cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/r7k64g26
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^(-1/2)*x^(-1/2)*log(1/x) on [0,1]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = -1/2, b = -1/2, are computed by a modified-moment-based method using the routine sr_jacobilog1(dig,32,100,-1/2,-1/2), where dig=34 has been determined by the routine dig_jacobilog1(100,-1/2,-1/2, 32,2,32), attesting to the high stability of the modified Chebyshev algorithm. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1, b &gt; -1, as well as for different precisions. If the logarithmic singularity occurs at the right endpoint, that is, the logarithmic factor in w(x) is log(1/(1-x)), then the recurrence coefficients are 1-c_k, d_k, where c_k, d_k are the recurrence coefficients for the weight function (1-x)^b*x*a*log(1/x) on [0,1]. For the relevant modified moments, see Section 3.2 of Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277. doi: doi:10.1007/s11075-010-9366-0.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = -1/2, b = -1/2
DOI: 10.4231/r76w981n
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^(1/2)*x^(1/2)*log(1/x) on [0,1]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal
 polynomials relative to the weight function w(x)=(1-x)^a*x^b*log(1/x)
 on [0,1], a=1/2, b=1/2, are computed by a modified-moment-based
 method using the routine sr_jacobilog1(dig,32,100,1/2,1/2), where
 dig=34 has been determined by the routine dig_jacobilog1(100,1/2,1/2,
 32,2,32), attesting to the high stability of the modified Chebyshev algorithm.
 The software provided in this dataset allows generating an arbitrary number
 N of recurrence coefficients for arbitrary a&gt;-1, b&gt;-1, as well as for different
 precisions. If the logarithmic singularity occurs at the right endpoint, that is,
 the logarithmic factor in w(x) is log(1/(1-x)), then the recurrence coefficients
 are 1-c_k, d_k, where c_k, d_k are the recurrence coefficients for the
 weight function (1-x)^b*x*a*log(1/x) on [0,1]. For the relevant modified
 moments, see Section 3.2 of Walter Gautschi, "Gauss quadrature routines
 for two classes of logarithmic weight functions", Numerical Algorithms 55
 (2010), 265-277. doi: doi:10.1007/s11075-010-9366-0.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = 1/2, b = 1/2
DOI: 10.4231/r7348hbp
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^(-1/2)*log(1/x) on [0,1]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = -1/2, b = 0, are computed by a modified-moment-based method using the routine sr_jacobilog1(dig,32,100,-1/2,0), where dig=34 has been determined by the routine dig_jacobilog1(100,-1/2,0, 32,2,32), attesting to the high stability of the modified Chebyshev algorithm. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1, b &gt; -1, as well as for different precisions. If the logarithmic singularity occurs at the right endpoint, that is, the logarithmic factor in w(x) is log(1/(1-x)), then the recurrence coefficients are 1-c_k, d_k, where c_k, d_k are the recurrence coefficients for the weight function (1-x)^b*x*a*log(1/x) on [0,1]. For the relevant modified moments, see Section 3.2 of Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277. doi: 10.1007/s11075-010-9366-0.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = -1/2, b = 0
DOI: 10.4231/r79p2zmr
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^(-1/2)*exp(-x)*(x-1-log(x)) on [0,Inf] obtained from moments

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=-1/2, are computed by a moment-based method using the routine sr_lalog(dig,32,100,-1/2), where dig=124 has been determined by the routine dig_laglog(100,-1/2,116,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a&gt;-1 as well as for different precisions. For the moments, see Section 2.1 of Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277. doi: &lt;a href="http://link.springer.com/article/10.1007/s11075-010-9366-0"&gt;10.1007/s11075-010-9366-0&lt;/a&gt;.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=-1/2
DOI: 10.4231/r7zc80vc
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^(1/2)*log(1/x) on [0,1]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = 1/2, b = 0, are computed by a modified-moment-based method using the routine sr_jacobilog1(dig,32,100,1/2,0), where dig=34 has been determined by the routine dig_jacobilog1(100,1/2,0, 32,2,32), attesting to the high stability of the modified Chebyshev algorithm. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1, b &gt; -1, as well as for different precisions. If the logarithmic singularity occurs at the right endpoint, that is, the logarithmic factor in w(x) is log(1/(1-x)), then the recurrence coefficients are 1-c_k, d_k, where c_k, d_k are the recurrence coefficients for the weight function (1-x)^b*x*a*log(1/x) on [0,1]. For the relevant modified moments, see Section 3.2 of Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277. doi: 10.1007/s11075-010-9366-0&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=(1-x)^a*x^b*log(1/x) on [0,1], a = 1/2, b = 0
DOI: 10.15468/dl.ukfd6k
title: Occurrence Download

description: A dataset containing 552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Petroica traversi"
 ]
}

 The dataset includes 552 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059345-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/r7m043cx
title: 32-digit values of the first 100 recurrence coefficients, obtained from modified moments, for the Laguerre weight function multiplied by a logarithmically singular function

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=0, are computed by a modified-moment-based method using the routine sr_laglogmm(dig,32,100,0), where dig=120 has been determined by the routine dig_laglogmm(100,0,112,4,32). For the modified moments, see Section 2.2 in Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277, doi: 10.1007/s11075-010-9366-0 and Section 1 for an application. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1 as well as for different precisions. See also doi:10.4231/R7T151N8 for a method based on ordinary moments. Both methods are equally efficient in terms of run time and produce the same answers.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=0
DOI: 10.4231/r7sq8xdm
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^(-1/2)*exp(-x)*(x-1-log(x)) on [0,Inf] obtained from modified moments

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=-1/2, are computed by a modified-moment-based method using the routine sr_laglogmm(dig,32,100,-1/2), where dig=120 has been determined by the routine dig_laglogmm(100,-1/2,112,4,32). For the modified moments, see Section 2.2 in Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277, doi: 10.1007/s11075-010-9366-0, and Section 1 for an application. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1 as well as for different precisions. See also doi:10.4231/R79P2ZMR for a method based on ordinary moments. Both methods produce the same answers, but the current one takes more than twice as long to run.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=-1/2
DOI: 10.4231/r7xg9p4b
title: 32-digit values of the first 100 recurrence coefficients for the second-order cardinal B-spline weight function obtained from moments

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=phi_m(x) on [0,m], m=2, are computed by a moment-based method using the routine sr_cBspline_cheb(100,2,dig,4,32), where dig=180 has been determined by the routine dig_Bspline_cheb(100,2,172,4,32). The results are in complete agreement with those produced by sr_Bspline_dis.m (cf. doi:10.4231/R74B2Z9Q), but are obtained more slowly by a factor of ten. For the moments, see Section 2 in Walter Gautschi, &quot;Polynomials orthogonal with respect to cardinal B-spline weight functions&quot;, submitted for publication.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=phi_m(x) on [0,m], m=2
DOI: 10.4231/r78p5xht
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^(1/2)*exp(-x)*(x-1-log(x)) on [0,Inf] obtained from modified moments

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=1/2, are computed by a modified-moment-based method using the routine sr_laglogmm(dig,32,100,1/2), where dig=116 has been determined by the routine dig_laglogmm(100,1/2,108,4,32). For the modified moments, see Section 2.2 in Walter Gautschi, &quot;Gauss quadrature routines for two classes of logarithmic weight functions&quot;, Numerical Algorithms 55 (2010), 265-277, doi: 10.1007/s11075-010-9366-0, and Section 1 for an application. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1 as well as for different precisions. See also doi:10.4231/R7JH3J5S for a method based on ordinary moments. Both methods produce the same answers, but the current one is about 60% faster to run.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*(x-1-log(x)) on [0,Inf], a=1/2
DOI: 10.4231/r72805kq
title: Gauss quadrature rules

description: &lt;p&gt;Variable-precision Matlab routine for generating in dig-digit  arithmetic the nodes and the weights of an n-point Gauss quadrature rule from the Nx2 array ab, N &gt;= n, of recurrence coefficients for the underlying weight function.&lt;/p&gt;
description: Variable-precision Matlab routine for generating the nodes and weights of a Gaussian quadrature rule
DOI: 10.4231/r7nz85nt
title: 32-digit values of the first 100 recurrence coefficients for a square-root-logarithmic weight function

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[log(1/x)]^b on [0,1], b = 1/2, are computed by a moment-based method using the routine sr_l_alglog(dig,32,100,0,1/2), where dig = 180 has been determined by the routine dig_l_alglog(100,0,1/2,172,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary b &gt; -1, as well as for different precisions. If the singularity, with the same exponent, occurs at the right endpoint, that is, if w(x)=[log(1/(1-x))]^b on [0,1], then the alpha-coefficients must be replaced by 1 minus the present ones, whereas the beta-coefficients remain the same.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=[log(1/x)]^b on [0,1], b=1/2
DOI: 10.4231/r7df6p63
title: Purdue University Buildings Demolition, Construction Images, November 2016, MATH Building Camera

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=DE_5hi7KBPk)]]&lt;/p&gt;

&lt;p&gt;Purdue&#39;s campus landscape continues to change with the construction of new buildings and the remodel of old buildings. In 2012 the Engineering Administration Building (ENAD) and the iconic Heat and Power Plant-North (HPN) were targeted for demolition to make way for the construction of the Wilmeth Active Learning Center (WALC); a building with offices, collaborative spaces and classrooms with an emphasis on new instructional models. The Purdue University Lyles School of Civil Engineering capitalized on a key opportunity for education and research. Five cameras were installed to capture images from the demolition of the Engineering Administration Building and the Power Plant, to the construction of the Wilmeth Active Learning Center. The images from 3 fixed cameras situated on the Mathematical Sciences Building (MATH) and the Potter (A. A.) Engineering Center (POTR) can be accessed and used for educational and research purposes.&lt;/p&gt;

&lt;p&gt;Representative time lapse videos were created from these three cameras for each month of the project. The time lapse videos created from these images - 08:00 to 18:00, 7 days a week in each month - are accessible here: &lt;a href="http://dx.doi.org/10.4231/R70P0WXD"&gt;http://dx.doi.org/10.4231/R70P0WXD&lt;/a&gt;.&lt;/p&gt;
description: Two iconic buildings on the Purdue University campus, the Engineering Administration Building and the retired Heating and Power Plant-North, were targeted for demolition in 2012 to make way for the construction of the Wilmeth Active Learning Center.
DOI: 10.4231/r7j67dxd
title: Purdue University Buildings Demolition, Construction Images, November 2016, POTR Building Camera 2

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=dHgHG1k8Xv0)]]&lt;/p&gt;

&lt;p&gt;Purdue&#39;s campus landscape continues to change with the construction of new buildings and the remodel of old buildings. In 2012 the Engineering Administration Building (ENAD) and the iconic Heat and Power Plant-North (HPN) were targeted for demolition to make way for the construction of the Wilmeth Active Learning Center (WALC); a building with offices, collaborative spaces and classrooms with an emphasis on new instructional models. The Purdue University Lyles School of Civil Engineering capitalized on a key opportunity for education and research. Five cameras were installed to capture images from the demolition of the Engineering Administration Building and the Power Plant, to the construction of the Wilmeth Active Learning Center. The images from 3 fixed cameras situated on the Mathematical Sciences Building (MATH) and the Potter (A. A.) Engineering Center (POTR) can be accessed and used for educational and research purposes.&lt;/p&gt;

&lt;p&gt;Representative time lapse videos were created from these three cameras for each month of the project. The time lapse videos created from these images - 08:00 to 18:00, 7 days a week in each month - are accessible here: &lt;a href="http://dx.doi.org/10.4231/R70P0WXD"&gt;http://dx.doi.org/10.4231/R70P0WXD&lt;/a&gt;.&lt;/p&gt;
description: Two iconic buildings on the Purdue University campus, the Engineering Administration Building and the retired Heating and Power Plant-North, were targeted for demolition in 2012 to make way for the construction of the Wilmeth Active Learning Center.
DOI: 10.4231/r7513w5p
title: Purdue University Buildings Demolition, Construction Images, November 2016, POTR Building Camera 1

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=b7rYXdXObvM)]]&lt;/p&gt;

&lt;p&gt;Purdue&#39;s campus landscape continues to change with the construction of new buildings and the remodel of old buildings. In 2012 the Engineering Administration Building (ENAD) and the iconic Heat and Power Plant-North (HPN) were targeted for demolition to make way for the construction of the Wilmeth Active Learning Center (WALC); a building with offices, collaborative spaces and classrooms with an emphasis on new instructional models. The Purdue University Lyles School of Civil Engineering capitalized on a key opportunity for education and research. Five cameras were installed to capture images from the demolition of the Engineering Administration Building and the Power Plant, to the construction of the Wilmeth Active Learning Center. The images from 3 fixed cameras situated on the Mathematical Sciences Building (MATH) and the Potter (A. A.) Engineering Center (POTR) can be accessed and used for educational and research purposes.&lt;/p&gt;

&lt;p&gt;Representative time lapse videos were created from these three cameras for each month of the project. The time lapse videos created from these images - 08:00 to 18:00, 7 days a week in each month - are accessible here: &lt;a href="http://dx.doi.org/10.4231/R70P0WXD"&gt;http://dx.doi.org/10.4231/R70P0WXD&lt;/a&gt;.&lt;/p&gt;
description: Two iconic buildings on the Purdue University campus, the Engineering Administration Building and the retired Heating and Power Plant-North, were targeted for demolition in 2012 to make way for the construction of the Wilmeth Active Learning Center.
DOI: 10.4231/r7g73bpn
title: Code and Dataset for Pattern Recognition Benchmarks

description: &lt;p&gt;Previous work has shown that image datasets have so much structure that a mere random projection of the dataset onto a 1D subspace is likely to uncover some of that structure. We subsequently built on this observation to propose a sequence of bounds for the error rate of a pattern recognition method based on image data. The code included computes these bounds from a training dataset. &lt;/p&gt;

&lt;p&gt;The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding each randomly selected feature in order to separate the patterns (the ``TARP&quot; approach). Our proposed bounds form a monotonically decreasing sequence that converges to a limit.  While this limit is the infimum of the sequence,  even the first few terms can be comparable or even below the error rate of more complex methods like support vector machines. The computational cost of a pattern recognition method should be offset by an accuracy gain over the TARP approach, otherwise the method does not provide any more insight into the pattern structure than random projections. This offset can be visualized in a two-dimensional space parametrized by error rate and computational cost;  only pattern recognition methods that lie on the left-hand-side of the curve defined by our proposed sequence of bounds can be considered insightful. &lt;/p&gt;
description: This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.
DOI: 10.4231/r7h70csk
title: 32-digit values of the first 100 recurrence coefficients for the weight function having an algebraic/logarithmic singularity with exponent a=1/2 and power b=3

description: &lt;p&gt; 32-digit values of the first 100 recurrence coefficients for orthogonal
 polynomials relative to the weight function w(x)=x^a*[log(1/x)]^b
 on [0,1], a = 1/2, b = 3, are computed by a moment-based method using
 the routine sr_l_alglog(dig,32,100,1/2,3), where dig = 176 has been
 determined by the routine dig_l_alglog(100,1/2,3,168,4,32). The software
 provided in this dataset allows generating an arbitrary number N of
 recurrence coefficients for arbitrary a &gt; -1, b &gt; -1, as well as for different
 precisions. If the singularities, with the same exponents, occur at the
 right endpoint, then the alpha-coefficients must be replaced by 1 minus
 the present ones, whereas the beta-coefficients remain the same.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^a*[log(1/x)]^b on [0,1], a=1/2, b=3
DOI: 10.4231/r71834gs
title: Biological, chemical and flow characteristics of five river sampling sites in the Wabash River watershed near Lafayette, Indiana – 2013.

description: &lt;p&gt;This dataset includes water quality data collected from five sites located in the Lafayette area watershed in 2013. Two of the sites are located upstream and downstream of Lafayette on the Wabash River. The other three sites are located on Little Pine Creek, Little Wea Creek, and Elliot Ditch. Data includes 15 minute flow data collected by the United States Geological Survey (USGS).&lt;/p&gt;
description: Water quality data collected from the Lafayette area watershed in 2013. There are five samples sites located on Little Pine Creek, Elliot Ditch, Little Wea Creek and two sites on the Wabash River.
DOI: 10.4231/r7wh2n0h
title: Biological, chemical and flow characteristics of five river sampling sites in the Wabash River watershed near Lafayette, Indiana – 2014.

description: &lt;p&gt;This dataset includes water quality data collected from five sites located in the Lafayette area watershed in 2014. Two of the sites are located upstream and downstream of Lafayette on the Wabash River. The other three sites are located on Little Pine Creek, Little Wea Creek, and Elliot Ditch. Data includes 15 minute flow data collected by the United States Geological Survey (USGS).&lt;/p&gt;
description: Water quality data collected from the Lafayette area watershed in 2014. There are five samples sites located on Little Pine Creek, Elliot Ditch, Little Wea Creek and two sites on the Wabash River.
DOI: 10.4231/r7rr1w7b
title: Biological, chemical and flow characteristics of five river sampling sites in the Wabash River watershed near Lafayette, Indiana – 2015.

description: &lt;p&gt;This dataset includes water quality data collected from five sites located in the Lafayette area watershed in 2015. Two of the sites are located upstream and downstream of Lafayette on the Wabash River. The other three sites are located on Little Pine Creek, Little Wea Creek, and Elliot Ditch. Data includes 15 minute flow data collected by the United States Geological Survey (USGS).&lt;/p&gt;
description: Water quality data collected from the Lafayette area watershed in 2015. There are five samples sites located on Little Pine Creek, Elliot Ditch, Little Wea Creek and two sites on the Wabash River.