Global Open Research Commons: Creating an International Model for Improved Interoperability and Collaboration
infodocketGARY's bookmarks 2024-12-13
Summary:
Abstract: In response to the global movement to implement national and cross-national or global commons, a Research Data Alliance (RDA) Interest Group was formed to work towards a community-developed typology for describing research commons. This Interest Group created a Working Group to develop an International Model (IM) describing the attributes of Global Open Research Commons (GORC). The vision of the GORC, once realised, will provide frictionless access to all research artifacts to everyone, everywhere, at all times, with the appropriate infrastructure, protocols, and support. This is the environment that will allow the research community to focus on their research questions and respond accordingly. To address what research commons and infrastructures would need to consider to be part of the GORC, the RDA GORC IM Working Group (WG) consolidated a large range of resources and expert feedback to generate the model. Between 2021 and 2023, the WG conducted a two-stage literature analysis, a 12-participant speaker series and multiple rounds of focused and casual community consultation. The model consists of 10 elements, with associated categories, subcategories, attributes and features, to be considered when undertaking the development of a commons of any kind, at any stage. The model does not mandate what should be implemented or in what way; the decisions on what is relevant and where resources should be invested will vary depending on the environment and priorities of the implementer. The model supports the development of individual commons and the work necessary to make them interoperable. It provides a firm, yet flexible, foundation for creating a set of recommendations and a roadmap for building the GORC. Use cases of the IM demonstrate that it provides guidance and structure for existing and intended commons, with directions for future work arising from these adoptions.
Link:
https://datascience.codata.org/articles/10.5334/dsj-2024-056From feeds:
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