Tallinn University of Technology

Research Data Management

Research Data Management

What is research data?

Research data is any systematic collection of information that is used by researchers for analysis. Typical examples of data include: 

  • Observational data: data captured in real-time, usually irreplaceable
    Examples: Sensor data, telemetry, survey data, sample data, neuroimages 

  • Experimental data: data from lab equipment, often reproducible, but can be expensive
    Examples: gene sequences, chromatograms, toroid magnetic field data

  • Simulation data: data generated from test models where model and metadata (inputs) are more important than output data. 
    Examples: climate models, economic models

  • Derived or compiled data: data that is reproducible (but very expensive)
    Examples: text and data mining, compiled database, 3D models, data gathered from public documents

Research data can also include video, sound, or text data, as long as it is used for systematic analysis. For example, a collection of video interviews use to gather and identify gesture and facial expressions in a study of emotional responses to stimuli would be considered research data.

All research data must be appropriately structured and documented in order for it to be used effectively for analysis. Additionally, any unique programs or models needed to analyze the data should also be preserved.

Data can take many forms:

  • Still images, video and audio

  • Survey results and interview transcripts

  • Experimental observations

  • Text corpuses

  • Notebooks and lab books

  • Models and software

  • Can be created in a digital form

  • Can be analogue that is converted to a digital form

Any definition of research data is likely to depend on the context in which the question is asked. The dividing line between 'research data' and 'primary materials' will not be clear in many cases.

Open Research Data is data that can be freely used, reused and redistributed by anyone – subject only, at most, to the requirement to credit the curator and share under the same license.

Open access to research data fits within the Open Science paradigm, situated within a context of ever greater transparency, accessibility and accountability. The main goals of these developments are to lower access barriers to research outputs, to speed up the research process and to increase the quality, integrity and longevity of the scholarly record.


Why openly share research data?
 

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Source

What is Research Data Management (RDM)?

As part of research data management, strategies are developed to organize and control the work processes that concern the generation of and handling of research data as efficiently as possible. Research data management thus accompanies research from the first planning to archiving, reuse or deletion of the data (i.e. over the entire data life cycle).

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Research data life cycle


Why manage research data

  • Protect your data from loss by maintaining good backups and documentation

  • Secure your data through effective management of sensitive data

  • Conduct research efficiently by analyzing your data practices

  • Simplify the use and reuse of your data through proper documentation and application of standards

  • Increase your research visibility by publishing your datasets and documentation

  • Meet funding agency, legal and ethical requirements for dissemination and documentation of your research

  • Preserve and provide access to your data in the long term, allowing future scholars to build on your work

References:
https://libguides.mst.edu/data
https://www.openaire.eu/what-is-open-research-data
https://doi.org/10.25592/uhhfdm.9199