Speaker
Description
A collaborative approach is the hallmark of modern science and research. Scientists and researchers, from different disciplines and countries, work together to advance humanity’s understanding of the world around us. As science and research become more “digital” – combining both digital assets and digital tools and services to increase the speed and excellence of scientific process – those digital assets and services must also be used collaboratively and increasingly provided and shared on a collaborative basis. Several terms have been used to describe this collaborative process, including open science commons and research ecosystems.Open science commons will face opportunities and challenges in the coming years. In this presentation we will illustrate some of them.Firstly, accessibility of distributed data, computing and storage services is a major requirement to enable data-intensive science.
Compute and Storage are the capabilities by which value is added to existing research data through its re-use and combination with other data - by researchers from any discipline and any country. In the current and future geopolitical landscape, data, computing and storage will be critical infrastructures to retain scientific excellence. It is important that policies are established to safeguard digital sovereignty of research performing organisations.Secondly, more and increasingly bigger scientific datasets are generated at research facilities and made available in ‘data holdings’ or ‘FAIR data repositories’. Although datasets are offered as resources in these holdings for primary and secondary use - as the size of holdings, the size of individual datasets and the complexity of datasets grow - the re-use of data becomes practically impossible without technical knowledge of compute environments, data staging, data analysis and AI techniques. To retain scientific excellence, the the data analysis capacity of research performing organisations needs to be significantly enhanced. This entails the coordinated sustainable provisioning of data as a service together with scalable computational platforms and integrating AI
frameworks.
This will enable more complex, data-intensive research, accelerating scientific discoveries and technological innovations. The advancement of interoperable
standards and protocols will be required to facilitate seamless data exchange and collaboration across different scientific domains, breaking down silos and enabling multidisciplinary research efforts.Lastly, AI democratisation will be necessary. AI needs to be put into the hands of researchers without specialised AI and technical knowledge. Large scale AI adoption requires open-source datasets, integrated computing infrastructure and tools which demand less knowledge of AI from the user so that they can build innovative AI software.
Research performing organisations need to integrate their capabilities to provide access to advanced computing, datasets, models, software, training and user support to researchers.We will illustrate how the EGI Federation, as reference digital infrastructure for data-intensive computing in Europe and beyond, is getting ready with its members and partners to face these opportunities and challenges, and an overview of its flagship Research and Development projects will be provided.
