Convert v2 HDF5 Dataset Summary CSV
CSV reference listing the four expected HDF5 datasets with shapes and units for lightweight converter assertions. Stable P8 artifact p8-convert-hdf5-summary-csv.
dataset,shape,units
/raw/detector_counts,32x8,counts
/raw/timestamp_s,32,s
/processed/normalised,32x8,1
/metadata/channel_index,8,1
Specifications
- Rows
- 4
- Columns
- 3
- Source Format
- h5
- Delivery Mode
- download-only
- Provider
- converter-v2
- Provenance
- Synthetic deterministic P8 fixture generated by generation/p8_content.py; seed namespace 2026082300
- Fixture Reserve
- convert-v2
Testing contract
Reference control- Scenario
- Compare recursively decoded HDF5 paths with the CSV rows.
- Expected result
- Four rows match the expected dataset paths, 32x8 shapes are preserved for raw and normalised arrays, and every row has units.
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
“Convert v2 HDF5 Dataset Summary CSV” is a deterministic Novus Examples fixture for Conversion testing, Scientific data, CSV parsing. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.
Documented properties for this file: 4 rows · 3 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("experiment-hierarchy-summary.csv")
print(df.head())
print(df.dtypes)Related files
- csvBinary versus Decimal Arithmetic — Where Floats Disagree (.csv)Eight arithmetic expressions where binary floating point and exact decimal disagree, with both answers and the signed error side by side. Adding one cent a hundred times does not give one, and this file says by exactly how much.
- csvCalibration 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.
- csvCatastrophic Cancellation — Naive versus Stable Formulas (.csv)Five computations done twice, once with the obvious formula and once with the numerically stable one, with the relative error of the naive version alongside. The one-pass variance row is the memorable one: five values a millisecond apart near 1e8 can produce a negative variance.
- csvCensored Values and Detection Limits — Non-Numeric Results (.csv)Nine laboratory results where only four are plain numbers: two are below the detection limit, one is above range, two are missing in different spellings, and one is a legitimate small negative near the blank. Coercing the censored strings to numbers or to NaN both bias the summary, and the file distinguishes every case explicitly.
- csvChemistry Fixture Molecule Index (.csv)One row per molecule used across the chemistry fixtures, with formula, molecular weight, SMILES and the atom and bond counts each file should yield. It is the oracle a cheminformatics toolkit can be scored against without needing a second toolkit to generate the answers.
- csvDICOM-Shaped Dataset — Element Table Reference (.csv)Every element of the synthetic phantom dataset as tag, value representation, meaning and encoded length, in the ascending tag order the standard mandates. Diff a parser's element list against it to check both the ordering rule and the two different length encodings.
Generated by generation/p8_content.py. Free for any use, no attribution required — license.