HDF5 Units and Uncertainty Attributes on Every Dataset (.h5)
Four measured quantities where the uncertainty lives in dataset attributes rather than a parallel column, with the coverage factor spelled out so k=1 and k=2 values cannot be confused. It is the fixture for a pipeline that must propagate uncertainty it read from metadata.
| Dataset | units | u (k=1) | U (k=2) |
|---|---|---|---|
| temperature | K | 0.15 | 0.3 |
| pressure | Pa | 25.0 | 50.0 |
| length | m | 1.2e-06 | 2.4e-06 |
| mole_fraction | 1 | 0.002 | 0.004 |
Specifications
- Datasets
- 4
- Points Each
- 20
- Units
- K, Pa, m, 1
- Coverage Factor
- 2
- Attributes Per Dataset
- 4
- Seed
- 20260818
Testing contract
Expected to pass- Scenario
- For each dataset read the values plus the units, standard_uncertainty and coverage_factor_k attributes and form the expanded uncertainty.
- Expected result
- All four datasets are 20 points, expanded_uncertainty equals 2 x standard_uncertainty in every case, and the units strings read back as 'K', 'Pa', 'm' and '1'.
What is a .h5 file?
HDF5 (.h5) is a binary container format for large, heterogeneous scientific data. It stores multidimensional arrays (datasets) in a hierarchical group structure with attributes and chunked, compressed storage, and is standard in ML, physics, and geoscience.
How to use this file
Use an example .h5 file to test HDF5 readers (h5py, PyTables), group and dataset traversal, and attribute extraction.
How to use this file for testing
“HDF5 Units and Uncertainty Attributes on Every Dataset (.h5)” is a deterministic Novus Examples fixture for Scientific data, Serialization 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: seed 20260818. 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.
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