What problem does it solve? Research data quickly becomes chaotic without structure: files get lost, versions conflict, sensitive participant information is mishandled, and analyses become impossible to reproduce. This Skill provides a complete system for organizing, documenting, securing, and preserving research data so your work stays reproducible and compliant with IRB and funder requirements. ## Core Features & Use Cases - Folder Structure & Naming Conventions: Establishes a numbered project hierarchy (raw data, processed data, analysis, documentation) with standardized file naming templates like INT_2024_001_2024-01-15_v1.mp3. - Documentation & Metadata: Provides templates for data dictionaries, qualitative codebooks, README files, and processing logs that record every transformation applied to your data. - Security & Preservation: Covers sensitivity classification, de-identification procedures, encryption, 3-2-1 backup strategy, long-term format preservation, and repository deposit with data availability statements. - Use Case: A doctoral student collecting 50 confidential interviews uses this Skill to set up an encrypted folder structure, de-identify transcripts with pseudonyms, maintain a version control log, and write a data management plan for their IRB protocol. ## Quick Start Help me set up a data management system for my dissertation research, including folder structure, file naming conventions, and a data dictionary for my interview and survey data.
