Update README.md

README.md

@@ -1,101 +1,129 @@
 ---
-dataset_info:
-  description: |
-    This dataset contains synthetic data generated from Discrete Elastic Rods (DER) simulations, a physical model commonly used to represent deformable slender structures such as hair strands, ropes, cables, and elastic fibers. The data was collected frame-by-frame during the simulations and includes geometric, kinematic, and physical information for each simulated rod vertex.
+language:
+- en
+
+pretty_name: Discrete Elastic Rods Simulation Dataset
+
+tags:
+- physics
+- simulation
+- synthetic-data
+- graph-neural-networks
+- gnn
+- geometric-deep-learning
+- physics-informed-machine-learning
+- regression
+- computational-physics
+- deformable-objects
+- discrete-elastic-rods
+- dynamics
+- physical-simulation
+- computer-graphics
+- trajectory-prediction
+
+task_categories:
+- graph-ml
+- time-series-forecasting
+
+task_ids:
+- multivariate-time-series-forecasting
+
+size_categories:
+- 1M<n<10M
+---
 
-    The dataset is intended for machine learning applications, particularly for training and evaluating Graph Neural Networks (GCNs) focused on physical regression, dynamics prediction, and approximation of deformable systems behavior.
+# Discrete Elastic Rods Simulation Dataset
 
-    Each sample represents the state of a vertex at a specific simulation frame, containing positional, velocity, force, curvature, torsion, and neighboring segment information.
+## Dataset Description
 
-  features:
-  - name: timestamp
-    dtype: timestamp[us]
+This dataset contains synthetic data generated from Discrete Elastic Rods (DER) simulations, a physical model used to represent deformable slender structures such as hair strands, ropes, cables, and elastic fibers.
 
-  - name: source
-    dtype: large_string
+The dataset was generated frame-by-frame during physical simulations and stores geometric, kinematic, and dynamic properties for each rod vertex.
 
-  - name: frame
-    dtype: int64
+The primary objective of this dataset is to support machine learning research involving:
 
-  - name: strand
-    dtype: int64
+- Graph Neural Networks (GNNs)
+- Physics-informed learning
+- Dynamics prediction
+- Physical regression
+- Deformable object simulation
 
-  - name: vertex_id
-    dtype: int64
+Each sample corresponds to a vertex at a specific simulation frame.
 
-  - name: pos_x
-    dtype: float64
+---
 
-  - name: pos_y
-    dtype: float64
+## Dataset Structure
 
-  - name: pos_z
-    dtype: float64
+The dataset is divided into three splits:
 
-  - name: vel_x
-    dtype: float64
+| Split | Samples |
+|---|---|
+| Train | 4,265,580 |
+| Validation | 376,200 |
+| Test | 460,920 |
 
-  - name: vel_y
-    dtype: float64
+---
 
-  - name: vel_z
-    dtype: float64
+## Features
+
+| Feature | Description |
+|---|---|
+| `frame` | Simulation frame index |
+| `strand` | Rod/strand identifier |
+| `vertex_id` | Vertex identifier |
+| `pos_x/y/z` | Vertex position |
+| `vel_x/y/z` | Vertex velocity |
+| `force_x/y/z` | Applied forces |
+| `curvature` | Local curvature |
+| `torsion` | Local torsion |
+| `prev_segment_direction` | Previous segment direction vector |
+| `next_segment_direction` | Next segment direction vector |
+| `prev_segment_length` | Previous segment length |
+| `next_segment_length` | Next segment length |
+| `boundary` | Boundary condition information |
 
-  - name: force_x
-    dtype: float64
+---
 
-  - name: force_y
-    dtype: float64
+## Data Generation
 
-  - name: force_z
-    dtype: float64
+The dataset was generated using a Discrete Elastic Rods simulation environment with randomized physical parameters and dynamic interactions.
 
-  - name: boundary
-    dtype: large_string
+The simulations include temporal evolution of elastic rods under physical constraints and external/internal forces.
 
-  - name: curvature
-    dtype: float64
+---
 
-  - name: torsion
-    dtype: int64
+## Intended Use
 
-  - name: prev_segment_direction
-    list: float64
+This dataset is intended for:
 
-  - name: prev_segment_length
-    dtype: float64
+- Training Graph Neural Networks
+- Physics regression tasks
+- Simulation approximation
+- Temporal dynamics prediction
+- Deformable object learning
 
-  - name: next_segment_direction
-    list: float64
+---
 
-  - name: next_segment_length
-    dtype: float64
+## Limitations
 
-  splits:
-  - name: train
-    num_bytes: 910701331
-    num_examples: 4265580
+- The dataset is fully synthetic.
+- Results may not perfectly generalize to real-world rod dynamics.
+- Physical behavior depends on the simulation assumptions and parameter ranges.
 
-  - name: validation
-    num_bytes: 80318700
-    num_examples: 376200
+---
 
-  - name: test
-    num_bytes: 98406420
-    num_examples: 460920
+## License
 
-  download_size: 485950046
-  dataset_size: 1089426451
+Specify the dataset license here.
 
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+---
 
-  - split: validation
-    path: data/validation-*
+## Citation
 
-  - split: test
-    path: data/test-*
----
+```bibtex
+@dataset{der_simulation_dataset,
+  title={Discrete Elastic Rods Simulation Dataset},
+  author={Samuel Ferreira Santos},
+  year={2026}
+}
+```