A FAIR Digital Object Lab Software Stack

Preprocessing data for research, like finding, accessing, unifying or converting, takes up to large parts of research time spans (Wittenburg and Strawn 2018). The FAIR (Findability, Accessibility, Interoperability, Reusability) principles (Wilkinson 2016) aim support facilitate the (re)use data, will contribute alleviating this problem. A Digital Object (FAIR DO) captures resources all kinds (raw metadata, software, ...) in order align them with principles. Objects are expressive, machine-actionable pointers (De Smedt et al. 2020). As such, each DO points one object. Additionally, they may link other DOs, explaining their relations. Lab (Pfeil 2022) is an extendable adjustable architecture (a software stack) generic tasks. It consists a set interacting components services tools creation, validation, discovery, curation, more. In talk, we present our plans explain decisions, which mostly based on experience gained previous developments. creation maintenance DOs not trivial, as persistent identifiers (PIDs) contain typed record information. When creating maintaining PID records required information has be validated, involving calls public Data Type Registry (DTR) (Lannom 2015). After successful transformed representation service. been registered successfully, should documented locally disseminated. Using these PIDs starting point, use machine-actionability maintain search indexes create collections. This enables researchers look by searching timestamp. We developing services, offering solution use-cases, call Lab. Its goal have production-ready configurable stack, easing development FAIR-DO-aware at least described use-cases. already some its predecessor, Testbed (2021a)), was introduced Research Alliance (RDA) Virtual Plenary 17 Poster Session 2021b). similar Testbed, service can omitted replaced satisfy specific needs while integrating top existing infrastructures. management validation using Typed Maker Jejkal 2021), following RDA Information Types (PIT) Working Group Recommendations (Weigel 2015) external (DTR), DTR stores profiles types, enabling typed, records. uses records, disseminates after creation. All created modified communicated message broker. way, notified about such activities. Our first making advanced indexing ingest into index, but also try extract from bit-sequence digital object itself. second step, considering automated collections utilizing Collection (Chelbi 2020), includes. require rules process those place new correct collection. implementation API specification 2017), published corresponding Collections Group. On conceptual side, hope gain more insight structure There ongoing discussions degree standardization required. Large talking concepts Kernel Profiles recognized that there gaps regarding roles types profiles. To bring reality, need them. But point diverse make decisions. what extent DO? observed too much flexibility makes decisions harder. suggestion therefore consider less infrastructure view, machine's view improve machine-actionability. expect insights feasibility ease further FAIR-aware particularly specialized exist use. feasible write every tool scratch. practical stronger focus interactive user interfaces provide easy-to-use research. current work granular base solid ground These course replace tools, easy still benefit integration services. supported program ‘Engineering Futures’ Helmholtz Association German Centers Metadata Collaboration Platform.