41 resultados para Ecosystem management -- Queensland -- Johnstone (Shire) -- Data processing.
em University of Queensland eSpace - Australia
Resumo:
This document records the process of migrating eprints.org data to a Fez repository. Fez is a Web-based digital repository and workflow management system based on Fedora (http://www.fedora.info/). At the time of migration, the University of Queensland Library was using EPrints 2.2.1 [pepper] for its ePrintsUQ repository. Once we began to develop Fez, we did not upgrade to later versions of eprints.org software since we knew we would be migrating data from ePrintsUQ to the Fez-based UQ eSpace. Since this document records our experiences of migration from an earlier version of eprints.org, anyone seeking to migrate eprints.org data into a Fez repository might encounter some small differences. Moving UQ publication data from an eprints.org repository into a Fez repository (hereafter called UQ eSpace (http://espace.uq.edu.au/) was part of a plan to integrate metadata (and, in some cases, full texts) about all UQ research outputs, including theses, images, multimedia and datasets, in a single repository. This tied in with the plan to identify and capture the research output of a single institution, the main task of the eScholarshipUQ testbed for the Australian Partnership for Sustainable Repositories project (http://www.apsr.edu.au/). The migration could not occur at UQ until the functionality in Fez was at least equal to that of the existing ePrintsUQ repository. Accordingly, as Fez development occurred throughout 2006, a list of eprints.org functionality not currently supported in Fez was created so that programming of such development could be planned for and implemented.
Resumo:
The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.
Resumo:
In order for policy makers to plan effectively for sustainable development, there is a need for measures of welfare that consider changes in the natural capital stock. Current measures based on conventional national accounting are flawed because they are based solely on flow measures and do not account for environmental effects. In this paper, we use an expanded measure of wealth to estimate the value of natural capital for Queensland. The state's stock of natural capital is valued at A$355.6 billion, of which non-timber forest resources account for 45.3%, ecosystem services 20.0%, and mineral resources 17.6%. This figure is a conservative estimate of the true value since some significant components such as the ecological and life-support functions of the environment are excluded. The estimates highlight the relative importance of different forms of natural capital and can be used to draw the attention of policymakers to the need to give adequate weight to the value of such services in decision-making processes. (c) 2005 Elsevier Ltd. All rights reserved.
Resumo:
Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs. (c) 2004 Elsevier Ltd. All rights reserved.
Resumo:
The dairy industry is a global industry that provides significant nutritional benefit to many cultures. in australia the industry is especially important economically, being a large export earner, as well as a vital domestic sector. in recent years the sector has come under increased competitive pressure and has restructured to cope with the changes. the industry recently undertook an eco-efficiency project to investigate where business and environmental improvements might be found. the project involved collecting and collating previous project data and surveying 38 companies in different dairy operations, from market milk to dried products. after the survey, 10 sites in two states were visited to discuss eco-efficiency issues in detail with key players. From the surveys, visits and data compilation, a comprehensive manual was prepared to help interested companies find relevant eco-efficiency data easily and assist them in the implementation process. ten fact sheets were also produced covering the topics of water management, water recycling and re-use, refrigeration optimisation, boiler optimisation, biogas, the use of treated wastewater, yield optimisation and product recovery, optimisation of ciP systems, chemical use and membranes the project highlighted the large amount of technical and engineering expertise within the sector that could result in eco-efficiency outcomes and also identified the opportunities that exist for changes to occur in some operations to save energy, input raw materials and water.
Resumo:
Background and purpose Survey data quality is a combination of the representativeness of the sample, the accuracy and precision of measurements, data processing and management with several subcomponents in each. The purpose of this paper is to show how, in the final risk factor surveys of the WHO MONICA Project, information on data quality were obtained, quantified, and used in the analysis. Methods and results In the WHO MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) Project, the information about the data quality components was documented in retrospective quality assessment reports. On the basis of the documented information and the survey data, the quality of each data component was assessed and summarized using quality scores. The quality scores were used in sensitivity testing of the results both by excluding populations with low quality scores and by weighting the data by its quality scores. Conclusions Detailed documentation of all survey procedures with standardized protocols, training, and quality control are steps towards optimizing data quality. Quantifying data quality is a further step. Methods used in the WHO MONICA Project could be adopted to improve quality in other health surveys.