HDF5 Deeply Nested Group Tree — Eight Levels (.h5)
Eight levels of nested groups, each holding a dataset whose values equal its own depth. Any tree walk that caps recursion depth or flattens names silently loses the lower levels, and the depth-valued arrays make that immediately visible.
| Field | Value |
|---|---|
| Depth | 8 nested groups |
| Datasets | one 'value' dataset at every level |
| Deepest path | /level1/level2/level3/level4/level5/level6/level7/level8/value |
| Path length | 62 characters |
| Attribute | each dataset carries its own depth as an attribute |
| Trap | recursion that flattens paths or caps depth loses the deepest values |
Specifications
- Depth
- 8
- Datasets Per Level
- 1
- Total Datasets
- 8
- Deepest Path
- /level1/level2/level3/level4/level5/level6/level7/level8/value
- Path Length
- 62
Testing contract
Expected to pass- Scenario
- Recursively enumerate every dataset and assert its values equal the number of group levels above it.
- Expected result
- Exactly eight datasets are found, the deepest is /level1/level2/level3/level4/level5/level6/level7/level8/value holding four copies of 8.0, and every dataset's depth attribute matches its position in the tree.
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 Deeply Nested Group Tree — Eight Levels (.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: H5 · 17,824 bytes. 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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