Zozus M. The Data Book: Collection and Management of Research Data

Chapman and Hall/CRC, 2017. — 354 p. — ISBN: 978-1498742245.Principles of research data collection and management is the first practical book written for researchers and research team members covering the do’s and don’ts about handling data for research. The book covers basic types of data and fundamentals of how data grow, move and change over time. Focusing on pre-publication data collection and handling, the text illustrates use of these key concepts to match data collection and management methods to a particular study, in essence, making good decisions about data.The first section of the book defines data, introduces fundamental types of data that bear on methodology to collect and manage them, and covers data management planning and research reproducibility. The second section covers basic principles of and options for data collection and processing emphasizing error resistance and traceability. The third section focuses on managing the data collection and processing stages of research such that quality is consistent and ultimately capable of supporting conclusions drawn from data. The final section of the book covers principles of data security, sharing and archival. This book will help graduate students and researchers systematically identify and implement appropriate data collection and handling methods.Dr. Zozus has spent over two decades managing data for research and studying methodology for doing so. She has worked in data management for life sciences, engineering and biomedical research. She received her undergraduate and masers degrees in Nuclear Engineering at North Carolina State University, led data management operations at the Duke Clinical Research Institute and later the Duke Translational Medicine Institute. Dr. Zozus received her Ph.D. in Health Informatics at the University of Texas at Houston School of Biomedical Informatics.Collecting and Managing Research DataData and Science.
Data Gone Awry.
Data Management Quality System.
Data Management’s Minimum Standards.
Determinates of Data Management Rigor.
Frameworks for Thinking about Managing Research Data.
Exercises.Defining Data and InformationData about Things in the Real World.
Data as a Model of the Real World.
Defining Data.
Three Fundamental Kinds of Data.
Data Elements.
Stevens’s Scales of Measurement.
Back to the Definition of Data and Information.
Categories of Data.Exercises.Deciding, Defining, and Documenting Data to Be CollectedConstructs and Concepts.
Operational and Conceptual Definitions.
Reliability and Validity.
Data Elements Revisited — ISO 11179.
Special Considerations for Continuous Data Elements.
Specifics for Discrete Data Elements.
Deciding What Data to Collect.
Choosing the Right Data Element When Options Exist.
Documenting Data Element Definitions.
Resources.
Exercises.Data Management PlanningData Sharing and Documentation Requirements.
What Is in a DMP?
Extent of Data Management Planning.
Data Flow and Workflow.
Exercises.Fundamental Dynamic Aspects of DataTime Aspects of Research Design.
Defining Data Accuracy.
Key Data-Related Milestones.
The Ideal Case.
Data Growing (or Shrinking) Over Time.
Data Moving and Changing Over Time.
Exercises.Data Observation and RecordingFour Possibilities for Observation and Recording.
Observation by Humans.
Obtaining Data by Asking Those Who Directly Observed or Experienced.
Recording by Humans.
Automated Observation and Recording.
Tasks Associated with Observation and Recording.
Exercises.Good Data Recording Practices Applicable to Man and MachineNecessity of Good Documentation.
Attributable.
Legible.
Contemporaneous.
Original.
Accurate.
An Urban Legend about Data Recording.
Exercises.Getting Data into Electronic FormatGetting Data into Electronic Format.
Preprocessing.
Scan-Based Methods.
Bar Codes.
OMR and OCR.
Speech Recognition.
Key Entry.
Quality Assurance and Control for Data Entry.
Timing of Data Entry.
Direct Electronic Capture of Data.
Exercises.Data StructuresData Collection Formats.
Data Storage Structures.
Data Integration.
Data Exchange Structures and Concepts.
Exercises.Data ProcessingData Cleaning.
Imputations.
Standardization.
Mapping.
Formatting Transformations.
Conversions and Calculations.
Workflow for Transformations.
Data Enhancement.
Exercises.Designing and Documenting Data Flow and WorkflowImportance of Diagrams.
Diagrams as Models.
Two Types of Diagrams.
Data Flow and Context.
DFD Symbols and Conventions.
Workflow Diagrams.
Workflow Diagram Symbols and Conventions.
Exercises.Selecting Software for Collecting and Managing DataAutomation.
Data Collection and Processing Tasks for Which Computers Are Often Used.
Types of Computer Systems Used for Data Processing and Storage.
Software Selection.
Exercises.The Data Management Quality SystemQuality Management Systems.
Defining Quality.
Frameworks for Managing Quality.
A Framework for Quality Management of Research Data.
Exercises.Calculating the Time and Cost for Data Collection and ProcessingThe Hidden Value of Project Budgeting.
Modeling Data Collection and Management for a Research Project.
The Project Budget.
Cost Forecasting, Tracking, and Management.
Exercises.Research Data SecuritySecurity as Protection against Loss of Confidentiality, Integrity, or Availability.
Steps Investigators Can Take to Prevent Loss.
Risk-Based Categorizations of Data and Information Systems.
Implications for Contractual Data Protections.
Components of a Research Data Security Plan.
Exercises.Data Ownership, Stewardship, and SharingInstitutional Policies for Research Data.
Data Ownership and Stewardship.
Federal Requirements for Data Sharing.
Effective Data Sharing.
Special Considerations for Sharing Data about Human Subjects.
Special Considerations for Sharing Data Received from Others.
Exercises.Data ArchivalArchiving and Reuse of Research Data.
What to Archive.
Formats for Archival.
Records Retention.
Discoverability of Archived Data and Research Records.
Exercises.