Datasets:

aerostratospheric
/

uogw

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema: string
date_utc: timestamp[s]
generated_at_utc: timestamp[s]
curator: string
disclaimer: string
methods: struct<absolute_thresholds: struct<temperature_c: struct<alert_high: int64, watch_high: int64, watch (... 363 chars omitted)
  child 0, absolute_thresholds: struct<temperature_c: struct<alert_high: int64, watch_high: int64, watch_low: int64, alert_low: int6 (... 197 chars omitted)
      child 0, temperature_c: struct<alert_high: int64, watch_high: int64, watch_low: int64, alert_low: int64>
          child 0, alert_high: int64
          child 1, watch_high: int64
          child 2, watch_low: int64
          child 3, alert_low: int64
      child 1, pressure_msl_hpa: struct<watch_low: int64, info_high: int64>
          child 0, watch_low: int64
          child 1, info_high: int64
      child 2, wind_speed_ms: struct<watch: int64, alert: int64>
          child 0, watch: int64
          child 1, alert: int64
      child 3, wave_height_m: struct<watch: int64, alert: int64>
          child 0, watch: int64
          child 1, alert: int64
      child 4, docs: string
      child 5, config: string
  child 1, zscore_normalization: struct<z_pop: string, z_sample: string, z_mad: string, s_range: string, percentile: string>
      child 0, z_pop: string
      child 1, z_sample: string
      child 2, z_mad: string
      child 3, s_range: string
      child 4, percentile: string
  child 2, examples_doc: string
counts: struct<total: int64, alert: int64, watch: int64, info: int64>
  
...
        child 0, pattern: string
          child 1, browse: string
          child 2, api: string
          child 3, site_id: string
          child 4, registration: string
          child 5, portal: string
          child 6, repository: string
          child 7, planned_api: string
          child 8, authoritative: string
          child 9, y2d: string
          child 10, igdr: string
          child 11, license: string
          child 12, realtime: string
          child 13, gdac: string
          child 14, aws: string
          child 15, cds: string
      child 6, upstream: string
      child 7, automation: string
      child 8, first_expected: timestamp[s]
      child 9, coverage: string
      child 10, citation: string
api_base: string
title: string
api_docs: string
subtitle: string
updated: timestamp[s]
homepage: string
layer_summary: struct<ground: string, marine: string, upper_air: string, stratospheric: string, satellite: string,  (... 15 chars omitted)
  child 0, ground: string
  child 1, marine: string
  child 2, upper_air: string
  child 3, stratospheric: string
  child 4, satellite: string
  child 5, flight: string
repository: string
data_hub: string
globe_registration: string
catalog_id: string
license_note: string
license_curated: string
related_repositories: struct<uogw: string, msds_data: string, igdr: string>
  child 0, uogw: string
  child 1, msds_data: string
  child 2, igdr: string
provider_note: string
contact: string
api_version: string
provider: string
to
{'api_version': Value('string'), 'catalog_id': Value('string'), 'title': Value('string'), 'subtitle': Value('string'), 'provider': Value('string'), 'provider_note': Value('string'), 'homepage': Value('string'), 'data_hub': Value('string'), 'repository': Value('string'), 'api_base': Value('string'), 'api_docs': Value('string'), 'license_curated': Value('string'), 'license_note': Value('string'), 'attribution': Value('string'), 'contact': Value('string'), 'globe_registration': Value('string'), 'updated': Value('timestamp[s]'), 'related_repositories': {'uogw': Value('string'), 'msds_data': Value('string'), 'igdr': Value('string')}, 'layer_summary': {'ground': Value('string'), 'marine': Value('string'), 'upper_air': Value('string'), 'stratospheric': Value('string'), 'satellite': Value('string'), 'flight': Value('string')}, 'datasets': List({'id': Value('string'), 'layer': Value('string'), 'title': Value('string'), 'status': Value('string'), 'frequency': Value('string'), 'access': {'pattern': Value('string'), 'browse': Value('string'), 'api': Value('string'), 'site_id': Value('string'), 'registration': Value('string'), 'portal': Value('string'), 'repository': Value('string'), 'planned_api': Value('string'), 'authoritative': Value('string'), 'y2d': Value('string'), 'igdr': Value('string'), 'license': Value('string'), 'realtime': Value('string'), 'gdac': Value('string'), 'aws': Value('string'), 'cds': Value('string')}, 'upstream': Value('string'), 'automation': Value('string'), 'first_expected': Value('timestamp[s]'), 'coverage': Value('string'), 'citation': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema: string
              date_utc: timestamp[s]
              generated_at_utc: timestamp[s]
              curator: string
              disclaimer: string
              methods: struct<absolute_thresholds: struct<temperature_c: struct<alert_high: int64, watch_high: int64, watch (... 363 chars omitted)
                child 0, absolute_thresholds: struct<temperature_c: struct<alert_high: int64, watch_high: int64, watch_low: int64, alert_low: int6 (... 197 chars omitted)
                    child 0, temperature_c: struct<alert_high: int64, watch_high: int64, watch_low: int64, alert_low: int64>
                        child 0, alert_high: int64
                        child 1, watch_high: int64
                        child 2, watch_low: int64
                        child 3, alert_low: int64
                    child 1, pressure_msl_hpa: struct<watch_low: int64, info_high: int64>
                        child 0, watch_low: int64
                        child 1, info_high: int64
                    child 2, wind_speed_ms: struct<watch: int64, alert: int64>
                        child 0, watch: int64
                        child 1, alert: int64
                    child 3, wave_height_m: struct<watch: int64, alert: int64>
                        child 0, watch: int64
                        child 1, alert: int64
                    child 4, docs: string
                    child 5, config: string
                child 1, zscore_normalization: struct<z_pop: string, z_sample: string, z_mad: string, s_range: string, percentile: string>
                    child 0, z_pop: string
                    child 1, z_sample: string
                    child 2, z_mad: string
                    child 3, s_range: string
                    child 4, percentile: string
                child 2, examples_doc: string
              counts: struct<total: int64, alert: int64, watch: int64, info: int64>
                
              ...
                      child 0, pattern: string
                        child 1, browse: string
                        child 2, api: string
                        child 3, site_id: string
                        child 4, registration: string
                        child 5, portal: string
                        child 6, repository: string
                        child 7, planned_api: string
                        child 8, authoritative: string
                        child 9, y2d: string
                        child 10, igdr: string
                        child 11, license: string
                        child 12, realtime: string
                        child 13, gdac: string
                        child 14, aws: string
                        child 15, cds: string
                    child 6, upstream: string
                    child 7, automation: string
                    child 8, first_expected: timestamp[s]
                    child 9, coverage: string
                    child 10, citation: string
              api_base: string
              title: string
              api_docs: string
              subtitle: string
              updated: timestamp[s]
              homepage: string
              layer_summary: struct<ground: string, marine: string, upper_air: string, stratospheric: string, satellite: string,  (... 15 chars omitted)
                child 0, ground: string
                child 1, marine: string
                child 2, upper_air: string
                child 3, stratospheric: string
                child 4, satellite: string
                child 5, flight: string
              repository: string
              data_hub: string
              globe_registration: string
              catalog_id: string
              license_note: string
              license_curated: string
              related_repositories: struct<uogw: string, msds_data: string, igdr: string>
                child 0, uogw: string
                child 1, msds_data: string
                child 2, igdr: string
              provider_note: string
              contact: string
              api_version: string
              provider: string
              to
              {'api_version': Value('string'), 'catalog_id': Value('string'), 'title': Value('string'), 'subtitle': Value('string'), 'provider': Value('string'), 'provider_note': Value('string'), 'homepage': Value('string'), 'data_hub': Value('string'), 'repository': Value('string'), 'api_base': Value('string'), 'api_docs': Value('string'), 'license_curated': Value('string'), 'license_note': Value('string'), 'attribution': Value('string'), 'contact': Value('string'), 'globe_registration': Value('string'), 'updated': Value('timestamp[s]'), 'related_repositories': {'uogw': Value('string'), 'msds_data': Value('string'), 'igdr': Value('string')}, 'layer_summary': {'ground': Value('string'), 'marine': Value('string'), 'upper_air': Value('string'), 'stratospheric': Value('string'), 'satellite': Value('string'), 'flight': Value('string')}, 'datasets': List({'id': Value('string'), 'layer': Value('string'), 'title': Value('string'), 'status': Value('string'), 'frequency': Value('string'), 'access': {'pattern': Value('string'), 'browse': Value('string'), 'api': Value('string'), 'site_id': Value('string'), 'registration': Value('string'), 'portal': Value('string'), 'repository': Value('string'), 'planned_api': Value('string'), 'authoritative': Value('string'), 'y2d': Value('string'), 'igdr': Value('string'), 'license': Value('string'), 'realtime': Value('string'), 'gdac': Value('string'), 'aws': Value('string'), 'cds': Value('string')}, 'upstream': Value('string'), 'automation': Value('string'), 'first_expected': Value('timestamp[s]'), 'coverage': Value('string'), 'citation': Value('string')})}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Aerostratospheric

Unified Open Global Weather (UOGW)

An open atmospheric data commons — curated by Aerostratospheric

Daily Hugging Face mirror of the latest packages from Midwest-Stratospheric/Unified-Open-Global-Weather.


Publisher	Aerostratospheric · Casey, Illinois
Live desk	xDataHub
GitHub	Unified-Open-Global-Weather
Sibling dataset	aerostratospheric/gir
Companion models	uogw-scientific-suite
License	CC BY 4.0
Cadence	GitHub Actions · 11:00 UTC daily (after the 09:00 research package)

UOGW is a versioned discovery and sampling layer for open atmospheric observations: ground, marine, upper-air, stratospheric / space, satellite / model, and first-party MSDS near-space flight products. It does not replace NOAA, NASA, ECMWF, or NWS systems of record.

Research screening only. Not an official forecast or warning product.

Dataset summary

Each sync copies the current GitHub data/latest/, catalog/, and reports/ trees (JSON / Markdown). Charts that are raw PNGs stay on GitHub; this mirror is the machine-readable science package.

Path	What you get
`data/latest/science-package.json`	Combined observations + summary for the current cycle
`data/latest/science-analytics.json`	Extremes, coverage, heat-index flags
`data/latest/anomaly-report.json`	Multi-method research anomaly screen (`none` / `info` / `watch` / `alert`)
`data/latest/global-cities.json`	Live city sample network (temperature, RH, wind, pressure, precip)
`data/latest/casey-hourly.json`	24-hour Casey, Illinois surface series (home site)
`data/latest/hub-endpoints.json`	Stable URL map for the public Data Hub
`catalog/`	Catalog of 25+ source datasets
`reports/`	Daily / weekly status notes

Typical city observation fields: temperature_c, relative_humidity_pct, wind_speed_ms, pressure_msl_hpa, precipitation_mm, lat, lon, ok.

Anomaly rows carry both a human severity and a methods block (z_pop, z_sample, z_mad, percentile, 7-day baseline mean / median / mad).

How to load

from huggingface_hub import hf_hub_download
import json

def load(name: str):
    path = hf_hub_download(
        repo_id="aerostratospheric/uogw",
        repo_type="dataset",
        filename=name,
    )
    return json.load(open(path))

pkg = load("data/latest/science-package.json")
cities = load("data/latest/global-cities.json")
anoms = load("data/latest/anomaly-report.json")
print(pkg.keys())
print("cities", len(cities.get("cities") or []))
print("anomaly counts", anoms.get("counts"))

Companion sklearn heads that consume this package:

from huggingface_hub import snapshot_download
root = snapshot_download("aerostratospheric/uogw-scientific-suite")
# see that repo's inference.py

Intended use

Research screening, education, and reproducible open-tier dashboards
Feature tables for small tabular models (see the companion suite)
Citation / provenance layer in front of agency feeds

Out of scope

Operational NWS / NCEP / ECMWF forecasting
Life-safety alerting
Claiming this package is ERA5, GFS, or a foundation-model weather corpus
Replacing the upstream systems of record

Collection and update process

GitHub Actions in UOGW refresh indexes and science packages through the day.
The 09:00 UTC research package is the usual daily snapshot.
huggingface-daily.yml copies JSON/Markdown here at 11:00 UTC.
Model retrain follows at 12:00 UTC.

Source observations come from public providers (Open-Meteo, NOAA NDBC, NOAA NCEI, NASA and others) plus first-party MSDS / Aerostratospheric flight and Casey ground products. Always cite those providers when you publish derived work.

Limitations and bias

City network is a sample, not a global analysis field.
Anomaly labels recover UOGW's published rule set; they are not independent “discovered” events.
Temporal depth on this mirror is “latest package,” not a multi-year reanalysis.
Historical anomaly snapshots used by the models live on the GIR dataset / repo.

Citation

Aerostratospheric / Midwest Stratospheric Data Systems (2026).
Unified Open Global Weather (UOGW).
https://www.midwestsds.com/
https://github.com/Midwest-Stratospheric/Unified-Open-Global-Weather
https://huggingface.co/datasets/aerostratospheric/uogw

Credits — Aerostratospheric

Curated and published by Aerostratospheric, an Illinois nonprofit corporation. Midwest Stratospheric Data Systems (MSDS) operates as a limited partnership under Aerostratospheric.

Resource	Link
Home	https://www.midwestsds.com/
About	https://midwestsds.com/about.html
xDataHub	https://midwestsds.com/msds-data-hub.html
Defense GIR	https://midwestsds.com/aerostratospheric-defense-gir.html
Contact	https://midwestsds.com/contact/
GitHub	https://github.com/Midwest-Stratospheric
Hugging Face	https://huggingface.co/aerostratospheric

Casey, Illinois (Clark County) · NASA GLOBE GO-4VW9B · Amateur radio KE9CFY · launchcontrol@midwestsds.com

Downloads last month: -

Total file size:

404 kB

Models trained or fine-tuned on aerostratospheric/uogw