for the usage of ML technologies, having a appropriate dataset is arguably the first and fundamental step one has to tackle by either aquiring a dataset from external sources or creating their own.
While datasets from external sources are of course marketed as beeing 'good' or 'high-quality' (and in most cases not explained on how the authors come to this conclusion), creating a dataset yourself doesn't come with these labels.
This brings me to my questions: How can one (objectively) quantify the quality of a dataset for a given problem?
This, of course, includes some points which are more or less 'accepted' within the community, e.g. the dataset has to contain enough datapoints for every modeled state (which leads to the question of what is 'enough' ...), or that the datapoints for each modeled state should be roughly equally split (e.g. a dataset for cat/dog image discrimination would not work well with one dog image and 10k cat images) and so on.
I recognize that this is a rather open ended and maybe even philosophical question, but I believe that, given the importance of data for ML (and other disciplines), I am in need of an objective way to evaluate my datasets in relation to the task at hand and determine their quality in an objective way. Also my goal here is clearly ML oriented, but since this topic is not only valid in the ML context (and ML is in its core more or less complex statistics), I don't want to restrict this only to the ML, but to datasets overall