Exploring the Core Components of Research Data Management
The Data Lifecycle
Understand each phase of your project’s data journey—from planning & collection, through processing & analysis, to sharing, preservation, and reuse—so you can anticipate needs and responsibilities at every step.
FAIR Principles
Learn how to make your data Findable (e.g., rich metadata), Accessible (clear access protocols), Interoperable (standard formats & vocabularies), and Reusable (licensing & provenance), and explore practical strategies for embedding FAIRness in your workflows.
Data Management Planning (DMP)
Master the art of drafting a robust DMP: specify your data types, choose appropriate metadata schemes, outline storage & sharing methods, and set forth long‑term preservation actions that satisfy funder and institutional requirements.
File Organization & Naming Conventions
Adopt best practices for structuring folders, applying consistent filenames, tracking version history, and documenting provenance, so that collaborators (and future you!) can navigate and reproduce your work effortlessly.
Metadata & Documentation
Select and implement domain‑relevant metadata standards (e.g., Dublin Core, DataCite), and learn to craft clear README files, codebooks, and lab notebooks that provide the context needed for discovery, interpretation, and reuse.
Storage, Backup & Security
Compare local versus cloud storage options; set up automated backup workflows; apply encryption and access controls; and understand best practices for handling sensitive or restricted data.
Data Sharing & Repositories
Evaluate institutional, domain‑specific, and generalist repositories (e.g., Zenodo, Figshare); assign DOIs; choose appropriate licenses (such as CC BY); and walk through a hands‑on deposit so you can share your data reliably and transparently.
