Fair Data Management Security and Ethics: Glossary

You can do more impactful research if you plan to share your outputs!

You can more efficiently publish if you plan to share your outputs!

Open Science increases transparency in research

Publicly funded science should be publicly available

A license is a promise not to sue - therefore attach license files

For data use Creative Commons Attribution (CC BY) license

For code use open source licenses such as MIT, BSD, or Apache license

The FAIR principles state that data should be Findable, Accessible, Interoperable, and Reusable.

FAIR data enhance impact, reuse, and transparancy of research.

FAIRification is an ongoing effort accross many different fields.

FAIR principles are a set of guiding principles, not rules or standards.

Findable, means findable long-term. This requires persistent identifiers (PIDs).

DOIs are one of more common PIDs and can be used to persistently identify software and datasets.

Research repositories often use the OAI-PMH or REST API protocols.

Understand that FAIR is about both humans and machines understanding data.

Interoperability means choosing a data format or knowledge representation language that helps machines to understand the data.

It is possible to publish public data that does not meet FAIR standards.

Different fields have variable standards for metadata.

Metadata provides contextual information so that other people can understand the data.

Metadata is key for data reuse and complying with FAIR guidelines.

Metadata should be added incrementally through out the project

Repositories are the main means for sharing research data.

You should use data-type specific repository whenever possible.

Repositories are the key players in data reuse.

A spectrum exists for FAIR data sharing.

Privacy, freedom, explainability, and fairness all go into managing data ethics.

There are simple steps to help make your data more secure

Implement these steps to avoid data loss