Metadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software... Show moreMetadata, data about other digital objects, play an important role in FAIR with a direct relation to all FAIR principles. In this paper we present and discuss the FAIR Data Point (FDP), a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles. We present the core components and features of the FDP, its approach to metadata provision, the criteria to evaluate whether an application adheres to the FDP specifications and the service to register, index and allow users to search for metadata content of available FDPs. Show less
While the FAIR Principles do not specify a technical solution for 'FAIRness', it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that... Show moreWhile the FAIR Principles do not specify a technical solution for 'FAIRness', it was clear from the outset of the FAIR initiative that it would be useful to have commodity software and tooling that would simplify the creation of FAIR-compliant resources. The FAIR Data Point is a metadata repository that follows the DCAT(2) schema, and utilizes the Linked Data Platform to manage the hierarchical metadata layers as LDP Containers. There has been a recent flurry of development activity around the FAIR Data Point that has significantly improved its power and ease-of-use. Here we describe five specific tools-an installer, a loader, two Web-based interfaces, and an indexer-aimed at maximizing the uptake and utility of the FAIR Data Point. Show less
Background The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers... Show moreBackground The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery. Show less
Background Occipital nerve stimulation (ONS) has shown promising results in small uncontrolled trials in patients with medically intractable chronic cluster headache (MICCH). We aimed to establish... Show moreBackground Occipital nerve stimulation (ONS) has shown promising results in small uncontrolled trials in patients with medically intractable chronic cluster headache (MICCH). We aimed to establish whether ONS could serve as an effective treatment for patients with MICCH.Methods The ONS in MICCH (ICON) study is an investigator-initiated, international, multicentre, randomised, double-blind, phase 3, electrical dose-controlled clinical trial. The study took place at four hospitals in the Netherlands, one hospital in Belgium, one in Germany, and one in Hungary. After 12 weeks' baseline observation, patients with MICCH, at least four attacks per week, and history of being non-responsive to at least three standard preventive drugs, were randomly allocated (at a 1:1 ratio using a computer-generated permuted block) to 24 weeks of occipital nerve stimulation at either 100% or 30% of the individually determined range between paraesthesia threshold and neardiscomfort (double-blind study phase). Because ONS causes paraesthesia, preventing masked comparison versus placebo, we compared high-intensity versus low-intensity ONS, which are hypothesised to cause similar paraesthesia, but with different efficacy. In weeks 25-48, participants received individually optimised open-label ONS. The primary outcome was the weekly mean attack frequency in weeks 21-24 compared with baseline across all patients and, if a decrease was shown, to show a group-wise difference. The trial is closed to recruitment (ClinicalTrials.gov NCT01151631).Findings Patients were enrolled between Oct 12, 2010, and Dec 3, 2017. We enrolled 150 patients and randomly assigned 131 (87%) to treatment; 65 (50%) patients to 100% ONS and 66 (50%) to 30% ONS. One of the 66 patients assigned to 30% ONS was not implanted and was therefore excluded from the intention-to-treat analysis. Because the weekly mean attack frequencies at baseline were skewed (median 15.75; IQR 9.44 to 24.75) we used log transformation to analyse the data and medians to present the results. Median weekly mean attack frequencies in the total population decreased from baseline to 7.38 (2.50 to 18.50; p<0.0001) in weeks 21-24, a median change of -5.21 (-11.18 to -0.19; p<0.0001) attacks per week. In the 100% ONS stimulation group, mean attack frequency decreased from 17.58 (9.83 to 29.33) at baseline to 9.50 (3.00 to 21.25) at 21-24 weeks (median change from baseline -4.08, -11.92 to -0.25), and for the 30% ONS stimulation group, mean attack frequency decreased from 15.00 (9.25 to 22.33) to 6.75 (1.50 to 16.50; -6.50, -10.83 to -0.08). The difference in median weekly mean attack frequency between groups at the end of the masked phase in weeks 21-24 was -2.42 (95% CI -5.17 to 3.33). In the masked study phase, 129 adverse events occurred with 100% ONS and 95 occurred with 30% ONS. None of the adverse events was unexpected but 17 with 100% ONS and eight with 30% ONS were labelled as serious, given they required brief hospital admission for minor hardware-related issues. The most common adverse events were local pain, impaired wound healing, neck stiffness, and hardware damage.Interpretation In patients with MICCH, both 100% ONS intensity and 30% ONS intensity substantially reduced attack frequency and were safe and well tolerated. Future research should focus on optimising stimulation protocols and disentangling the underlying mechanism of action. Copyright (C) 2021 Elsevier Ltd. All rights reserved. Show less
