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Alaska Region Interim Data Management User Guide
  • Alaska Region Interim Data Management User Guide
  • Background
    • Why Data Managment?
    • The Big Picture: Integrating Data Management with Project Management
    • Definition of Project and Product (aka Data Resources)
  • Four Fundamental Activities of Data Management
    • Establish Roles and Responsibilities
    • Quality Management
    • Security and Preservation
    • Documentation
  • Alaska Data Management 101
    • Workflow
    • File Organization and Best Practices
      • Best Practices in Naming Conventions
      • Best Practices for Version Control
      • Changelog Best Practices
    • Alaska Regional Data Repository
    • Data Management Policy
  • Plan
    • Why Data Planning?
    • Data Management Plan Templates
      • Data Standards in brief
    • Project & Data Management Integration
    • Considerations for Projects with External Partners
  • ACQUIRE
    • Common Data Types
      • Open Formats
      • Best Practices in Tabular Data
      • Best Practices in Databases
      • Best Practices in Geospatial Data
      • Best Practices with Collections of Similar Types of Data
      • Best Practices with Source Data
    • Quality Management Procedures
      • Incorporating Data Standards
      • Using Unique Identifiers
  • MAINTAIN
    • Update Metadata
  • Access & Share
    • Open Data Requirements
      • Obtaining a Digital Object Identifier (DOI)
      • Obtaining a URL
      • Sharing without a URL
  • Long-term Storage Options
    • Using the Regional Data Repository
    • Public Accessible Repositories
  • Records Schedule & Disposition
  • Data Management Actions Quick Guide
  • Glossary
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  1. Four Fundamental Activities of Data Management

Quality Management

PreviousEstablish Roles and ResponsibilitiesNextSecurity and Preservation

Last updated 5 years ago

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Data quality management is composed of quality assurance (QA) and quality control (QC). Quality assurance begins before data are collected and are procedures used to prevent errors from entering the data (e.g., using a mobile app for data collection that limits possible values that can be entered (pick-lists)). Quality control is the discovery and correction of errors in the data and generally occurs during or after data collection (e.g., detection of outliers, typographical error, a character datum where a numeric value is expected, using an incorrect species code, and etc.). Quality control should occur as soon as possible after collecting the data and before submitting data to the archive record or sharing. QA and QC procedures should be identified during the project planning phase in consultation with the program’s biometrician and/or data manager. quality management practices (QA and QC) in the mdEditor for all documented products.

Best Practices in Quality Assurance

  • Use documented protocols and standard methods

  • Use high-quality instrumentation and regularly check accuracy

  • Provide consistent training

  • Develop standardized data collection forms (data sheet templates or computer input with data validation formats)

Best Practices in Quality Control

  • Inspect data values using summary functions (tabling unique values, calculating means and variances, etc.) or by applying complex analysis algorithms.

  • In Excel files, use sort and filter functions to look for data anomalies or outliers.

  • Visually inspect data using scatterplots, regressions, and histograms.

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