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 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.
Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec
Orf virus (ORFV), the prototype species of the Parapoxvirus genus, is an important zoonotic virus, causing great economic losses in livestock production. At present, there are no effective drugs for orf treatment. Therefore, it is crucial to develop accurate and rapid diagnostic approaches for ORFV. Over decades, various diagnostic methods have been established, including conventional methods such as virus isolation and electron microscopy; serological methods such as virus neutralization test (VNT), immunohistochemistry (IHC) assay, immunofluorescence assay (IFA), and enzyme-linked immunosorbent assay (ELISA); and molecular methods such as polymerase chain reaction (PCR), real-time PCR, loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and recombinase-aided amplification (RAA) assay. This review provides an overview of currently available diagnostic approaches for ORFV and discusses their advantages and limitations and future perspectives, which would be significantly helpful for ORFV early diagnosis and surveillance to prevent outbreak of orf. Key points• Orf virus emerged and reemerged in past years • Rapid and efficient diagnostic approaches are needed and critical for ORFV detection • Novel and sensitive diagnostic methods are required for ORFV detection Keywords Orf virus • Diagnostic approach • Conventional methods • Serological methods • Molecular methods
Plant scientists use Functional Structural Plant Models (FSPMs) to model plant systems within a limited space-time range. To allow FSPMs to abstract complex plant systems beyond a single model's limitation, an integration that compounds different FSPMs could be a possible solution. However, the integration involves many technical dimensions and a generic software infrastructure for all integration cases is not possible. In this paper, we analyze the requirements of the integration with all the technical dimensions. Instead of an infrastructure, we propose a generic architecture with specific process-related components as a logical level solution by combining an ETL (Extract, Transform and Load) based sub architecture and a C/S (Client/Server) based sub architecture. This allows the integration of different FSP models hosted on the same and different FSP modeling platforms in a flexible way. We demonstrate the usability of the architecture by the implementation of a full infrastructure for the integration of two specific FSPMs, and we illustrate the effectiveness of the infrastructure by several integrative tests. CCS Concepts• Software and its engineering➝Integration frameworks, Software and its engineering➝Cooperating communicating processes, Software and its engineering➝Data flow architectures Keywords Functional structural plant model, simulation, multiscale, Multiscale tree graph, OpenAlea and GroIMP platform.
Grape quality is regulated by complex interactions between environments and cultivars. Growing suitable cultivars in a given region is essential for maintaining viticulture sustainability, particularly in the face of climate change. We created a database composed of three different subsets of data. The first subset was created by digitizing and curating the seminal report of Amerine and Winkler (1944), which provided grape harvest dates (GHDs), the quality of musts and wines, and wine tasting notes for 148 cultivars from 1935–1941 across five contrasting climatic regions of California. To put this dataset into a climate change context, we collected GHDs and must sugar content (°Brix) records from 1991 to 2018 for four representative cultivars in one of the five studied regions (Napa). Finally, we integrated meteorological data of the five regions during 1911–2018 and calculated bioclimatic indices important for grape. The resulting database is unique and valuable for assessing the fitness between cultivars across environments in order to mitigate the effects of climate change. Design Type(s) Cultivars design • Regions design Measurement Type(s) Climate data • Harvest date • Quality • Tasting notes Technology Type(s) Phenology characterization • Quality determination Sample Characteristic(s) Grape harvest dates • °Brix • Tannin • Total acid • pH • Alcohol • Fixed acid • Extract Measurement(s) maximum air temperature • minimum air temperature • total soluble solids ( o Brix) • must total acid • must pH • wine alcohol • wine extract • wine tannin • wine total acid • wine volatile acid Technology Type(s) weather station • a o Brix hydrometer • titration with sodium hydroxide to a phenolphthalein end point • a quinhydrone electrode or a Beckman pH meter • hydrometer • a special 0° to 8° Balling hydrometer • the Association of Official Agricultural Chemists method • titration with phenolphthalein as an indicator • titration with pretreated wines by method II of the Association of Official Agricultural Chemists
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