"granularity of data" is a correct and usable phrase in written English.
It is typically used in the context of data analysis and refers to the level of detail or specificity of the data being examined. Example: "In order to fully understand the trends in our sales data, we need to consider the granularity of data we are using. Are we looking at daily, weekly, or monthly data? Are we examining individual product sales or overall revenue? The granularity of data will greatly impact our analysis and decision-making."
We also give an algorithm for determining the optimal granularity of data ''chunks" to be used for each web service call.
For the first time in history, the scale and the diversity and the granularity of data that we have is actually beginning to be commensurate to the complexity of the social problems that confront us.
Balancing privacy with providing the granularity of data needed for more sophisticated analysis is an ongoing concern, although increasingly a shared set of policies and practices are being developed around privacy protection.
Coarse granularity of data records has negative influence on contact detection.
Some of the have nots aren't collecting the same granularity of data, or don't have a way to take lots of different data sources and use them together.
It takes a different set of AI tools – focusing more on granularity of data, and applying AI in a more "internal" way, where it is looking at traditional prediction models and fine-tuning them to make them better.
It is a further improvement of l-diversity group based anonymization that is used to preserve privacy in data sets by decreasing the granularity of a data representation.
One of the main issues in this context is the trade-off between the granularity of the data and the amount of information on each single interaction.
In contrast, the primary model used by Dryad offers coarser granularity of one data record per associated publication whereby the complete Dryad data set is linked with a peer reviewed journal publication.
The spatial granularity of our data is 100 meters, while the temporal granularity of our data is the minute.
To test this explicitly, we created data sets representing comparable time points by pooling the expression data from adjacent D. virilis developmental stages to match the granularity of available data sets from D. melanogaster, based on the morphological observations (fig. 1) and the within-species microRNA transcriptome similarities (fig. 2).
