What is Research Data Management?

Reesearch Data management (RDM for short) refers to all steps of the data lifecycle, from data generation to processing and analysis to publishing and preserving. Digital resources can be preserved and published via public discipline-specific repositories.

For consistent and structured descriptions of corresponding data, independent bibliographic/administrative or descriptive/technical metadata should be stored with data. Common metadata standards (e.g. Dublin Core, MARC) ensure machine-readability and therefore enable open data discovery and quality assessment. Data are usually provided with a persistent identifier (e.g. ISO standard digital object identifier - DOI).

In addition, sets of research data with appropriate documentation can be published in specific journals, such as GigaScience, F1000Research, Scientific Data or Data.

Over the last few years, increasing numbers of gatekeepers such as funding organizations and journals have started to demand RDM according to the FAIR principles in order to ensure transparency and reusability of research. 

The FAIR Data Principles

At TU Graz, research outputs will be treated according to the FAIR (Findability, Accessibility, Interoperability and Reusability) principles. Adherence to these guidelines enables transparency and reproducibility of research. The principles do not only apply to data, but also to algorithms, tools and workflows leading to the data.

Findability can be ensured by using a unique or persistent identifier with sufficient description of data and their characteristics in machine-readable metadata and storage of data in archives or repositories. 

Accessibility requires that metadata are always available via standardized, universally implementable communication protocols and corresponding datasets have clearly defined access conditions.

Interoperability needs data and metadata kept in common, published standards of data formats, variables or ontologies in order to enable their integration into existing applications or workflows.

Reusability is the ultimate aim of research. Detailed description of characteristics according to community standards with clear conditions enable the reuse of research data for future endeavours.

Implementation of the FAIR principles provides advantages for different stakeholders such as: 

  • researchers, who receive credit for their work and benefit from data shared by other researchers
  • funding agencies aiming for long-term data stewardship
  • professional data publishers getting credit for their software, tools and services for data handling
  • the data science community for exploratory analyses


Dr. Tony Ross-Hellauer
Digitale TU Graz-Handlungsfeld Forschung

Brockmanngasse 84, 8010 Graz
Phone: +43 316 873 32800
Email: ross-hellauernoSpam@tugraz.at