The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images

The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50–500 km and 10–100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an object-based image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast

An Ontological Solution to the Mind-Body Problem

I argue for an idealist ontology consistent with empirical observations, which seeks to explain the facts of nature more parsimoniously than physicalism and bottom-up panpsychism. This ontology also attempts to offer more explanatory power than both physicalism and bottom-up panpsychism, in that it does not fall prey to either the ‘hard problem of consciousness’ or the ‘subject combination problem’, respectively. It can be summarized as follows: spatially unbound consciousness is posited to be nature’s sole ontological primitive. We, as well as all other living organisms, are dissociated alters of this unbound consciousness. The universe we see around us is the extrinsic appearance of phenomenality surrounding—but dissociated from—our alter. The living organisms we share the world with are the extrinsic appearances of other dissociated alters. As such, the challenge to artificially create individualized consciousness becomes synonymous with the challenge to artificially induce abiogenesis

FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance

The principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) have been put forward to guide optimal sharing of data. The potential for industrial and social innovation is vast. Domain-specific metadata standards are crucial in this context, but are widely missing in the energy sector. This report provides a collaborative response from the low carbon energy research community for addressing the necessity of advancing FAIR metadata standards. We review and test existing metadata practices in the domain based on a series of community workshops. We reflect the perspectives of energy data stakeholders. The outcome is reported in terms of challenges and elicits recommendations for advancing FAIR metadata standards in the energy domain across a broad spectrum of stakeholders

Microtheories for SDI - Accounting for diversity of local conceptualisations at a global level

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The categorization and conceptualization of geographic features is fundamental to cartography, geographic information retrieval, routing applications, spatial decision support and data sharing in general. However, there is no standard conceptualization of the world. Humans conceptualize features based on numerous factors including cultural background, knowledge, motivation and particularly space and time. Thus, geographic features are prone to multiple, context-dependent conceptualizations reflecting local conditions. This creates semantic heterogeneity and undermines interoperability. Standardization of a shared definition is often employed to overcome semantic heterogeneity. However, this approach loses important local diversity in feature conceptualizations and may result in feature definitions which are too broad or too specific. This work proposes the use of microtheories in Spatial Data Infrastructures, such as INSPIRE, to account for diversity of local conceptualizations while maintaining interoperability at a global level. It introduces a novel method of structuring microtheories based on space and time, represented by administrative boundaries, to reflect variations in feature conceptualization. A bottom-up approach, based on non-standard inference, is used to create an appropriate global-level feature definition from the local definitions. Conceptualizations of rivers, forests and estuaries throughout Europe are used to demonstrate how the approach can improve the INSPIRE data model and ease its adoption by European member states