CSVs and Tabular Data

LinkML can support both complex interlinked normalized relational data as well as flat/denormalized data as typically found in spreadsheets and in CSVs used by data scientists.

Our philosophy is “always have a schema” even when working with simple tabular data


the linkml-convert script can be used to convert between CSVs/TSVs and other formats like JSON/RDF. The same tooling for validating-data operate in the same way.

Conventions for working with tabular data

LinkML allows you to create schemas with complex nested data - these don’t necessarily have a simple unified mapping to tables/TSVs. However, you can still work with tabular representations if your schema has a certain “shape” and you provide sufficient hints.

Container objects

See part 2 of the tutorial for more on container objects.

To serialize your data objects as TSVs, it’s assumed that you have a class in your schema that serves the role of container. It can be called whatever you like. You can also annotate this with tree_root set to true. This class will have a multivalued slot pointing at the list of things you want to serialize in the TSV. This slot is known as the index slot

For example, in the PersonSchema schema, the Container class has two possible index slots:


  • persons: points at a list of Person objects

  • organizations: points at a list of Organization objects

You can only serialize one of these in any one TSV (using more advanced techniques you could create a union class for Person and Organization and serialize this, but this is outside the scope of this tutorial)

The linkml command line tools for conversion and validation will do their best to guess the index slot and the container, but if there is no unambiguous choice, then have to provide these using the following arguments:

  -C, --target-class TEXT         name of class in datamodel that the root
                                  node instantiates

  -S, --index-slot TEXT           top level slot. Required for CSV

For example, to serialize the organizations in the provided YAML data file in this repository, you can run:

linkml-convert -t tsv -s examples/PersonSchema/personinfo.yaml -C Container -S organizations examples/PersonSchema/data/example_personinfo_data.yaml

Note that currently serializing the person objects won’t work, as the Person class is too nested to be serialized as TSV

On the fly denormalization

The json-flattener/ library is used to do on-the-fly denormalizations. For example:

  • multivalued slots are serialized using a | separator

  • nested slots are flattened to paths, e.g if Container has a slot persons, and Person has a slot name, then the path with be persons_name

Working with Relational Databases

Inference of schemas from tabular data

Use tsvs2linkml in the enrichment toolkit