add dataset card

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+  - table-question-answering
+  - text-classification
+language:
+  - ko
+  - en
+tags:
+  - finance
+  - disclosure
+  - dart
+  - edgar
+  - sec
+  - xbrl
+  - korea
+  - financial-statements
+  - corporate-filings
+pretty_name: DartLab Company Data
+size_categories:
+  - 1K<n<10K
+---
+
+<div align="center">
+
+# DartLab Data
+
+**Structured company data from DART & EDGAR disclosure filings**
+
+[![GitHub](https://img.shields.io/badge/GitHub-dartlab-ea4647?style=for-the-badge&labelColor=050811&logo=github&logoColor=white)](https://github.com/eddmpython/dartlab)
+[![PyPI](https://img.shields.io/pypi/v/dartlab?style=for-the-badge&color=ea4647&labelColor=050811&logo=pypi&logoColor=white)](https://pypi.org/project/dartlab/)
+[![Docs](https://img.shields.io/badge/Docs-GitHub_Pages-38bdf8?style=for-the-badge&labelColor=050811&logo=github-pages&logoColor=white)](https://eddmpython.github.io/dartlab/)
+[![Sponsor](https://img.shields.io/badge/Sponsor-Buy_Me_A_Coffee-ffdd00?style=for-the-badge&labelColor=050811&logo=buy-me-a-coffee&logoColor=white)](https://buymeacoffee.com/eddmpython)
+
+</div>
+
+## What is this?
+
+Pre-collected [Parquet](https://parquet.apache.org/) files from [DartLab](https://github.com/eddmpython/dartlab) — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map.
+
+This dataset is the **data layer** behind DartLab. When you run `dartlab.Company("005930")`, the library automatically downloads the relevant parquet from this repo.
+
+## Dataset Structure
+
+```
+dart/
+├── docs/          6 companies      ~42 MB     disclosure text (sections, tables, markdown)
+├── finance/       2,735 companies  ~586 MB    financial statements (BS, IS, CF, XBRL)
+└── report/        2,711 companies  ~319 MB    structured disclosure APIs (28 types)
+```
+
+Each file is one company: `{stockCode}.parquet`
+
+### docs — Disclosure Text
+
+Full-text sections from annual/quarterly reports, parsed into structured blocks.
+
+| Column | Description |
+|--------|------------|
+| `rcept_no` | DART filing ID |
+| `rcept_date` | Filing date |
+| `stock_code` | Stock code |
+| `corp_name` | Company name |
+| `report_type` | Annual/quarterly report type |
+| `section_title` | Original section title |
+| `section_order` | Section ordering |
+| `content` | Section text (markdown) |
+| `blockType` | `text` / `table` / `heading` |
+| `year` | Filing year |
+
+### finance — Financial Statements
+
+XBRL-based financial data from DART OpenAPI (`fnlttSinglAcntAll`).
+
+| Column | Description |
+|--------|------------|
+| `bsns_year` | Business year |
+| `reprt_code` | Report quarter code |
+| `stock_code` | Stock code |
+| `corp_name` | Company name |
+| `fs_div` | `CFS` (consolidated) / `OFS` (separate) |
+| `sj_div` | Statement type (BS/IS/CF/SCE) |
+| `account_id` | XBRL account ID |
+| `account_nm` | Account name (Korean) |
+| `thstrm_amount` | Current period amount |
+| `frmtrm_amount` | Prior period amount |
+| `bfefrmtrm_amount` | Two periods prior amount |
+
+### report — Structured Disclosure APIs
+
+28 DART API categories covering governance, compensation, shareholding, and more.
+
+| Column | Description |
+|--------|------------|
+| `apiType` | API category (e.g., `dividend`, `employee`, `executive`) |
+| `year` | Year |
+| `quarter` | Quarter |
+| `stockCode` | Stock code |
+| `corpCode` | DART corp code |
+| *(varies)* | Category-specific columns |
+
+**28 API types:** dividend, employee, executive, majorHolder, treasuryStock, capitalChange, auditOpinion, stockTotal, outsideDirector, corporateBond, and more.
+
+## Usage
+
+### With DartLab (recommended)
+
+```bash
+pip install dartlab
+```
+
+```python
+import dartlab
+
+c = dartlab.Company("005930")   # Samsung Electronics
+c.sections                      # full company map (topic x period)
+c.BS                            # balance sheet
+c.ratios                        # financial ratios
+c.show("businessOverview")      # narrative text
+
+# US companies work the same way
+us = dartlab.Company("AAPL")
+us.BS
+us.ratios
+```
+
+DartLab auto-downloads from this dataset. No manual download needed.
+
+### Direct download
+
+```python
+import polars as pl
+
+# Single file
+url = "https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/dart/finance/005930.parquet"
+df = pl.read_parquet(url)
+```
+
+```bash
+# wget
+wget https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/dart/finance/005930.parquet
+```
+
+## Data Source
+
+- **DART** (Korea): [dart.fss.or.kr](https://dart.fss.or.kr) — Korea's electronic disclosure system operated by the Financial Supervisory Service
+- **EDGAR** (US): [sec.gov/edgar](https://www.sec.gov/edgar) — SEC's Electronic Data Gathering, Analysis, and Retrieval system
+
+All data is sourced from public government disclosure systems. Financial figures are preserved as-is from the original filings — no rounding, no estimation, no interpolation.
+
+## Update Schedule
+
+This dataset is updated automatically via GitHub Actions (twice weekly). New filings are collected incrementally.
+
+## License
+
+MIT — same as [DartLab](https://github.com/eddmpython/dartlab).
+
+## Support
+
+If DartLab is useful for your work, consider supporting the project:
+
+[![Buy Me A Coffee](https://img.shields.io/badge/Buy_Me_A_Coffee-Support-ffdd00?style=for-the-badge&labelColor=050811&logo=buy-me-a-coffee&logoColor=white)](https://buymeacoffee.com/eddmpython)
+
+- [GitHub Issues](https://github.com/eddmpython/dartlab/issues) — bug reports, feature requests
+- [Blog](https://eddmpython.github.io/dartlab/blog/) — 120+ articles on Korean disclosure analysis