Added description

README.md

@@ -7,7 +7,42 @@ license: cc-by-4.0
 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.**
 
 ## Dataset Description
-... coming soon
+CSTS (**C**orrelation **S**tructures in **T**ime **S**eries) 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. With its controlled properties and ground truth labels, 
+CSTS provides algorithm developers clean benchmark data that bridges the gap between theoretical models 
+and messy real-world data.
+
+### Key Applications
+- Evaluating the ability of time series clustering algorithms to segment and group by correlation structures
+- Assessing clustering validation methods for correlation-based clusters
+- Investigating how data preprocessing affects correlation structure discovery
+- Establishing performance thresholds for high-quality clustering result
+
+### Dataset Structure
+CSTS provides **two main splits** (exploratory and confirmatory) with **30 subjects** each, enabling proper statistical validation.
+The dataset structure includes:
+- **12 data variants** across four generation stages × three completeness levels for each split
+- **Generation stages**: raw (unstructured), correlated (normal-distributed), non-normal distribution shifts, downsampled (1s→1min)
+- **Completeness levels**: complete (100%), partial (70%), sparse (10%) of observations retained
+
+### Subjects
+Each subject contains 100 segments of varying lengths and each segment encodes one of the 23 specific correlation 
+structures. Each subject uses all 23 patterns 4-5 times.
+
+For each subject, CSTS includes:
+- a time series **data file** with three variates
+- a **label file** specifying the ground truth segmentation and clustering
+- 67 **bad clustering label files** with controlled degradations (varying numbers of segmentations and/or cluster assignment mistakes) spanning the full Jaccard Index range [0,1]
+
+### Additional Splits
+CSTS also includes versions (configured as splits) that allow exploration of how cluster and segment counts affect 
+algorithm performance. They follow the same structure as above and are:
+- Reduced clusters (11 or 6 instead of 23)
+- Reduced segments (50 or 25 instead of 100)
+
+Our accompanying paper provides complete methodological details, baseline findings, and usage guidance.
 
 ## Authors
 - Isabella Degen, University of Bristol