📄️ Research Data
Research data refers to the information, records, and observations collected or generated during a research project to support or validate its development, results or findings, including contextual information. Research data does not include physical objects, but it does include information/description about them. Some examples are:
📄️ Research Data Management
Research Data Management (RDM) is the systematic organization, storage, documentation, and sharing of data generated or collected during research endeavors to ensure its integrity, accessibility, and reusability. It involves planning for data handling throughout the research lifecycle to support valid and transparent scientific practices.
📄️ Research Data Lifecycle
Data management is a crucial aspect of the Research Data Lifecycle, which involves the various stages of data handling from its inception to its ultimate disposition. Throughout the research data lifecycle, data management includes tasks such as planning your research, collecting data using various methods, processing and analysing data, organizing and documenting data, securely storing and preserving it, sharing it responsibly, and eventually properly disposing of or archiving it for future reuse. This systematic approach to data management ensures that research data remains reliable, accessible, and compliant with ethical and legal requirements, promoting transparency and contributing to better and more efficient scientific research.
📄️ FAIR Principles
The FAIR principles are the standard for responsible data management and practicing open science. They focus on ensuring that research data are reusable, will actually be reused and will become as valuable as possible. FAIR is not only aimed at human beings but puts emphasis on enhancing the ability of machines to automatically find and use the data. FAIR stands for: