Update README.md

README.md

@@ -1,32 +1,51 @@
 ---
 license: cc-by-4.0
+pretty_name: GPlates Tectonic Intelligence Dataset
+
 task_categories:
 - other
 - feature-extraction
+
 tags:
 - geoscience
 - geology
 - tectonics
 - plate-tectonics
 - geospatial
-- earth-observation
+- gis
+- geoai
 - remote-sensing
+- earth-observation
+- earth-science
+
 language:
 - en
-pretty_name: GPlates Tectonic Intelligence Dataset
-size_categories:
-- 10K<n<100K
 ---
 
-![GPlates Tectonic Intelligence Dataset](https://i.ibb.co/GvgsCy4d/Chat-GPT-Image-Aug-18-2026-12-48-59-AM.png)
+<p align="center">
+<img src="https://i.ibb.co/GvgsCy4d/Chat-GPT-Image-Aug-18-2026-12-48-59-AM.png" alt="NORA Research Lab Banner" width="100%">
+</p>
 
 # GPlates Tectonic Intelligence Dataset
 
-Global, AI-ready tectonic context for every point on Earth — built from GPlates/EarthByte plate reconstruction data.
+[![GitHub](https://img.shields.io/badge/GitHub-NORA_Research_Lab-black?logo=github)](https://github.com/Nora-Research-Lab)
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-NORA-yellow?logo=huggingface)](https://huggingface.co/NoraResearchLab)
+[![LinkedIn](https://img.shields.io/badge/LinkedIn-NORA-blue?logo=linkedin)](https://www.linkedin.com/company/nora-research-lab)
+[![X](https://img.shields.io/badge/X-@noraresearchlab-black?logo=x)](https://x.com/noraresearchlab)
+
+## Overview
+
+GPlates Tectonic Intelligence Dataset is a standardized geospatial dataset maintained by NORA Research Lab, converting the Müller et al. (2019) global plate reconstruction model into AI-ready spatial features.
 
-## Dataset Summary
+Every cell in a 1.0° global grid (360 × 180 = 64,800 points, present day / 0 Ma) is tagged with its reconstruction plate ID, seafloor age, and distance to the nearest mid-ocean ridge, subduction trench, transform fault, and coastline — turning raw tectonic geometry into tabular features for geological, geophysical, exploration, and environmental ML applications.
 
-This dataset converts the Müller et al. (2019) global plate reconstruction model into a standardized, gridded feature set. Every cell in a 1.0° global grid (360 × 180 = 64,800 points, present day / 0 Ma) is tagged with its reconstruction plate ID, seafloor age, and distance to the nearest mid-ocean ridge, subduction trench, transform fault, and coastline — turning raw tectonic geometry into tabular features ready for geological, geophysical, exploration, and environmental ML applications.
+## Quick Links
+
+- GitHub: https://github.com/Nora-Research-Lab
+- Hugging Face: https://huggingface.co/NoraResearchLab
+- Dataset: https://huggingface.co/datasets/NoraResearchLab/gplates-tectonic-intelligence
+- LinkedIn: https://www.linkedin.com/company/nora-research-lab
+- X: https://x.com/noraresearchlab
 
 ## Dataset Structure
 
@@ -49,8 +68,6 @@ This dataset converts the Müller et al. (2019) global plate reconstruction mode
 - **Seafloor age grid:** Seton, M. et al. (2020), present-day oceanic crustal age and spreading parameters.
 - **Access layer:** [`gplately`](https://github.com/GPlates/gplately) (Mather et al.), built on `pygplates`.
 
-Original data distributed by EarthByte / GPlates under their respective open licenses; this derived grid is released under CC-BY-4.0 — verify against upstream terms before redistribution.
-
 ## Processing Pipeline
 
 1. Downloaded core reconstruction files (rotation model, topologies, static polygons) and supporting layers (coastlines, continents, COBs, age grid) via `gplately.DataServer`.
@@ -59,16 +76,31 @@ Original data distributed by EarthByte / GPlates under their respective open lic
 4. Built a global 1.0° grid, assigned plate IDs via point-in-polygon partitioning, sampled seafloor age, and computed nearest-boundary distances (reprojected to an equidistant CRS for real-world km).
 5. Published the standardized dataset here.
 
-## Usage
+## Data Format
 
-```python
-import pandas as pd
+Vector datasets stored as GeoParquet. Standard coordinate reference system: EPSG:4326 (WGS84).
 
-df = pd.read_parquet("global_tectonic_context.parquet")
-df.head()
-```
+## Attribution
 
-## Known Limitations
+All third-party scientific datasets remain the property of their original creators (EarthByte / GPlates). NORA Research Lab performs aggregation, feature generation, format conversion, metadata generation, and cloud distribution.
+
+Users should cite both the original dataset providers and NORA Research Lab where appropriate.
+
+## Licensing
+
+This repository includes an MIT LICENSE file for repository code, scripts, and documentation created by NORA Research Lab. The underlying plate model and age grid retain the licenses of their original providers and are not relicensed under MIT.
+
+## Intended Use
+
+This repository is intended for:
+
+- plate tectonics and structural geology research
+- geospatial analysis and GIS
+- machine learning / GeoAI
+- mineral and resource exploration
+- academic research and data engineering
+
+## Limitations
 
 - 1.0° resolution is coarse; finer grids (e.g. 0.25°) trade runtime for detail.
 - Bilinear interpolation on the age grid can return null values near coastlines even over oceanic crust, slightly undercounting true oceanic coverage.
@@ -77,12 +109,21 @@ df.head()
 
 ## Citation
 
-If you use this dataset, please cite the underlying sources:
+If you use this dataset, please cite:
 
 - Müller, R.D. et al. (2019), *Tectonics*, 38(6).
 - Seton, M. et al. (2020), *Geochemistry, Geophysics, Geosystems*, 21(10).
 - Mather, B. et al., *GPlately* (GitHub: GPlates/gplately).
+- NORA Research Lab
+
+## Maintainer
+
+NORA Research Lab
+
+GitHub: https://github.com/Nora-Research-Lab
+
+Hugging Face: https://huggingface.co/NoraResearchLab
 
-## License
+Dataset: https://huggingface.co/datasets/NoraResearchLab/gplates-tectonic-intelligence
 
-CC-BY-4.0
+Last updated: 2026