data sensitivities

USAGE SUMMARY

The phrase "data sensitivities" is correct and usable in written English.
It can be used when discussing the importance of handling sensitive information or data with care, particularly in contexts like data privacy, security, or compliance. Example: "When conducting research, it is crucial to be aware of data sensitivities to protect participants' privacy."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

4 human-written examples

The concluded analytical data (sensitivities) for the studied biosensors obtained from the calibration curves are presented in Figure 9.

Nanoscale Research Letters

However, this approach yields large inaccuracies in the evaluation of data sensitivities based on the adjoint problem solution, as described in the next section.

Earth, Planets and Space

This paper presents an inversion method based on direct integration of magnetic fields in the time domain, and using the adjoint solution for fast evaluation of data sensitivities to model perturbations.

Earth, Planets and Space

More specifically, this means that data collection needs to be sensitive to: (a) reducing biases in specific types of data, (b) the specific data sensitivities with regards to vulnerable groups, (c) processing and analytics in order to remain truthful, and (d) being cautious not to perpetuate or increase the vulnerabilities of groups by emphasizing differences.

Journal of International Humanitarian Action

Human-verified similar examples from authoritative sources

Similar Expressions

56 human-written examples

Specifically, Neokami's AI tech is being applied to what Christo calls "cloud data sensitivity management" — that is identifying and locating sensitive documents that are, perhaps inappropriately, stored in the cloud.

TechCrunch

For model parameterisation, one relies on a combination of field data, sensitivity analysis and expert judgement.

Reliability Engineering & System Safety

Using illustrative data, sensitivity results help evaluate the neighborhood rough set technique's robustness with various reverse logistics performance factors.

Journal of Cleaner Production

The nuclear data sensitivity in 3D Monte Carlo burnup calculations of minor actinide transmutation in Energy Amplifier Systems is assessed.

Annals of Nuclear Energy

The system is intended to extract certain parameters from network map, risk analysis, fault tree, user interface analysis, data sensitivity and gateways analysis.

Applied Mathematics and Computation

The present analysis will be completed by a full nuclear data sensitivity and uncertainty analysis of the reactivity coefficients and keff in a companion paper.

Progress in Nuclear Energy

It considers data sensitivity before a job's execution.

Journal of Big Data

Expert writing Tips

Best practice

When discussing research involving human subjects, always explicitly address the "data sensitivities" and the measures taken to protect participant privacy.

Common error

Avoid generalizing "data sensitivities". Instead, identify which data points are sensitive (e.g., personal health information, financial records) and explain why.

Linguistic Context

The phrase "data sensitivities" functions as a noun phrase referring to the degree of care and ethical considerations required when handling data that may contain personal or confidential information. It highlights the need for cautious and responsible practices, as confirmed by Ludwig.

Expression frequency: Rare

Frequent in

Science

75%

News & Media

25%

Formal & Business

0%

Less common in

Academia

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "data sensitivities" is a noun phrase that highlights the importance of responsible data handling, especially concerning privacy and ethical considerations. As Ludwig AI confirms, this phrase is grammatically correct and applicable across various contexts, especially in science and news media. Recognizing and addressing "data sensitivities" is crucial for maintaining trust and avoiding potential risks associated with data use. When writing, be mindful of the specific data types and potential harms involved and consider using alternatives such as "data privacy concerns" or "sensitive data handling" for clarity and impact.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

data privacy concerns

Focuses specifically on the privacy aspect of data handling.

data confidentiality issues

Highlights the importance of keeping data confidential.

sensitive data handling

Emphasizes the act of managing sensitive data.

data protection measures

Focuses on the actions taken to protect data.

data security protocols

Highlights the security procedures implemented for data.

privacy considerations for data

Directly addresses the considerations needed regarding data privacy.

responsible data management

Emphasizes accountability in how data is managed.

data governance policies

Refers to the overarching rules and standards for data.

data risk assessment

Highlights the process of evaluating risks associated with data.

ethical data practices

Focuses on the moral and ethical aspects of data usage.

FAQs

How do I address "data sensitivities" in a research paper?

Clearly outline the potential risks associated with the data, and then describe the specific steps taken to mitigate those risks and protect privacy, such as anonymization or encryption.

What's the difference between "data sensitivities" and data security?

"Data sensitivities" refers to the inherent potential for harm if data is misused or exposed. Data security involves the technical and procedural measures implemented to prevent unauthorized access and ensure data integrity.

What are some alternatives to "data sensitivities"?

Depending on the context, you could use alternatives like "data privacy concerns", "data confidentiality issues", or "sensitive data handling".

Why is it important to consider "data sensitivities"?

Failing to address "data sensitivities" can lead to breaches of privacy, legal repercussions, reputational damage, and erosion of public trust. It is a critical aspect of responsible data management.