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Data management: Introduction

Introduction data management

Data management is an essential part of research practice and includes planning, creating or collecting, storing, organizing, maintaining, long-term preservation, making accessible, sharing, describing and publishing research data. According to the international FAIR principles, research data must be findable, accessible, interoperable and reusable (see this LibGuide). FAIR data management is the set of decisions and measures taken during the life cycle of research data to deliver research data as FAIR as possible. Important aspects include the choice of a file format, naming convention and data license, the recording of data documentation and metadata, etc. (see this LibGuide). A data management plan is an indispensable tool for managing everything and mapping out all aspects during a research project (see this LibGuide).
Attention to ethical aspects (see this LibGuide) and GDPR principles (see this LibGuide) is necessary.

Core values
Within data management, integrity and reproducibility are important core values. Integrity and reproducibility not only lead to reliable but also to (re-)usable research.
♦ Integrity
   Is based on the principles of honesty, scrupulousness, transparency, independence and responsibility. More about this and the Netherlands Code of Conduct for Research Integrity can be found in this LibGuide.
♦ Reproducibility
   Research is reproducible if you provide all the necessary (meta)data, software and computer scripts that make it possible to either redo the research or to re-analyse the research data. This requires good data
   documentation, see this LibGuide. Setting up reproducible research also requires thorough preparation in the planning phase. See this LibGuide for tips and tools.

Why data management?

Good data management has many advantages: for yourself, for your research institute, for your field of expertise and for the world around you.

  • It shows that you meet internal and external requirements, e.g. subsidy providers
  • It shows that you comply with certain legislation, such as the GDPR and the Medical Research Involving Human Subjects Act (WMO).
  • It increases the chance of FAIR data
  • It promotes the integrity of your research
  • It increases the impact of your research
  • It supports future (re-)use of your research by others
  • It promotes transparency and enables verification of your research ( reproducible)
  • It increases the digital durability of your research data

Research Data Lifecycle

A research lifecycle is a visual representation of the different phases of a research project and describes the steps a researcher takes in each phase, from the planning phase to its completion.

What are research data?

Research data are data collected in the context of scientific and practice-oriented research to answer a research question or to test a hypothesis. What the research data look like depends on the field of study, the research process and the research method. Research data can come in many forms and formats.

A distinction is made between: 

  • Raw data (= primary data)
    These are the unprocessed data, i.e. the data purely as collected; no processing or analysis has yet taken place.
  • Derived (processed) data (= secondary data)
    These are research data on which an analysis has already taken place, from which some variables have already been selected or which have been otherwise edited.

Methods to collect data:

  • Observation (e.g. telemetry, surveys, neuroimaging, audio/video recordings)
  • Interviews
  • Questionnaires
  • Measurements
  • Experiment (e.g. data from laboratory equipment)
  • Simulation (e.g. climate models, economic models)
  • Derived data, e.g. text and data mining, statistical analysis
  • Source research, e.g. scans or transcripts of archive documents

See this LibGuide also.

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