Field type identification

Field-type identification

Summary

Identify and classify fields in JSON project documents according to their designated types (e.g., URL, string, number) as defined in a JSON schema, enabling downstream validation and processing rules specific to each field category.

When to use

When working with JSON project documents that must conform to a schema-defined structure, and you need to apply type-specific validation, sanitization, or transformation rules (e.g., URL whitespace detection, numeric range checking, mandatory field enforcement).

When NOT to use

• Input is a non-JSON or unstructured document (e.g., CSV, plain text)
• The project JSON does not declare which schema version it conforms to
• No schema file is available or schema is incomplete/malformed

Inputs

• Project JSON document
• JSON schema definition file (app/public/schema.json)

Outputs

• Field-type mapping (field name → type classification)
• Categorized field list (URLs, strings, numbers, etc.)
• Validation rule assignments per field type

How to apply

Load the project JSON schema (e.g., app/public/schema.json) and parse its field type definitions. Iterate through each field in a project JSON document and cross-reference its name against the schema to determine its declared type. For fields with URL type, flag them for URL-specific validation (whitespace detection); for string fields, apply text constraints; for numeric fields, apply range checks. This classification step is prerequisite to any schema-aware validation workflow and ensures validation rules are applied only to relevant field categories.

Related tools

• npm (Runtime environment for loading, parsing, and iterating JSON schema and project documents) — https://www.npmjs.com/
• paired-data-form repository (Source of the JSON schema definition and project structure documentation) — https://github.com/iomega/paired-data-form

Evaluation signals

• All fields in the project JSON are successfully mapped to a type declared in the schema
• Fields classified as URL type match the schema's URL field list (e.g., project URLs, reference links, data repository links)
• No fields are left unclassified or assigned an unknown type
• Type-specific validation rules (e.g., whitespace checks for URLs) can be applied to the correct field subset
• Schema conformance check passes without type mismatch errors

Limitations

• Requires the schema file to be present and valid; breaks silently if schema is missing or malformed
• Does not validate field values themselves, only identifies their types; whitespace or format errors are caught by downstream validation, not this step
• Schema version mismatch between document and schema definition may lead to incorrect type assignments
• Platform historically had issues with spaces in URLs not being properly handled, suggesting field-type identification alone is insufficient without downstream whitespace validation

Evidence

• [other] The platform stores paired omics data projects using a JSON schema format (app/public/schema.json) that defines project structure: "The platform stores paired omics data projects using a JSON schema format (app/public/schema.json) that defines project structure, which serves as the basis for implementing field-level validation"
• [other] Identify all fields designated as URL type in the schema (e.g., project URLs, reference links, data repository links): "Identify all fields designated as URL type in the schema (e.g., project URLs, reference links, data repository links). Iterate through each URL field and check for the presence of whitespace"
• [readme] The JSON schema (app/public/schema.json) describes the format of an project: "The JSON schema (app/public/schema.json) describes the format of an project."
• [discussion] Warning to not include spaces in urls: "Spaces in URLs not properly handled [section=discussion; evidence='Warning to not include spaces in urls ([#75]'"