1 Background
2 The trend of shaping the research data infrastructure in research infrastructure framework
Figure 1. Landscape of global RDIs (Sorted based on the roadmap of USA, EU, France, Germany, UK, China’s RIs, and scheduled running time in parentheses. Many high-performance computers are carriers of research data analytics processing technologies, so they are treated and listed as a subcategory). |
Table 1. The evolution of the categories of RDIs. |
Version | Description of the categories of RDIs and their predecessors |
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2006 (ESFRI 2006) | ● Seven domains or topics were organized in the first edition at 2006 and one of them was Computation and Data Treatment, which mainly focused on high-performance and high-throughput computing, grid architectures, software and middleware for performing computing, data management and curation techniques for handling vast masses of data. |
2008 (ESFRI 2008) | ● European high-performance computing service, later called PRACE, was established to form the partnership and network for advanced computing in Europe, aiming to utilize the top-level computing machines to fulfill the requirements of different scientific domains. ● Emphasized on the efficient use of the e-infrastructure to deal with the production and use of unprecedented quantities of research data, not only those coming directly from facilities, but also those contained in scientific publications. |
2016 (ESFRI 2016) | ● Pointed out an impression of the European RI Ecosystem, ● The e-infrastructure was transversal to all domains to develop the convergent and sustainable services in landscape analysis. |
2018 (ESFRI 2018) | ● Enhanced the concept of the European RI Ecosystem, and the role of e-infrastructure. ● Widely acknowledged and accepted the importance of data infrastructures for solving cutting-edge and interdisciplinary complex scientific and social problems, by policy-makers, researchers, funders, industry and society. ● Renamed the domain e-infrastructure to Data, Computing and Digital Research Infrastructures (DIGIT), highlighting the more general and explicit mandate of data. ● Ensured convergence of strategies and implementation actions with the European Open Science Cloud (EOSC). |
2021 (ESFRI 2021) | ● Continuing to deepen the ecological concept of European RI Ecosystem. ● Announced 11 new projects filling gaps in European RI capacities. eight of them were data infrastructures (horizontal) and data-driven facilities (vertical), involving computing and data science, brain science, population, ecology and environment, energy and social science. |
Table 2. Three elements of USA’s new RI framework. |
Elements | Description and the position in the dataflow |
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Experimental and observational infrastructure | ● closer to the traditional concept of physical or “hard” facilities, such as accelerators, telescopes, etc., and provided the tools and platforms for scientists ● Played the role of observation, generation and collection of research data with the extreme experimental approaches and conditions from the perspective of dataflow. |
Knowledge infrastructures | ● Included research data assets and resources, such as scientific collections, reference libraries, data repositories, intellectual property, etc.; the standards, protocols, and services enabling data management and remote access; analytic and computational algorithms; even human capital infrastructure. ● covered the aggregation, storage, management, sharing and provision procedures in research dataflow, as well as the mechanisms to guarantee them. It should be noted that these infrastructures maintain historical data, information, references, knowledge that may be demanded in the future. |
Research cyberinfrastructure | ● Offered interconnected ecosystem, like co-designed advanced computing resources, data and software service, research and education networks. ● Scientific results and outputs from certain research were shared, analyzed, near real time distributed, accessed across global physical or virtual networks through them, also ensured the efficiency and trustworthiness. |
3 Four new missions for research data infrastructures
3.1 As a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights
Figure 3. Pattern of RDIs (discipline-based vs problem-oriented). |
3.2 As an architect, to establish a digital, intelligent, flexible research and knowledge services environment
Figure 4. The interface of one-station panoramic digital research environment. |
3.3 As a platform, to foster the high-end academic communication
Figure 5. The role of RDI as a high-end communication platform. |