Workflows, processes and technical solutions for seeding the research data commons

Queensland University of Technology (QUT) completed an Australian National Data Service (ANDS) funded “Seeding the Commons Project” to contribute metadata to Research Data Australia. The project employed two Research Data Librarians from October 2009 through to July 2010. Technical support for the project was provided by QUT’s High Performance Computing and Research Support Specialists. ---------- The project identified and described QUT’s category 1 (ARC / NHMRC) research datasets. Metadata for the research datasets was stored in QUT’s Research Data Repository (Architecta Mediaflux). Metadata which was suitable for inclusion in Research Data Australia was made available to the Australian Research Data Commons (ARDC) in RIF-CS format. ---------- Several workflows and processes were developed during the project. 195 data interviews took place in connection with 424 separate research activities which resulted in the identification of 492 datasets. ---------- The project had a high level of technical support from QUT High Performance Computing and Research Support Specialists who developed the Research Data Librarian interface to the data repository that enabled manual entry of interview data and dataset metadata, creation of relationships between repository objects. The Research Data Librarians mapped the QUT metadata repository fields to RIF-CS and an application was created by the HPC and Research Support Specialists to generate RIF-CS files for harvest by the Australian Research Data Commons (ARDC). ---------- This poster will focus on the workflows and processes established for the project including: ---------- • Interview processes and instruments • Data Ingest from existing systems (including mapping to RIF-CS) • Data entry and the Data Librarian interface to Mediaflux • Verification processes • Mapping and creation of RIF-CS for the ARDC

Network structure, knowledge governance, and firm performance : evidence from innovation networks and SMEs in the UK

It is increasingly understood that learning and thus innovation often occurs via highly interactive, iterative, network-based processes. Simultaneously, economic development policy is increasingly focused on small and medium-sized enterprises (SMEs) as a means of generating growth, creating a clear research issue in terms of the roles and interactions of government policy, universities, and other sources of knowledge, SMEs, and the creation and dissemination of innovation. This paper analyses the contribution of a range of actors in an SME innovation creation and dissemination framework, reviewing the role of various institutions therein, exploring the contribution of cross-locality networks, and identifying the mechanisms required to operationalise such a framework. Bivariate and multivariate (regression) techniques are employed to investigate both innovation and growth outcomes in relation to these structures; data are derived from the survey responses of over 450 SMEs in the UK. Results are complex and dependent upon the nature of institutions involved, the type of knowledge sought, and the spatial level of the linkages in place but overall highlight the value of cross-locality networks, network governance structures, and certain spillover effects from universities. In general, we find less support for the factors predicting SME growth outcomes than is the case for innovation. Finally, we outline an agenda for further research in the area.

The effect of presbyiopic vision corrections on nighttime driving performance

Purpose. To investigate the effect of various presbyopic vision corrections on nighttime driving performance on a closed-road driving circuit. Methods. Participants were 11 presbyopes (mean age, 57.3 ± 5.8 years), with a mean best sphere distance refractive error of R+0.23±1.53 DS and L+0.20±1.50 DS, whose only experience of wearing presbyopic vision correction was reading spectacles. The study involved a repeated-measures design by which a participant's nighttime driving performance was assessed on a closed-road circuit while wearing each of four power-matched vision corrections. These included single-vision distance lenses (SV), progressive-addition spectacle lenses (PAL), monovision contact lenses (MV), and multifocal contact lenses (MTF CL) worn in a randomized order. Measures included low-contrast road hazard detection and avoidance, road sign and near target recognition, lane-keeping, driving time, and legibility distance for street signs. Eye movement data (fixation duration and number of fixations) were also recorded. Results. Street sign legibility distances were shorter when wearing MV and MTF CL than SV and PAL (P < 0.001), and participants drove more slowly with MTF CL than with PALs (P = 0.048). Wearing SV resulted in more errors (P < 0.001) and in more (P = 0.002) and longer (P < 0.001) fixations when responding to near targets. Fixation duration was also longer when viewing distant signs with MTF CL than with PAL (P = 0.031). Conclusions. Presbyopic vision corrections worn by naive, unadapted wearers affected nighttime driving. Overall, spectacle corrections (PAL and SV) performed well for distance driving tasks, but SV negatively affected viewing near dashboard targets. MTF CL resulted in the shortest legibility distance for street signs and longer fixation times.

Analysis of the effect of negation on information retrieval of medical data

Most information retrieval (IR) models treat the presence of a term within a document as an indication that the document is somehow "about" that term, they do not take into account when a term might be explicitly negated. Medical data, by its nature, contains a high frequency of negated terms - e.g. "review of systems showed no chest pain or shortness of breath". This papers presents a study of the effects of negation on information retrieval. We present a number of experiments to determine whether negation has a significant negative affect on IR performance and whether language models that take negation into account might improve performance. We use a collection of real medical records as our test corpus. Our findings are that negation has some affect on system performance, but this will likely be confined to domains such as medical data where negation is prevalent.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

Critical success factors of project management for Brunei construction projects : improving project performance

The problem of delays in the construction industry is a global phenomenon and the construction industry in Brunei Darussalam is no exception. The goal of all parties involved in construction projects – owners, contractors, engineers and consultants in either the public or private sector is to successfully complete the project on schedule, within planned budget, with the highest quality and in the safest manner. Construction projects are frequently influenced by either success factors that help project parties reach their goal as planned, or delay factors that stifle or postpone project completion. The purpose of this research is to identify success and delay factors which can help project parties reach their intended goals with greater efficiency. This research extracted seven of the most important success factors according to the literature and seven of the most important delay factors identified by project parties, and then examined correlations between them to determine which were the most influential in preventing project delays. This research uses a comprehensive literature review to design and conduct a survey to investigate success and delay factors and then obtain a consensus of expert opinion using the Delphi methodology to rank the most needed critical success factors for Brunei construction projects. A specific survey was distributed to owners, contractors and engineers to examine the most critical delay factors. A general survey was distributed to examine the correlation between the identified delay factors and the seven most important critical success factors selected. A consensus of expert opinion using the Delphi methodology was used to rank the most needed critical success factors for Brunei building construction. Data was collected and evaluated by statistical methods to identify the most significant causes of delay and to measure the strength and direction of the relationship between critical success factors and delay factors in order to examine project parties’ evaluation of projects’ critical success and delay factors, and to evaluate the influence of critical success factors on critical delay factors. A relative importance index has been used to determine the relative importance of the various causes of delays. A one and two-way analysis of variance (ANOVA) has been used to examine how the group or groups evaluated the influence of the critical success factors in avoiding or preventing each of the delay factors, and which success factors were perceived as most influential in avoiding or preventing critical delay factors. Finally the Delphi method, using consensus from an expert panel, was employed to identify the seven most critical success factors used to avoid the delay factors, and thereby improve project performance.

The hydrodynamic and capture/retention performance of a gross pollutant trap

This research shows that gross pollutant traps (GPTs) continue to play an important role in preventing visible street waste—gross pollutants—from contaminating the environment. The demand for these GPTs calls for stringent quality control and this research provides a foundation to rigorously examine the devices. A novel and comprehensive testing approach to examine a dry sump GPT was developed. The GPT is designed with internal screens to capture gross pollutants—organic matter and anthropogenic litter. This device has not been previously investigated. Apart from the review of GPTs and gross pollutant data, the testing approach includes four additional aspects to this research, which are: field work and an historical overview of street waste/stormwater pollution, calibration of equipment, hydrodynamic studies and gross pollutant capture/retention investigations. This work is the first comprehensive investigation of its kind and provides valuable practical information for the current research and any future work pertaining to the operations of GPTs and management of street waste in the urban environment. Gross pollutant traps—including patented and registered designs developed by industry—have specific internal configurations and hydrodynamic separation characteristics which demand individual testing and performance assessments. Stormwater devices are usually evaluated by environmental protection agencies (EPAs), professional bodies and water research centres. In the USA, the American Society of Civil Engineers (ASCE) and the Environmental Water Resource Institute (EWRI) are examples of professional and research organisations actively involved in these evaluation/verification programs. These programs largely rely on field evaluations alone that are limited in scope, mainly for cost and logistical reasons. In Australia, evaluation/verification programs of new devices in the stormwater industry are not well established. The current limitations in the evaluation methodologies of GPTs have been addressed in this research by establishing a new testing approach. This approach uses a combination of physical and theoretical models to examine in detail the hydrodynamic and capture/retention characteristics of the GPT. The physical model consisted of a 50% scale model GPT rig with screen blockages varying from 0 to 100%. This rig was placed in a 20 m flume and various inlet and outflow operating conditions were modelled on observations made during the field monitoring of GPTs. Due to infrequent cleaning, the retaining screens inside the GPTs were often observed to be blocked with organic matter. Blocked screens can radically change the hydrodynamic and gross pollutant capture/retention characteristics of a GPT as shown from this research. This research involved the use of equipment, such as acoustic Doppler velocimeters (ADVs) and dye concentration (Komori) probes, which were deployed for the first time in a dry sump GPT. Hence, it was necessary to rigorously evaluate the capability and performance of these devices, particularly in the case of the custom made Komori probes, about which little was known. The evaluation revealed that the Komori probes have a frequency response of up to 100 Hz —which is dependent upon fluid velocities—and this was adequate to measure the relevant fluctuations of dye introduced into the GPT flow domain. The outcome of this evaluation resulted in establishing methodologies for the hydrodynamic measurements and gross pollutant capture/retention experiments. The hydrodynamic measurements consisted of point-based acoustic Doppler velocimeter (ADV) measurements, flow field particle image velocimetry (PIV) capture, head loss experiments and computational fluid dynamics (CFD) simulation. The gross pollutant capture/retention experiments included the use of anthropogenic litter components, tracer dye and custom modified artificial gross pollutants. Anthropogenic litter was limited to tin cans, bottle caps and plastic bags, while the artificial pollutants consisted of 40 mm spheres with a range of four buoyancies. The hydrodynamic results led to the definition of global and local flow features. The gross pollutant capture/retention results showed that when the internal retaining screens are fully blocked, the capture/retention performance of the GPT rapidly deteriorates. The overall results showed that the GPT will operate efficiently until at least 70% of the screens are blocked, particularly at high flow rates. This important finding indicates that cleaning operations could be more effectively planned when the GPT capture/retention performance deteriorates. At lower flow rates, the capture/retention performance trends were reversed. There is little difference in the poor capture/retention performance between a fully blocked GPT and a partially filled or empty GPT with 100% screen blockages. The results also revealed that the GPT is designed with an efficient high flow bypass system to avoid upstream blockages. The capture/retention performance of the GPT at medium to high inlet flow rates is close to maximum efficiency (100%). With regard to the design appraisal of the GPT, a raised inlet offers a better capture/retention performance, particularly at lower flow rates. Further design appraisals of the GPT are recommended.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

Availability of advance visual information constrains association-football goalkeeping performance during penalty kicks

A pressing concern within the literature on anticipatory perceptual-motor behaviour is the lack of clarity on the applicability of data, observed under video-simulation task constraints, to actual performance in which actions are coupled to perception, as captured during in-situ experimental conditions. We developed an in-situ experimental paradigm which manipulated the duration of anticipatory visual information from a penalty taker’s actions to examine experienced goalkeepers’ vulnerability to deception for the penalty kick in association football. Irrespective of the penalty taker’s kick strategy, goalkeepers initiated movement responses earlier across consecutively earlier presentation points. Overall goalkeeping performance was better in non-deception trials than in deception conditions. In deception trials, the kinematic information presented up until the penalty taker initiated his/her kicking action had a negative effect on goalkeepers’ performance. It is concluded that goalkeepers are likely to benefit from not anticipating a penalty taker’s performance outcome based on information from the run-up, in preference to later information that emerges just before the initiation of the penalty taker’s kicking action.

Throwing frame and performance of elite male seated shot-putters

Seated shot-putters rely on a customized assistive device called a throwing frame. Currently, the construction of each individual throwing frame is mainly driven by an empirical approach. One way to refine the conception is to improve the basic understanding of performance of seated shot-putters. The relationship between performance and throwing technique has been well described. Remarkably, the relationship between performance and throwing frame characteristics has received limited attention. The primary objective of this study was to present a cataloguing of characteristics of throwing frames used by seated shot-putters. This cataloguing consisted of defining and grouping 26 characteristics into three main categories (i.e., whole body, foot and upper limb specific characteristics) and seven sub-categories. The secondary objective of this study was to provide raw characterisations of the throwing frames for a group of athletes who participated in a world-class event. The characterisation consisted of describing the characteristics of each throwing frame. Potential relationships between characteristics, performance and classification were also identified. The cataloguing was achieved using a 6-step heuristic approach, involving expert opinions and the analysis of 215 attempts produced by 55 male athletes during the 2006 IPC Athletics World Championships. The distribution of samples across characteristics suggested a relevant level of comprehensiveness for the proposed cataloguing. The raw data, the profile of best athletes and the frequency of characteristics provided key benchmark information for construction of a throwing frame as well as coaching, classification and officiating. Analysis of data sets relating to characteristics, performance and classification were inconclusive.

A Data Picture of Australia's Arts and Entertainment Sector 2010

Australia’s Arts and Entertainment Sector underpins cultural and social innovation, improves the quality of community life, is essential to maintaining our cities as world class attractors of talent and investment, and helps create ‘Brand Australia’ in the global marketplace of ideas (QUT Creative Industries Faculty 2010). The sector makes a significant contribution to the Australian economy. So what is the size and nature of this contribution? The Creative Industries Faculty at Queensland University of Technology recently conducted an exercise to source and present statistics in order to produce a data picture of Australia’s Arts and Entertainment Sector. The exercise involved gathering the latest statistics on broadcasting, new media, performing arts, and music composition, distribution and publishing as well as Australia’s performance in world markets.

Getting research data ‘out there’ : collaborative solutions to identifying, describing and making research data more visible

At QUT research data refers to information that is generated or collected to be used as primary sources in the production of original research results, and which would be required to validate or replicate research findings (Callan, De Vine, & Baker, 2010). Making publicly funded research data discoverable by the broader research community and the public is a key aim of the Australian National Data Service (ANDS). Queensland University of Technology (QUT) has been innovating in this space by undertaking mutually dependant technical and content (metadata) focused projects funded by ANDS. Research Data Librarians identified and described datasets generated from Category 1 funded research at QUT, by interviewing researchers, collecting metadata and fashioning metadata records for upload to the Australian Research Data commons (ARDC) and exposure through the Research Data Australia interface. In parallel to this project, a Research Data Management Service and Metadata hub project were being undertaken by QUT High Performance Computing & Research Support specialists. These projects will collectively store and aggregate QUT’s metadata and research data from multiple repositories and administration systems and contribute metadata directly by OAI-PMH compliant feed to RDA. The pioneering nature of the work has resulted in a collaborative project dynamic where good data management practices and the discoverability and sharing of research data were the shared drivers for all activity. Each project’s development and progress was dependent on feedback from the other. The metadata structure evolved in tandem with the development of the repository and the development of the repository interface responded to meet the needs of the data interview process. The project environment was one of bottom-up collaborative approaches to process and system development which matched top-down strategic alliances crossing organisational boundaries in order to provide the deliverables required by ANDS. This paper showcases the work undertaken at QUT, focusing on the Seeding the Commons project as a case study, and illustrates how the data management projects are interconnected. It describes the processes and systems being established to make QUT research data more visible and the nature of the collaborations between organisational areas required to achieve this. The paper concludes with the Seeding the Commons project outcomes and the contribution this project made to getting more research data ‘out there’.

A Modified inverse integer Cholesky decorrelation method and the performance on ambiguity resolution

One of the research focuses in the integer least squares problem is the decorrelation technique to reduce the number of integer parameter search candidates and improve the efficiency of the integer parameter search method. It remains as a challenging issue for determining carrier phase ambiguities and plays a critical role in the future of GNSS high precise positioning area. Currently, there are three main decorrelation techniques being employed: the integer Gaussian decorrelation, the Lenstra–Lenstra–Lovász (LLL) algorithm and the inverse integer Cholesky decorrelation (IICD) method. Although the performance of these three state-of-the-art methods have been proved and demonstrated, there is still a potential for further improvements. To measure the performance of decorrelation techniques, the condition number is usually used as the criterion. Additionally, the number of grid points in the search space can be directly utilized as a performance measure as it denotes the size of search space. However, a smaller initial volume of the search ellipsoid does not always represent a smaller number of candidates. This research has proposed a modified inverse integer Cholesky decorrelation (MIICD) method which improves the decorrelation performance over the other three techniques. The decorrelation performance of these methods was evaluated based on the condition number of the decorrelation matrix, the number of search candidates and the initial volume of search space. Additionally, the success rate of decorrelated ambiguities was calculated for all different methods to investigate the performance of ambiguity validation. The performance of different decorrelation methods was tested and compared using both simulation and real data. The simulation experiment scenarios employ the isotropic probabilistic model using a predetermined eigenvalue and without any geometry or weighting system constraints. MIICD method outperformed other three methods with conditioning improvements over LAMBDA method by 78.33% and 81.67% without and with eigenvalue constraint respectively. The real data experiment scenarios involve both the single constellation system case and dual constellations system case. Experimental results demonstrate that by comparing with LAMBDA, MIICD method can significantly improve the efficiency of reducing the condition number by 78.65% and 97.78% in the case of single constellation and dual constellations respectively. It also shows improvements in the number of search candidate points by 98.92% and 100% in single constellation case and dual constellations case.

The impact of gender diversity on performance in services and manufacturing organizations

We present three competing predictions of the organizational gender diversity-performance relationship: a positive linear prediction, a negative linear prediction, and an inverted U-shaped curvilinear prediction. The paper also proposes a moderating effect of industry type (services vs. manufacturing). The predictions were tested using archival quantitative data with a longitudinal design. The results show partial support for the positive linear and inverted U-shaped curvilinear predictions as well as for the proposed moderating effect of industry type. The results help reconcile the inconsistent findings of past research. The findings also show that industry context can strengthen or weaken gender diversity effects.

Developing research data management services at QUT

QUT Library and the High Performance Computing and Research Support (HPC) Team have been collaborating on developing and delivering a range of research support services, including those designed to assist researchers to manage their data. QUT’s Management of Research Data policy has been available since 2010 and is complemented by the Data Management Guidelines and Checklist. QUT has partnered with the Australian Research Data Service (ANDS) on a number of projects including Seeding the Commons, Metadata Hub (with Griffith University) and the Data Capture program. The HPC Team has also been developing the QUT Research Data Repository based on the Architecta Mediaflux system and have run several pilots with faculties. Library and HPC staff have been trained in the principles of research data management and are providing a range of research data management seminars and workshops for researchers and HDR students.