Calibration Certificate — Error and Expanded Uncertainty (.csv)
Five calibration points with the nominal value, the measured value, the signed error and the expanded uncertainty at k=2 with its confidence level stated. It is a synthetic illustration of the layout, not an accredited certificate, and nothing in it refers to a real instrument or laboratory.
nominal_value,unit,measured_value,error,expanded_uncertainty_U,coverage_factor_k,confidence_level,verdict
1.0,g,1.00012,+0.00012,0.000120,2,95%,pass
10.0,g,10.00031,+0.00031,0.000300,2,95%,pass
100.0,g,99.99870,-0.00130,0.002100,2,95%,pass
500.0,g,500.00420,+0.00420,0.010100,2,95%,pass
1000.0,g,999.98900,-0.01100,0.020100,2,95%,pass
Specifications
- Rows
- 5
- Columns
- 8
- Unit
- g
- Coverage Factor
- 2
- Confidence Level
- 95%
- Nominal Range
- 1 g to 1000 g
- Synthetic
- true
- Not An Accredited Certificate
- true
Testing contract
Expected to pass- Scenario
- Parse the certificate and evaluate each point against the acceptance rule that the error magnitude must not exceed five times U.
- Expected result
- All five rows parse with the signed error matching measured minus nominal to five decimals, and every verdict column agrees with the rule you evaluated independently.
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Calibration Certificate — Error and Expanded Uncertainty (.csv)” is a deterministic Novus Examples fixture for Scientific data, CSV parsing, Editor testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.
Documented properties for this file: 5 rows · 8 columns. 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.
Scientific fixtures are small, valid, and fully synthetic — no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.
Code examples
import pandas as pd
df = pd.read_csv("calibration-certificate.csv")
print(df.head())
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