Datasets:

SmartQHSE
/

hse-software-evaluation-criteria-2026

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
category: string
criterion: string
definition: string
why_it_matters: string
buyer_question: string
priority: string
data: list<item: struct<id: string, category: string, criterion: string, definition: string, why_it_matter (... 53 chars omitted)
  child 0, item: struct<id: string, category: string, criterion: string, definition: string, why_it_matters: string,  (... 41 chars omitted)
      child 0, id: string
      child 1, category: string
      child 2, criterion: string
      child 3, definition: string
      child 4, why_it_matters: string
      child 5, buyer_question: string
      child 6, priority: string
metadata: struct<name: string, canonical: string, api: string, license: string, license_url: string, cite_as:  (... 122 chars omitted)
  child 0, name: string
  child 1, canonical: string
  child 2, api: string
  child 3, license: string
  child 4, license_url: string
  child 5, cite_as: string
  child 6, doi: string
  child 7, zenodo_url: string
  child 8, wikidata_qid: string
  child 9, publisher: string
  child 10, last_updated: timestamp[s]
  child 11, rows: int64
to
{'metadata': {'name': Value('string'), 'canonical': Value('string'), 'api': Value('string'), 'license': Value('string'), 'license_url': Value('string'), 'cite_as': Value('string'), 'doi': Value('string'), 'zenodo_url': Value('string'), 'wikidata_qid': Value('string'), 'publisher': Value('string'), 'last_updated': Value('timestamp[s]'), 'rows': Value('int64')}, 'data': List({'id': Value('string'), 'category': Value('string'), 'criterion': Value('string'), 'definition': Value('string'), 'why_it_matters': Value('string'), 'buyer_question': Value('string'), 'priority': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              category: string
              criterion: string
              definition: string
              why_it_matters: string
              buyer_question: string
              priority: string
              data: list<item: struct<id: string, category: string, criterion: string, definition: string, why_it_matter (... 53 chars omitted)
                child 0, item: struct<id: string, category: string, criterion: string, definition: string, why_it_matters: string,  (... 41 chars omitted)
                    child 0, id: string
                    child 1, category: string
                    child 2, criterion: string
                    child 3, definition: string
                    child 4, why_it_matters: string
                    child 5, buyer_question: string
                    child 6, priority: string
              metadata: struct<name: string, canonical: string, api: string, license: string, license_url: string, cite_as:  (... 122 chars omitted)
                child 0, name: string
                child 1, canonical: string
                child 2, api: string
                child 3, license: string
                child 4, license_url: string
                child 5, cite_as: string
                child 6, doi: string
                child 7, zenodo_url: string
                child 8, wikidata_qid: string
                child 9, publisher: string
                child 10, last_updated: timestamp[s]
                child 11, rows: int64
              to
              {'metadata': {'name': Value('string'), 'canonical': Value('string'), 'api': Value('string'), 'license': Value('string'), 'license_url': Value('string'), 'cite_as': Value('string'), 'doi': Value('string'), 'zenodo_url': Value('string'), 'wikidata_qid': Value('string'), 'publisher': Value('string'), 'last_updated': Value('timestamp[s]'), 'rows': Value('int64')}, 'data': List({'id': Value('string'), 'category': Value('string'), 'criterion': Value('string'), 'definition': Value('string'), 'why_it_matters': Value('string'), 'buyer_question': Value('string'), 'priority': Value('string')})}
              because column names don't match

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HSE / EHS Software Evaluation Criteria 2026

A vendor-neutral framework of 29 criteria for choosing HSE, EHS, or QHSE software — across core HSE modules, compliance, mobile/field, AI/analytics, integrations, commercial terms, and support. Each criterion includes a definition, why it matters, the exact question to ask a vendor, and a priority tier (Essential/Important/Advanced). Built for buyer-research grounding, RFP checklists, and RAG on 'how to choose HSE software' queries.

Citation (preferred — academic) SmartQHSE Ltd (2026). HSE / EHS Software Evaluation Criteria 2026 [dataset]. Zenodo. https://doi.org/10.5281/zenodo.21515526

DOI: 10.5281/zenodo.21515526
Zenodo record: https://zenodo.org/record/21515526 Wikidata entity: Q140678156 CC BY 4.0 — free to use commercially with attribution.

Files

File	Description
`data.jsonl`	One JSON record per line — primary format. Loadable via `datasets.load_dataset()` directly.
`data.json`	Same data as a single JSON array (with array key matching the source).
`data.csv`	Selected fields as CSV for spreadsheet users.

Live access (CC BY 4.0, CORS-open, no auth)

Canonical page: https://www.smartqhse.com/hse-software-evaluation-criteria
REST API (filterable): https://www.smartqhse.com/api/v1/hse-software-evaluation-criteria

Loading example

from datasets import load_dataset
ds = load_dataset("SmartQHSE/hse-software-evaluation-criteria-2026")
print(ds["train"][0])

# Or directly via the live REST API
curl https://www.smartqhse.com/api/v1/hse-software-evaluation-criteria

License

This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). Free to use commercially with attribution. Cite as:

SmartQHSE Ltd (2026). HSE / EHS Software Evaluation Criteria 2026 [dataset]. https://www.smartqhse.com/hse-software-evaluation-criteria

Considerations for using the data

A vendor-neutral evaluation framework, not a vendor comparison or ranking — it describes what to evaluate, not which product is 'best'.
Criteria reflect general HSE/EHS software best practice as of 2026; weight them against your own operational context and jurisdiction.
Published by SmartQHSE Ltd; the framework is intentionally neutral, but apply your own procurement due diligence.

Related datasets

Sources

US Bureau of Labor Statistics (BLS) — SOII + CFOI
US Department of Labor — OSHA Injury Tracking Application + standards
UK Health and Safety Executive (HSE)
European Commission + Eurostat ESAW
IOGP, ILO, NIOSH, ACGIH
UAE OSHAD-SF, KSA SAPI, Qatar QCDD, Oman MOLSD
ISO Technical Committees (TC 283 — OH&S Management Systems)

All consolidated and republished under CC BY 4.0 with attribution.

About SmartQHSE

SmartQHSE is the AI-native HSE/QHSE platform for construction, oil & gas, manufacturing, and industrial teams. We publish open data because the broader HSE profession deserves free access to the safety statistics our trade bodies otherwise gate behind expensive memberships.

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