LibGuides: Datamanagement: English

Introduction datamanagement

Data management is an essential part of research practice and includes planning, creating or collecting, storing, organising, maintaining, long-term preservation (preservation), making accessible, sharing, describing and publishing research data. According to the international FAIR principles, research data must be findable, accessible, interoperable and re-usable (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 data 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 (see this LibGuide) to keep everything on track and to map out all aspects during a research project.
Attention to ethical aspects and AVG principles is necessary (see this LibGuide).

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.Kernwaarden
♦ Integrity
Is based on the principles of honesty, diligence, transparency, independence and responsibility. More about this and the Netherlands Code of Conduct for Scientific 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 reanalyse the enable test data.This requires good data documentation, see this LibGuide.Setting up reproducible research also requires thorough preparation in the planning phase. See for tips and tools this LibGuide.

Why datamanagement?

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

It shows that you meet internal and external requirements, e.g. from subsidy providers.
It shows that you comply with certain legislation, such as the GDPR and The Medical Research with People Act
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 Cycle

An research cycle is a visual representation of the different phases of an research and describes the steps a researcher takes in each phase, from planning an investigation to its completion.

What are research data?

Research data are data collected in the context of scientific and practical research to answer a research question or to test a hypothesis. How the research data look exactly depends on the field of study, the research process and the research method. Research data come in many forms and formats.

A distinction is made between:

Raw data (= primary data)
These are the raw data, i.e. purely the data as collected; no processing or analysis has taken place yet.
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 for collecting 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
Survey of sources, e.g. scans or transcripts of archival documents

Zuyd support

Team Research Support
Zuyd Bibliotheek

Marsha Bokhorst
Nieuw Eyckholt - Heerlen
marsha.bokhorst@zuyd.nl
045-4006029

Lilian van de Burgt
Brusselseweg - Maastricht
lilian.vandeburgt@zuyd.nl
043-3466620

Ingrid Kars
Ligne - Sittard
ingrid.kars@zuyd.nl
046-4207020