Interoperable

Data need to be integrated with and/or compared to other datasets, while computers must be able to interpret and exchange the information. Ideally, they are compatible with standard applications and can thus be integrated into (automated) processing and analysis workflows. Interoperability often functions as a precursor to reusability, as it ensures the compatibility across systems.

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation

Machines need to be able to understand how to exchange and interpret information. Similar to humans, a uniform and standard language aids in this understanding. In chemistry, a great typical example of such an information exchange standard is the crystallographic information (CIF). This standard also adheres to the aspects described in I2 for a given analytical method ensure the data and the associated metadata, which typically include measurement details, for example, follows a prescribed format. This ensures both humans and machines receive the information required to interpret the data.

Especially when looking at metadata, effective and efficient machine readability greatly depends on being able to reduce ambiguity. Metadata provides context to datasets. However, machines need to be able to interpret this context. Therefore, the structured schemas chosen by the repositories should include universally applied ontologies and controlled vocabularies to define relationships and avoid ambiguity. For example, chemistry-specific repositories should be designed to include ontologies such as the Chemical Methods Ontology (CHMO) or the Chemical Information Ontology (CHEMINF) to accurately describe the (meta)data provided. Such ontologies should be based on widely-applied data models, for instance, the Resource Description Framework (RDF).

I2. (meta)data use vocabularies that follow FAIR principles

The applied vocabularies or ontologies should be well-documented and resolvable using a PID. For instance, CHMO mentioned above is publicly available on Github.

I3. (meta)data include qualified references to other (meta)data

Related datasets should be linked in a reliable manner, preferably via their PIDs. This includes any previous versions, datasets required to fully use and comprehend the current dataset, or datasets that the dataset builds upon. This relationship should also be described in a meaningful manner. For example, if dataset X is a previous version of dataset Y, it would be described as such rather than simply being described as a related or an associated dataset. Repositories should include a method of referring to other datasets in their metadata form.