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Objective: To address the growing need for effective data reuse in health research, healthcare institutions need to make their data Findable, Accessible, Interoperable, and Reusable (FAIR). A prevailing method to model databases for interoperability is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative. A European repository for OMOP CDM-converted databases called the “European Health Data & Evidence Network (EHDEN) portal” was developed, aiming to make these databases Findable and Accessible. This paper aims to assess the FAIRness of databases on the EHDEN portal. Materials and methods: Two researchers involved in the OMOP CDM conversion of separate Dutch Intensive Care Unit (ICU) research databases each manually assessed their own database using seventeen metrics. These were defined by the FAIRsFAIR project as a list of minimum requirements for a database to be FAIR. Each metric is given a score from zero to four based on how well the database adheres to the metric. The maximum score for each metric varies from one to four based on the importance of the metric. Results: Fourteen out of the seventeen metrics were unanimously rated: seven were rated the highest score, one was rated half of the highest score, and five were rated the lowest score. The remaining three metrics were assessed differently for the two use cases. The total scores achieved were 15.5 and 12 out of a maximum of 25. Conclusion: The main omissions in supporting FAIRness were the lack of globally unique identifiers such as Uniform Resource Identifiers (URIs) in the OMOP CDM and the lack of metadata standardization and linkage in the EHDEN portal. By implementing these in future updates, the EHDEN portal can be more FAIR.

At the end of 2022, an author group led by the EOSC Association Task Force on FAIR Metrics and Data Quality published ‘Community-driven governance of FAIRness assessment: an open issue, an open discussion’ which, as the title suggests, aims to foster an open debate with the community around FAIRness governance and the mechanism(s) that could be used to implement it. The FAIR-IMPACT project is keen to take part in this open community discussion, as many tasks and activities in this project focus on advancing FAIR assessment metrics and tools in various ways. FAIR-IMPACT supports the implementation of FAIR-enabling practices, tools and services across scientific communities at a European, national and institutional level. One of our overall objectives is to expand FAIRness assessment metrics and tools to cover a broader scope of digital objects (data, software, semantic objects) as well as to add community-specificity to the metrics and tools. Working so closely with FAIR assessment metrics and tools, as well as being involved in the projects’ synchronisation efforts to better harmonise the current landscape, we acknowledge and experience the complications that the lack of governance entails for the field of FAIR and assessment. This document presents our thoughts and views on the whitepaper and is our public effort for participation in the community discussion.

At the end of 2022, an author group led by the EOSC Association Task Force on FAIR Metrics and Data Quality published ‘Community-driven governance of FAIRness assessment: an open issue, an open discussion’ which, as the title suggests, aims to foster an open debate with the community around FAIRness governance and the mechanism(s) that could be used to implement it. The FAIR-IMPACT project is keen to take part in this open community discussion, as many tasks and activities in this project focus on advancing FAIR assessment metrics and tools in various ways.

A sustainability sheet for the Photon and Neutron FAIR implementation framework after the ExPaNDS project, reporting in a condensed manner on the following items: Description of the framework, its target audience, its benefits Accessibility, including links to relevant documentation and feedback mechanism Competitors, TRL and EOSC integration status Plans for sustainability Exploitability potential and associated actions The source document is available for comments or reproducibility here.

FAIR-IMPACT aims to realise a FAIR EOSC using proven solutions, tools and methods developed during the FAIRsFAIR and other initiatives. One of the goals of the project is to enable the ‘FAIRification’ of different research objects such as datasets, software and semantic artefacts originating from a large range of scientific disciplines. This includes the provision and extension of FAIR assessment metrics and associated tools and their adoption to the needs and requirements of a variety of research communities. In particular FAIRsFAIR data object assessment metrics as well as the F-UJI tool are intended to become more disciplinary-context aware and to include more discipline-specific tests in cooperation with FAIR-IMPACT use case partners, domain data repositories, research infrastructures and e-infrastructures. This deliverable provides the first set of discipline specific tests and metrics developed in cooperation with FAIR-IMPACT Social Sciences and Humanities (SSH) use case partners. We present an analysis of SSH community FAIR-aligned habits and practices carried out using available literature and whitepapers, data collected using standard interfaces provided by the community, as well as FAIR Implementation Profiles (FIPs) from a number of SSH data repositories. Based on this analysis we identified an appropriate SSH sub-community, the social sciences, for which we defined a set of discipline specific metrics and tests derived from the FAIRsFAIR data assessment metrics which are also presented in this deliverable.

English: This overview presents a selection of FAIR assessment tools. The categories also use the acronym FAIR but stand for: Fully Configurable Tools, Automatic Tools, Improved Survey Tools and Regular List Tools. The classification of tools into these categories is not always 100% clear, especially when certain properties overlap. In preparation for the poster, an evaluation of all known FAIR assessment tools was carried out between July 2022 and February 2023. Each tool was evaluated by three independent people. The categories used included the time taken to carry out the FAIR assessment, the presence of feedback and the required prior knowledge. With regard to the technical specifics, the research revealed that only generic tools are currently on offer. The poster was created for the "3. Sächsische FDM-Tagung" and the "E-Science-Tage 2023". Following tools weren't evaluated: FAIRdat (unfinished, beta version of SATISFYD?) FAIR-Aware (awarenness, not evaluation questions for dataset) Files: Poster as PDF Data as XLSX (full table) or CSV (split in data and codebook file) ------------------------------------------- German: Diese Übersicht stellt eine Auswahl von FAIR Assessment Tools vor. Die verwendeten Kategorien nutzen ebenfalls das Akronym FAIR stehen hier aber für: Fully Configurable Tools, Automatic Tools, Improved Survey Tools und Regular List Tools. Die Einordnung der Werkzeuge in diese Kategorien ist nicht immer hundertprozentig eindeutig, besonders, wenn sich bestimmte Eigenschaften überschneiden. In der Vorbereitung des Posters wurde zwischen Juli 2022 und Februar 2023 eine Auswertung aller bekannten FAIR Assessment Tools vorgenommen. Jedes Werkzeug wurde von drei unabhängigen Personen bewertet. Die verwendeten Kategorien waren unter anderem die Zeit zur Durchführung des FAIR Assessments, das Vorhandensein von Feedback und die vorausgesetzten Kenntnisse. In Bezug auf die Fachspezifik ergab die Recherche, dass zurzeit ausschließlich generische Werkzeuge angeboten werden. Das Poster wurde für die Veranstaltung "3. Sächsische FDM-Tagung" und die " E-Science-Tage 2023 "erstellt. Folgende Werkzeuge wurden nicht evaluiert: FAIRdat (unfertig, beta-Version von SATISFYD?) FAIR-Aware (Bewusstseinsfragen, nicht für Datensatz) Dateien: Poster als PDF Datensatz als XLSX (ganze Tabelle) oder CSV (aufgespalten in Daten und Codebuch), Version 2.1: added "CSIRO 5-star Data Rating tool" and "FAIR Enough" tool to evaluation, removed old files

For research data to be reusable by scientists or machines, the data and associated meta-data should comply with the so-called "FAIR principles", i.e. it should be findable, accessible, interoperable, and reusable [1]. To realize this, is not a straightforward task, as researchers do not know how FAIR or un-fair their data actually is and how to improve their FAIRness. A quantitative measure, which is easy to apply could help. The Helmholtz Metadata Collaboration (HMC) works on improving tools to automate the assessment of the FAIRness of publications. The F-UJI framework [2] originating from the FAIRsFAIR project is a powerful tool that provides a score for the FAIRness for machine findable and readible metadata of a given publication with respect to the FAIRsFAIR metric [3]. We co-develop F-UJI to explore and evaluate ways to apply it in user-sided tools. On this poster we present a FAIR assessment through F-UJI of Helmholtz data repositories. With our work, we want to identify gaps in the Helmholtz data infrastructure with respect to the FAIRness of (meta)data and how these gaps can be closed effectively. We also provide an outlook on possible research into the development of FAIRness over time within communities. [1] Wilkinson, M.D.et al. Sci Data 3, 160018 (2016) [2] Devaraju, A., Huber, R. (2020). F-UJI, Zenodo. https://doi.org/10.5281/zenodo.4063720 [3] Devaraju, A., et al. (2020). FAIRsFAIR Metrics. Zenodo. https://doi.org/10.5281/zenodo.6461229

A presentation on PresQT at CNI Fall 2020 Virtual Membership Meeting

Data quality and documentation are at the core of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016). Regarding biodiversity and more broadly ecology domains, complementary solutions of the well-known data standard (notably through Darwin Core (Wieczorek et al. 2012)) orientation are emerging from the intensive use of EML (Ecological Metadata Language (Michener et al. 1997)) metadata standard. These notably capitalize on using: semantic annotation from EML metadata documents that describe data attributes, and FAIR quality assessment as proposed by DataOne (Data Observation Network for Earth) network. semantic annotation from EML metadata documents that describe data attributes, and FAIR quality assessment as proposed by DataOne (Data Observation Network for Earth) network. Here we propose to present this point of view by orchestrating the production of rich (with attributes description and links with terminological resources terms) EML metadata from raw datafiles and, through the generation of FAIR metrics for direct assessment of FAIRness and creation of data standards like Darwin Core. Using EML, we can describe each data attribute (e.g., name, type, unit) and associate each attribute one to several terms coming from terminological resources. Using the Darwin Core vocabulary as a terminological resource, we can thus associate, on the metadata file, original attributes terms to corresponding Darwin Core ones. Then, the data and their metadata files can be processed in order to automatically create the necessary files for a Darwin Core Archive. By acting at the metadata level, associated with accessible raw data files, we can associate raw attribute names to standardized ones, and then, potentially create data standards.