The data life cycle. Source: RDMkit by ELIXIR-CONVERGE, CC BY 4.0.

The data life cycle (DLC) is a conceptual tool which helps to understand the different steps that data follow from data generation to knowledge creation and specifically focuses on the role of data. It strongly suggests that a professional approach to research data management involves more than just collection and analysis, but begins with detailed planning. In order to cope with the heterogeneity of biological datasets, data users, and data producers, a large variety of DLC´s are commonly used within the community. DLC´s often include between 5 and 10 steps, depending on the institution and respective research mission. However, the message and content are quite similar across DLC´s and disciplines. All DLCs share the commonality of adhering to the FAIR principles at each individual step. The differentiation between research domains relates to specific tools and services that are used within the respective community. The figure displays the exemplary DLC RDM-Kit developed by a diverse community of experts and educators spanning various disciplines within the life sciences. The RDM-Kit is a suitable DLC for introducing RDM to a wider audience. The seven phases of a data cycle according to the RDM-Kit are planning, collecting, processing, analysing, preserving, sharing and reusing; an unlimited number of subsequent cycles can follow. Each step of the DLC contains further explanations and specifications in order to cope with the challenges in biodiversity research. Ideally, the FAIR principles should be considered throughout the life cycle of research data. However, the realisation might vary depending on the type of conducted research, the type of collected data and the type of researchers. Here, we focus on the steps that are specifically important for researchers, which work with biodiversity and environmental data.

As a researcher, it is worthwhile to always consider all phases when making decisions and to find out at an early stage which tools and options are available to optimise your practice in dealing with research data. It starts with the planning, then the data is collected, processed and analysed. The life cycle continues with the preservation and sharing of data, until it re-enters the process by data reuse.

This video [1] explains the research data lifecycle.