Frictionless Data

Define a Table Schema that describes the structure and data types of the research data.

Use the Frictionless Data validation tools to validate the data against the schema.

Review the validation report to identify any errors or inconsistencies.

Correct any errors in the data and revalidate until the data passes validation.

Create a Data Package by defining a datapackage.json file that describes the data resources and their metadata.

Package the data files and the datapackage.json file into a single archive (e.g., a ZIP file).

Share the Data Package with collaborators.

Collaborators can unpack the Data Package and use the metadata to understand the structure and content of the data.

Integrate the Frictionless Data validation tools into the data pipeline.

Configure the tools to validate data against a Table Schema at each stage of the pipeline.

Set up alerts to notify data engineers when data fails validation.

Implement automated error correction or data rejection based on validation results.

Use the Data Package standard to package data and metadata for each data asset.

Create a metadata registry to store the metadata for all Data Packages.

Implement a search interface that allows users to search for data assets based on metadata.

Provide links to the Data Packages so that users can download and access the data.

Use the Frictionless Data validation libraries to validate data uploads against a Table Schema.

Display validation errors to the user and prompt them to correct the errors.

Only store the data in the database if it passes validation.

Provide clear and informative error messages to guide users in correcting the errors.

Identify the core data elements used by different departments.

Define a common Table Schema that captures these core data elements.

Develop data transformation scripts to convert data from different formats to the standard format defined by the Table Schema.

Implement data validation to ensure all departments adhere to the standard.

Define data governance policies and standards using Table Schemas.

Implement data validation tools to ensure compliance with these standards.

Monitor data quality metrics to identify potential compliance issues.

Generate reports to demonstrate compliance with regulatory requirements.