Adding descriptive fields to the dataset card updating text on readme

Including highlevel description of correlation structures. Removing some of the "coming soon" make the dataset card better with link to code, description, summary and funding details

README.md

@@ -1361,17 +1361,17 @@ tags:
 - correlation-structure
 - synthetic
 - benchmark
-pretty_name: 'CSTS: Correlation Structures in Time Series'
+pretty_name: CSTS
+funded_by: "UK Research and Innovation (UKRI), through the UKRI Doctoral Training in Interactive Artificial Intelligence (AI) under grant EP/S022937/1"
+repo: https://github.com/isabelladegen/corrclust-validation
+demo: https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb
+dataset_summary: "CSTS (Correlation Structures in Time Series) is a synthetic benchmarking dataset for evaluating correlation structure discovery in time series data, featuring controlled properties with ground truth labels to bridge the gap between theoretical models and real-world data."
+description: "CSTS (Correlation Structures in Time Series) is a comprehensive synthetic benchmarking dataset for evaluating correlation structure discovery in time series data. The dataset systematically models known correlation structures between three different time series variates and enables examination of how these structures are affected by distribution shifting, sparsification, and downsampling."
 ---
-
 # CSTS - Correlation Structures in Time Series
+GitHub Repository: [CSTS GitHub Repository](https://github.com/isabelladegen/corrclust-validation)
 
-## Important Notice
-This dataset is published as a pre-publication release. An accompanying research paper is forthcoming on arXiv. 
-**All usage of this dataset must include proper attribution to the original authors as specified below.**
-
-## TL/DR
-Try our dataset loading examples in Google Colab to get started quickly!
+To get started quickly you can follow our dataset loading demo in Google Colab:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb)
 
 ## Dataset Description
@@ -1388,6 +1388,9 @@ and messy real-world data.
 - Investigating how **data preprocessing** affects correlation structure discovery
 - Establishing **performance thresholds** for high-quality clustering result
 
+## Correlation Structures
+The dataset features 23 distinct correlation structures representing different combinations of strong positive, negligible, and strong negative correlations between three time series variates. These structures are based on meaningful thresholds for strong negative ([-1,-0.7]), negligible ([-0.2,0.2]), and strong positive ([0.7,1]) correlations.
+
 ### Dataset Structure
 CSTS provides **two main splits** (exploratory and confirmatory) with **30 subjects** each, enabling proper statistical validation.
 The dataset structure includes:
@@ -1396,7 +1399,7 @@ The dataset structure includes:
 - **Completeness levels**: complete (100% of observations), partial (70% of observations), sparse (10% of observations)
 
 ### Subjects
-Each subject contains 100 segments of varying lengths (900-36000) and each segment encodes one of the 23 specific correlation 
+There are in total 60 subjects. Each subject contains 100 segments of varying lengths (900-36000) and each segment encodes one of the 23 specific correlation 
 structures. Each subject uses all 23 patterns 4-5 times. For the complete data variants each subject consists of ~1.26 mio
 observations.
 
@@ -1452,10 +1455,6 @@ Additional splits are:
 
 ### Quick Start
 
-Try our dataset loading examples in Google Colab to get started quickly!
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb)
-
-#### Example
 Steps to load the data and labels for the complete correlated data variant:
 
 1. Load the data for all 30 exploratory subjects for the complete and correlated data variant into pandas df:
@@ -1475,6 +1474,10 @@ df_correlated_labels = correlated_labels.to_pandas()
 df_correlated_labels.head()
 ```
 
+Comprehensive examples can be found here:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/isabelladegen/corrclust-validation/blob/main/src/utils/hf_tooling/CSTS_HuggingFace_UsageExample.ipynb)
+
+
 ## Authors
 - Isabella Degen, University of Bristol
 - Zahraa S Abdallah, University of Bristol
@@ -1501,7 +1504,7 @@ Please use the following temporary citation until our paper is published:
   year         = {2025},
   publisher    = {Hugging Face},
   howpublished = {Pre-publication dataset release},
-  url          = {https://huggingface.co/datasets/[your-username]/[dataset-name]}
+  url          = {https://huggingface.co/datasets/idegen/csts}
   note         = {ArXiv preprint forthcoming}  # Uncomment when preprint is available
 }
 ```
@@ -1510,4 +1513,5 @@ Once our paper is published on arXiv, we will update this README with the proper
 **Please check back for updates.**
 
 ## Acknowledgements
-... coming soon
+We would like to thank UK Research and Innovation (UKRI) for funding author ID's PhD research through the UKRI Doctoral Training in Interactive Artificial Intelligence (AI) under grant EP/S022937/1. The authors extend their gratitude to the faculty, staff and colleagues of the Interactive AI Centre for Doctoral Training at Bristol University for their valuable support and guidance throughout this research.
+We acknowledge the use of Claude 3.7 Sonnet by Anthropic as a research dialogue tool throughout the development of this work, assisting with dataset documentation, iterative refinement of ideas, and evaluating the clarity of our methods and contributions.