Research data management
Cross-cutting issue
This cross-section addresses the critical role of Research Data Management (RDM) in the lifecycle of Data Science projects. We aim to highlight cutting-edge practices, challenges, and solutions related to making research data FAIR (Findable, Accessible, Interoperable, Reusable). Topics of interest include (but aren't limited to):
- Data Management Plans (DMPs): Evaluation of effective DMPs for data-intensive projects
- Data Storage & Infrastructure: Evaluation of data storage solutions, cloud-based resources, and data transfer protocols
- Data Documentation & Metadata: Best practices for creating rich, standardized metadata to enhance data discoverability and understanding
- Data Sharing & Preservation: Policies, tools, and workflows for responsible data sharing and long-term data preservation
- Reproducibility & Open Science: Methods for ensuring the reproducibility of Data Science research and promoting open science principles
- Automation in RDM: Approaches to automating RDM workflows using scripting, APIs, and other tools
This cross-section seeks contributions from researchers, data scientists, librarians, and RDM professionals who are actively working to advance the state of the art in RDM for Data Science.