cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/r7n014h9
title: 32-digit values of the first 100 recurrence coefficients, obtained from moments, for a radiative transfer weight function with parameter c=2/3

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp(-1/x) on [0,c], c=2/3, are computed by a moment-based method using the routine sr_radtrans_cheb(dig,32,100,2/3), where dig=194 has been determined by the routine dig_radtrans_cheb(100,2/3,186,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary parameter c &gt; 0 as well as for different precisions. The polynomials here obtained are closely related to the radiative transfer polynomials in Section 4 of Martin J. Gander and Alan H. Karp, &quot;Stable computation of high order Gauss quadrature rules using discretization for measures in radiation transfer&quot;, Journal of Quantitative Spectroscopy &amp; Radiative Transfer&quot; 68 (2001), 213-223. The alpha-coefficients of the latter, as well as the first beta-coefficient, are obtained by dividing ours by c, and the remaining beta-coefficients by dividing ours by c^2. The results are in complete agreement with those produced by sr_radtrans_dis.m (cf. doi:10.4231/R7CF9N35) but are obtained about six times faster.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp(-1/x) on [0,c], c=2/3
DOI: 10.4231/r7cf9n35
title: 32-digit values of the first 100 recurrence coefficients, obtained by discretization, for a radiative transfer weight function with parameter c=2/3

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp(-1/x) on [0,c], c=2/3, are computed by a general-purpose discretization procedure using the routine sr_radtrans_dis(32,100), where c=2/3 and dig=34 have to be entered when prompted. The value dig=34 has been determined by the routine dig_radtrans_dis(100,32,2,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary parameter c &gt; 0 as well as for different precisions. The polynomials here obtained are closely related to the radiative transfer polynomials in Section 4 of Martin J. Gander and Alan H. Karp, &quot;Stable computation of high order Gauss quadrature rules using discretization for measures in radiation transfer&quot;, Journal of Quantitative Spectroscopy &amp; Radiative Transfer 68 (2001), 213-223. The alpha-coefficients of the latter, as well as the first beta-coefficient, are obtained by dividing ours by c, and the remaining beta-coefficients by dividing ours by c^2. The results are in complete agreement with those produced by sr_radtrans_cheb.m (cf. doi: &lt;a href="https://purr.purdue.edu/publications/2315/1" target="_blank"&gt;&lt;span class="doi"&gt;10.4231/R7N014H9&lt;/span&gt;&lt;/a&gt;), but are obtained about six times more slowly.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp(-1/x) on [0,c], c=2/3
DOI: 10.4231/r77s7kr9
title: 32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=2

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=2, are computed by a moment-based method using the routine sr_fermidirac(dig,32,100,2), where dig=168 has been determined by the routine dig_fermidirac(100,2,160,4,32). For the respective moments, see Section 5 of Walter Gautschi, &quot;Variable-precision recurrence coefficients for nonstandard orthogonal polynomials&quot;, Numerical Algorithms 52 (2009), 409-418. doi: 10.1007/s11075-009-9283-2. The software in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary integer r &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=2
DOI: 10.4231/r7vh5kt8
title: 32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=3

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=3, are computed by a moment-based method using the routine sr_fermidirac(dig,32,100,3), where dig=196 has been determined by the routine dig_fermidirac(100,3,188,4,32). For the respective moments, see Section 5 of Walter Gautschi, &quot;Variable-precision recurrence coefficients for nonstandard orthogonal polynomials&quot;, Numerical Algorithms 52 (2009), 409-418. doi:10.1007/s11075-009-9283-2. The software in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary integer r &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=3
DOI: 10.4231/r708639z
title: 32-digit values of the first 100 recurrence coefficients for the Fermi-Dirac-type weight function with exponent r=4

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r = 4, are computed by a moment-based method using the routine sr_fermidirac(dig,32,100,4), where dig=220 has been determined by the routine dig_fermidirac(100,4,212,4,32). For the respective moments, see Section 5 of Walter Gautschi, &quot;Variable-precision recurrence coefficients for nonstandard orthogonal polynomials&quot;, Numerical Algorithms 52 (2009), 409-418. doi:10.1007/s11075-009-9283-2. The software in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary integer r &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=[1/(exp(x)+1)]^r on [0,Inf], r=4
DOI: 10.4231/r7416v2r
title: 32-digit values of the first 100 recurrence coefficients for the half-range Freud weight function with exponents mu=0, nu=4

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal
 polynomials relative to the weight function w(x)=x^mu*exp(-x^nu) on [0,Inf],
 mu=0, nu=4, are computed by a moment-based method using the routine
 sr_hrfreud(dig,32,100,0,4), where dig=156 has been determined by the routine
 dig_hrfreud(100,0,4,148,4,32). For the respective moments, see Exercise 2.20(a)
 in Walter Gautschi, "Orthogonal polynomials in MATLAB. Exercises and solutions,
 Software, Environments, and Tools, SIAM, Philadelphia, PA, 2016. The software
 provided in this dataset allows generating an arbitrary number N of recurrence
 coefficients for arbitrary exponents mu&gt;-1/2, nu&gt;0, as well as for different
precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^nu) on [0,Inf], mu=0, nu=4
DOI: 10.4231/r7qr4v3z
title: 32-digit values of the first 100 recurrence coefficients for the Schroedinger weight function with exponent mu=5

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for
 orthogonal polynomials relative to the weight function
 w(x)=x^mu*exp(-x^4/16) on [0,Inf], mu = 5, are computed by a
 moment-based method using the routine sr_schroedinger(dig,100,32,5),
 where dig = 156 has been determined by the routine dig_hrfreud(100,5,4,148,4,32).
 The software in this dataset allows generating an arbitrary number N of
 recurrence coefficients for arbitrary exponent mu &gt; -1 as well as for different
 precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^mu*exp(-x^4/16) on [0,Inf], mu=5
DOI: 10.4231/r7bk19b6
title: 32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=1

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp[-(x^2-1)^2/(4*&epsilon;)] on [-Inf,Inf], &epsilon;=1, are computed by a multicomponent discretization procedure using the routine sr_OPbimod(32,100) with dig=34, epsi=1 entered at the prompt. The value dig=34 has been determined by the routine dig_sOPbimod(100,32,2,32), attesting to the high stability of the procedure. (Both routines may take several hours to run.) For details, see Exercise 2.38(c) in Walter Gautschi, &quot;Orthogonal polynomials in MATLAB: Exercises and solutions&quot;, SIAM, Philadelphia, PA (2016). Auxiliary routines muOPbimod_gp.m and explore_mu.m are intended to help determine suitable values for the parameter &lt;span lang="el"&gt;&mu;&lt;/span&gt; needed when &epsilon; &lt; 1/10. (This requires a minor temporary change in the routine smcdis.m as explained on p.130 of the cited reference.) The value of &lt;span lang="el"&gt;&mu;&lt;/span&gt; should be taken to be at least equal to 1; when N=100, other selected values of &mu; for &epsilon;=.008:-.001:.001 are found to be, respectively, &mu;=1.7, 4.7, 8.0, 15.3, 26.2, 44.4, 84.2, 201.3. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary &epsilon; &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=1
DOI: 10.4231/r7319swh
title: 32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.02

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp[-(x^2-1)^2/(4*&epsilon;)] on [-Inf,Inf], &epsilon;=.02, are computed by a multicomponent discretization procedure using the routine sr_OPbimod(32,100) with dig=34, epsi=.02 entered at the prompt. The value dig=34 has been determined by the routine dig_sOPbimod(100,32,2,32), attesting to the high stability of the procedure. (Both routines may take several hours to run.) For details, see Exercise 2.38(c) in Walter Gautschi, &quot;Orthogonal polynomials in MATLAB: Exercises and solutions&quot;, SIAM, Philadelphia, PA (2016). Auxiliary routines muOPbimod_gp.m and explore_mu.m are intended to help determine suitable values for the parameter &mu; needed when &epsilon; &lt; 1/10. (This requires a minor temporary change in the routine smcdis.m as explained on p.130 of the cited reference.) The value of &mu; should be taken to be at least equal to 1; when N=100, other selected values of &mu; for &epsilon;=.008:-.001:.001 are found to be &mu;=1.7, 4.7, 8.0, 15.3, 26.2, 44.4, 84.2, 201.3. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary &epsilon; &gt; 0 as well as for different precisions.&lt;/p&gt;

&lt;p&gt; &lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.02
DOI: 10.4231/r76t0jnx
title: 32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.1

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp[-(x&lt;sup class="moz-txt-sup"&gt;&lt;span style="display:inline-block;width:0;height:0;overflow:hidden"&gt;^&lt;/span&gt;2&lt;/sup&gt;-1)&lt;sup class="moz-txt-sup"&gt;&lt;span style="display:inline-block;width:0;height:0;overflow:hidden"&gt;^&lt;/span&gt;2&lt;/sup&gt;/(4*&epsilon;)] on [-Inf,Inf], &epsilon;=.1, are computed by a multicomponent discretization procedure using the routine sr_OPbimod(32,100) with dig=34, epsi=.1 entered at the prompt. The value dig=34 has been determined by the routine dig_sOPbimod(100,32,2,32), attesting to the high stability of the procedure. (Both routines may take several hours to run.) For details, see Exercise 2.38(c) in Walter Gautschi, &quot;Orthogonal polynomials in MATLAB: Exercises and solutions&quot;, SIAM, Philadelphia, PA (2016). Auxiliary routines muOPbimod_gp.m and explore_mu.m are intended to help determine suitable values for the parameter &mu; needed when &epsilon; &lt; 1/10. (This requires a minor temporary change in the routine smcdis.m as explained on p.130 of the cited reference.) The value of &mu; should be taken to be at least equal to 1; when N = 100, other selected values of &mu; for &epsilon; = .008:-.001:.001 are found to be &mu; = 1.7, 4.7, 8.0, 15.3, 26.2, 44.4, 84.2, 201.3. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary &epsilon; &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.1
DOI: 10.4231/r7z899dm
title: 32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.005

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp[-(x^2-1)^2/(4*&epsilon;)] on [-Inf,Inf], &epsilon;=.005, are computed by a multicomponent discretization procedure using the routine sr_OPbimod(32,100) with dig=34, epsi=.005 entered at the prompt. The value dig=34 has been determined by the routine dig_sOPbimod(100,32,2,32), attesting to the high stability of the procedure. (Both routines may take several hours to run.) For details, see Exercise 2.38(c) in Walter Gautschi, &quot;Orthogonal polynomials in MATLAB: Exercises and solutions&quot;, SIAM, Philadelphia, PA (2016). Auxiliary routines muOPbimod_gp.m and explore_mu.m are intended to help determine suitable values for the parameter &mu; needed when &epsilon; &lt; 1/10. (This requires a minor temporary change in the routine smcdis.m as explained on p.130 of the cited reference.) The value of &mu; should be taken to be at least equal to 1; when N=100, other selected values of &mu; for &epsilon;=.008:-.001:.001 are found to be &mu;=1.7, 4.7, 8.0, 15.3, 26.2, 44.4, 84.2, 201.3. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary &epsilon; &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.005
DOI: 10.4231/r7th8jpw
title: 32-digit values of the first 100 recurrence coefficients for the bimodal weight function with parameter ε=.001

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=exp[-(x^2-1)^2/(4*&epsilon;)] on [-Inf,Inf], &epsilon;=.001, are computed by a multicomponent discretization procedure using the routine sr_OPbimod(32,100) with dig=34, epsi=.001 entered at the prompt. The value dig=34 has been determined by the routine dig_sOPbimod(100,32,2,32), attesting to the high stability of the procedure. (Both routines may take several hours to run.) For details, see Exercise 2.38(c) in Walter Gautschi, &quot;Orthogonal polynomials in MATLAB: Exercises and solutions&quot;, SIAM, Philadelphia, PA (2016). Auxiliary routines muOPbimod_gp.m and explore_mu.m are intended to help determine suitable values for the parameter &mu; needed when &epsilon; &lt; 1/10. (This requires a minor temporary change in the routine smcdis.m as explained on p. 130 of the cited reference.) The value of &mu; should be taken to be at least equal to 1; when N=100, other selected values of &mu; for &epsilon;=.008:-.001:.001 are found to be &mu;=1.7, 4.7, 8.0, 15.3, 26.2, 44.4, 84.2, 201.3. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary &epsilon; &gt; 0 as well as for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=exp[-(x^2-1)^2/(4*ε)] on [-Inf,Inf], ε=.001
DOI: 10.4231/r79k4878
title: Generalized Hermite polynomials

description: &lt;p&gt;A Matlab variable-precision routine is provided for generating the first N recurrence coefficients of generalized Hermite polynomials orthogonal relative to the weight function w(x)=&lt;code class="moz-txt-verticalline"&gt;&lt;span class="moz-txt-tag"&gt;|&lt;/span&gt;x&lt;span class="moz-txt-tag"&gt;|&lt;/span&gt;&lt;/code&gt;^(2*&mu;)*exp(-x&lt;sup class="moz-txt-sup"&gt;&lt;span style="display:inline-block;width:0;height:0;overflow:hidden"&gt;^&lt;/span&gt;2&lt;/sup&gt;) on [-Inf,Inf], 2*&mu; &gt;- 1.&lt;/p&gt;
description: Matlab routine for the first N recurrence coefficients of generalized Hermite polynomials
DOI: 10.4231/r7pr7sz5
title: Jacobi polynomials

description: &lt;p&gt;
Matlab variable-precision routines are provided for generating
 the first N recurrence coefficients of Jacobi and shifted Jacobi polynomials
 orthogonal relative to the weight function w(x)=(1-x)^a*(1+x)^b on
 [-1,1] resp. w(x)=(1-x)^a*x^b on [0,1], a &gt; -1, b &gt; -1.&lt;/p&gt;
description: Matlab routines for the first N recurrence coefficients of Jacobi polynomials
DOI: 10.4231/r7k07280
title: Generalized Laguerre polynomials

description: &lt;p&gt;A Matlab variable-precision routine is provided for generating
 the first N recurrence coefficients of the generalized Laguerre polynomial
 orthogonal relative to the weight function w(x)=x^a*exp(-x) on [0,Inf],
 a &gt; -1.&lt;/p&gt;
description: Matlab routines for the first N recurrence coefficients of generalized Laguerre polynomials
DOI: 10.4231/r75t3hg3
title: Meixner-Pollaczek polynomials

description: &lt;p&gt;A Matlab variable-precision routine is provided for generating the first N recurrence coefficients of Meixner-Pollaczek polynomials orthogonal relative to the weight function w(x)=(1/(2*&pi;))*exp((2*&phi;-&pi;)*x)*|&lt;span lang="el"&gt;&Gamma;&lt;/span&gt;(&lambda;+i*x)|^2 on [-Inf,Inf], &lambda; &gt; 0, 0 &lt; &phi; &lt; &pi;.&lt;/p&gt;
description: Matlab routine for the first N recurrence coefficients of Meixner-Pollaczek polynomials
DOI: 10.5067/orbview-2/seawifs/l2/gac/iop/2022.0
title: OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Inherent Optical Properties (IOP) Data, version 2022.0
DOI: 10.15468/dl.683cc9
title: Occurrence Download

description: A dataset containing 10899 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sarcopoterium spinosum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10899 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059347-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/r7wm1bmh
title: Fragments of Coarseware from the Rough Cilicia Survey Pottery Study Collection

description: &lt;p&gt;The Rough Cilicia Survey Pottery Study Collection consisting of more than 300 useful diagnostic sherds of the most commonly observed forms of the region, based on the collections of the survey team that took place between 1996-2011. The collection is a result of a non-intrusive walking  survey of that provided a highly accurate chronological assessment of the pottery recovered from a systematically organized and narrowly delineated set of spatial locations.&lt;/p&gt;
description: This dataset is contains coarseware form the Rough Cilicia Survey Pottery Study Collection, a collection of more than 300 useful diagnostic sherds of the most commonly observed forms of the region.
DOI: 10.4231/r7222rrp
title: Evaluation of MARWIS for Indiana Winter Road Operations

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=nh6w4dS5-MA)]]&lt;/p&gt;

&lt;p&gt;Purdue University&rsquo;s Field Infrastructure Assessment class investigated the accuracy of Mobile Advanced Road Weather Information Sensor (MARWIS) roadway sensors for their ability to assess winter weather conditions. These vehicle-mounted sensors collect and display real-time data such as roadway temperature, surface conditions (e.g., dry, wet, or icy), and estimated available friction on the roadway. Purdue&rsquo;s CE 597 class evaluated each of these features using a series of experiments documented in this video.&lt;/p&gt;
description: This video documents the evaluation of the MARWIS data sensors as performed by Purdue University’s CE 597 class.
DOI: 10.4231/r7x9288s
title: Underdrain Assessment Along the I-65 Corridor

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=XIh8Hkb5LnY)]]&lt;/p&gt;

&lt;p&gt;Purdue University&rsquo;s Field Infrastructure Assessment class performed a study to assess the condition of underdrains installed in 2011 on I-65. This study was conducted as a follow-up to a study performed in spring 2013. The purpose of the 2016 study was to assess the condition of the underdrains and compare their conditions to those determined in 2013. This video documents the study process and describes the results and conclusions.&lt;/p&gt;
description: This video documents the assessment of the condition ratings of underdrains on I-65, as determined by Purdue University’s CE 597 class.
DOI: 10.4231/r7sj1hj2
title: Virtual Weigh-in-Motion Infrastructure Overview

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=ZTDdrUoYL3I)]]&lt;/p&gt;

&lt;p&gt;Purdue University&rsquo;s Field Infrastructure Assessment class investigated the virtual weigh-in-motion (VWIM) station infrastructure and how the Indiana State Police utilizes it to selectively and routinely stop and inspect potentially overweight vehicles. The VWIM infrastructure investigated in this video is installed on the Hoosier Heartland Highway, near Logansport, Indiana.&lt;/p&gt;
description: This video documents the VWIM station infrastructure and how it is being used by the Indiana State Police.
DOI: 10.5067/orbview-2/seawifs/l2/gac/oc/2022.0
title: OrbView-2 SeaWiFS Level-2 Regional Global Area Coverage (GAC) Ocean Color (OC) Data, version 2022.0
DOI: 10.4231/r7ns0rvs
title: Porter County Regional Airport Runway Construction

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=VrmS7t2gTWQ)]]&lt;/p&gt;

&lt;p&gt;[[Video(http://www.youtube.com/watch?v=wk5_WEALDwc)]]&lt;/p&gt;

&lt;p&gt;These videos illustrate how time-lapse photography can be used to develop training and education materials pertaining to airfield construction practices. This material is beneficial for training construction workers on airfield awareness and providing an overall understanding of the complexities of airside construction. Though these videos document work at a small general aviation airport, with some modifications and support the techniques illustrated could be applied to all sizes and types of airports.&lt;/p&gt;
description: These time-lapse videos summarize construction activity on runway 9/27 at Porter County Regional Airport in Valparaiso, Indiana.
DOI: 10.5067/orbview-2/seawifs/l2/mlac/iop/2022.0
title: OrbView-2 SeaWiFS Level-2 Regional Merged Local Area Coverage (MLAC) Inherent Optical Properties (IOP) Data, version 2022.0