OAI12 Session 2
Digital research data in the era of EOSC and FAIR
Chaired by Elena Giglia and Frank Manista
Data produced by public funds must be openly accessible and reusable: the European Open Science Cloud is the virtual environment in which researchers, innovators, service providers meet and create innovation for the benefit of society at large. FAIR (Findable, Accessible, Interoperable, Reusable) data is the pillar of the European Open Science Cloud.
The COVID pandemic made clear the importance of Research Data sharing, in a fast and open way; fast data sharing enables discovery and can save lives . As data is the foundation of sound research, the crisis of reproducibility, although complex and thorny, can and will be overcome by virtue of encouraging and enabling researchers to share their RD in a FAIR way, as the Research Data pilot for Horizon 2020 underscored and Horizon Europe will reinforce.
By “data” we mean all materials and assets scholars collect, generate and use during all stages of the research cycle, in every discipline
In this session we will aim to take a snapshot of what is happening around the issue of FAIR research data and their implementation according to disciplinary specificities. We will also explore the landscape for how things can be improved to support the sharing and re-use of data within the open research ecosystem and the European Open Science Cloud. As with open publishing, external factors include the rise of open access, a global pandemic, available tools and infrastructures, local research funding, and researcher assessment mechanisms, as well as rewarding.
FAIR data management and data sharing imply changing some practices in the everyday workflow.
Tuesday 07 Sept. 2021
All dates and times are Central European Summer Time (CEST)
Data as first-class citizens in the Science of the 21st century
The last science paradigm has marked the beginning of the e-Science, or Science 2.0: we are immersed in an enormous amount of data and are equipped with the computational resources and infrastructure needed to make sense of these data. However, the process of scholarly communication and especially the one of research evaluation need to still shift the focus from the traditional research outputs (aka, the paper) to data.
In this talk, I will make the case that the 21st century academic production can no longer be PDF-centric, but needs to look at data as first-class citizens of science, recognizing that the publishing system, as well as the assessment criteria, need to move towards dataset publication, citation, evaluation.
Making research data FAIR: the building of a CIDOC CRM extension for humanities and social sciences
This talk addresses the issue of interoperability of data generated by historical research in order to make them re-usable for new research agendas (as a realisation of the FAIR principles). The reasons for adopting the CIDOC CRM as a core ontology for this field, but extending it with some relevant, missing high-level classes, will be discussed while taking advantage of the methodological tools provided by the foundational ontologies DOLCE and DnS. Finally, the talk will show how collaborative data modelling carried out in the ontology management environment OntoME (ontome.net) makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain.
Accelerating Open and FAIR Data Practices Across the Earth, Space, and Environmental Sciences
Data underlying published studies is difficult to find or access, which can hinder new scientific research. Currently, only about 20% of published papers[1] have their supporting data in discoverable and accessible repositories. The AGU, working with our partners (Dryad, CHORUS, ESIP, Wiley), and supported by NSF grant 2025364, will focus on improving guidance and workflows to properly manage, link, and track data and software references throughout the publication pipeline. The resulting best practices will serve as a resource for AGU editors, reviewers and authors and help advance data and software publication policies. Beyond the AGU, this work will serve as a model for linking information across funders, data repositories and publishers, and improving public access to research outputs. In this talk, current publication practices as they relate to the FAIR principles will be described, together with lessons learned, and how workflows and guidance are being improved.
Coffee break and Music
FAIR Data Science in Africa on COVID-19 Prevalence
VODAN Africa has pioneered FAIR-Data Science for clinical patient and science data on COVID prevalence in nine countries in Africa. The concept is based on data visiting of data held in residence, and/or with strict data ownership controls. A return of data benefits to point of care is an explicit part of the model. The technical execution is based on data visiting of distributed data over the internet. The data is produced as both human and machine-readable instances. A proof of concept was realised in September 2020 and a Minimal Viable Product will be tested in September 2021. The approach resolved critical issues of data management in view of GDPR and the need for increased data protection in Africa which has suffered from data extraction, including in relation to health data. The research is a project if the Virus Outbreak data Network (VODAN) - Africa and Asia. It is coordinated by Kampala International University. It received seed funding from the Philips Foundation and support from the Dutch Development Bank, Philips Foundation, Cordaid and the Go-FAIR Foundation.
Questions to a live Panel session with the speakers of today’s session
