README.md

---
dataset_info:
  features:
  - name: query
    dtype: string
  - name: image
    dtype: 'null'
  - name: annot
    dtype: string
  - name: reasoning
    dtype: 'null'
  - name: rationale
    dtype: string
  - name: cate
    dtype: string
  - name: task
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 210741
    num_examples: 76
  download_size: 54390
  dataset_size: 210741
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
tags:
- smart-manufacturing
- sft
- industrial
license: other
extra_gated_prompt: This dataset is released for **research use**. Access is reviewed
  and granted **manually** by the maintainers. Please state your name, affiliation,
  and intended use.
pretty_name: PF-D13
---
# PF-D13

The **agentic-scenario layer** of PHMForge, reformatted into the unified SFT schema. Each scenario becomes a **T-E3** (agentic tool-use) record; Cost-Benefit and Safety/Policy scenarios additionally become a **T-D1** (decision) record.

> The repository name is an internal code. See **Provenance** below for the underlying dataset.

## Records

**76** records. `query` = scenario question, `annot` = ground truth (answer + acceptance criteria, with the rationale removed). `reasoning` is **`null`** (these scenarios ship no chain-of-thought); the scenario's native **templated** rationale is preserved in a dedicated **`rationale`** field — a D13-specific column beyond the unified 7-field schema. 8 scenarios built on Week2-overlap datasets (CWRU / IMS / XJTU) are excluded (cross-paper de-dup) → 67 scenarios → 76 records.

## Unified SFT schema (8 fields)

| field | type | meaning |
|---|---|---|
| `query` | str | the question / query / instruction |
| `image` | Image \| null | always `null` in this dataset |
| `annot` | str \| list[str] | ground-truth answer + acceptance criteria (the scenario rationale is removed and kept separately in `rationale`) |
| `reasoning` | str \| null | always `null` here — these scenarios carry no chain-of-thought / thinking trace |
| `rationale` | str \| null | **D13-specific field.** The scenario's native answer-justification — a *templated* one-liner derived from the ground-truth labels (e.g. *“Based on ground truth RUL values from RUL_FD001.txt …”*), **not** an LLM/CoT reasoning trace; `null` when the source has none |
| `cate` | "A".."E" | one of the five SFT categories (this dataset: E, D) |
| `task` | "T-xx" | unified task id (this dataset: T-E3 + T-D1) |
| `metadata` | str (JSON) | all other info; carries a "split" key when the source has train/val/test |

## Load

```python
from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/PF-D13")
```
_Gated — request access on the dataset page; access is granted manually by the maintainers._

## Provenance & license

This dataset is a **reformatted derivative** (unified SFT schema) of:

PHMForge — *Evaluating LLM Agents on Industrial Prognostics through MCP-Native, Algorithm-Grounded Tools* (Columbia + IBM).

- Paper: https://arxiv.org/abs/2604.01532
- Code: https://github.com/DeveloperMindset123/PHMForge-A-Scenario-Driven-Agentic-Benchmark-for-Industrial-Asset-Lifecycle-Maintenance

Refer to the upstream source for the original licensing terms; this reformatted version is shared for research use. Please cite the upstream work.

## Not yet included

**Raw signal layer (T-C1 / T-C2) — not yet included.** PHMForge's underlying sensor-signal datasets (13 PDMBench subsets + C-MAPSS + EngineMT-QA) map to time-series tasks whose input is *signal → time-series image*. That encoding step is owned by the team and **not yet frozen**, so the signal layer is intentionally **not** converted here yet; it will be added once the encoding is fixed.