Harmonised Data is Actionable Data: DiSSCo’s solution to data mapping

Predictability is one of the core requirements for creating machine actionable data. The better predictable the data, the more generic the service acting on the data can be. The more generic the service, the easier we can exchange ideas, collaborate on initiatives and leverage machines to do the work. It is essential for implementing the FAIR Principles (Findable, Accessible, Interoperable, Reproducible), as it provides the “I” for Interoperability (Jacobsen et al. 2020). The FAIR principles emphasise machine actionability because the amount of data generated is far too large for humans to handle. While Biodiversity Information Standards (TDWG) standards have massively improved the standardisation of biodiversity data, there is still room for improvement. Within the Distributed System of Scientific Collections (DiSSCo), we aim to harmonise all scientific data derived from European specimen collections, including geological specimens, into a single data specification. We call this data specification the open Digital Specimen (openDS). It is being built on top of existing and developing biodiversity information standards such as Darwin Core (DwC), Minimal Information Digit