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FAIR+ Principles

Combatting Bias is aimed to tackle both the technological and ethical complexities surrounding data collection and dataset creation.

FAIR

For the former, FAIR principles (Findable, Accessible, Interoperable, Reusable) have been established to guide the creation of human (re)usable and machine-usable scholarly data. In the field of SSH, the FAIR principles are a useful guide to make historical data easily (re)usable in the long-term, to the benefit of current and future research. Moreover, FAIR principles emphasise the importance of machine-usability as well.

Findable:

  • Datasets are easily discoverable through clear metadata and unique identifiers.
  • Metadata include diverse terminologies and perspectives.

Accessible:

  • Data (and metadata) is retrievable through standardised protocols.
  • Access conditions are clearly stated, considering sensitive information and ethical implications.

Interoperable:

  • Data uses standardised vocabularies and formats that are critically examined for inclusivity and historical biases.
  • Metadata includes clear descriptions of concepts, potential biases, and ethical considerations to facilitate responsible cross-domain use.

Reusable:

  • Clear usage licences are provided, along with guidance on ethical reuse and potential pitfalls to avoid.

See more about FAIR: https://www.go-fair.org/

+

We believe it is necessary for historical data to be scrutinised at an ethical level. Historical sources are never objective and must be contextualised - the same goes for historical datasets based on those sources. This includes (re)considering certain concepts (such as gender and ethnicity), as well as critically examining the positionalities of the author(s) and researcher(s). This is what is looked at within the ‘+’.

+ (ethical considerations):

  • Inclusivity: Represent diverse perspectives and experiences
  • Transparency: Disclose data sources, methods, and potential biases
  • Context: Provide necessary historical and cultural information
  • Harm Prevention: Assess and mitigate negative impacts on represented groups
  • Community Involvement: Engage relevant communities in data governance
  • Accountability: Establish feedback and correction mechanisms