Dataset Viewer
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
pipeline_version: string
unit_of_split: string
split_policy: string
session_index: string
segment_index: string
timeline_index: string
review_queue: string
hard_rejection_policy: list<item: string>
child 0, item: string
semantic_policy: string
privacy_policy: string
score_contract: struct<activity_score: string, activity_relative_probability: string, activity_margin: string, activ (... 18 chars omitted)
child 0, activity_score: string
child 1, activity_relative_probability: string
child 2, activity_margin: string
child 3, activity_label: string
model: string
labels: list<item: string>
child 0, item: string
model_revision: string
prompt_ensembles: struct<coding_or_terminal: list<item: string>, web_browsing: list<item: string>, paper_or_document: (... 211 chars omitted)
child 0, coding_or_terminal: list<item: string>
child 0, item: string
child 1, web_browsing: list<item: string>
child 0, item: string
child 2, paper_or_document: list<item: string>
child 0, item: string
child 3, training_monitoring: list<item: string>
child 0, item: string
child 4, file_or_desktop: list<item: string>
child 0, item: string
child 5, communication: list<item: string>
child 0, item: string
child 6, other_screen_work: list<item: string>
child 0, item: string
child 7, non_screen_or_idle: list<item: string>
child 0, item: string
prompt_manifest_sha256: string
to
{'pipeline_version': Value('string'), 'model': Value('string'), 'model_revision': Value('string'), 'labels': List(Value('string')), 'prompt_ensembles': {'coding_or_terminal': List(Value('string')), 'web_browsing': List(Value('string')), 'paper_or_document': List(Value('string')), 'training_monitoring': List(Value('string')), 'file_or_desktop': List(Value('string')), 'communication': List(Value('string')), 'other_screen_work': List(Value('string')), 'non_screen_or_idle': List(Value('string'))}, 'prompt_manifest_sha256': Value('string'), 'score_contract': {'activity_score': Value('string'), 'activity_relative_probability': Value('string'), 'activity_margin': Value('string'), 'activity_label': 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
pipeline_version: string
unit_of_split: string
split_policy: string
session_index: string
segment_index: string
timeline_index: string
review_queue: string
hard_rejection_policy: list<item: string>
child 0, item: string
semantic_policy: string
privacy_policy: string
score_contract: struct<activity_score: string, activity_relative_probability: string, activity_margin: string, activ (... 18 chars omitted)
child 0, activity_score: string
child 1, activity_relative_probability: string
child 2, activity_margin: string
child 3, activity_label: string
model: string
labels: list<item: string>
child 0, item: string
model_revision: string
prompt_ensembles: struct<coding_or_terminal: list<item: string>, web_browsing: list<item: string>, paper_or_document: (... 211 chars omitted)
child 0, coding_or_terminal: list<item: string>
child 0, item: string
child 1, web_browsing: list<item: string>
child 0, item: string
child 2, paper_or_document: list<item: string>
child 0, item: string
child 3, training_monitoring: list<item: string>
child 0, item: string
child 4, file_or_desktop: list<item: string>
child 0, item: string
child 5, communication: list<item: string>
child 0, item: string
child 6, other_screen_work: list<item: string>
child 0, item: string
child 7, non_screen_or_idle: list<item: string>
child 0, item: string
prompt_manifest_sha256: string
to
{'pipeline_version': Value('string'), 'model': Value('string'), 'model_revision': Value('string'), 'labels': List(Value('string')), 'prompt_ensembles': {'coding_or_terminal': List(Value('string')), 'web_browsing': List(Value('string')), 'paper_or_document': List(Value('string')), 'training_monitoring': List(Value('string')), 'file_or_desktop': List(Value('string')), 'communication': List(Value('string')), 'other_screen_work': List(Value('string')), 'non_screen_or_idle': List(Value('string'))}, 'prompt_manifest_sha256': Value('string'), 'score_contract': {'activity_score': Value('string'), 'activity_relative_probability': Value('string'), 'activity_margin': Value('string'), 'activity_label': Value('string')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
PIE2F-LongHorizon
A large-scale dataset of 147 long-horizon screen recording sessions (~646 hours) covering real computer workflows across IDEs, terminals, browsers, research, notes, and experiment monitoring.
PIE2F-LongHorizon preserves complete long-form sessions and provides structured temporal indexes, sampled visual activity labels, quality signals, and session metadata for studying extended computer use.
Dataset Summary
| Sessions | Hours | Temporal Segments |
|---|---|---|
| 147 | 646.21 | 11,696 |
Data
Each session contains a long-form screen recording together with structured metadata and temporal indexes.
The release includes:
- 147 complete session videos
- 646.21 hours of computer workflows
- 11,696 temporal activity segments
- timeline samples every 30 seconds
- weak visual activity labels
- quality measurements
- session metadata
- content hashes
Data Organization
data/videos/
indexes/sessions.parquet
indexes/segments.parquet
indexes/timeline_samples.parquet
indexes/review_queue.parquet
indexes/activity_taxonomy.json
indexes/data_contract.json
metadata/videos/
Intended Use
PIE2F-LongHorizon is designed for research on:
- long-horizon computer-use models
- workflow understanding
- temporal representation learning
- long-context retrieval
- video understanding
- workflow segmentation
- visual state retrieval
- further annotation of computer workflows
Annotations
The session timelines are sampled every 30 seconds and assigned weak visual activity labels.
Nearby predictions are temporally smoothed and grouped into activity segments, producing a lightweight index over the complete 646-hour dataset.
These labels are intended for navigation, retrieval, sampling, and further annotation rather than as human-verified task descriptions.
Notes
PIE2F-LongHorizon contains visual workflow data and does not include synchronized mouse or keyboard actions.
Activity labels are automatically generated weak annotations and may contain errors.
License
CC BY 4.0
Parergon-created annotations, indexes, metadata, and processed dataset components are released under CC BY 4.0.
Processing code released by Parergon is licensed separately under Apache-2.0.
Parergon
Teach AI how great work gets done.
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