---
license: cc-by-4.0
task_categories:
- tabular-classification
- time-series-forecasting
language:
- en
tags:
- tsfile
- timeseries
- internet-censorship
- network-measurement
- internet-freedom
- ooni
- ioda
- censoredplanet
- time-series
- format:tsfile
pretty_name: Voidly Benchmark v1 (TsFile)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: evidence
    path: evidence.tsfile
  - split: incident_evidence
    path: incident_evidence.tsfile
  - split: incidents
    path: incidents.tsfile
---

# Voidly Benchmark v1 (TsFile)

Apache TsFile version of [`emperor-mew/voidly-bench-v1`](https://huggingface.co/datasets/emperor-mew/voidly-bench-v1).

## Overview

An evidence-backed internet-measurement benchmark: individual observations from public measurement sources (IODA, OONI, …) with their signal level and confidence, the incident-to-evidence links that group them, and the resulting incident records with severity, mechanism and duration.

- **Tables:** 3 TsFile table(s), 86,546 rows in total.
  - `evidence`: 49,596 rows, 2016-07-15 00:00:00 → 2026-04-16 18:00:00.093000 (UTC)
  - `incident_evidence`: 35,376 rows, 1970-01-01 00:00:00 → 2026-04-17 03:47:14.498000 (UTC)
  - `incidents`: 1,574 rows, 2016-07-15 00:00:00 → 2026-04-12 08:00:00.001000 (UTC)

## Schema (TsFile structure)

### `evidence.tsfile`

- **Time** (INT64, milliseconds) — the source `observed_at` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (21): `id` (int64), `source` (string), `kind` (string), `source_ref` (string), `source_url` (string), `retrieved_at` (string), `country_code` (string), `region_code` (string), `asn` (double), `signal_type` (string), `signal_value` (double), `signal_baseline` (double), `signal_level` (string), `upstream_severity` (string), `domain` (string), `continent` (string), `un_subregion` (string), `risk_tier` (double), `domain_category` (string), `blocking_method` (string), `confidence` (double)

### `incident_evidence.tsfile`

- **Time** (INT64, milliseconds) — the source `linked_at` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (4): `incident_id` (string), `evidence_id` (int64), `link_confidence` (double), `link_reason` (string)

### `incidents.tsfile`

- **Time** (INT64, milliseconds) — the source `first_seen` column.
- **TAG**: none — the table is stored as a single device.
- **FIELD** (18): `id` (string), `readable_id` (string), `country_code` (string), `title` (string), `incident_type` (string), `mechanism` (string), `severity` (string), `confidence` (double), `status` (string), `last_seen` (string), `duration_hours` (double), `affected_domains` (string), `affected_services` (string), `affected_asns` (string), `measurement_count` (int64), `anomaly_rate` (double), `sources` (string), `revision` (int64)

## Conversion notes

- One TsFile per source Parquet (3 tables), each stored as a single device.
- Time = the observation timestamp of each table as INT64 milliseconds: `observed_at` (internet-measurement evidence), `linked_at` (incident-to-evidence links) and `first_seen` (incidents). `incidents.last_seen` is kept as a FIELD so both ends of an incident survive.
- No column is dropped apart from `affected_services` / `affected_asns`, which the source stores with Arrow type `null` and which are empty in every row.
- Not converted: `reproduce.py, reproduce_results.json, source_reliability.json, DATA_HASHES.txt, LICENSE` — the reproduction script, its recorded results and integrity hashes are not time series; they are mirrored into the repository as reference files.

## Usage

Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:

```python
from pathlib import Path
from tsfile import TsFileReader

path = Path("evidence.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())
```

## Source & license

- Original dataset: [`emperor-mew/voidly-bench-v1`](https://huggingface.co/datasets/emperor-mew/voidly-bench-v1)
- Source card title / publisher: Voidly Benchmark v1
- License: cc-by-4.0 (as declared by the original dataset).