30 results for Dataset, 2010

The End of the Rhineland Model? Changing Labour Relations in Germany  Evidence from the Minimum Wage Debate
Reiling, Pascal (2010)
Masters thesis
Victoria University of WellingtonHypothesis: Effects of globalisation, European Integration and reunification have pushed the German political economy away from its unique institutional setting, framed as Rhineland Capitalism or the Rhineland Model. Legislative decisions in the last years and current positions of politicoeconomic actors in wage setting mechanisms  a distinctive part of the Rhineland Model  seem to foster that shift and illustrate the incremental 'AngloSaxonisation' of the German political economy.
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Matlab application for fitting progress curves to the Equilibrium Model
Peterson, Michelle E.; McDowall, James; Goodhue, Nigel David; Bryan, Karin R.; Hailstone, Daniel; Monk, Colin R. (2010)
Dataset
University of WaikatoThe general procedures for carrying out the necessary rate determinations required for accurate determination of the Equilibrium Model parameters, and fitting this data to the mathematical model to generate the parameters, are described in "Peterson, M.E., Daniel, R.M., Danson, M.J. & Eisenthal, R. (2007) The dependence of enzyme activity on temperature: determination and validation of parameters. Biochemical Journal, 402, 331337". It should be borne in mind that the Equilibrium Model equation contains exponentials of exponentials – quite small deviations from ideal behaviour, or a failure to obtain true Vmax values, may lead to difficulty in obtaining reliable Equilibrium Model parameters.
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Combined Vertical Ozone Profile Database
Bodeker, Greg; Hassler, Birgit; Young, Paul; Portmann, Robert (2011)
Dataset
Victoria University of WellingtonBodeker Scientific produces a combined monthly mean vertical ozone profile database spanning the period 1979 to 2007. The database is completely filled such that there are no missing data. A publication describing the construction of this database is currently in preparation. The raw individual ozone data are sourced from the BDBP database (see The BDBP). Monthly means are calculated from individual ozone measurements extracted from the BDBP in much the same way as in Hassler et al. (2009). These are referred to as Tier 0 data. A regression model is fitted to the Tier 0 data at each of 70 pressure/altitude levels. The regression model is of the form: Ozone(t,lat) = A(t,lat) + Offset and seasonal cycle B(t,lat) x t + Linear trend C(t,lat) x EESC(t,AoA) + Ageofair dependent equivalent effective stratospheric chlorine D(t,lat) x QBO(t) + Quasibiennial Oscillation E(t,lat) x QBOorthog(t) + Orthogonalized QBO F(t,lat) x ENSO(t) + ElNiño Southern Oscillation G(t,lat) x Solar(t) + Solar cycle H(t,lat) x Pinatubo(t) + Mt. Pinatubo volcanic eruption R(t) Residual Regression model fit coefficients are expanded in Fourier series to account for seasonality and in Legendre polynomials in latitude to account for meridional structure in the fit coefficients. Regression model output is then used to produce 4 gap free Tier 1 data sets, viz.: Tier 1.1 (Anthropogenic): This comprises the mean annual cycle plus contributions from the EESC and linear trend basis functions. Tier 1.2 (Natural): This comprises the mean annual cycle plus contributions from the QBO, solar cycle and El Niño basis functions. Tier 1.3 (Natural & volcanoes): Tier 1.2 but now also including contributions from volcano basis functions. Tier 1.4 (All): Constructed by summing the contributions from all basis functions. There are 20 files available named CCMVal2_REFB1_BSOzoneXXYYY_TierZZ_T2Mz_O3.nc where: CCMVal2 indicates that these data files have been formatted to allow easy use in the CCMVal2 project. REFB1 indicates that the time period covered is similar to that for the REFB1 simulations. XX is either 'MR' for mixing ratio or 'ND' for number density. YYY is either 'PRS' to denote that the data are on pressure levels or 'ALT' to denote that the data are on altitude levels. ZZ denotes the Tier: '0', '1_1', '1_2', '1_3' or '1_4'. T2Mz denotes that these are monthly means in two dimensions (latitude and altitude/pressure). At present Bodeker Scientific has no financial support to maintain this database and so if there is anyway that you can contribute towards the maintenance of this database, that would be much appreciated. That said, this database is made freely available to any notforprofit organisation or individual. If you are going to be using this database in a publication, please let me know. At the very least please include the following acknowledgement: We would like to thank Greg Bodeker (Bodeker Scientific) and Birgit Hassler (NOAA) for providing the combined vertical ozone profile database.
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CALM: Computer Assisted Learning for the Mind website
Fernando, Antonio; Moir, Fiona; Davis, PG; Kumar, Shailesh; Doherty, Iain (2010)
Dataset
The University of Auckland LibraryDevelopment of an online selfcare package to help students manage stresses of student life. The website was first released to medical students and is now open access on the web. One of the initial ideas with the CALM website, was to do some research as soon as the website was created. With this in mind, the website was initially only available to particpants in a research project (medical students), who were each given unique identifers whch oculd grant them access. After the conclusion of the study, the website was released to the public
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Seawater Temperature dataset at Goat Island, Leigh New Zealand from 1967 to 2011
Evans, J; Atkins, John (2013)
Dataset
The University of Auckland LibrarySeawater Temperature dataset at Goat Island, Leigh New Zealand from October 2011 to present available at http://hdl.handle.net/2292/21850 Collected seawater temperatures at the Leigh Marine Laboratory. Dataset contains an archive of material to 2011. The location of the laboratory is lat: 36.26929, lng: 174.79840. 1001 Leigh Road Matakana Auckland New Zealand. Creative Commons licence applied acknowledge attribution. http://www.marine.auckland.ac.nz/uoa/home/about/ourdepartment/contactdetailsandlocationmaps.
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GeneralizedHyperbolic, Version 0.70, The Generalized Hyperbolic Distribution
Scott, David (2011)
Dataset
The University of Auckland LibraryThis package provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skewLaplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data.
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DistributionUtils, Version 0.50, Distribution Utilities
Scott, David (2011)
Dataset
The University of Auckland LibraryThis package contains utilities which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, loghistogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function.
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VGAM 1.03
Yee, Thomas (20170110)
Dataset
The University of Auckland LibraryAn implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and VGAM package. Currently only fixedeffects models are implemented, i.e., no randomeffects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are datadriven VGLMs (i.e., with smoothing). The other classes are RRVGLMs (reducedrank VGLMs), quadratic RRVGLMs, reducedrank VGAMs, RCIMs (rowcolumn interaction models)—these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO).
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VGAMdata 1.02
Yee, Thomas; Gray, J (20160531)
Dataset
The University of Auckland LibraryAn implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and VGAM package. Currently only fixedeffects models are implemented, i.e., no randomeffects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are datadriven VGLMs (i.e., with smoothing). The other classes are RRVGLMs (reducedrank VGLMs), quadratic RRVGLMs, reducedrank VGAMs, RCIMs (rowcolumn interaction models)—these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO).
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VGAM 1.01
Yee, Thomas (20160315)
Dataset
The University of Auckland LibraryAn implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and VGAM package. Currently only fixedeffects models are implemented, i.e., no randomeffects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are datadriven VGLMs (i.e., with smoothing). The other classes are RRVGLMs (reducedrank VGLMs), quadratic RRVGLMs, reducedrank VGAMs, RCIMs (rowcolumn interaction models)—these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO).
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VGAMdata 1.03
Yee, Thomas; Gray, J (20170111)
Dataset
The University of Auckland LibraryAn implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and VGAM package. Currently only fixedeffects models are implemented, i.e., no randomeffects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are datadriven VGLMs (i.e., with smoothing). The other classes are RRVGLMs (reducedrank VGLMs), quadratic RRVGLMs, reducedrank VGAMs, RCIMs (rowcolumn interaction models)—these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO).
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VGAM 1.02
Yee, Thomas (20160527)
Dataset
The University of Auckland LibraryAn implementation of about 6 major classes of statistical regression models. At the heart of it are the vector generalized linear and additive model (VGLM/VGAM) classes, and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and VGAM package. Currently only fixedeffects models are implemented, i.e., no randomeffects models. Many (150+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE, using Fisher scoring. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are datadriven VGLMs (i.e., with smoothing). The other classes are RRVGLMs (reducedrank VGLMs), quadratic RRVGLMs, reducedrank VGAMs, RCIMs (rowcolumn interaction models)—these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO).
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SPAN. New release
Marshall, Roger (20110701)
Dataset
The University of Auckland LibrarySearch Partitiona Analysis programme. New Windows 7 compatible version release.
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SPARX: Computerised Cognitive Behavioural Therapy for Adolescents seeking help for depression (CDRom)
Merry, S; Stasiak, Karolina; Shepherd, M; Fleming, T; Lucassen, M (2010)
Dataset
The University of Auckland LibrarySPARX is a selfhelp computer programme for young people with symptoms of depression. The programme has been developed by a team of specialists in treating adolescent depression from the University of Auckand
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VGAM 0.84
Yee, TW (2011)
Dataset
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The University of Auckland Library 
DSpace
Tansley, R; Downing, J; Jones, R; Mircea, G; Jürgen, C; Donohue, T; Yeadon, S; Phillips, S; Rodgers, R; Lewis, Stuart; Bollini, A; Diggory, M; Rutherford, J; Tirggs, G; Bosman, B; Wood, M; Shepherd, Kim; Stone, L; Gilbertson, K; Trimble, J; Taylor, R; Dietz, P (2010)
Dataset
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The University of Auckland Library 
DTWave, Dynamic Time Warping for spectrogram Alignment and AVErage sequence computation
Ranjard, Louis (2010)
Dataset
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The University of Auckland Library 
The Journal: an online selfmanagement programme
Hatcher, Simon (201006)
Dataset
The University of Auckland LibraryNationwide self help programme for people with mild to medium depression incorporating social marketing, problem solving and telephone support.
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PhyML: Phylogenetic estimation using Maximum Likelihood
Guindon, Stephane (2010)
Dataset
The University of Auckland LibraryPhyML is a software that estimates maximum likelihood phylogenies from alignments of nucleotide or amino acid sequences. It implements a wide range of nucleotide and aminoacid substitution models and relies on fast graph algorithms to explore the space of tree topologies.
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Physiological changes during anaesthesia for surgery with potential for moderate blood loss
Harrison, Michael; Cumin, David (2012)
Dataset
The University of Auckland LibraryThis dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the dataset are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ Data in this collection was originally collected, with local ethics committee and patient consent, at 0.1 Hz from the DatexOhmeda S5 anaesthetic monitor via a serial connection using an ‘inhouse’ datacollection program (A.Lowe). The data collected were converted to text files and stored in a Microsoft Excel™ file. The concurrent timed comments from clinicians included an assessment of the state of blood volume, whether there was sympathetic activity or a fall in cardiac output, amongst others, were collected in a separate file. This data was collected for the purpose of investigating methods of enhancing intraoperative diagnoses by an expert system [41]. A VBA (v6.5, Microsoft) script was used to merge and convert these files into the XML schema. Note that blood pressure values are 100 x the true value.
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