 The research data life cycle is a key concept in research data management. It describes the different stages that research data go through in the course of the research process, and helps identify data management activities at different points in a research project. Thinking about the data life cycle for your own project is often the first step to draft a high-quality data management plan. Research data management spans across all stages of the research cycle. It begins with planning, even before your research has started. It follows through the active phase of research, where you create data, process it and analyze it. And it continues afterwards, once your results are published, with the preservation and publication of your data, so that these can be potentially reused, starting or feeding a new cycle. And what's important, is that choice is made in one phase of the cycle, influence the next. If you have seen our introduction to RDM video, you may remember that the final goal of all data management activities is to make sure that data are secure, sustainably preserved, easy to find, understand and reuse. Let's have a look at what specific data management activities can be taken in each of the different stages of the data life cycle. During the planning stage, decisions are made about the following stages. The typical outcome of this phase is a data management plan. One of the considerations to make while planning, is to anticipate the resources needed to implement the data management decisions. This is important because many research funders will allow you to budget for at least some data management related activities. In the planning phase it is also essential to consider the different policies and regulations that you need to comply with, and obtain the necessary ethical approval, consent, and any other permissions needed not only to collect data, but also to share and preserve them. Check whether existing data sources are available that you could reuse, and make sure to design how you gather and organize the data you collect. If your discipline has developed data collection or processing protocols, make sure to follow them or plan out yourself how you will document the process. Also, design data security measures to safeguard your data. Data collection involves all different activities and methods to acquire existing data, or collect and generate new data. Metadata, or data about your data, should be created along with research data. During this stage of the research data life cycle, it is also recommended to introduce measures to avoid errors during data collection, to ensure data quality and integrity. The result of this phase is a set of data inputs to be further processed and analyzed during later stages of the research cycle. Data processing comprises all the steps and procedures to prepare the data for its subsequent use in analysis. Data analysis refers to the actions taken, or methods used, to extract patterns and information from data and to test a hypothesis. During these stages, activities such as entering, digitizing, transcribing, and deriving data are performed. But also, transforming and integrating diverse or unstructured data into a common format. Data quality checks, data validation and data cleaning are part of this stage as well. And, course analysis and interpretation of the data. At this stage, it is important to continue to describe and document the research data, as well as the applied procedures. Consider versioning, organizing and storing data during their active life, while they're still being used. Data preservation refers to keeping data available beyond the end of your research project to facilitate data reuse, to comply with applicable policies, and to guarantee research integrity and reproducibility. This involves selecting the data to keep, preparing them for preservation. Think of choosing a file format suitable for long-term sustainability, and storing data and accompanying documentation in an appropriate infrastructure, such as a data repository or archive. Data publication refers to releasing or sharing your data, making it available for others outside your research project to reuse. Best practices to publish your data include its deposition in a data repository, rather than making it available as supplementary material to a publication. Preferably, choose a trusted domain-specific repository if one is available. When you deposit your data, you will also need to provide discovery metadata, and you will have to choose a specific license for your data set. Besides, it is also possible to publish a data paper containing all relevant contextual information needed to understand your data. Ultimately, shared data from completed research may be reused for further research, for teaching, or to verify published findings. Data reuse causes the data lifecycle to start over again. Potential data re-users, first of all, need to find suitable datasets, for example by searching online data catalogs. Checking and complying with any access and use conditions for the data is also key. In addition, citing reused data in your publication ensures that the original data creators receive proper credit for their work. More information about each of these phases and further support can be found at our web pages. So, check them out.