FAIR data
& Research Data Management
Research data is any information that is generated in the course of scientific work and serves to formulate, verify or refute research hypotheses. It can take the form of numerical values, measurements, texts, images, audio recordings, biological or chemical samples, software code or simulation outputs. It also includes accompanying materials such as laboratory notebooks, protocols, questionnaires or documentation of experiments. Research data can be primary (directly measured), secondary (taken or processed from other sources), qualitative or quantitative.
Data is all around us
- measurements
- microscope photographs
- interviews
- economic databases
- geographic map layers
- genomic data
- animal behavior records
- telescope datasets
- clinical data
- software
- data analysis scripts
- music recording
- video
- shopping list from wife
- letter to Santa
- sports results summary
Research Data Management (RDM)
Research data management involves the systematic planning, organization, storage, security, and long-term preservation of data generated during a research project. Its goal is to ensure that the data is of high quality, secure, clearly described, and available for future use – whether by the authors themselves or by the wider scientific community.
Why does it matter?
Although effective data management can be time-consuming and organizationally demanding, it is an integral part of good scientific practice and brings a number of essential benefits. The most important of these include:
Posílení integrity a kvality výzkumu
Well-managed data promotes transparency, increases the credibility of results, and contributes to a positive reputation.
Meeting the requirements of grant providers
All grant agencies today require clearly defined data handling procedures (e.g. through DMP).
Possibility to defend results in case of doubt
Quality data management makes it possible to demonstrate that research was conducted honestly, even if the results are questioned.
Robustnost a replikovatelnost výzkumu
Clearly structured and documented data allows others to verify your processes and results.
Compliance with legislative requirements
Od roku 2027 bude účinný zákon o výzkumu, vývoji, inovacích a transferu znalostí, který zpřesňuje povinnosti nakládání s daty.
Ensuring continuity of long-term projects
Structured data is essential where team members, doctoral students, or multiple researchers or institutions collaborate.
Preventing problems during the project
Thoughtful work with data allows you to identify risks such as inconsistent formats, incompatible software, legal restrictions, etc.
Minimizing the risk of errors or data loss
Thoughtful backups and proper file naming protect data from technical failures and human errors.
Contribution to global scientific progress
High-quality, described and accessible data enable their reuse, thus saving time and money in scientific progress.