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README.md

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 # QuAnTS: Question Answering on Time Series
 
+[![Dataset Generation GitHub Repo](https://img.shields.io/badge/GitHub-Dataset%20Generation-blue?logo=github)](https://github.com/mauricekraus/quants-generate)
+
 QuAnTS is a challenging dataset designed to bridge the gap in question-answering research on time series data.
 The dataset features a wide variety of questions and answers concerning human movements, presented as tracked skeleton trajectories.
 QuAnTS also includes human reference performance to benchmark the practical usability of models trained on this dataset.
 
 ![Q: "What is the person doing first?", A: "They are waving.", Q: "How many times are they jumping after that?", A: "..."](doc/intro-chat.png "Example chat motivating time series question answering")
 
-For more information, please refer to the paper: *Under Review*
-
 At present, there is no official leaderboard for this dataset.
 
+## Dataset Generation Overview
+
+![QuAnTS is generated in several steps: An action sequence is sampled ➀, where for each we sample five question and answer types ➁. For diversity, each of them is then instantiated from a sampled template ➂. The time series from the human motion diffusion ➃ is then combined with the QA-pair and auxiliary data ➄. Example QA pairs are shown below. Dice indicate randomized operations for dataset diversity.](doc/overview.png "Dataset generation overview")
+
+For details, please refer to the paper: *Under Review*
+
 ## Task and Format
 
 The primary task for the QuAnTS dataset is Time Series Question Answering.