Update README.md

README.md

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-license: cc-by-4.0
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+---
+language:
+  - en
+  - th
+license: cc-by-4.0
+tags:
+  - thailand
+  - bangkok
+  - road-safety
+  - traffic-injuries
+  - injury-surveillance
+  - public-health
+  - tabular-data
+  - csv
+dataset_type: tabular
+pretty_name: Thailand Road-Traffic Injury Aggregates (2018)
+---
+
+# Thailand Road-Traffic Injury Aggregates (2018)
+
+**Canonical dataset page (EN/TH):** https://bangkokfamilylawyer.com/datasets-injury-th/  
+**DOI (this version):** https://doi.org/10.5281/zenodo.17538573  
+**Concept DOI (latest):** https://doi.org/10.5281/zenodo.17538574  
+
+**Author:** Jean Maurice Cecilia Menzel  
+**Publisher:** AppDevBangkok / UdonLaw  
+**License:** CC BY 4.0
+
+This repository provides aggregated indicators derived from Thailand’s official Injury Surveillance
+data (Department of Disease Control, Ministry of Public Health) for **calendar year 2018**.
+
+All outputs are privacy-preserving aggregates; individual-level records are **not** included.
+
+## Contents
+
+Key CSV files (2018 slice):
+
+- `province_2018.csv` — Cases by province.
+- `bkk_quarter_2018.csv` — Bangkok cases by quarter.
+- `age_bins_2018.csv` — Cases by age group.
+- `sex_2018.csv` — Cases by sex.
+- `mode_mix_bkk_2018.csv` — Mode / road-user mix for Bangkok.
+- `top10_provinces_2018.csv` — Top 10 provinces by case count.
+- `bkk_top_amphoe_2018.csv` — Leading Bangkok districts.
+- `qa_year_counts_2018.csv` — Year-level row counts (QA).
+- `qa_coverage_province_2018.csv` — Coverage / completeness indicators.
+- `qa_summary.json` — Human-readable QA summary.
+
+File names may be extended as new QA tables or visualizations are added.
+
+## Source Data
+
+Source dataset (not redistributed here):
+
+- **Injury Surveillance – DDC**  
+  Injury Prevention Division, Department of Disease Control, Ministry of Public Health, Thailand.  
+  Catalog: https://data.go.th/en/dataset/injury-surveillance
+
+The original dataset remains under its own terms and conditions.
+This repository only provides derived aggregates.
+
+## Methodology (summary)
+
+- Filtered to events in **2018**.
+- Robust date parsing with support for Thai formats and Buddhist Era → Gregorian conversion.
+- Records with invalid or missing core fields are excluded from aggregates.
+- Aggregations computed by:
+  - Province and (for Bangkok) district
+  - Quarter (Q1–Q4)
+  - Age groups
+  - Sex
+  - Selected mode / road-user categories
+- Small cells may be combined or omitted to reduce re-identification risk.
+
+For full details, see the canonical documentation at:
+
+> https://bangkokfamilylawyer.com/datasets-injury-th/
+
+## How to use
+
+Example (Python):
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+ds = load_dataset(
+    "appdevbangkok/thailand-road-traffic-injury-aggregates-2018",
+    split="train"
+)
+# or load specific CSVs directly via raw URLs if preferred