Big data, machine learning, artificial intelligence, and data science technologies have led to numerous success stories across various fields. These technologies offer innovative methods to integrate, reuse, and analyze vast data volumes, encouraging scientists in disciplines such as physics, chemistry, materials science, and medicine to explore how they can enhance scientific research.

However, leveraging these technologies comes with its own set of challenges. Many existing software tools and systems were not designed with scientific research or the unique needs of scientists in mind. Scientists who lack programming or computer science expertise may find these tools challenging to use, while computer scientists may struggle to understand domain-specific issues without adequate background knowledge.

This workshop aims to bridge the gap between domain scientists and computer scientists. It will explore avenues for creating and utilizing tools, systems, and methodologies to advance scientific discovery. Participants will share success stories, insights from lessons learned, and address challenges that need to be overcome to foster fruitful collaborations across different fields.

### Research Topics Included in the Workshop:
Topics related to big data tools, systems, and methods include, but are not limited to:

- Scientific data processing (integration, standardization, sampling, etc.)
- Artificial intelligence and machine learning
- Text and graph mining
- Database management, query processing, and query optimization
- Parallel computation and high-performance computing
- Visualization and user interface/HCI
- Parallelization, performance, and scalability of data tools
- High-performance computing
- Uncertainty quantification
- Combinational usage of simulation, experiment, machine learning models, and data
- Data fusion

These topics facilitate innovation and discovery in scientific domains.

We are organizing a demo and tutorial session, along with research paper presentations, for this year's workshop to broaden its reach and attract more researchers. This session will offer attendees the opportunity to showcase their work, exchange ideas, and receive hands-on training from field experts. We welcome tutorial submissions in a short-paper format. Tutorials can be either 30 minutes or 1 hour long. Topics will include, but are not limited to, scientific tools and software, AI/ML applications in science, and data processing and management for large-scale scientific data.

Please submit a short paper (minimum 4 pages, up to 6 pages, IEEE 2-column format) or full paper (minimum 8 pages, up to 10 pages, IEEE 2-column format) through the IEEE BigData 2024 online submission system (https://wi-lab.com/cyberchair/2024/bigdata24/index.php). Papers should be formatted according to IEEE Computer Society Proceedings Manuscript Formatting Guidelines.