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HawkFranklin-Research
/

UAP-May8

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Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 192, in _split_generators
                  raise ValueError(f"Found metadata files with different extensions: {list(metadata_ext)}")
              ValueError: Found metadata files with different extensions: ['.csv', '.parquet']
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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UFO Government Files

This dataset is a local staging copy of the publicly released U.S. government UFO/UAP files from the May 8, 2026 release.

It is organized for direct upload to Hugging Face Datasets and is intended for archive analysis, metadata review, and reproducible document inspection.

Context

Based on the tremendous interest shown, I will be directing the Secretary of War, and other relevant Departments and Agencies, to begin the process of identifying and releasing Government files related to alien and extraterrestrial life, unidentified aerial phenomena (UAP), and unidentified flying objects (UFOs), and any and all other information connected to these highly complex, but extremely interesting and important, matters. GOD BLESS AMERICA!

metadata.parquet: cleaned canonical metadata table
metadata.csv: cleaned canonical metadata table in CSV form
metadata_raw.csv: exact export of the source metadata
files/: mirrored PDFs, videos, and images organized by agency

Dataset Snapshot

Metric	Value
Raw metadata rows	162
Distinct documents	159
Agencies	4
File groups	3
PDFs	120
Videos	25 distinct / 28 raw rows
Images	14

Folder Layout

hf-usa-gov/
├── README.md
├── metadata.csv
├── metadata.parquet
├── metadata_raw.csv
└── files/
    ├── Department of State/
    ├── Department of War/
    ├── FBI/
    └── NASA/

Metadata Fields

Column	Description
`Title`	Record title from the release table
`Agency`	Source agency or collection label
`Release Date`	Release date for the public file set
`Incident Date`	Date associated with the underlying incident, if present
`Incident Location`	Location associated with the underlying incident, if present
`PDF	Image Link`
`Type`	Source file type label
`Document Link`	Canonical link used in the local mirror
`Source Filename`	Sanitized source filename
`File Group`	Normalized storage group, such as `PDF`, `VID`, or `IMG`
`Output Path`	Relative path to the mirrored file

Notes

The source release contains 162 metadata rows, but two video items are duplicated, leaving 159 distinct documents.
metadata.csv and metadata.parquet are deduplicated on Output Path.
metadata_raw.csv preserves the exact exported table before deduplication.
Missingness is concentrated in Incident Date and Incident Location, so the dataset is best suited for descriptive and archival analysis rather than direct quantitative inference.
The local file tree preserves the source agency structure so documents remain easy to trace.