When you create a project, the type of project defines which project-wide analyses are going to be executed (and how). The different types are:
A mixed collection of genomes, metagenomes, and viromes. This is the most basic type of project, with no support for any project-wide analyses. It is intended for projects that are only concerned with datasets preprocessing, e.g., read trimming, assembly, etc.
A collection of genomes. This is the most typical type of project, storing a set of genomes from different taxonomic groups. It can be useful for anything from indexing a reference database, to create a collection of metagenomic bins, and anything in between.
A collection of closely-related genomes (ANI >= 90%). This is a project for a collection of genomes in the same species (or closely-related species) that require higher resolution but don't require support for a large distance range.
Once you have a project, the type of the datasets define which analyses are going to be executed for that particular entry (and how). The different types are:
The genome from an isolate. This is the most typical case, in which you have a genome (complete or draft) from a pure culture (excluding SAGs).
A Single-cell Amplified Genome (SAG). This is the particular case in which you are dealing with an amplified genome from a single cell. These datasets typically have very uneven coverage (resulting in very incomplete assemblies) and sometimes have contamination from external DNA.
A population genome (including metagenomic bins). This is the type of dataset that includes sequences from different strains of the same species, such as metagenomic bins or metagenomes of highly enriched (but not pure) cultures.
A metagenome (excluding viromes).
A viral metagenome.
In addition to the dataset types, some analyses may differ depending on the status of a dataset as query or reference. Reference datasets are those that integrate the database of the project; i.e., those that can be queried by analyses with other datasets like distances. In contrast, query datasets are more isolated: they can use data from other datasets (or the project), but don't get to form part of the project database. Defining query datasets is useful when, for example, you have a reference framework for taxonomy (formed by reference datasets) and want to find the best classification for a genome without affecting the project itself. By default, datasets are created as reference datasets.