ABSTRACT
INTRODUCTION: Rare disease patient data are typically sensitive, present in multiple registries controlled by different custodians, and non-interoperable. Making these data Findable, Accessible, Interoperable, and Reusable (FAIR) for humans and machines at source enables federated discovery and analysis across data custodians. This facilitates accurate diagnosis, optimal clinical management, and personalised treatments. In Europe, twenty-four European Reference Networks (ERNs) work on rare disease registries in different clinical domains. The process and the implementation choices for making data FAIR ('FAIRification') differ among ERN registries. For example, registries use different software systems and are subject to different legal regulations. To support the ERNs in making informed decisions and to harmonise FAIRification, the FAIRification steward team was established to work as liaisons between ERNs and researchers from the European Joint Programme on Rare Diseases. RESULTS: The FAIRification steward team inventoried the FAIRification challenges of the ERN registries and proposed solutions collectively with involved stakeholders to address them. Ninety-eight FAIRification challenges from 24 ERNs' registries were collected and categorised into "training" (31), "community" (9), "modelling" (12), "implementation" (26), and "legal" (20). After curating and aggregating highly similar challenges, 41 unique FAIRification challenges remained. The two categories with the most challenges were "training" (15) and "implementation" (9), followed by "community" (7), and then "modelling" (5) and "legal" (5). To address all challenges, eleven types of solutions were proposed. Among them, the provision of guidelines and the organisation of training activities resolved the "training" challenges, which ranged from less-technical "coffee-rounds" to technical workshops, from informal FAIR Games to formal hackathons. Obtaining implementation support from technical experts was the solution type for tackling the "implementation" challenges. CONCLUSION: This work shows that a dedicated team of FAIR data stewards is an asset for harmonising the various processes of making data FAIR in a large organisation with multiple stakeholders. Additionally, multi-levelled training activities are required to accommodate the diverse needs of the ERNs. Finally, the lessons learned from the experience of the FAIRification steward team described in this paper may help to increase FAIR awareness and provide insights into FAIRification challenges and solutions of rare disease registries.
Subject(s)
Rare Diseases , Software , Humans , Europe , Rare Diseases/therapy , RegistriesABSTRACT
Gran parte de las personas que padecen trastorno mental severo (TMS) sufren una situación que se caracteriza por los aspectos defectuales que tienen su origen en la propia patología y que desembocan, a nivel funcional, en la inactividad, la falta de expectativas y la dificultad para desempeñar roles productivos socialmente reconocidos. En 1998 el Ayuntamiento de Sant Boi de Llobregat (Concejalía de Colectivos de Atención Especial) recibió una demanda desde el servicio de Centro de Día de Benito Menni. Se trataba de iniciar un programa de inserción laboral para usuarios del Centro de Día y del Centro de Salud Mental de Adultos. En ese mismo año comenzó el Programa de Inserción Laboral de Personas con TMS de Sant Boi fruto de la estrecha colaboración entre CORESSA (empresa municipal que gestiona el Área de Promoción Económica del Ayuntamiento de Sant Boi), el propio Ayuntamiento (Concejalía de Colectivos de Atención Especial) y los servicios asistenciales del Centro de Día y el Centro de Salud Mental de Adultos, ambos integrados en Benito Menni, Complejo Asistencial en Salud Mental. Las páginas que siguen explican como se concretó esta iniciativa destinada a facilitar la (re)inserción laboral articulando y coordinando las acciones y apoyos necesarios para que los usuarios puedan superar las dificultades que muestran en la adquisición de hábitos laborales, capacitación ocupacional y búsqueda y mantenimiento del empleo conseguido (AU)
