 Data repositories? What are they in? How are they useful in research data management? Let's take a look at why we need data repositories. First of all, data repositories allow you to share data. By depositing data in a data repository, you enable review and verification of research and facilitate reuse of data by others. Second, data can be preserved or archived in a data repository. This is why depositing data in a repository is increasingly required by research funders, journals and institutions. Use of data repositories is beneficial for you as a researcher as your data gains visibility, and is beneficial for the broader community as reuse and transparency are increased. So what is a data repository? A data repository is a general term for a database infrastructure that collects, manages and stores data for preservation and sharing. A data repository takes input from researchers who want to preserve and share their data. Often, it has an easy-to-use input form which allows to import the data as well as the metadata. During data import, the data is registered and possibly also standardized. Once the data import is finalized, the data is shared and preserved. Others can now search, view or export the data. A data repository is the go-to place to find data and identify data unambiguously. This is enabled through the use of metadata and persistent and globally unique identifiers. A data repository ensures that data remains available for the long-term in a secure manner. Moreover, it makes sure that it is clear how the data can be accessed and reused, thereby generating fair data. Input forms of repositories will typically ask you to enter different types of metadata, such as descriptive metadata, technical metadata, administrative metadata, and structural metadata. These input forms are based on metadata standards to ensure that sufficient and clear metadata are provided to allow data reuse and that metadata can be exchanged across datasets and repositories. So how do you choose a suitable repository? Different types of repositories exist. There are generic repositories, which allow to deposit data independent of the scientific domain or data type. Besides, there are domain-specific repositories, which are narrower in scope and only manage specific data types. It depends on your field whether a suitable domain-specific repository already exists. Further, there are institutional repositories that hold data from a particular research institution. A good starting point is to check whether any repositories are recommended in your field. In some research domains, a consensus is reached regarding the best-suited repositories for a specific purpose or data type. You can reach out to fellow researchers to learn about best practices. Moreover, journals in which you aim to publish your research sometimes recommend specific data repositories. Finally, registries for data repositories such as re3data.org can be consulted. In addition to these recommendations, trustworthy repositories are preferred. These repositories have received certification, are domain-specific repositories that are commonly used and endorsed by the scientific community, or have essential functionalities to enable fair data. So what do you do when you have identified a suitable repository? To prepare for the data deposit in a repository, browse to the repository's website and check the data submission guidelines of the repository. Often these guidelines describe the scope of the repository, accepted data types and formats, required metadata and documentation, possible access restrictions and its policy regarding processing of personal data. The next step is to prepare a data package for depositing data in a data repository. A data package consists of the data, metadata and other documentation on the data. First, you need to select the data that are valuable for verification, reproducibility or reuse, and compile these data from the different storage locations in which they reside. Next, metadata is collected either manually or from automatically generated sources. Finally, further documentation such as study level, file level or variable level documentation is compiled. The documentation addresses the provenance of the data by specifying who generated the data, what data was generated, where, when and how was it generated and why. When your data package is ready, you can enter metadata and upload data and documentation through the repository's input form. When curation services are available, exchange with the data curator might be needed to improve your data submission. Start the submission early to account for time to improve it. Some final tips. It is advisable to plan ahead and identify suitable repositories and metadata standards at the beginning of the project. Preferably, the preparation of a data package and the compilation of metadata and other documentation occur during research, when this information is fresh in your mind. And finally, regularly check back for newly emerging repositories and metadata standards relevant to your field. For more information, have a look at our web pages.