Dissertations / Theses on the topic 'Data management and data science not elsewhere classified'

This thesis presents the results from an investigation into the merits of analysing Magnetoencephalographic (MEG) data in the context of dynamical systems theory. MEG is the study of both the methods for the measurement of minute magnetic flux variations at the scalp, resulting from neuro-electric activity in the neocortex, as well as the techniques required to process and extract useful information from these measurements. As a result of its unique mode of action - by directly measuring neuronal activity via the resulting magnetic field fluctuations - MEG possesses a number of useful qualities which could potentially make it a powerful addition to any brain researcher's arsenal. Unfortunately, MEG research has so far failed to fulfil its early promise, being hindered in its progress by a variety of factors. Conventionally, the analysis of MEG has been dominated by the search for activity in certain spectral bands - the so-called alpha, delta, beta, etc that are commonly referred to in both academic and lay publications. Other efforts have centred upon generating optimal fits of "equivalent current dipoles" that best explain the observed field distribution. Many of these approaches carry the implicit assumption that the dynamics which result in the observed time series are linear. This is despite a variety of reasons which suggest that nonlinearity might be present in MEG recordings. By using methods that allow for nonlinear dynamics, the research described in this thesis avoids these restrictive linearity assumptions. A crucial concept underpinning this project is the belief that MEG recordings are mere observations of the evolution of the true underlying state, which is unobservable and is assumed to reflect some abstract brain cognitive state. Further, we maintain that it is unreasonable to expect these processes to be adequately described in the traditional way: as a linear sum of a large number of frequency generators. One of the main objectives of this thesis will be to prove that much more effective and powerful analysis of MEG can be achieved if one were to assume the presence of both linear and nonlinear characteristics from the outset. Our position is that the combined action of a relatively small number of these generators, coupled with external and dynamic noise sources, is more than sufficient to account for the complexity observed in the MEG recordings. Another problem that has plagued MEG researchers is the extremely low signal to noise ratios that are obtained. As the magnetic flux variations resulting from actual cortical processes can be extremely minute, the measuring devices used in MEG are, necessarily, extremely sensitive. The unfortunate side-effect of this is that even commonplace phenomena such as the earth's geomagnetic field can easily swamp signals of interest. This problem is commonly addressed by averaging over a large number of recordings. However, this has a number of notable drawbacks. In particular, it is difficult to synchronise high frequency activity which might be of interest, and often these signals will be cancelled out by the averaging process. Other problems that have been encountered are high costs and low portability of state-of-the- art multichannel machines. The result of this is that the use of MEG has, hitherto, been restricted to large institutions which are able to afford the high costs associated with the procurement and maintenance of these machines. In this project, we seek to address these issues by working almost exclusively with single channel, unaveraged MEG data. We demonstrate the applicability of a variety of methods originating from the fields of signal processing, dynamical systems, information theory and neural networks, to the analysis of MEG data. It is noteworthy that while modern signal processing tools such as independent component analysis, topographic maps and latent variable modelling have enjoyed extensive success in a variety of research areas from financial time series modelling to the analysis of sun spot activity, their use in MEG analysis has thus far been extremely limited. It is hoped that this work will help to remedy this oversight.

When an astronomical observer discovers a transient event in the sky, how can the information be immediately shared and delivered to others? Not too long time ago, people shared the information about what they discovered in the sky by books, telegraphs, and telephones. The new generation of transferring the event data is the way by the Internet. The information of astronomical events is able to be packed and put online as an Internet feed. For receiving these packed data, an Internet feed listener software would be required in a terminal computer. In other applications, the listener would connect to an intelligent robotic telescope network and automatically drive a telescope to capture the instant Astrophysical phenomena. However, because the technologies of transferring the astronomical event data are in the initial steps, the only resource available is the Perl-based Internet feed listener developed by the team of eSTAR. In this research, a Java-based Internet feed listener was developed. The application supports more features than the Perl-based application. After applying the rich Java benefits, the application is able to receive, parse and manage the Internet feed data in an efficient way with the friendly user interface. Keywords: Java, socket programming, VOEvent, real-time astronomy

Sense-making of spatial data on an urban level and large-scale decisions on new infrastructure projects need teamwork from experts with varied backgrounds. Technology can facilitate this collaboration process and magnify the effect of collective intelligence. Therefore, this work explores new useful collaboration interactions and visualizations for map-exploration software with a strong focus on usability. Additionally, for same-time and same-place group work, interactive table-top displays serve as a natural platform. Thus, the second aim of this project is to develop a user-friendly concept for integrating table-top displays with collaborative map-exploration. To achieve these goals, we continuously adapted the user-interface of the map-exploration software RAISE. We adopted a user-centred design approach and a simple iterative interaction design lifecycle model. Alternating between quick prototyping and user-testing phases, new design concepts were assessed and consequently improved or rejected. The necessary data was gathered through continuous dialogue with users and experts, a participatory design workshop, and a final observational study. Adopting a cross-device concept, our final prototype supports sharing information between a user’s personal device and table-top display(s). We found that this allows for a comfortable and practical separation between private and shared workspaces. The tool empowers users to share the current camera-position, data queries, and active layers between devices and with other users. We generalized further findings into a set of recommendations for designing user-friendly tools for collaborative map-exploration. The set includes recommendations regarding the sharing behaviour, the user-interface design, and the idea of playfulness in collaboration.

This research is focused on the intersection of secondary teachers’ data-use to inform instructional decisions and their teaching of STEM in STEM-focused high schools. Teaching STEM requires presenting more than just the content knowledge of the STEM domains. The methods of inquiry (e.g., scientific inquiry, engineering design) are skills that should be taught as part of STEM activities (e.g., science labs). However, under the data- and standards-based accountability focus of education, it is unclear how data from STEM activities is used in instructional decision-making. While teachers give tremendous weight to the data they collect directly from their observations of their classrooms, it is data from standardized testing that strongly influences practices through accountability mandates. STEM education alters this scenario because, while there is a growing focus on teaching STEM, important aspects of STEM education are not readily standardized. This mixed-methods study will examine the perspectives of 9th through 12th grade science and mathematics teachers, in STEM-focused schools, on data-use and STEM teaching. We developed a framework, adapted from existing frameworks of data-use, to categorize these perspectives and outline contexts influencing them. Through a concurrent triangulation design we will combine quantitative and qualitative data for a comprehensive synthesis of these perspectives.

The author investigated the condition under which competition effect and contagion effect impact the suppliers of the firm encountering data breach. An event study was conducted to analyze the stock price of 104 suppliers of Target after the large-scale data breach in 2013. The result showed that suppliers with high dependence on Target experienced negative abnormal return on the day after Target’s announcement, while those with low dependence experienced positive abnormal return. After regressing the abnormal return on some explanatory variables, the result showed that firms with better operational performance and high information technology capability were less negatively affected. This study suggested that suppliers who relatively highly rely on one customer company are susceptible for the negative shock from that customer because of contagion effect. Furthermore, maintaining good performance and investing in information technology can help firms reduce losses from negative events happened in customer companies.

External representations (ERs) used in science education are multimodal ensembles consisting of design elements to convey educational meanings to the audience. As an example of a dynamic ER, an animation presenting its content features (i.e., scientific concepts) via varying the feature’s depiction over time. A production team invited the dissertation author to inspect their creation of a biochemistry animation about the role of the actin cytoskeleton in cell motility and the animation’s implication on learning. To address this, the author developed a four-step methodology entitled the Multimodal Variation Analysis of Dynamic External Representations (MVADER) that deconstructs the animation’s content and design to inspect how each content feature is conveyed via the animation’s design elements.

This dissertation research investigated the actin animation’s educational value and the MVADER’s utility in animation evaluation. The research design was guided by descriptive case study methodology and an integrated framework consisting of the variation theory, multimodal analysis, and visual analytics. As stated above, the animation was analyzed using MVADER. The development of the actin animation and the content features the production team members intended to convey via the animation were studied by analyzing the communication records between the members, observing the team meetings, and interviewing the members individually. Furthermore, students’ learning experiences from watching the animation were examined via semi-structured interviews coupled with post- storyboarding. Moreover, the instructions of MVADER and its applications in studying the actin animation were reviewed to determine the MVADER’s usefulness as an animation evaluation tool.

Findings of this research indicate that the three educators in the production team intended the actin animation to convey forty-three content features to the undergraduate biology students. At least 50% of the student who participated in this thesis learned thirty-five of these forty-three (> 80%) features. Evidence suggests that the animation’s effectiveness to convey its features was associated with the features’ depiction time, the number of identified design elements applied to depict the features, and the features’ variation of depiction over time.

Additionally, one-third of the student participants made similar mistakes regarding two content features after watching the actin animation: the F-actin elongation and the F-actin crosslink structure in lamellipodia. The analysis reveals the animation’s potential design flaws that might have contributed to these common misconceptions. Furthermore, two disruptors to the creation process and the educational value of the actin animation were identified: the vagueness of the learning goals and the designer’s placement of the animation’s beauty over its reach to the learning goals. The vagueness of the learning goals hampered the narration scripting process. On the other hand, the designer’s prioritization of the animation’s aesthetic led to the inclusion of a “beauty shot” in the animation that caused students’ confusion.

MVADER was used to examine the content, design, and their relationships in the actin animation at multiple aspects and granularities. The result of MVADER was compared with the students’ learning outcomes from watching the animation to identify the characteristics of content’s depiction that were constructive and disruptive to learning. These findings led to several practical recommendations to teach using the actin animation and create educational ERs.

To conclude, this dissertation discloses the connections between the creation process, the content and design, and the educational implication of a biochemistry animation. It also introduces MVADER as a novel ER analysis tool to the education research and visualization communities. MVADER can be applied in various formats of static and dynamic ERs and beyond the disciplines of biology and chemistry.

This dissertation contextualises the forensic computing domain in terms of validation of tools and processes. It explores the current state of forensic computing comparing it to the traditional forensic sciences. The research then develops a classification system for the disciplines functions to establish the extensible base for which a validation system is developed.
Thesis (PhD)--University of South Australia, 2010

In forensic chemistry, a quicker and more accurate analysis of a sample is always being pursued. Speedy analyses allow the analyst to provide quick turn-around times and potentially decrease back-logs that are known to be a problem in the field. Accurate analyses are paramount with the futures and lives of the accused potentially on the line. One of the most common methods of analysis in forensic chemistry laboratories is gas chromatography, chosen for the relative speed and efficiency afforded by this method. Two major routes were attempted to further improve on gas chromatography applications in forensic chemistry.
The first route was to decrease separation times for analysis of ignitable liquid residues by using micro-bore wall coated open-tubular columns. Micro-bore columns are much shorter and have higher separation efficiencies than the standard columns used in forensic chemistry, allowing for faster analysis times while maintaining the expected peak separation. Typical separation times for fire debris samples are between thirty minutes and one hour, the micro-bore columns were able to achieve equivalent performance in three minutes. The reduction in analysis time was demonstrated by analysis of ignitable liquid residues from simulated fire debris exemplars.
The second route looked at a relatively new detector for gas chromatography known as a vacuum ultraviolet (VUV) spectrophotometer. The VUV detector uses traditional UV and far-ultraviolet light to probe the pi and sigma bonds of the gas phase analytes as well as Rydberg traditions to produce spectra that are nearly unique to a compound. Thus far, the only spectra that were not discernable were from enantiomers, otherwise even diastereomers have been differentiated. The specificity attained with the VUV detector has achieved differentiation of compounds that mass spectrometry, the most common detection method for chromatography in forensic chemistry labs, has difficulty distinguishing. This specificity has been demonstrated herein by analyzing various classes of drugs of abuse and applicability to “real world” samples has been demonstrated by analysis of de-identified seized samples.