The phrase "are useful for data" is correct and usable in written English.
It can be used when discussing the utility or applicability of certain tools, methods, or resources in relation to data.
Example: "These analytical techniques are useful for data interpretation and decision-making."
Alternatives: "beneficial for data" or "valuable for data analysis".
These discriminative results with Deep Learning are useful for data tagging and information retrieval and can be used in search engines.
The resulting controlled vocabularies are useful for data integration and disambiguation, e.g., terms could be used as keywords for scholarly works, enabling "similar" and/or "relevant" scholarly works to be identified.
Likewise, data preprocessing, dimension reduction, data mining, and machine learning methods are useful for data reduction at different levels in big data systems.
The intermittent contacts between randomly roaming users are useful for data sharing, which has been explored and studied extensively in variety of network settings, from military warfare [17] to disaster recovery [18].
The structuring of the data, the creation of additional calculation fields and the integration with nucleotide sequences allow users to make many types of comparisons and calculations that are useful for data retrieval and analysis.
The fully structured local database obtained (Supplementary Fig. S1) allows users to make many types of comparisons and do a variety of calculations that are useful for data retrieval and analysis.
Principal components analysis (PCA) was useful for data reduction, to assess the continuity/overlap of clusters or clustering/similarities in the data and was used to determine the sources of variation between parameters (Guler et al. 2002).
This system is useful for data communication between vehicles on the road or between vehicles and terminals over the road; and is composed of laser transceivers and respondent devices to realize two-way communication using only one optical laser source.
Unsupervised learning finds hidden patterns in huge sets of data, which is useful for data mining and big data analytics.
But Harris also noted that Snowflake meant tweet IDs would no longer be useful for data analysis.
