suitable format for analysis.