The general rule of making research data accesible: Data should be as open as possible and as closed as necessary.

Research data are usually made accessible in a form of datasets, which means sets which are a separate elements including data related to one publication, scientific project or experiment.

FAIR rules have been created  to help scientists to prepare and make research data accessible.
FAIR stands for an acronym of four English adjectives depicting how the research data should look like:

Findable
  • a dataset provided with metadata allowing to find this set both by people and by computer machines
  • the dataset has a unique identifier (i.e. DOI), which is also the element of metadata description
  • metadata are indexed in widely available databases enabling to perform a database search
Accessible
  • the access to the dataset, at least, to metadata is directly possible due to a unique identifier and does not require any additional tools or software
  • metadata are always available, even if the dataset has been deleted or transferred
Interoperable
  • data and metadata are delivered in a format enabling to be read easily and processed both by people and computers
  • datasets and metadata describing them include references to other related resources
Reusable
  • metadata include numerous attributes describing precisely the dataset and enabling users to define their usefulness for their own research
  • the dataset includes the license defining the explicit conditions for reusing and processing data
  • metadata distinctly define the author and the place of creating data
  • metadata are created according to generally accepted specific standards for each discipline and type of data

The rules are not rigid, but should be treated as guidelines to use data appropriately. FAIR rules have been created and have been still developed mainly to make data accessible both for users and searching databases computer software without the help of a man.

The CARE principles (Collective Benefit, Authority to Control, Responsibility, Ethics) were developed as a complement to the FAIR principles (Findable, Accessible, Interoperable, Reusable) in response to the need to consider the rights and interests of Indigenous nations and other descendant communities in data management.

FAIR focuses on the technical aspects of data management, such as its accessibility and interoperability, while CARE emphasizes the ethical and social dimensions, highlighting the importance of data for innovation and self-determination of Indigenous Peoples. The CARE principles were developed by the International Indigenous Data Sovereignty Interest Group within the Research Data Alliance. They complement the FAIR principles (Findable, Accessible, Interoperable, Reusable) by focusing on people and purpose rather than just data characteristics.

The CARE principles are:

Collective Benefit
  • Data should be used in ways that benefit Indigenous communities. This includes promoting innovation, governance, and self-determination.
Authority to Control
  • Indigenous Peoples should have the authority to control the collection, access, and use of their data. This principle emphasizes the importance of Indigenous governance over data.
Responsibility
  • Those handling Indigenous data should be responsible for ensuring that their practices do not harm Indigenous communities. This includes respecting cultural values and ensuring data is used ethically.
Ethics
  • Data practices should be grounded in ethical considerations that respect the rights and interests of Indigenous Peoples. This includes ensuring that data use aligns with Indigenous values and priorities.

These principles aim to create a more equitable data ecosystem that acknowledges and respects the unique rights and interests of Indigenous communities. By integrating the CARE Principles into research data management, organizations can help ensure that data practices are both fair and respectful.