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Aid Memoire Data Management in Research

Data management is important throughout the lifecycle of project. I have compiled the following as a ready reckoner based on my understanding of data management, These are more of bullet points and interpretation is left unto the reader:

  1. How data is collected a. Instruments
    1. Forms and Versioning
    2. Instructions
    3. Modality: Paper/ electronic
  2. Organized a. Database
    1. Structure
    2. Codes
    3. Labels
    4. Relationships across tables
    5. File naming
    6. Location
  3. Data Related Activities
    1. Training
    2. Data Collection
    3. Data Quality monitoring
    4. Data entry: Independent, Double data entry
    5. Transcribing
    6. Translation
    7. Data Cleaning
    8. Data Quality Assessment
    9. Completeness / Missingness
    10. Consistency Checks
    11. Audit log
  4. Analysis plan
    1. Dummy tables
    2. Data Analysis – Interim; Final
    3. Code used for analysis: reproducibility, integrity
  5. Publication
    1. Data File
    2. Commands
  6. Security and Safety
    1. Storage: Locations, Copies
    2. Backed up i. Frequency ii. Locations
    3. Access Control
    4. Encryption
    5. Safety during Transportation
    6. Confidentiality
    7. De-identification
  7. Data Dissemination Plan
  8. Data Sharing Plan a. Licensing Policy
  9. Archival
    1. Accessibility in Future
    2. Study Metadata
    3. Data Archive
  10. Legal requirements
    1. Data Custodian
    2. Institutional Contacts
    3. Duration of retention
    4. De-identification
    5. Encryption
    6. Raw and Clean data
    7. Primary Case Record Forms: Paper CRFs, eCRFs

Needless to day, data management is Important throughout the lifecycle of project