PowerCoT-1

PowerCoT-1 is an instruction-tuning dataset for power-system dispatch and control. It contains multimodal image-text reasoning samples and general text reasoning samples, each annotated with a chain-of-thought (CoT). The dataset is built through a multi-stage data-engineering pipeline with rule-based cleaning and manual spot-checks, totaling roughly 1.8 million samples.

Dataset Structure

Config	Source	Samples	Modality	CoT	Shards
multimodal_power	Power-scenario web images	27,464	Image-text	Yes	11
multimodal_pdf	Academic-paper figures	6,173	Image-text	Partial (see thinking)	5
text_reasoning	General-domain corpus	1,771,671	Text-only	Yes	9
Total		1,805,308			25

Default configs are multimodal_power and multimodal_pdf. text_reasoning is excluded by default because its 1.77M rows are too large for the dataset viewer to index; load it manually via data_files (see below).

Each config is a single train split that can be loaded independently — no need to download the full 8 GB.

Field Descriptions

Multimodal configs (multimodal_power, multimodal_pdf)

• messages: list[{role, content}] — conversation turns with role ∈ {user, assistant}. The user content carries an image placeholder where the image appears; the assistant content starts with a chain-of-thought block followed by the final answer. (The loading example below shows the exact tag form.)
• images: list[Image] — images embedded in the Parquet via the HuggingFace Image feature. After load_dataset they are returned as PIL images and can be previewed in the HF Viewer.

Additional field for multimodal_pdf

• thinking: bool — whether the sample includes a chain-of-thought block (True) or is a direct answer (False).

Text config (text_reasoning)

• messages: list[{role, content}] — a user question paired with an assistant answer that includes a chain-of-thought block.

Loading Examples

from datasets import load_dataset

# 1) Multimodal power scenarios (with images)
ds = load_dataset("PowerCoT-1", "multimodal_power", split="train")
print(ds[0]["messages"][0]["content"])   # '<image>What does this image roughly describe?...'
img = ds[0]["images"][0]                  # PIL.Image — call img.show() directly
print(ds[0]["messages"][1]["content"])   # '<think>\n...</think>\n...'

# 2) Paper figures (filter by the thinking field)
ds = load_dataset("PowerCoT-1", "multimodal_pdf", split="train")
with_think = ds.filter(lambda x: x["thinking"])

# 3) General text reasoning (not a default config — specify files manually).
#    1.77M rows: streaming is recommended.
ds = load_dataset("PowerCoT-1", data_files="data/text_reasoning/train-*.parquet", split="train", streaming=True)
for ex in ds:
    print(ex["messages"])
    break

Data Engineering Pipeline

Data construction has three stages:

1. Data Source Collection

• IndustryCorpus2 broadens domain coverage.
• 31k public images covering 78 keywords in the power-system and control domain.
• Academic papers selected by keyword prominence and citation count (recent publications prioritized).
• Domain documents (e.g., power-system operation-and-maintenance manuals) form a structured knowledge base.

2. Data Generation

• Mineru parses papers into Markdown.
• Qwen3-32B (via vLLM on an H20 GPU, thinking mode on) generates question–CoT–answer triplets in parallel, yielding 7.5k high-quality text samples.
• Image captions come from Qwen3-VL-235B-A22B-Thinking, GLM4.5V, and a locally deployed GLM4.1V-9B-Thinking.
• In total the pipeline produces 34k multimodal samples and 1.771M general-reasoning triplets.

3. Rule-Based Cleaning

• Text filtering removes low-quality samples by length and repetition statistics.
• Multimodal consistency check verifies image–text alignment and discards mismatched pairs.
• Manual spot-checks further ensure quality.
• The 31k images are refined down to 28k high-quality samples.

Reproduction Script

scripts/build_powercot1.py rebuilds this Parquet dataset from the original jsonl files:

python scripts/build_powercot1.py --src <original-jsonl-dir> --out data

It streams the input, repairs image paths, embeds image bytes, and writes Parquet shards of ~500 MB each.

Limitations & License

• Released under CC-BY-NC-4.0, for non-commercial research use only.
• Sources include public web images, derivatives of academic papers, and IndustryCorpus2. Copyright of each original item remains with its owner; users must verify and comply with the underlying licenses.
• The data is provided "as is", without any express or implied warranties.

Citation

The paper is currently under submission; a BibTeX citation will be added upon formal publication.