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Supporting FAIR Research: Virtual SciDataCon 2021 Strand

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In the final week of Virtual SciDataCon 2021, we see the continuation of the strand on Core Interoperability and two new strands.  The first of these covers ’Supporting FAIR Research’, including emerging Research Infrastructure and various activities to support researchers and research groups in making data – and other outputs – FAIR.

SoBigData: An European Research Infrastructure for Big Data and Social Mining, Tuesday 26 October, 11:00-12:30 UTC: REGISTER FOR THIS SESSION

Data science is an opportunity for boosting social progress, and data analysis tools are triggering new services with a clear impact on our daily life. is a multi-disciplinary research infrastructure aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. The SoBigdata RI’s service platform empowers researchers for the design and execution of large-scale social mining experiments. Pushing the FAIR (findable, accessible, Interoperable, responsible) and FACT (Fair, Accountable, Confidential and Transparent) principles, the RI renders social mining experiments more efficiently designed, and repeatable by leveraging concrete tools that operationalize ethics, incorporating values and norms for privacy, fairness, transparency and pluralism; also touching upon how data science helps us to make more informed choices, underlining the need to achieve collective intelligence without compromising the rights of individuals. The format of the session is a mix of research and practice presentations where the SoBigData++ project is presented in all its parts.

Unlock scientific collaboration through technology, Tuesday 26 October, 13:00-14:30 UTC: REGISTER FOR THIS SESSION

This 90min practice session will showcase with real examples how the Science Mesh (explained below) is supporting collaboration of distributed science teams across disciplines dealing with data. The session will have presentations from Science Mesh developers and also representatives of each one of the use-cases. There will be opportunity for interaction and discussion with audience. The Science Mesh, a service developed by CS3MESH4EOSC project, enables the users to retain control over their remote or domestic datasets, while becoming FAIR (Findable, Acessible, Interoperable and Reusable)compatible and integrated with the EOSC (European Open Science Cloud) at the same time. Users are able to directly access the service provided by Science Mesh from easy-to-use interfaces and discover the different functionalities, as well as develop application plugins or easily set-up federations in their own scope and develop their own activities on top of them. The Science Mesh goes beyond the general-purpose storage services from multinational companies, by providing an interconnected platform suited to the particular needs of researcher and students at European academic institutions and increasing scientific knowledge coming from both the academia and the research industry.
Certifying FAIR: The GO FAIR Foundation’s Pioneer Program to bootstrap community development of FAIR certification for events, people, and technology, Wednesday 27 October, 11:00-12:30 UTC: REGISTER FOR THIS SESSION

The GO FAIR Foundation (GFF) has been asked by various sectors to provide FAIR certification for FAIR-related resources. This request has come from a broad range of stakeholders and concerns the widely perceived need for independent third-party criteria and validation of resources with respect to the FAIR Principles, for technical components, domain-relevant standards, FAIR-related training, for FAIR implementation events (e.g. M4M and FIP workshops), for people (demonstrating various FAIR-related competencies) and organizations that aspire are committed to FAIR practices. It is believed that certification, when appropriately applied, can be a powerful accelerator of convergence onto wide-spread FAIR implementations. In response, the GFF has initiated a Pioneer Program to bootstrap an approach to certification beginning with a limited number of early mover experts and organizations that have clearly established themselves as global leaders in FAIR implementation. The GFF invites these experts and organizations as “GO FAIR Pioneers” to work together to build the first generation criteria for FAIR certification.

FAIRsFAIR – Tools and Support to foster FAIR Data practices in Europe, Wednesday 27 October, 13:00-14:30 UTC: REGISTER FOR THIS SESSION

This 90min practice session will showcase practical solutions for the use of the FAIR data principles throughout the research data life cycle, namely fostering FAIR data culture and the uptake of good practices in making data FAIR. The session will have presentations from different FAIRsFAIR tools and support programs to repository managers, research data managers, service providers, data stewards and higher education institutes. Some implementation stories will be showcased of how FAIRsFAIR is supporting the improvement of data FAIRness and interoperability, across disciplines dealing with data. There will be opportunity for interaction and discussion with the audience.

Towards computable publications: Author-driven FAIR data production, Wednesday 27 October, 16:00-17:30 UTC: REGISTER FOR THIS SESSION

Producing FAIR (findable, accessible, interoperable, and reusable) data cannot be accomplished solely by data curators in all disciplines. In biology, we have seen that phenotypic data curation is not only costly, but it is burdened with inter-curator variation. Variation in the curation results is stemmed from the complicated intellectual processes of interpreting published scientific findings and re-expressing the findings in machine languages (e.g., ontologies) by different curators. Inter-curator variation is also well-known in other intellectual activities involving human participants, for example: inter-cataloger variation/agreement among library catalogers and inter-coder variation/agreement in content analyses used in social science studies. Since inter-curator variation is inherent in any human-based intellectual activity, this issue should be recognized as one major issue in the post-publication curation approach to produce FAIR data. Data scientists have been investigating an alternative approach to FAIR data production that is grounded on making scientific publications semantically clear (i.e., computable) at the time of publication. So FAIR data can be harvested immediately after publication. We believe authors are the most authoritative in interpreting their datasets, methods, and finding (or “data” collectively) and we need to design intuitive semantic models and software platforms to support authors to efficiently express their data and produce computable publications. The theme of this session is enabling scientific authors to publish FAIR data along with human readable articles. It addresses two conference themes: (1) Policy and Practice of Data in Research, and (2) Data and Education. Younger generation of scientists should learn to write computable publications.

CODATA and WDS are very grateful to SpringerNature for sponsorship of Virtual SciDataCon 2021This sponsorship has assisted us in running the conference without a cost-recovery access charge, for which we are extremely grateful.

Virtual SciDataCon 2021 is organised by CODATA and the World Data System, the two data organisations of the International Science Council – PROGRAMME AT A GLANCE – FULL PROGRAMME – please note that registration is free, but participants must register for each session they wish to attend.