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Data used when studying vaccination programs and vaccine evaluations are collectedfrom multiple, and constantly changing data sources. These data sources are often isolateddata silos, that are hard for researchers and public health organizations to connect, andthus hinders when researchers and public health organizations are conducting these studies. A solution for this hindrance are well-designed ontologies. Ontologies are becomingincreasingly relevant with the massive increase of data online. Equally relevant is handling ontologies according to the FAIR principles, since it is considered the best practicesof handling scientific data. This thesis presents a design process for modeling an ontology in order to representvaccine coverage, safety and adverse events, and vaccine effectiveness for real-time evaluation when existing ontologies are taken into consideration. The design process was inspired by the METHONTOLOGY method and the XD method. The scope was specified viathree stories each representing one aspect (vaccine coverage, safety and adverse events,and vaccine effectiveness), and relevant terms were gathered via knowledge acquisition.They were both validated by a domain expert before integration and implementation ofeach story, forming a module, were performed. In the integration and implementation,noticeable impact of the FAIR principles was seen since FAIRsFAIR’s 17 recommendationswere followed. The impact of the guidelines suggested foundational ontologies and reuseof ontologies which also follow the FAIR principles should be used to the extent possible.This thesis also presents how the modeled ontology could be evaluated by three aspects:internal evaluation, external evaluation, and FAIRness. Both the internal and external evaluation regarded the quality criterion accuracy and coverage. The internal evaluation included verification of requirements, also the coverage, and predicting the accuracy by semantic variance. The external evaluation was conducted b

Denna rapport beskriver ett kandidatarbete som utfördes av åtta studenter i kursen TDDD96 - Kandidatprojekt i programvaruutvecklingvid Linköpings universitet våren 2021. Projektet handlade om att skapa ett system för att spåra och visualisera förekomsten av farliga ämnen som kan förekomma på olycksplatser och på så sätt underlätta räddningstjänstens arbete. Resultatet av projektet bestod av en sensorenhet som fästs på räddningspersonalens hjälm. Sensorenheten detekterar halten butan, vätgas, ammoniak, sulfider och bensen i luften och skickar dessa värden tillsammans med GPS och acceleration till en instrumentbräda. Där visas all data och kan användas avbakre led. Rapporten beskriver projektets arbete beställt av forskningsgruppen Ubiquitous Computing and Analytics Group vid Institutionen för Datavetenskap på Linköpings universitet. Teorin kring systemet lägger grunden till förståelse av rapporten, samt hur arbete kan effektiviseras med hjälp av till exempel Scrum, parprogrammering och ärendespårning (eng.issue tracking). Rapporten redovisar även projektets resultat och hur den slutgiltiga produktens hårdvara, server och användargränssnitt fungerar. Till sist presenteras gruppmedlemmarnas individuella bidrag som innehåller djupdykningar inom olika områden med koppling till projektet.