Aims & Scope Discovery Science 2024 conference provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The conference focus is on the use of Artificial Intelligence, Data Science and Big Data Analytics methods in science. Its scope includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, and big data analytics, as well as their application in various domains. Possible topics include, but are not limited to: Artificial Intelligence (machine learning, knowledge representation and reasoning, natural language processing, statistical methods, etc.) applied to science Machine Learning: supervised learning (including ranking, multi-target prediction and structured prediction), unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, transfer learning, etc. Knowledge Discovery and Data Mining Anomaly and Outlier Detection Time-Series Analysis Spatial, Temporal and Spatiotemporal Data Analysis Unstructured Data Analysis (textual and web data) Data and Knowledge Visualization Complex Network Analysis Causal Modelling Explainable AI and Interpretable Machine Learning Human-Machine Interaction for Knowledge Discovery and Management Data Streams, Evolving Data, Change Detection & Concept drift AutoML, Meta-Learning, Planning to Learn AI and High-performance Computing, Grid and Cloud Computing AI and Cybersecurity Computational Creativity Learning from Complex Data Process Discovery and Analysis Evaluation of Models and Predictions in Discovery Setting Applications of the above techniques in scientific domains, such as Physical sciences (e.g., materials sciences, particle physics), Life sciences (e.g., systems biology/systems medicine), Environmental sciences, Natural and social sciences