The digital health landscape in Uganda is plagued by problems with interoperability and sustainability, due to fragmentation and a lack of integrated digital health solutions. This can be partly... Show moreThe digital health landscape in Uganda is plagued by problems with interoperability and sustainability, due to fragmentation and a lack of integrated digital health solutions. This can be partly attributed to the absence of policies on the interoperability of data, as well as the fact that there is no common goal to make digital data and data infrastructure interoperable across the data ecosystem. The promulgation of the FAIR Guidelines in 2016 brought together various data stewards and stakeholders to adopt a common vision on data management and enable greater interoperability. This article explores the potential of enhancing digital health interoperability through FAIR by analysing the digital solutions piloted in Uganda and their sustainability. It looks at the factors that are currently hindering interoperability by examining existing digital health solutions in Uganda, such as the Digital Health Atlas Uganda (DHA-U) and Uganda Digital Health Dashboard (UDHD). The level of FAIRness of the two dashboards was determined using the FAIR Evaluation Services tool. Analysis was also carried out to discover the level of FAIRness of the digital health solutions within the dashboards and the most frequently used software applications and data standards by the different digital health interventions in Uganda. Show less
Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; ... ; Mons, B. 2019
Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand... Show moreBackground: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e. g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as "which particular data was input to a particular workflow to test a particular hypothesis?", and "which particular conclusions were drawn from a particular workflow?".Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.Availability: The Research Object is available at http://www.myexperiment.org/packs/428 The Wf4Ever Research Object Model is available at http://wf4ever.github.io/ro Show less
