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Plant phenotyping experiments are increasingly generating vast amounts of data. The question of how to organize this data quickly arises. However, choosing a means of organizing such data is anon-trivial matter, as many solutions are possible. This has led to a lack of standardization in thefield. This hampers the use and reuse of data, as users of a dataset have to write/adapt their owncode to account for the particular structure of a new dataset. Furthermore, often dataset formatsdo not enable the detailed recording of metadata such as provenance information, potentiallyleading to a lack of transparency around the activities and structures within which the dataset wascollected. In this document, we consider the question of dataset organization within the context of plantphenotyping, with a special emphasis on computer vision data. A mapping/gapping analysis ispresented based on summaries of interviews with the imaging experts involved in setting up/controlling the imaging pipeline within phenotyping research infrastructure in Europe, tounderstand their way of working and their expectations from EMPHASIS-PREP. Subsequently, we discuss PHIS (Phenotyping Hybrid Information System), a prominent communityeffort specialized in the organization of plant phenotyping datasets. The discussion includes coresemantic web concepts, as well as an overview of PHIS’ approach to organizing information.Annexes are included which further expand on the information presented in the main matter,allowing the interested reader to learn more and experiment with using PHIS. This includes theuploading of a case study dataset containing hyperspectral image data. In addition, we consider thequestion of how to best preserve PHIS’ ability to capture provenance metadata while connecting thesystem to subsequent machine learning workflows (with a special emphasis on deep learning).

Thesis (Ph.D. (Accounting))--North-West University, Vaal Triangle Campus, 2004. The work done in this study is based on three dimensions; theory, research and practices. The objectives of this study are: Firstly, to determine what the literature says about strategic, project and risk management; Secondly, to establish if project managers use project risk management systems; to determine the maturity level of organizations in terms of risk management in capital projects; and to recommend a model for the improvement of the maturity level of organizations in terms of project management in general and risk management in particular when they implement their goals through projects; Thirdly, to give practical risk studies that can be used as examples by project teams in future. On the achievement of these objectives the following is applicable: The literature review on strategic, project and risk management, was achieved because a comprehensive review on these three subjects was undertaken as can be read in chapters three, four and five; Project managers' use of project risk management systems, and the maturity level of organizations in terms of risk management in capital projects, was researched and is given in chapters six and seven; The research recommendation can be read in chapter seven. Practical risk studies are explained in chapter eight. Doctoral

Thesis (M.Sc.)--Potchefstroom University for Christian Higher Education, 2000. "No Abstract available" Masters