"fundamental dataset" is a correct phrase in written English.
You could use it in the context of a scientific or technical report. For example: "The development of this new model requires an exhaustive analysis of the fundamental dataset."
Streamflow discharge is a fundamental dataset required to effectively manage water and land resources.
In this study, we have collected complete genome sequences of 14 model species as the fundamental dataset.
We expect these limitations to have minimal effects on our data because (a) the entire stretch of coast examined in the present study has been the object of extensive research providing a fundamental dataset of the spatial and temporal extent of the species' geographic range; and (b) F. vesiculosus is a scientifically popular species with well-known biology.
Results obtained are considered to serve as fundamental datasets for the practical development of MFC technologies for WWT.
The detection of retrocopies in eukaryotic genomes relies on two fundamental datasets: (i) a reference genome sequence and (ii) a set of known transcripts from each organism.
The results are shown in Figure 4. We can see that the best result is obtained for B = 3, although the underlying dataset only has two fundamental patterns.
It is important to distinguish between private companies' data, which is the basis for internal research and development for new drugs and treatments, from public research datasets, fundamental to advance general scientific research.
With the increasingly accumulated large volumes of information about human, animals or microbe, researchers are starting to grapple with massive datasets and further elucidate the fundamental implications of those datasets in biology.
Learn about different areas of NLP, machine learning and the digital humanities; gain hands-on experience with a creating a dataset (a fundamental step in data science).
Such approaches define the boundaries for mass balance, allow comparison with lithologic descriptions and mineralogy, help ground the ensuing statistical treatments, and, in a way that is hard to quantify, ensure that the researcher is intimately familiar with the fundamental underpinnings of each dataset.
PCA is a linear dimensionality reduction technique that can effectively extract the fundamental structure of a dataset without any need for modeling of the data.
