Update README.md

README.md

@@ -1,32 +1,98 @@
 ---
-dataset_info:
-  features:
-  - name: node_feat
-    sequence:
-      sequence: int64
-  - name: edge_index
-    sequence:
-      sequence: int64
-  - name: edge_attr
-    sequence:
-      sequence: int64
-  - name: y
-    sequence: float64
-  - name: num_nodes
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 8491164
-    num_examples: 7836
-  - name: val
-    num_bytes: 1739548
-    num_examples: 998
-  - name: test
-    num_bytes: 2093540
-    num_examples: 999
-  download_size: 581912
-  dataset_size: 12324252
+license: mit
 ---
-# Dataset Card for "AQSOL"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for AQSOL
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [External Use](#external-use)
+  - [PyGeometric](#pygeometric)
+- [Dataset Structure](#dataset-structure)
+  - [Data Properties](#data-properties)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+- **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)**
+- **Paper:**:  (see citation)
+
+
+### Dataset Summary
+The AQSOL dataset comes "from the Benchmarking Graph Neural Networks paper based on AqSolDB, a standardized database of 9,982 molecular graphs with their aqueous solubility values, collected from 9 different data sources" (PyGeometric doc).
+
+### Supported Tasks and Leaderboards
+`AQSOL` should be used for graph regression, on aqueous solubility. 
+
+## External Use
+### PyGeometric
+To load in PyGeometric, do the following:
+
+```python
+from datasets import load_dataset
+
+from torch_geometric.data import Data
+from torch_geometric.loader import DataLoader
+
+dataset_hf = load_dataset("graphs-datasets/<mydataset>")
+# For the train set (replace by valid or test as needed)
+dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
+dataset_pg = DataLoader(dataset_pg_list)
+```
+
+## Dataset Structure
+
+### Data Properties
+| property | value |
+|---|---|
+| #graphs | 9,833 |
+| average #nodes | 17.6 |
+| average #edges | 35.8 |
+
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `node_feat` (list: #nodes x #node-features): nodes
+- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
+- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
+- `y` (list:  #labels): contains the number of labels available to predict
+- `num_nodes` (int): number of nodes of the graph
+- `pos` (list: 2 x #node): positional information of each node
+
+### Data Splits
+
+This data is split. It comes from the PyGeometric version of the dataset.
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under MIT license.
+
+### Citation Information
+```
+@article{DBLP:journals/corr/abs-2003-00982,
+  author    = {Vijay Prakash Dwivedi and
+               Chaitanya K. Joshi and
+               Thomas Laurent and
+               Yoshua Bengio and
+               Xavier Bresson},
+  title     = {Benchmarking Graph Neural Networks},
+  journal   = {CoRR},
+  volume    = {abs/2003.00982},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2003.00982},
+  eprinttype = {arXiv},
+  eprint    = {2003.00982},
+  timestamp = {Sat, 23 Jan 2021 01:14:30 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+```