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0.29.0 • Public • Published

Data Format for Digital Linguistics (DaFoDiL)

The DLx data format is a standardized, human-readable, web-compatible format for storing linguistic data, following best practices for managing data on the modern web. It is part of a broader project called Digital Linguistics (DLx), which has the goal of creating web tools for managing linguistic data. This project will be useful for anyone who manages a linguistic database.

This repository contains the specification of the data format, in the form of a number of schemas. There is one schema for each type of linguistic object (e.g. Language, Morpheme, Text, etc), and schemas for various non-linguistic objects as well (e.g. Person, Location, etc.). The schemas follow the JSON Schema format for describing the structure of JSON data.

See the documentation for human-readable versions of the schemas, and an example of the schema in use.

Please consider citing this specification in scholarly articles using this repository's Zenodo DOI:

Hieber, Daniel W. 2018. Data Format for Digital Linguistics. DOI:10.5281/zenodo.1438589

GitHub stars npm downloads GitHub issues npm version Build Status license DOI

Contents & Quick Links

Note on Versioning

This project is using v0 for initial development. While the specification is largely stable, it may still change at any time, and should not yet be considered 100% production-ready. Once the specification has been tested in a production environment with a live project, its version will be incremented to v1.0.0, and the specification considered stable. Until that time, both major and minor changes will increment the minor version number (the second number), and patches / fixes will increment the patch version number (the third number). Individual schemas however start at v1.0.0.

Basic Usage


  • Install the schemas: npm install @digitallinguistics/spec

  • Install a JSON Schema validator to validate your data against the schemas: npm install ajv

  • Validate the data:

    // Imports
    const AJV     = require(`ajv`);
    const schemas = require(`@digitallinguistics/spec`);
    const ajv      = AJV(); // Initialize ajv
    const language = { /* your data to validate */ };
    // Validate your data
    const valid = ajv.validate(schemas.Language, data);
    if (!valid) console.error(ajv.errorsText());


  • Download the schemas to your project in a /schemas folder.

  • Include a JSON Schema validator in your project to validate your data against the schemas:

    <script src=></script>
  • Validate the data:

    (async function validate() {
      const ajv      = Ajv(); // Initialize ajv
      const language = { /* your data to validate */ };
      // Load the schema
      const res    = await fetch(`schemas/Language`);
      const json   = await res.json();
      const schema = JSON.parse(json);
      // Validate the data against the schema
      const valid = ajv.validate(schema, data);
      if (!valid) console.error(ajv.errorsText());

Data Validation

If you need to validate your linguistic data against the DLx schemas, you can use one of the JSON Schema validators. This project uses the ajv validator for testing.


Schemas are located in the /schemas folder in both JSON and YAML formats.

You can install the schemas to your computer using npm (npm install @digitallinguistics/spec), or access them programmatically at the following URLS:

URL Result{schema} latest version of the schema, as JSON{schema}.json latest version of the schema, as JSON{schema}.yml latest version of the schema, as YAML{schema}-{X.X.X} specified version of the schema, as JSON{schema}-{X.X.X}.json specified version of the schema, as JSON{schema}-{X.X.X}.yml specified version of the schema, as YAML

For example, you could access v1.0.0 of the Language schema in each of the following ways:


Best Practices

The following is a list of principles and best practices to keep in mind when working with linguistic data in DLx format.

  • Storage vs. Use

    • This specification describes how data should be stored, i.e. in a database or JSON file. It does not recommend how that data should be formatted when it is being managed or manipulated. Required properties could be missing during data entry, or data could be represented using (for instance) an Object instead of an Array while the data is being manipulated. It is only when you store that data in a file or database that it must be in valid DLx format.
  • Documents

    • The DLx format is designed to work well with document databases, where each item is stored as a single document (typically in JSON) rather than as records in a table. Schemas that include a comment that they are top-level database objects should be their own documents in the database. Other schemas will be subparts of those documents.
  • IDs & Cross-References

    • The DLx schemas support unique identifiers in the form of opaque IDs or human-readable keys, or both (recommended - see below). Many schemas include an optional id field, which is meant to be representative of whatever opaque identifier scheme best suits your database. You could name this field id, ID, dbid (database ID), localid, uuid, uri, etc. etc. Thus when the id property is referenced in the schemas, it should not be taken literally as being the id field. Instead it refers to whichever field your database uses for unique, opaque identifiers. Whichever name you choose for the property should be used consistently in place of the id fields. No restrictions are placed on the format of the ID field other than that it must be non-empty, although best practice is for this field to be a string (preferably a UUID) or a number.

    • If your database depends on unique, opaque identifiers (e.g. a UUID), you should also use human-readable keys. For example, a Lexeme object representing the word "book" (the noun) might have an ID of d0e51fcb-84af-44aa-ba16-67561e21c793, but should also have a key book1. This helps users identify it as the lexeme "book", while simultaneously helping distinguish it from other "book" homonyms in the database (such as the word "book" used as verb, which might be book2).

    • Generally speaking, DLx data should be stored in denormalized (embedded) format whenever it is practical to do so. Sometimes, however, data needs to be normalized when embedding the data directly would be impractical or create infinite recursion. The DLx specification provides a DatabaseReference object for this purpose, used to reference other items in a database. For example, a Lexeme may have a reference to an Utterance in its examples field, and that Utterance might also have a reference to the Lexeme. Because it would be impossible to represent such a recursive relationship in a single JSON document, database references are used instead. Each DatabaseReference object contains key, url, id, index, and referenceType properties which allow you to uniquely identify other items in the database. Other properties are required or permitted as appropriate.

  • URLs

    • Top-level schemas (and some subschemas) include a url property which should be used to indicate a URL where that resource can be retrieved in JSON format. It should not be used for the URL of a human-readable presentation of the resource (the link property should be used for this purpose). The resource does not have to be publicly available at this URL; it may require permissions to access.

    • Top-level schemas also include a link property. This is used to indicate a URL where a presentational format of the given resource can be viewed, for instance a text viewer or lexicon editor, etc.

  • Completeness

    • An exported database should be complete, self-contained, and human-readable in the sense that a user should be able to find and follow any cross-references easily. For example, if the Lexeme book1 has a cross-reference to the Lexeme book2, the book1 Lexeme should reference the other Lexeme as book2 and not just a database ID like d0e51fcb-84af-44aa-ba16-67561e21c793. Moreover, both book1.json and book2.json should be included in the export. The Text schema has optional lexemes, orthographies, and texts properties, allowing you to save/export all the data in a language corpus in a single file (if this is feasible for your project).
  • Optional, Required, & Empty Properties

    • Typically, if an optional property is present, it should have data in it. If the data in the property is empty (e.g. an empty Array, an empty String, an Object without properties, etc.), you should remove that property before saving the data. In other words, do not store empty Strings, empty Arrays, etc. unless those properties are required. This helps keep storage costs to a minimum, while reducing clutter and maintaining human-readability.

    • Occasionally, the description for a schema imposes restrictions or guidelines that the schema itself technically does not. (This occurs in cases where it is impossible to capture a requirement in the JSON Schema format.) In these cases, implementations should adhere to the requirements of the schema description in addition to the requirements enforced by the JSON schema itself.

  • Context

    • Schemas sometimes have different uses or interpretations depending on the context in which they appear. For example, when an Utterance appears in the "utterances" property of a Lexeme, it is an example utterance. When it appears in the "utterances" property of a Text, it is a transcribed utterance from that Text. When a schema appears within another schema, its "description" field will tell you how it should be used in that context.

    • Most schemas have an optional type field. This field is not required because it may conflict with database implementations which already make use of a type keyword for other purposes. The type field or some other similar field is however strongly recommended. If using the type field conflicts with your database model, dlxType is recommended instead.

  • Dates


Tests are run using Jasmine in Node.js. Run them from the command line using npm test.


npm i @digitallinguistics/spec

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