Fakultät für Informatik

Permanent URI for this collection

https://open-science.ub.ovgu.de/handle/684882692/48

Browse

Browsing Fakultät für Informatik by Issue Date

Filter results by year or month
Now showing 1 - 6 of 6
  • Item
    2D MRI liver slices with navigator frames. A test data set for image based 4D MRI reconstruction
    (Otto-von-Guericke-Universität Magdeburg, 2019) Gulamhussene, Gino; Joeres, Fabian; Rak, Marko; Lübeck, Cindy; Pech, Maciej; Hansen, Christian
    This data contains 2D MRI slices of the abdomens of 13 healthy subjects and corresponding navigator slices as basis to reconstruct 4D MRIs of the breathing motion of the subjects abdomen. Contained is a total of 19 data sets.
  • Item
    2.5D Thermometry Maps for MRI-guided Tumor Ablation
    (Otto-von-Guericke-Universität Magdeburg, 2021) Alpers, Julian; Reimert, Daniel; Rötzer, Maximilian; Gerlach, Thomas; Gutberlet, Marcel; Wacker, Frank; Hensen, Bennet; Hansen, Christian
    Fast and reliable monitoring of volumetric heat distribution during MRI-guided tumor ablation is an urgent clinical need. In this work, we introduce a method for generating 2.5D thermometry maps from uniformly distributed 2D MRI phase images rotated around the applicator’s main axis. The images can be fetched directly from the MR device, reducing the delay between image acquisition and visualization. For reconstruction, we use a weighted interpolation on a cylindric coordinate representation to calculate the heat value of voxels in a region of interest. A pilot study on 13 ex vivo bio protein phantoms with flexible tubes to simulate a heat sink effect was conducted to evaluate our method. After thermal ablation, we compared the measured coagulation zone extracted from the post-treatment MR data set with the output of the 2.5D thermometry map. The results show a mean Dice score of 0.75±0.07, a sensitivity of 0.77±0.03, and a reconstruction time within18.02ms±5.91ms. Future steps should address improving temporal resolution and accuracy, e.g., incorporating advanced bioheat transfer simulations.
  • Item
    2D MRI liver slices with navigator frames. A training and test data set for image based 4D MRI reconstruction. (Part II)
    (Otto-von-Guericke-Universität Magdeburg, 2021) Gulamhussene, Gino; Meyer, Anneke; Rak, Marko; Lübeck, Cindy; Omari, Jazan; Pech, Maciej; Hansen, Christian
    This data contains fully anonymized MRI data of the abdomens of 20 healthy subjects as basis to reconstruct 4D MRIs of the breathing motion of the subjects’ abdomen. This data publication uses the same protocol and is a continuation (part 2) of the data publication “2D MRI liver slices with navigator frames. A test data set for image based 4D MRI reconstruction” (https://doi.org/10.24352/UB.OVGU-2019-093)
  • Item
    LAMM - Learning Analytics Metadata Model
    (Otto-von-Guericke-Universität Magdeburg, 2022) Wolff, Ian; Broneske, David; Köppen, Veit
    Research data management is an emerging topic in all sciences. In this work, we introduce a metadata model called LAMM – Learning Analytics Metadata Model – for learning analytics. We derived metadata entities, by conducting a literature review in the special learning analytics field on observing collaborative programming scenarios, and together with researchers from our project context from three different universities. The main focus lies on the subject-specific metadata entities, to describe the learning analytics data provenance and learning process measurement in detail to increase third-party researcher reusability for learning analytics datasets. The result of the Version 0.1 takes metadata entities of existing metadata standards into consideration and provides the opportunity besides general descriptive metadata, to describe the learning-analytics-specific entities environment, learner, measurement instrument and learning-process-measurement and gives the option to link datasets with other related research.
  • Item
    Information Hiding in Cyber-Physical Systems - Covert Channel Dataset
    (Otto-von-Guericke-Universität Magdeburg, 2023) Lamshöft, Kevin
    This repository contains datasets, relevant documentation and code snippets for Covert Channels and other methods of Information Hiding in Cyber-Physical Systems with a focus of Industrial Control Systems. This repository is the basis for the PhD thesis titled "Information Hiding in Cyber-Physical Systems." The datasets provided here are meant to facilitate transparency, reproducibility, and further exploration of the research findings.
  • Item
    Learning Analytics Data from Collaborative SQL Exercises using the SQLValidator
    (Otto-von-Guericke-Universität Magdeburg, 2023) Broneske, David; Obionwu, Victor; Berndt, Sarah; Hawlitschek, Anja
    This data set is part of the DiP-iT project (http://www.dip-it.ovgu.de), which tests and evaluates different didactic concepts to improve collaborative learning of programming languages. This data set resembles student data of 83 students recorded at the university of Magdeburg in the Summer Semester 2021 (2022), where the SQLValidator is used in the weekly exercises to practice SQL. It consists of student questionnaire answers, submissions of students’ task solutions (and trials of their solution), as well as their chat and submissions of their semester’s team project. The questionnaire contains items on socio-demographic characteristics (e.g. gender, age), on factors relevant to the course (e.g. subject semester, degree program), on attitudes and attitudes towards the course (e.g. self-efficacy expectations, interest) and on teamwork (e.g. experience, relevance, openness towards teamwork). The students' previous knowledge is also surveyed subjectively (e.g. previous knowledge of programming, previous knowledge of SQL) and objectively (knowledge test in the area of teamwork). In the task solutions, students test their SQL queries in the SQLValidator for the weekly exercises in a trial-and-error fashion. The aim was to analyze how students learn from the shown error messages and what problem-solving skills they employ. In the semester’s team project, a group of students is working on the tasks, chat about the tasks and submit their solutions. The aim of this part was to analyze collaboration behavior between students and factors for a successful collaboration.