Programme 2019

Abstract

[Presentation]

EOSC has been envisioned as a coordinated effort to provide an European, open environment for scientific data and related processing that promotes convergence of infrastructures and services provided at national or European level. Realizing this support to the European Research Area requires coordinated actions to provide services to access, process and compute on research data in a scalable way at the European level, ensuring research data is broadly exploitable as a public good. Integration and harmonization are especially important points to be addressed by European-wide Research Communities and scientific computing infrastructures to bridge the “digital gap” between EC countries with different readiness levels. A fundamental aspect is the convergence and alignment of common practices that would lead to the promotion of an open environment for data sharing, software and exploitation potentiated by new services, new users and new data.

To fulfil this ambition goals a key step is the development and implementation of policies to support the engagement of all the relevant stakeholders that need to cooperate to make EOSC a viable ecosystem. In this sense it is important understanding the current challenges as opportunities to remove the obstacles that currently prevent an effective sharing and reuse of scientific data, enabling exploitation of the full data potential and harnessing the capacity of regional investments. This presentation will summarize the work and conclusions of the 2nd EOSC HLEG, with a focus on the mechanism of engagement and incentives development from the point of view of the actors that need to develop the EOSC ecosystem.

Abstract

[Presentation]

With the rapid and widespread adoption of the FAIR Principles, a range of stakeholders including scientists, publishers, funding agencies and policy makers, are seeking ways to objectively evaluate the “FAIRness” of digital resources – that is, the degree to which the resource is Findable, Accessible, Interoperable, and Reusable. Implicit in this desire is the recognition that FAIRness is not abstract, but rather is something that can be objectively measured, and the means of achieving it can be defined by increased adherence to non-qualitative indicators. Spurred by these stakeholders, a self-selected group of the original FAIR Principles co-authors assembled the FAIR Metrics Authoring Group. This resulted in a set of semi-quantitative metrics having universal applicability for the evaluation of FAIRness, and a rubric within which additional metrics can be generated by the community. These were used to as the basis of a questionnaire that was undertaken by a variety of data hosts to self-evaluate their degree of FAIRness. In parallel, a software tool was designed that would attempt to validate their answers to the questionnaire in an automated manner by exploration of the (meta)data resource itself. The outcomes of this study provided us a general overview of the diversity of technologies and standards that were being used by resource hosts to achieve FAIRness. Using this as a basis, a second-generation of Metrics is now being designed that will perform fully-automated FAIRness assessments by testing a resource’s compliance with the best-practices we identified. In this presentation, I will discuss these second-generation FAIR Metrics, the community-driven process for designing new Metrics, the requirements of a FAIR Metrics Test, and The FAIR Evaluator software that executes compliance tests.

Abstract

[Presentation]

Open access to research data and making data findable, accessible, interoperable and reusable (according to the FAIR data principles) are topics that have gained increasing traction in recent years among funders, librarians, researchers and policy makers across the globe.
Much of the conversation focuses on the technical challenges and proposed solutions involved in opening, sharing and searching for research data. These processes are not purely technical, but are situated in and shaped by the norms and practices of research communities. Creating sustainable tools and policies to support data practices demands deeper understanding of the research practices of different epistemic communities.
This presentation will examine the sociotechnical practices of data search and reuse, probing the “F” and “R” of FAIR, by presenting findings from semi-structured interviews and a recent cross-disciplinary survey. I will suggest how these findings can be applied to designing systems for data discovery and to the development of policies for open research data.