Collaborative notes https://docs.google.com/document/d/13fb03DimFqrvq5dDe_eqUg17RTHnYEYe/edit

The FAIR Data Maturity Model WG completed the FAIR Data Maturity Model (FDMM) as its key output in 2020, since then we have been advocating its adoption in various disciplines to make digital assets FAIR-compliant. The FAIR Data Maturity Model was developed specifically to assess the FAIRness of data assets. However, the FAIR ecosystem continues to expand, encompassing all kinds of digital assets (e.g. data, software, models, physical specimens and reports) that researchers rely on in their work. Attempts have been made to apply the model to other types of assets, with limited success.

In this session we will bring together various initiatives that have experience in FAIR assessment of research assets such as data, software, and physical samples and its potential application in the field of publishing. They will share the awareness the FDMM has generated, their experiences with applying and aligning their work with the model. These insights will help inform the model’s future development. In addition, we aim to identify the gaps in FAIR assessment the model does not currently address, in order to help define the next steps or priorities of the Working Group.

The session will include three invited talks as below, followed by a panel discussion exploring next steps for the FAIR Assessment and more specifically the FDMM.

FAIR Physical Samples: Natalie Raia, University of Arizona, will reflect on how FAIR assessment informed the process of creating and managing PIDs for physical samples, and the difficulty in linking these PIDs to publications and other digital objects.

FAIR Software: Ghana Bharathy, Research Data Specialist AI/ML at the Australian Research Data Commons, will update us on the progress being made to identify the elements of FAIR software and code, relevant differences compared to FAIR data assessment and the FDMM, and will provide reflections on FAIR assessment into the future.

FAIR Publishing: Solange Santos, SciELO, will share the patient and persistent work in Latin America, Portugal, Spain and South Africa in education, the development of value propositions, and the incremental implementation of journal practices that support the FAIR principles. Ms Santos will provide her reflections on the use of the FDMM in SciELO and the challenges faced when applying to audiences with varying maturity across the sector.

1. Welcome and Introduction to the FAIR Data Maturity Model, its current state and adoptions, Shelley Stall
2. Speakers
   1. FAIR Physical Samples, Natalie Raia (in-person)
   2. FAIR Software, Gnana Bharathy (in-person)
   3. FAIR Publishing, Solange Santos (virtual)
3. Panel Discussion – Addressing the Gaps and Opportunities in FAIR Assessment
   1. Moderated Discussion
      How can the development of the model be informed by this discussion.
4. Wrap Up – Implications and lessons learned for the FDMM, Shelley Stall

We are seeking input from those who have adopted or attempted to adopt the FDMM. We are eager to learn the reasoning behind the use as well as from those who chose not to adopt it. We also welcome those who wish to learn more about the model.

The session will be of interest to researchers, data service owners, funders and infrastructures from different scientific and research disciplines, the industry and public sector, who are active and/or interested in the FAIR data principles and in particular in assessment criteria and methodologies for evaluating their real-life uptake and implementation level. We are interested to meet the newcomers and those who are starting their FAIR journey, but also those that are implementing methods around the globe.