In order to deposit the data, researchers will develop a Data Management Plan (DMP) and choose the repository to upload the dataset. Researchers have a significant amount of guidelines for the elaboration of a research DMP.

Most calls for project funding require the preparation of a DMP from the outset. A DMP is a "living" document (in the sense that it can be changed or reviewed during his “live”) that describes how the data are collected or generated during and after the research project. A DMP might describe:

• The data set: the kind of data will be collected or generated by the project. Furthermore, who can use the data later.
• International standards and metadata: A DMP will answer what the data is about, who creates it and why. This plan must explain in what formats it is available as well. Metadata answers these questions so that it can be found and understood and according to the specific international standards of your scientific discipline equally.
• Data sharing: legitimate reasons for not sharing research data should be detailed in the DMP.
• Data archiving and preservation: The data might be used in the future based on the correct storage, backups and software preservation.
   

The DMP is not a static document (in the sense that it's not unchangeable), but evolves and acquires more precision and content during the development of the investigation. The first version of the DMP should be completed during the first months of the project. Then, it should be updated at least halfway and at the end of the project. The researcher has to adjust it to the data generated and the uses identified by the project research group.

• The kind of research data that will be created or collected.
• The person who will be responsible for each aspect of the management plan you are developing.
• The policies (financial, institutional and legal) that will be applied to the data.
• The method through which data will be organised (folder structures, file naming conventions, file versioning).
• The method how the data will be documented during the collection and analysis phase of the research.
• The data management practices that you will use to store and protect your data (backup, storage, access control, archiving).
• The facilities and equipment that will be needed (hard disk space, backup server, repository).
• The person that will have the ownership of the data (access rights).
• The way how the data will be preserved and made available, once the research is completed.

Remember that::

• The DMP is an integral part of a funding request, not an afterthought.
• DMPs are improved and strengthened through collaboration. Actually, few people possess all the skills related to data management. You could seek support from others who have the appropriate knowledge, if you need it. And you could use available tools developed by your institution or the scientific community.
• DMPs are “living” documents and may be changed. The plan submitted for the funding application is an initial idea. Once funding is obtained, you would need to expand it by developing policies and procedures or implementing guidelines for your research group, department or institution. Processes often evolve over time to respond to new opportunities or changes in research. Your DMP can help you to give documentary evidence to this process of change.

In this section we provide you with several documents to help you in the elaboration of the DMPs.

Examples of DMPs:

10 steps to develop a Data Management Plan

1. Review the funding’s agency requirements.
2. Identify the data: typology, origin, volume, formats and files.
3. Define how the data will be organised and managed: file names, version control, software requirements, etc.
4. Explain how the data will be documented: identify the information to be processed, check if there are international standards or metadata schemes, indicate tools to manage them.
5. Describe the processes to ensure good data quality.
6. Prepare a strategy for data storage (in-process) and preservation (repository).
7. Define the project's data policy: intellectual property issues and the handling of sensitive and personal data.
8. Describe how the data will be disseminated: where, what, when it will be disseminated. Whether you will publish the data in a repository, as supplementary information to the article or as a data paper.
9. Assign roles and responsibilities to the people and organisations involved in the project.
10. Prepare a realistic budget: data management costs time and money in terms of software, hardware, services and staff.

An open access repository stores and provides free access to a digital collection of research outputs. A repository can provide a persistent identifier that makes it easier to find publications. Many data repositories also accept publications and allow linking between publications and their underlying data.

If no thematic or institutional repository is available, researchers can use the Zenodo repository, provided by the European Commission and hosted by CERN.
There are also data harvesters. These are systems that make metadata searchable, but instead of hosting the data itself, they point to the repositories where it is stored.