## Publish and Share

The research landscape heavily promotes Open Science, and under this umbrella falls Open Data Publishing. Sharing your data with others can help promote collaboration, further scientific research, and increase the recognition of your research. Publishing your data in a repository is a great way to share your research and increase your research outputs.

The way you publish your research data depends on the nature of your project, the type of data involved, and any legal or ethical considerations. Generally, there are three common approaches to data publishing.

#### 1. Open Data Publishing:

- The data is fully accessible to the public via an online repository. Anyone can view, download, and reuse the data, provided they comply with the terms of the associated license (e.g., Creative Commons).

#### 2. Restricted Data Publishing:

- The data can be reused, but the access is controlled. This may apply to the entire dataset or only specific parts of it. Potential users must submit a request and meet specific requirements, often outlined in the reuse license, before accessing the data. While the data itself is not publicly accessible, metadata and descriptions of the data are available online.
- Data may be restricted due to potential privacy risks.

#### 3. Closed Data Publishing:

- The data is not available for reuse under any circumstances, often due to privacy concerns, ethical considerations, or intellectual property restrictions. However, descriptive information about the dataset (metadata) is still made publicly available.

Regardless of the chosen publishing approach, data must remain available for verification purposes. This ensures that the integrity of the research can be confirmed if questions arise about the validity of the findings.

Once you have determined what data you can and cannot share publicly, you can determine which repository you will publish in. You can find out more information on specific repositories here. Each repository will have their own requirements, but it is always required to include the code you used to analyze your data, as well as clear reuse documentation along with a reuse license.