Lab Measurements (JSON)
Synthetic lab readings (temperature, pressure, pH) with ISO timestamps for scientific ETL tests. JSON twin.
[
{
"sample_id": "LAB-2026001",
"collected_at": "2026-01-10T09:00:00Z",
"temp_c": 20.0,
"pressure_kpa": 101.3,
"ph": 6.8,
"operator": "Ada"
},
{
"sample_id": "LAB-2026002",
"collected_at": "2026-01-10T10:00:00Z",
"temp_c": 20.3,
"pressure_kpa": 101.4,
"ph": 6.85,
"operator": "Ben"
},
{
"sample_id": "LAB-2026003",
"collected_at": "2026-01-10T11:00:00Z",
"temp_c": 20.6,
"pressure_kpa": 101.5,
"ph": 6.9,
"operator": "Chen"
},
{
"sample_id": "LAB-2026004",
"collected_at": "2026-01-10T12:00:00Z",
"temp_c": 20.9,
"pressure_kpa": 101.6,
"ph": 6.95,
"operator": "Ada"
},
{
"sample_id": "LAB-2026005",
"collected_at": "2026-01-10T13:00:00Z",
"temp_c": 21.2,
"pressure_kpa": 101.3,
"ph": 7.0,
"operator": "Ben"
},
{
"sample_id": "LAB-2026006",
"collected_at": "2026-01-10T14:00:00Z",
"temp_c": 20.0,
"pressure_kpa": 101.4,
"ph": 7.05,
"operator": "Chen"
},
{Specifications
- Records
- 16
- Schema
- sample_id, collected_at, temp_c, pressure_kpa, ph, operator
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Lab Measurements (JSON)” is a deterministic Novus Examples fixture for Data import, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: 16 records · schema: sample_id, collected_at, temp_c, pressure_kpa, ph, operator. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("measurements.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
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Generated by generation/data_domains_wave_c.py. Free for any use, no attribution required — license.