RDM Basics
This page provides a basic information on research data management (RDM), which should quickly instruct scientists (especially from the MSE domain) about responsible research data handling and how to easily get involved into data sharing and re-use. Only absolute necessities are explained. For more detailed information and in-depth understanding of discussed topics, we recommend training material from carefully selected resources.
Research Data
Research data encompass measured, recorded, observed and simulated outputs such as tables, spectra, images, videos, text, simulations and models, questionnaires, data mining results. These can be quantitative or qualitative, digital or digitalized (the source is non-digital). Protocols (laboratory notebooks – ELNs or handwritten copies), workflows, code and software, publications and peer review documents should be stored along with or linked to the data (Fig. 1).Research data encompass measured, recorder, observed and simulated outputs such as tables, spectra, images, videos, text, simulations and models, questionnaires, data mining results. These can be quantitative or qualitative, digital or digitalized (the source is non-digital). Protocols (laboratory notebooks – ELNs or handwritten copies), workflows, code and software, publications and peer review documents can be stored along with or linked to the data (Fig. 1).
Research Data Managment
The RDM is the management of (research) data throughout their full life cycle: planning, acquisition/collection, processing, analysis, preservation, sharing and reuse (Fig. 2). It is important to plan what will happen with the data long before the project or experiments start (see Data Management Plan). More here and here. Terminology is here.The RDM is the management of (research) data throughout their full life cycle: planning, acquisition/collection, processing, analysis, preservation, sharing and reuse (Fig. 2). It is important to plan what will happen with the data long before the project or experiments start (see Data Management Plan). More here and here. Terminology is here.
Data Managment Plan
Data lifecycle starts with planning (Fig. 2). By creating Data Management Plan (DMP) researcher focuses on data, which play a key role in modern data-driven research. It is a formal document describing what kind of data will be collected, what will happen with the data during and after a project, and what is needed to accomplish this plan. It is a living document. It should be updated regularly. But IMPORTANTLY, it helps researcher to predict data management-related issues, which may limit the output of the project. More here and here.To create DMP, it is recommended to use online tools: Data Stewardship Wizard, DMP Tool, DMPonline. These often work with templates from the main funders. There are many more useful tools*.
Data Sharing and Long-term Preservation
There is a difference between data storage and data repository. Unstructured data without metadata can be stored in a data storage. Repositories require more structured data with metadata in order to enable data sharing within a group, with collaborators or even publicly. For long-term data preservation (usually > 10 years), the use of certified repositories is recommended (e.g., Zenodo with CoreTrustSeal). If available, use domain-specific repositories (e.g., NOMAD or DICE for material sciences and engineering).
There is a difference between data storage and data repository. Unstructured data without metadata can be stored in a data storage. Repositories require more structured data with metadata in order to enable data sharing within a group, with collaborators or even publicly. For long-term data preservation (usually > 10 years), the use of certified repositories is recommended (e.g., Zenodo with CoreTrustSeal). If available, use domain-specific repositories (e.g., NOMAD or DICE for material sciences and engineering).
Open Science and Open Data
Sharing your data accelerates research. Open data can also contribute to a better reproducibility of research. To share your data, the data should be prepared in line with FAIR data principles. A basic principle is to store data in a certified repository with an appropriate license (see more), provide as rich as possible metadata and stick to standard formats, which can be handled with an open-source or free software. Sharing your data openly (without any limitations) helps researchers all around the world to access and re-use it. 

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