BackgroundThe Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs.Methods and resultsThe current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis.ConclusionsmalariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2500-5) contains supplementary material, which is available to authorized users.
This paper focuses on demonstrating the feasibility of applying expert system methodology as a new approach for assisting the development of engineering degree curricula particularly in developing countries. A number of subdomains in which a rule-based system can be applied have been identified in the field of curriculum development. The subdomain developed and presented in this paper concerns methods of identifying curriculum content, and the major subgoal of developing a profile of 'staff experience' in this context is investigated in depth. Knowledge in this subdomain has been encapsulated in an expert system which has been refined to the satisfaction of a curriculum expert and tested by potential end-users of the system. REsUME On s'est concentre dans cet article sur une demonstration de la possibilite d'appliquer les methodes d'Intelligence Artificelle, au deueloppement des programmes d'etudes pour la formation des ingenieurs au niveau de licence (surtout dans les pays en voie de deueloppement). On a identifie, dans le domaine du deoeloppement des programmes d'etudes, plusieurs sous-domaines auxquels s'appliquerait un systeme d'Intelligence Artificielle a base de regles. Le sous-domaine qu'on presente ici concerne les methodes qu'on se sert pour determiner le contenu des programmes d'etudes; en meme temps, un autre but majeur de nos recherches a lite d'examiner a fond le profil d'. experience du personnel enseignant en ce qui s'agit du deueloppement des programmes d'etudes. On a encapsule le savoir dans ce sous-domaine au moyen d'un systeme d'Intelligence Artijicielle qui a ete mis au point a la satisfaction d'un expert en matieres de programmes d'etudes et, qui a lite, de plus, uerifie par des representants de ceux qui, en toute probabilite, se serviront d'un tel systeme.
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