The phrase "applicability of data" is correct and usable in written English.
It can be used when discussing how relevant or useful certain data is in a specific context or situation.
Example: "The applicability of data in this research study is crucial for drawing accurate conclusions."
Alternatives: "relevance of data" or "usefulness of data".
Other speakers showed the broad applicability of data science approaches to many fields in addition to healthcare.
Her own research concentrates on understanding the applicability of data science approaches in social welfare research and practice settings.
This paper explores the applicability of data mining techniques, particularly, association rule learning, for developing guidelines for universal design.
We achieve a good compromise between these goals and help establish the applicability of data mining for characterizing wireless systems performance.
The interactivity of analysis of build-up structures can only be achieved by re-using results of component analyses, therefore, the applicability of data based analysis to the O-O approach is investigated in this paper.
Understanding the effectiveness and the applicability of data reduction as a technique for designing heuristics for intractable problems has been one of the main motivations in studying the phase transition of randomly-generated instances of NP-complete problems.
Given the inclusion criteria (described in full in Appendix 1 of [ 12]), the dataset is ideal for the present research in regards to clinical applicability, completeness of data, and comparability of results.
43 45 We will need to consider whether the chosen indicators need to be weighted to reflect both suitability (in terms of adherence to the accountability criteria) and applicability (quality of data collection and adjustment for patient mix).
It may therefore be necessary to undertake further analysis of available data or to make assumptions regarding the applicability of the data to a particular policy question.
To conclude, mobile phone data has a great potential for good but its high dimensionality limits the applicability of traditional data anonymization methods.
Such comparisons help in ensuring the data is correct (the projects use different code and mechanisms) and also help identify the applicability of the data from each project.
