“What’s going on here?” The pedagogy of a data analysis session

Data analysis sessions are a common feature of discourse analytic communities, often involving participants with varying levels of expertise to those with significant expertise. Learning how to do data analysis and working with transcripts, however, are often new experiences for doctoral candidates within the social sciences. While many guides to doctoral education focus on procedures associated with data analysis (Heath, Hindmarsh, & Luff, 2010; McHoul & Rapley, 2001; Silverman, 2011; Wetherall, Taylor, & Yates, 2001), the in situ practices of doing data analysis are relatively undocumented. This chapter has been collaboratively written by members of a special interest research group, the Transcript Analysis Group (TAG), who meet regularly to examine transcripts representing audio- and video-recorded interactional data. Here, we investigate our own actual interactional practices and participation in this group where each member is both analyst and participant. We particularly focus on the pedagogic practices enacted in the group through investigating how members engage in the scholarly practice of data analysis. A key feature of talk within the data sessions is that members work collaboratively to identify and discuss ‘noticings’ from the audio-recorded and transcribed talk being examined, produce candidate analytic observations based on these discussions, and evaluate these observations. Our investigation of how talk constructs social practices in these sessions shows that participants move fluidly between actions that demonstrate pedagogic practices and expertise. Within any one session, members can display their expertise as analysts and, at the same time, display that they have gained an understanding that they did not have before. We take an ethnomethodological position that asks, ‘what’s going on here?’ in the data analysis session. By observing the in situ practices in fine-grained detail, we show how members participate in the data analysis sessions and make sense of a transcript.

Large scale participatory acoustic sensor data analysis : tools and reputation models to enhance effectiveness

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Safeguarding rural Australia : addressing masculinity and violence in rural settings : framework and scope for secondary data analysis in a rural Australia context

This report is an update of an earlier version produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report provides an introduction to our analyses of extant secondary data with respect to violent acts and incidents relating to males living in rural settings in Australia using data which were available in public data bases at the time of production. It clarifies important aspects of our overall approach primarily by concentrating on three elements that required early scoping and resolution.

Safeguarding rural Australia: addressing masculinity and violence in rural settings : intentional violent harm to others in an Australian rural context: analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. This report focuses on our examination of extant data which have been sourced with respect to intentional violence perpetrated or experienced by males living in regional and remote Australia . and which were available in public data bases at production. The nature of intentional violent acts can be physical, sexual or psychological or involve deprivation or neglect.

Safeguarding rural Australia: addressing masculinity and violence in rural settings : unintentional violent harm including injuries in an Australian rural context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. This report focuses on our examination of extant data which have been sourced with respect to unintentional serious and violent harm, including injuries, to males living in regional and remote Australia . and which were available in public data bases at production. Such harm typically might be caused by, for example, transport accidents, occupational exposures and hazards, burns and so on. Thus unintentional violent harm can cause physical trauma the consequences of which can lead to chronic conditions including psychological harm or substance abuse.

Safeguarding rural Australia : addressing masculinity and violence in rural settings : risky behaviour in a rural Australian context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report focus on our examination of extant data which have been sourced with respect to personally and socially risky behaviour associated with males living in regional and remote Australia and which were available in public data bases at production.

Safeguarding rural Australia : addressing masculinity and violence in rural settings : consequences of violent harm in a rural Australian context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report considers extant data which have been sourced with respect to some of the consequences of violent acts, incidents, harms and risky behaviour involving males living in regional and remote Australia and which were available in public data bases at production.

Numerical methods and analysis for a class of fractional advection-dispersion models

In this paper, a class of fractional advection–dispersion models (FADMs) is considered. These models include five fractional advection–dispersion models, i.e., the time FADM, the mobile/immobile time FADM with a time Caputo fractional derivative 0 < γ < 1, the space FADM with two sides Riemann–Liouville derivatives, the time–space FADM and the time fractional advection–diffusion-wave model with damping with index 1 < γ < 2. These equations can be used to simulate the regional-scale anomalous dispersion with heavy tails. We propose computationally effective implicit numerical methods for these FADMs. The stability and convergence of the implicit numerical methods are analysed and compared systematically. Finally, some results are given to demonstrate the effectiveness of theoretical analysis.

Data analysis skills for teachers

Although accountability in the form of high stakes testing is in favour in the contemporary Australian educational context, this practice remains a highly contested source of debate. Proponents for high stakes tests claim that higher standards in teaching and learning result from their implementation, whereas others believe that this type of testing regime is not required and may even in fact be counterproductive. Regardless of what side of the debate you sit on, the reality is that at present, high stakes testing appears to be here to stay. It could therefore be argued it is essential that teachers understand accountability and possess the specific skills to interpret and use test data beneficially.

Reputation modelling in citizen science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, as the gathered information is from the crowd, the data quality is always hard to manage. There are many ways to manage data quality, and reputation management is one of the common approaches. In recent year, many research teams have deployed many audio or image sensors in natural environment in order to monitor the status of animals or plants. The collected data will be analysed by ecologists. However, as the amount of collected data is exceedingly huge and the number of ecologists is very limited, it is impossible for scientists to manually analyse all these data. The functions of existing automated tools to process the data are still very limited and the results are still not very accurate. Therefore, researchers have turned to recruiting general citizens who are interested in helping scientific research to do the pre-processing tasks such as species tagging. Although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Therefore, this research aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we aim to investigate how to use reputation management to enhance data reliability. Reputation systems have been used to solve the uncertainty and improve data quality in many marketing and E-Commerce domains. The commercial organizations which have chosen to embrace the reputation management and implement the technology have gained many benefits. Data quality issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. However, research on reputation management in this area is relatively new. We therefore start our investigation by examining existing reputation systems in different domains. Then we design novel reputation management approaches for Citizen Science projects to categorise participants and data. We have investigated some critical elements which may influence data reliability in Citizen Science projects. These elements include personal information such as location and education and performance information such as the ability to recognise certain bird calls. The designed reputation framework is evaluated by a series of experiments involving many participants for collecting and interpreting data, in particular, environmental acoustic data. Our research in exploring the advantages of reputation management in Citizen Science (or crowdsourcing in general) will help increase awareness among organizations that are unacquainted with its potential benefits.

Reputation modelling in Citizen Science for environmental acoustic data analysis

Citizen Science projects are initiatives in which members of the general public participate in scientific research projects and perform or manage research-related tasks such as data collection and/or data annotation. Citizen Science is technologically possible and scientifically significant. However, although research teams can save time and money by recruiting general citizens to volunteer their time and skills to help data analysis, the reliability of contributed data varies a lot. Data reliability issues are significant to the domain of Citizen Science due to the quantity and diversity of people and devices involved. Participants may submit low quality, misleading, inaccurate, or even malicious data. Therefore, finding a way to improve the data reliability has become an urgent demand. This study aims to investigate techniques to enhance the reliability of data contributed by general citizens in scientific research projects especially for acoustic sensing projects. In particular, we propose to design a reputation framework to enhance data reliability and also investigate some critical elements that should be aware of during developing and designing new reputation systems.

An innovative information gathering and data analysis platform for railway level crossing safety data

This paper describes an innovative platform that facilitates the collection of objective safety data around occurrences at railway level crossings using data sources including forward-facing video, telemetry from trains and geo-referenced asset and survey data. This platform is being developed with support by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper provides a description of the underlying accident causation model, the development methodology and refinement process as well as a description of the data collection platform. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

The big data upgrade : new dimensions in data analysis - risks and opportunities [das big data upgrade : neue dimensionen der datennutzung – chancen und risiken]

Summary: More than ever before contemporary societies are characterised by the huge amounts of data being transferred. Authorities, companies, academia and other stakeholders refer to Big Data when discussing the importance of large and complex datasets and developing possible solutions for their use. Big Data promises to be the next frontier of innovation for institutions and individuals, yet it also offers possibilities to predict and influence human behaviour with ever-greater precision

Over time assessment data analysis tracking a school's on-going performance

In Australia, as in some other western nations, governments impose accountability measures on educational institutions (Earl, 2005). One such accountability measure is the National Assessment Program - Literacy and Numeracy (NAPLAN) from which high-stakes assessment data is generated. In this article, a practical method of data analysis known as the Over Time Assessment Data Analysis (OTADA) is offered as an analytical process by which schools can monitor their current and over time performances. This analysis developed by the author, is currently used extensively in schools throughout Queensland. By Analysing in this way, teachers, and in particular principals, can obtain a quick and insightful performance overview. For those seeking to track the achievements and progress of year level cohorts, the OTADA should be considered.

The importance of good data, analysis and interpretation for showing the economics of reducing healthcare-associated infection

To the Editor—In a recent review article in Infection Control and Hospital Epidemiology, Umscheid et al1 summarized published data on incidence rates of catheter-associated bloodstream infection (CABSI), catheter-associated urinary tract infection (CAUTI), surgical site infection (SSI), and ventilator- associated pneumonia (VAP); estimated how many cases are preventable; and calculated the savings in hospital costs and lives that would result from preventing all preventable cases. Providing these estimates to policy makers, political leaders, and health officials helps to galvanize their support for infection prevention programs. Our concern is that important limitations of the published studies on which Umscheid and colleagues built their findings are incompletely addressed in this review. More attention needs to be drawn to the techniques applied to generate these estimates...