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Datasets:
aerostratospheric
/
gir

Tasks:
Tabular Classification
Text Classification
Modalities:
Geospatial
Languages:
English
Size:
n<1K
Tags:
geospatial
hazards
open-data
eonet
usgs
nws
DOI:
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
status: string
secret: string
tier: string
anomalies: list<item: struct<kind: string, severity: string, subject: string, metric: string, value: double, th (... 252 chars omitted)
  child 0, item: struct<kind: string, severity: string, subject: string, metric: string, value: double, threshold: do (... 240 chars omitted)
      child 0, kind: string
      child 1, severity: string
      child 2, subject: string
      child 3, metric: string
      child 4, value: double
      child 5, threshold: double
      child 6, detail: string
      child 7, extra: struct<methods: struct<n: int64, x: double, z_pop: double, z_sample: double, z_mad: double, s_range: (... 110 chars omitted)
          child 0, methods: struct<n: int64, x: double, z_pop: double, z_sample: double, z_mad: double, s_range: double, percent (... 93 chars omitted)
              child 0, n: int64
              child 1, x: double
              child 2, z_pop: double
              child 3, z_sample: double
              child 4, z_mad: double
              child 5, s_range: double
              child 6, percentile: double
              child 7, mean: double
              child 8, median: double
              child 9, pstdev: double
              child 10, sample_stdev: double
              child 11, mad: double
counts: struct<total: int64, alert: int64, watch: int64, info: int64>
  child 0, total: int64
  child 1, alert: int64
  child 2, watch: int64
  child 3, info: int64
schema: string
date_utc: timestamp[s]
generated_a
...

          child 8, median: double
          child 9, pstdev: double
          child 10, sample_stdev: double
          child 11, mad: double
disclaimer: string
curator: 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
to
{'schema': Value('string'), 'date_utc': Value('timestamp[s]'), 'generated_at_utc': Value('timestamp[s]'), 'curator': Value('string'), 'disclaimer': Value('string'), 'methods': {'absolute_thresholds': {'temperature_c': {'alert_high': Value('int64'), 'watch_high': Value('int64'), 'watch_low': Value('int64'), 'alert_low': Value('int64')}, 'pressure_msl_hpa': {'watch_low': Value('int64'), 'info_high': Value('int64')}, 'wind_speed_ms': {'watch': Value('int64'), 'alert': Value('int64')}, 'wave_height_m': {'watch': Value('int64'), 'alert': Value('int64')}, 'docs': Value('string'), 'config': Value('string')}, 'zscore_normalization': {'z_pop': Value('string'), 'z_sample': Value('string'), 'z_mad': Value('string'), 's_range': Value('string'), 'percentile': Value('string')}, 'examples_doc': Value('string')}, 'counts': {'total': Value('int64'), 'alert': Value('int64'), 'watch': Value('int64'), 'info': Value('int64')}, 'anomalies': List({'kind': Value('string'), 'severity': Value('string'), 'subject': Value('string'), 'metric': Value('string'), 'value': Value('float64'), 'threshold': Value('float64'), 'detail': Value('string'), 'extra': {'methods': {'n': Value('int64'), 'x': Value('float64'), 'z_pop': Value('float64'), 'z_sample': Value('float64'), 'z_mad': Value('float64'), 's_range': Value('float64'), 'percentile': Value('float64'), 'mean': Value('float64'), 'median': Value('float64'), 'pstdev': Value('float64'), 'sample_stdev': Value('float64'), 'mad': Value('float64')}}}), 'zscore_by_city': List({'id': Value('string'), 'name': Value('string'), 'methods': {'n': Value('int64'), 'x': Value('float64'), 'z_pop': Value('float64'), 'z_sample': Value('float64'), 'z_mad': Value('float64'), 's_range': Value('float64'), 'percentile': Value('float64'), 'mean': Value('float64'), 'median': Value('float64'), 'pstdev': Value('float64'), 'sample_stdev': Value('float64'), 'mad': Value('float64')}}), 'inputs': {'global_cities': Value('bool'), 'ndbc': Value('bool'), 'casey': Value('bool'), 'baseline_files_used': Value('int64')}}
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
              status: string
              secret: string
              tier: string
              anomalies: list<item: struct<kind: string, severity: string, subject: string, metric: string, value: double, th (... 252 chars omitted)
                child 0, item: struct<kind: string, severity: string, subject: string, metric: string, value: double, threshold: do (... 240 chars omitted)
                    child 0, kind: string
                    child 1, severity: string
                    child 2, subject: string
                    child 3, metric: string
                    child 4, value: double
                    child 5, threshold: double
                    child 6, detail: string
                    child 7, extra: struct<methods: struct<n: int64, x: double, z_pop: double, z_sample: double, z_mad: double, s_range: (... 110 chars omitted)
                        child 0, methods: struct<n: int64, x: double, z_pop: double, z_sample: double, z_mad: double, s_range: double, percent (... 93 chars omitted)
                            child 0, n: int64
                            child 1, x: double
                            child 2, z_pop: double
                            child 3, z_sample: double
                            child 4, z_mad: double
                            child 5, s_range: double
                            child 6, percentile: double
                            child 7, mean: double
                            child 8, median: double
                            child 9, pstdev: double
                            child 10, sample_stdev: double
                            child 11, mad: double
              counts: struct<total: int64, alert: int64, watch: int64, info: int64>
                child 0, total: int64
                child 1, alert: int64
                child 2, watch: int64
                child 3, info: int64
              schema: string
              date_utc: timestamp[s]
              generated_a
              ...
              
                        child 8, median: double
                        child 9, pstdev: double
                        child 10, sample_stdev: double
                        child 11, mad: double
              disclaimer: string
              curator: 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
              to
              {'schema': Value('string'), 'date_utc': Value('timestamp[s]'), 'generated_at_utc': Value('timestamp[s]'), 'curator': Value('string'), 'disclaimer': Value('string'), 'methods': {'absolute_thresholds': {'temperature_c': {'alert_high': Value('int64'), 'watch_high': Value('int64'), 'watch_low': Value('int64'), 'alert_low': Value('int64')}, 'pressure_msl_hpa': {'watch_low': Value('int64'), 'info_high': Value('int64')}, 'wind_speed_ms': {'watch': Value('int64'), 'alert': Value('int64')}, 'wave_height_m': {'watch': Value('int64'), 'alert': Value('int64')}, 'docs': Value('string'), 'config': Value('string')}, 'zscore_normalization': {'z_pop': Value('string'), 'z_sample': Value('string'), 'z_mad': Value('string'), 's_range': Value('string'), 'percentile': Value('string')}, 'examples_doc': Value('string')}, 'counts': {'total': Value('int64'), 'alert': Value('int64'), 'watch': Value('int64'), 'info': Value('int64')}, 'anomalies': List({'kind': Value('string'), 'severity': Value('string'), 'subject': Value('string'), 'metric': Value('string'), 'value': Value('float64'), 'threshold': Value('float64'), 'detail': Value('string'), 'extra': {'methods': {'n': Value('int64'), 'x': Value('float64'), 'z_pop': Value('float64'), 'z_sample': Value('float64'), 'z_mad': Value('float64'), 's_range': Value('float64'), 'percentile': Value('float64'), 'mean': Value('float64'), 'median': Value('float64'), 'pstdev': Value('float64'), 'sample_stdev': Value('float64'), 'mad': Value('float64')}}}), 'zscore_by_city': List({'id': Value('string'), 'name': Value('string'), 'methods': {'n': Value('int64'), 'x': Value('float64'), 'z_pop': Value('float64'), 'z_sample': Value('float64'), 'z_mad': Value('float64'), 's_range': Value('float64'), 'percentile': Value('float64'), 'mean': Value('float64'), 'median': Value('float64'), 'pstdev': Value('float64'), 'sample_stdev': Value('float64'), 'mad': Value('float64')}}), 'inputs': {'global_cities': Value('bool'), 'ndbc': Value('bool'), 'casey': Value('bool'), 'baseline_files_used': Value('int64')}}
              because column names don't match

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Aerostratospheric

Aerostratospheric Defense GIR

Open-tier Geospatial Information Repository — published by Aerostratospheric

Daily Hugging Face mirror of Midwest-Stratospheric/aerostratospheric-defense-gir.

Publisher Aerostratospheric · Casey, Illinois
Live page Defense GIR
GitHub aerostratospheric-defense-gir
Sibling dataset aerostratospheric/uogw
Companion models gir-open-tier-suite
License MIT
Cadence GitHub Actions · 19:30 UTC daily (after the 18:00 ingest)

GIR is a public “briefing binder”: storms already warned, earthquakes already measured, free satellite catalog cards, public cyber patch lists, and open research-flight summaries. Partner or restricted sensor products stay out of this tree on purpose.

Open public feeds only. Not classified. Not DEFCON / FPCON.
Not a substitute for NWS, USGS, CISA, or any official alert channel.

Dataset summary

Path What you get
data/manifests/manifest_latest.json Per-source ok/total for the latest ingest
data/events/ NWS active alerts, USGS M≥2.5 day, EONET, DONKI
data/anomalies/ Dated UOGW anomaly snapshots plus uogw_anomalies_latest.json
data/imagery_index/ Sentinel-2 STAC index (library cards, not pixels)
data/defense_open/ CISA KEV, OpenSky / OurAirports samples, other public lists
data/status/us_open_conditions_latest.json GREEN / YELLOW / ORANGE / RED open-tier banner
data/flight_logs/ Public research balloon / test summaries when published
catalog/ Source catalog
reports/ Daily executive summaries (reports/latest.md)

US open-status is a published rule over public counts (NWS volume, max USGS magnitude, UOGW alert/watch flags, active public flights). It is explicitly not military readiness.

How to load 

from huggingface_hub import hf_hub_download
import json

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

manifest = load("data/manifests/manifest_latest.json")
status = load("data/status/us_open_conditions_latest.json")
print("overall", status.get("overall_level"), status.get("overall_message"))

NWS GeoJSON example:

nws = load("data/events/nws_active_alerts_latest.geojson")
print("nws features", len(nws.get("features") or []))

Intended use

  • Morning open briefings for research / STEM / uncleared partners
  • Audit of “what is already public” for a region
  • Feature tables for the companion open-tier models
  • Grant / SAM.gov narrative support for a responsible open-data posture

Out of scope

  • Classified basing, targeting, or kinetic control
  • Sole life-safety alerting
  • Treating the banner as DEFCON or FPCON
  • Replacing NWS, USGS, NASA EONET, or CISA official products

Collection and update process

  1. GIR ingest runs at 06:00 and 18:00 UTC.
  2. Pipeline: open-tier ingest → US open-status → charts → daily exec summary.
  3. huggingface-daily.yml copies JSON / CSV / Markdown here at 19:30 UTC.
  4. Model retrain follows at 20:00 UTC.

Limitations and bias

  • Snapshot depth is “latest + a short dated archive,” not a multi-decade hazard climatology.
  • NWS alert volume is a count, not a population-weighted risk index.
  • Airport name heuristics marked “military” are not official basing lists.
  • OpenSky / flight samples can be empty outside public research windows.

Citation 

Aerostratospheric / Midwest Stratospheric Data Systems (2026).
Aerostratospheric Defense GIR.
https://www.midwestsds.com/
https://midwestsds.com/aerostratospheric-defense-gir.html
https://github.com/Midwest-Stratospheric/aerostratospheric-defense-gir
https://huggingface.co/datasets/aerostratospheric/gir

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
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Models trained or fine-tuned on aerostratospheric/gir