attributes of data

USAGE SUMMARY

The phrase "attributes of data" is correct and usable in written English.
It can be used when discussing the characteristics or properties that define or describe data in various contexts, such as data analysis or database management. Example: "The attributes of data, such as its type, format, and source, are crucial for effective data processing."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

9 human-written examples

G.I.S. is software in which attributes of data like elevation are linked directly to their location, making possible the creation of maps that show at a glance, for example, rivers and streams in a watershed.

The New York Times - Tech

To design QoI-aware network control algorithms, such as resource control algorithms, attributes of data must be mapped to QoI.

Pervasive and Mobile Computing

Method performance is primarily controlled by the inherent geomorphological characteristics (surface morphology and elevation) of the data; however, the attributes of data structure are significant.

Geomorphology

This paper presents a numerical investigation of factors that influence the design of precise measurement networks based on the random error reduction attributes of data reconciliation.

Minerals Engineering

From the meaningful and globally approximate optimal abstraction, users can navigate a large multivariate graph with an overview+detail to explore and rapidly find the potential correlations between the graph structure and the attributes of data items.

Journal of Visual Languages & Computing

However, it cannot be assumed that all feature selection sets will be stable, especially when they are built from a myriad of heterogeneous sources in a constantly evolving and hostile environment where the diversity in attributes of data sets can vary considerably.

Journal of Big Data

Human-verified similar examples from authoritative sources

Similar Expressions

51 human-written examples

For example, the vulnerability type attribute of Data source 2 includes values such as "not assigned," "?," and "&novuln".

Data Science and Engineering

Overdispersion, when the variance is greater than expected under a given model, is a common attribute of data in a GLMM and can increase the probability of a Type I error (Crawley 2013).

Evolution: Education and Outreach

However, if there exists correlation among data records, the attributes of absent data records can still be inferred.

Human-centric Computing and Information Sciences

The PsyGeNET results can be filtered by the score, data source and attributes of the data.

Bioinformatics

In both cases, the results can be filtered by the DisGeNET score, data source and some of the attributes of the data, such as the PANTHER protein class, MeSH disease class or the DisGeNET association type class.

Database : the journal of biological databases and curation

Expert writing Tips

Best practice

In scientific writing, ensure that the "attributes of data" are clearly described in the methods section to allow for reproducibility.

Common error

Avoid using "attributes of data" without specifying which attributes are relevant. For example, instead of saying 'The attributes of data were analyzed', specify 'The size, format, and source of the data were analyzed'.

Linguistic Context

The phrase "attributes of data" functions as a noun phrase, typically serving as the subject or object of a sentence. As Ludwig AI states, it refers to the characteristics or properties that define a dataset.

Expression frequency: Uncommon

Frequent in

Science

80%

News & Media

15%

Formal & Business

5%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

The phrase "attributes of data" is a noun phrase used to describe the characteristics or properties that define a dataset. According to Ludwig AI, it is grammatically correct and usable in written English. Predominantly found in scientific and technical contexts, as demonstrated by the source analysis, its purpose is to provide detailed information about the data's nature, structure, and context, facilitating better understanding and analysis. While alternatives like "data characteristics" and "data properties" exist, using "attributes of data" requires specificity to avoid vagueness. In essence, understanding and clearly defining these attributes is crucial for effective data processing, analysis, and interpretation in various fields.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Data attributes

Slight reordering of the words in the original query.

Data characteristics

Focuses on the qualities that distinguish data.

Data properties

Emphasizes the inherent attributes possessed by data.

Qualities of data

Emphasizes the inherent nature or essence of data.

Data features

Highlights specific aspects or elements of data.

Data elements

Refers to individual components that constitute data.

Data metrics

Focuses on measurable aspects of data.

Data indicators

Highlights elements that point to specific trends or conditions in the data.

Data specifications

Emphasizes the documented requirements and standards for data.

Data parameters

Refers to the variables or settings that define data.

FAQs

How can I describe the "attributes of data" in a research paper?

In a research paper, describe the "attributes of data" such as its source, type (e.g., numerical, categorical), format (e.g., CSV, JSON), and any relevant metadata. This ensures transparency and reproducibility.

What are some alternatives to using the phrase "attributes of data"?

You can use alternatives like "data characteristics", "data properties", or "data features" depending on the specific context.

Why is it important to understand the "attributes of data"?

Understanding the "attributes of data" is crucial for effective data processing, analysis, and interpretation. It helps ensure data quality, validity, and reliability in decision-making.

How do the "attributes of data" affect data analysis?

The "attributes of data", such as data type, scale, and distribution, influence the choice of appropriate statistical methods and techniques for data analysis. Different attributes may require different preprocessing steps and analytical approaches.