Added usage examples

README.md

@@ -467,7 +467,60 @@ Our accompanying paper provides complete methodological details, baseline findin
 GitHub codebase includes the generation, validation and use case code and is configured to automatically load the data.
 
 ## Usage Guidance
-... coming soon
+### Configuration Concept
+The configuration follows the convention: `<generation_stage>_<completeness_level>_<file_type>` and allows
+access to a specific subset of the data.
+
+Possible values are:
+
+Generation Stages:
+- **raw**: raw data, segmented but not correlated
+- **correlated**: correlated data according to a specific correlation strtucture, normal distributed
+- **nonnormal**: distribution shifted, correlated data
+- **downsampled**: resampled non-normal data from 1s to 1min
+
+Completeness Levels:
+- **complete**: 100% of the data
+- **partial**: 70% of the data (30% of observations dropped at random)
+- **sparse**: 10% of the data (90% of observations dropped at random)
+
+**File Type**
+- **data**: loads the times series data file (needed for training algorithms)
+- **labels**: loads the labels file for the ground truth (perfect) segmentation and clustering (needed for validating the results)
+- **badclustering_labels**: loads the labels file for a degraded clustering with controlled segmentation and/or cluster assignment mistakes
+
+### Splits
+The main splits are:
+- **exploratory**: for experimentation and training
+- **confirmatory**: for testing and validation
+Consider that depending on the application and study design, a single subject might be sufficient for training.
+
+Additional splits are:
+- **reduced_11_clusters**(_exploratory or _confirmatory): same data including 11 of the original 23 clusters (selected at random)
+- **reduced_6_clusters**(_exploratory or _confirmatory): same data including t of the original 23 clusters (selected at random)
+- **reduced_50_segments**(_exploratory or _confirmatory): same data including 50 of the original 100 segments (selected at random)
+- **reduced_25_segments**(_exploratory or _confirmatory): same data including 25 of the original 100 segments (selected at random)
+
+### Quick Start
+#### Example 1 - complete and correlated data variant
+1. Load the data for all 30 exploratory subjects for the complete and correlated data variant into pandas df:
+```python
+import pandas as pd
+from datasets import load_dataset
+correlated_data = load_dataset("idegen/csts", name="correlated_complete_data", split="exploratory")
+df_correlated = correlated_data.to_pandas()
+df_correlated.head()
+```
+2. Load the ground truth labels for these subjects
+```python
+import pandas as pd
+from datasets import load_dataset
+correlated_labels = load_dataset("idegen/csts", name="correlated_complete_labels", split="exploratory")
+df_correlated_labels = correlated_labels.to_pandas()
+df_correlated_labels.head()
+```
+
+... more examples coming soon
 
 ## Authors
 - Isabella Degen, University of Bristol