From data pipelines to FAIR data infrastructures: A vision for the new horizons of bio- and geodiversity data for scientific research

Natural science collections are vast repositories of bio- and geodiversity specimens. These collections, originating from natural history cabinets or expeditions, increasingly becoming unparalleled sources data facilitating multidisciplinary research (Meineke et al. 2018, Heberling 2019, Cook 2020, Thompson 2021). Due to various global mobilization digitisation efforts (Blagoderov 2012,Nelson Ellis 2018), this digitised information about specimens includes database records along with two/three-dimensional images, sonograms, sound video recordings, computerised tomography scans, machine-readable texts labels on the as well media items notes related discovery sites acquisition (Hedrick 2020,Phillipson 2022). The scope practice specimen gathering also evolving. term extended was coined refer associated extending beyond singular physical object other digital entities such chemical composition, genetic sequence species data. Thus becomes an interconnected network resources that have incredible potential enhance integrative data-driven (Webster 2017,Lendemer 2019,Hardisty practices reflect role curatorial life-cycle starting initial material sampling process downstream analysis. We seeing growing acknowledgement disparate domain specific elements prevent interdisciplinarity which is crucial for a holistic understanding biodiversity climate crisis (Hicks 2010, Craven Folk Siniscalchi not just rows in pipelines going one repository another. They become self-describing artefacts can revolutionise how machines interpret work Within context, Distributed System Scientific Collections (DiSSCo), new European Research Infrastructure envisions infrastructure based FAIR Digital Objects (FDO) unify more than 170 under common FAIR-compliant (Findable, Accessible, Interoperable, Reusable) (Wilkinson 2016) access curation policies practices. DiSSCo’s key element achieving implementation Specimen (a FDO) closely aligns idea behind – FDO acts surrogate collection influenced by conversations around Object Architecture (De Smedt Islam 2020,Hardisty 2020). main purpose talk explain vision create only take advantage existing databases but at same time provide support innovative services AI twinning. With scientific use cases mind, will highlight few components (persistent identifiers, metadata, ontologies) within collaborative modelling activity specification. template specifying should look so DiSSCo build service ecosystem FDOs (Addink give examples envisioned help image feature extraction, model training (Grieb 2021,Hardisty 2022) twinning (Schultes believe exciting paradigm powered both humans accelerate From objects curated over hundred years, we developed pipelines, aggregators (Barberousse Now solutions where these enable wider research.