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DCF dictionary

The DCF data dictionary is a baseline dictionary, it allows users to create their own dictionaries by serving as a start point for new dictionaries. The flexibility of the DCF dictionary makes the process of creating a new dictionary more efficient and easy. DCF dictionary is used for CCLE, CPTAC and Canine datasets.

The DCF data dictionary provides the first level of validation for all data stored in it. Written in YAML, JSON schemas define all the individual entities (nodes) in the data model. Moreover, these schemas define all of the relationships (links) between the nodes. Finally, the schemas define the valid key-value pairs that can be used to describe the nodes.

Data Dictionary Structure

The Data Model covers all of the nodes within the dictionary as well as the relationships between the different types of nodes. All of the nodes in the data model are strongly typed and individually defined for a specific data type. For example, submitted files can come in different forms, such as aligned or unaligned reads; within the model we have two separately defined nodes for Submitted Unaligned Reads and Submitted Aligned Reads. Doing such allows for faster querying of the data model as well as providing a clear and concise representation of the data.

Beyond node type, there are also a number of extensions used to further define the nodes within the data model. Nodes are grouped up into categories that represent broad roles for the node such as analysis or biospecimen. Additionally, nodes are defined within their Program or Project and have descriptions of their use. All nodes also have a series of systemProperties; these properties are those that will be automatically filled by the system unless otherwise defined by the user. These basic properties define the node itself but still need to be placed into the model.

The model itself is represented as a graph. Within the schema are defined links; these links point from child to parent with Program being the root of the graph. The links also contain a backref that allows for a parent to point back to a child. Other features of the link include a semantic label that describes the relationship between the two nodes, a multiplicity property that describes the numeric relationship from the child to the parent, and a requirement property to define whether a node must have that link. Taken all together the nodes and links create the directed graph of the Data Model.

Node Properties and Examples

Each node contains a series of potential key-value pairs (properties) that can be used to characterize the data they represent. Some properties are categorized as required or preferred. If a submission lacks a required property, it cannot be accepted. Preferred properties can denote two things: the property is being highlighted as it has become more desired by the community or the property is being promoted to required. All properties not designated either required or preferred are still sought, but submissions without them are allowed.

The properties have further validation through their entries. Legal values are defined in each property. For the most part these are represented in the enum categories although some keys, such as submitter_id, will allow any string value as a valid entry. Other numeric properties can have maximum and minimum values to limit valid entries. For examples of what a valid entry would look like, each node has a mock submission located in the examples/valid/ directory.


We welcome all comments, feature requests, and pull requests using GitHub issues or the Gen3 community.