csvlabdokat

csvlabdokat is a proposed data standard and accompanying toolkit designed to manage laboratory tabular data in a structured, machine-readable form. It extends the widely used CSV format by introducing a separate metadata layer and optional validation mechanisms to improve reproducibility and data integrity.

The core idea is to keep data as plain CSV while supplying a companion metadata file that defines the schema and provenance. A typical dataset consists of a data file named sample.csv and an optional metadata file named sample.csvlabdokat.json. The metadata describes column names, data types, units, and constraints, and it can include dataset-wide information such as project, instrument, operator, timestamps, and version.

Data types supported include string, integer, float, boolean, and datetime. Each column may specify a unit and, where appropriate, acceptable value ranges or categorical enums. Validation tools check consistency between the data and the schema, flag missing values, and verify unit compatibility. The standard supports both wide and long data representations and can accommodate missing data via standard markers.

Example:

Data file sample.csv

time,temperature,ph

2025-01-01T12:00:00Z,23.4,7.2

2025-01-01T12:05:00Z,23.5,7.25

Accompanying metadata file sample.csvlabdokat.json

{

"csvlabdokat_version": "1.0",

"project": "enzyme assay",

"instrument": "SpectroX 300",

"columns": {

"time": {"type": "datetime", "unit": "ISO8601"},

"temperature": {"type": "float", "unit": "C"},

"ph": {"type": "float"}

}

}

Status: The concept is described in community documentation and has limited adoption. It is used mainly in demonstration datasets and experimental workflows to illustrate metadata-driven validation, and it competes with other metadata practices and standardization efforts.

See also:

- CSV

- metadata standards

- data provenance

- open data formats