. . . . "ITC Bok"@en . . "201900064" . "GIP_0001" . "5"^^ . "140"^^ . "10"^^ . "2022-11-10T23:00:00Z"^^ . "f2f" . "online" . "In this course, students will learn the fundamentals of big geodata processing. Then, they will be introduced (via lectures, demos and exercises) to various distributed big data solutions as well as the role of cloud computing. After that, they will work on a real-life problem involving a big geo-dataset. They will work in groups and create the necessary workflows to process the data. This requires programming skills and critical thinking to select the \"best\" algorithm and computational solution.\n\nIn this course, there will also be a strong emphasis on Open Science principles, with a focus on scientific reproducibility and triangulation. Lectures on archiving data and code will be provided too."@en . . . . . . . "Basic Programming skills ,The knowledge gained during the Scientific Geocomputing course is advantageous but not strictly necessary to follow this course. Some self-study material will be provided through Canvas for students that do not follow the Geoinformatics specialisation. You are advised to contact the course coordinator to discuss the materials' relevance for you."@en . . . . . . . . . "3"^^ . "1" . "1A " . . "2022-09-04T22:00:00Z"^^ . "Thanks to the digital, mobile and sensor revolutions, massive amounts of data are becoming available at unprecedented spatial, temporal, and thematic scales. This leads to the practical problem of transforming big geodatasets into actionable information that can support a variety of decision-making processes. In this respect, geodata science workflows are not only key to processing big geospatial datasets but also to sharing the extracted information and knowledge and to ensuring the reproducibility of the results.\n\nTo handle and analyse massive and potentially heterogeneous amounts of spatio-temporal data, scientists need to 1) understand the particular characteristics of big geodata, 2) learn to work with scalable data management and processing systems, and 3) develop scalable and robust data mining and machine learning workflows. Hence, this course presents theories, methods, and techniques to build scalable solutions for handling and analysing big geodata."@en . "Big Geodata Processing"@en . . "Big Geodata Processing"@en . "Big Geodata Processing"@en . . "2.5" . "70.0" . "5.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Foundation courses M-Geo"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "2.0" . "The concept of (public) participation in geospatial research has a long tradition. However, the adoption of Web 2.0 technologies facilitates the generation and sharing of and collaboration on digital content with a geospatial component, and has therefore expanded possibilities and practice. This course gives an overview of its history and new developments, on examples of successful and unsuccessful projects to identify criteria for sustainable crowdsourcing or volunteering, including issues of privacy and ethical research. It is particularly relevant for eliciting and arguing the needs, interests, and positions of any stakeholder that incorporates or directly works with the public. A main focus lies on the technologies that enable new forms of participatory sensing, and techniques to assess and improve the quality of such data. "@en . "Volunteered Geographic Information and Geo Citizen Science"@en . . "Volunteered Geographic Information and Geo Citizen Science"@en . "Volunteered Geographic Information and Geo Citizen Science"@en . . . . . . . . . "Reproducibility and sustainability of crowdsourced research and citizen science "@en . "Volunteered Geographic Information and Geo Citizen Science: LU8"@en . "https://ltb.itc.utwente.nl/page/792/concept/152867" . . "Reproducibility"@en .