Update README.md

README.md

@@ -60,19 +60,31 @@ size_categories:
 # Dataset Card for U3T
 
 This dataset stores refactored data of [Monte Carlo Search Tree Evaluations](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) edited from arnowaczynski's [utttai](https://github.com/arnowaczynski/utttai), for the game [Ultimate-tic-tac-toe](https://en.wikipedia.org/wiki/Ultimate_tic-tac-toe) (U3T).
-One can use this data to train a model to evaluate positions or predict moves.
+MCTS evaluations provide probabilistic estimates of how advantageous a given game position or move is for a player.
+
+
+### About Ultimate Tic-Tac-Toe
+From Wikipedia:
+```
+Ultimate tic-tac-toe is a board game composed of nine tic-tac-toe boards arranged in a 3 × 3 grid. Players take turns playing on the smaller tic-tac-toe boards until one of them wins on the larger board. Compared to traditional tic-tac-toe, strategy in this game is conceptually more difficult and has proven more challenging for computers.
+```
+
+Ultimate Tic-Tac-Toe remains a largely unexplored domain in AI research. While there have been some academic papers and small-scale attempts to develop AI for this game (with some being quite successful), not much effort has been put in to making advanced bots. This dataset provides a structured and scalable resource for researchers and developers looking to build AI models for U3T.
+By leveraging MCTS evaluations, this dataset offers a foundation for training AI to better understand strategic play in a complex yet structured environment. Given the lack of extensive research in this field, this dataset represents a unique opportunity to contribute to an emerging area of AI-driven gameplay.
 
 ## Dataset Details
 
-This dataset contains over 8 million evaluated positions at varying depth (via [utttai](https://github.com/arnowaczynski/utttai/tree/main/datasets)):  
+This dataset contains **over 8 million** evaluated positions at varying depths of play (via [utttai](https://github.com/arnowaczynski/utttai/tree/main/datasets)):  
 ![Depth Graph from UTTTAI](assets/depth_graph.png)  
 
-Each evaluated positions counts the number of wins, draws, and loses, giving an estimation as to how good a position is. Each position stores an array of legal moves, and their respective
+Each evaluated positions counts the number of wins, draws, and loses, giving a probabilisitc estimation as to how good a position is. Each position stores an array of legal moves, and their respective
 MCTS searches.
 
 ## Uses
 
-One can use this dataset in an way they want. But, it is mainly intended for making a deep learning model to play U3T, or evaluate a static position.
+The MCTS evaluation can be used for many types of game evaluations. The MCTS of every position can be used for making a static evaluator, or the evaluations of every 
+legal move from a position can be used to train a model to predict the best moves. There are many ways to use this set, but the dataset is specifically tuned for making making deep learning models that can play U3T, or evaluate a static position.
+It is further recommended that any bot you make with this as initial training should be fine-tuned with reinforcement learning.
 
 ## Dataset Structure
 
@@ -135,11 +147,16 @@ The dataset was split into a train, test, and validation set, with a distributio
 
 ### Curation Rationale
 
-The original dataset by arnowaczynski was not stored in a conventional big data format, and used some unconventional index storing [(detailed here)](https://github.com/markstanl/uttt-bots/blob/main/utttai_conversion/utttai.md).
-So, we decided to refactor it and upload it here, to Hugging Face, in a dataset that is more efficient for NN training.
+The original dataset by arnowaczynski was not structured for large-scale machine learning and used an unconventional indexing method [(detailed here)](https://github.com/markstanl/uttt-bots/blob/main/utttai_conversion/utttai.md).
+To optimize for neural network training, we refactored it and uploaded the improved version to Hugging Face.
 
 
 ### Source Data
 
 arnowaczynski generated these datasets in their [GitHub Repository](https://github.com/arnowaczynski/utttai/tree/main/datasets). Further documentation on how
 the dataset was generated is available at that link.
+
+### Contribution
+
+Contributing is highly encouraged and appreciated. This does not need to be expanding upon this dataset. It can be building different models or datasets.
+Opening PRs here or reaching out to me individually is encouraged.