cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/r7bz640b
title: 32-digit values of the first 100 recurrence coefficients for the Bose-Einstein-type weight function with exponent 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/(exp(x)-1)]^r on [0,Inf], r=3, are computed by a moment-based method using the routine sr_boseeinstein(dig,32,100,3), where dig=200 has been determined by the routine dig_boseeinstein(100,3,192,4,32). For the respective moments, see Section 4 in 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 provided in this dataset allows generating an arbitrary number N of recurrence coefficients for 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 the weight function w(x)=[x/(exp(x)-1)]^r on [0,Inf], r=3
DOI: 10.4231/r7765c8x
title: 32-digit values of the first 100 recurrence coefficients for the Bose-Einstein-type weight function with exponent 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/(exp(x)-1)]^r on [0,Inf], r=4, are computed by a moment-based method using the routine sr_boseeinstein(dig,32,100,4), where dig=224 has been determined by the routine dig_boseeinstein(100,4,216,4,32). For the respective moments, see Section 4 in 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 provided in this dataset allows generating an arbitrary number N of recurrence coefficients for 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 the weight function w(x)=[x/(exp(x)-1)]^r on [0,Inf], r=4
DOI: 10.4231/r73f4mkn
title: 32-digit values of the first 100 recurrence coefficients for a modified Bessel 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)=c*K_nu(x) on [0,Inf], c=2cos(nu*pi/2)/pi, are computed for nu=1/3 by a moment-based method using the routine sr_modbess(dig,32,100,1/3), where dig=124 has been determined by the routine dig_modbess(100,1/3,116,4,32). For the respective moments, see Section 4.1 in Walter Gautschi, &quot;Computation of Bessel and Airy functions and of related Gaussian quadrature formulae&quot;, BIT 42 (2002), 110-118. doi: doi:10.1023/A:1021974203359. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary order nu&gt;0 (not an integer) in w(x) and different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=c*K_nu(x) on [0,Inf], c=2cos(nu*pi/2)/pi, nu=1/3
DOI: 10.4231/r7zp443r
title: 32-digit values of the first 100 recurrence coefficients for the half-range generalized Hermite weight function with exponent 0

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^(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 [0,Inf], mu=0, are computed by a moment-based method using the routine sr_hrghermite(dig,32,100,0), where dig=140 has been determined by the routine dig_hrghermite(100,0,132,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary mu &gt; -1/2, 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^(2*mu)*exp(-x^2) on [0,Inf], mu=0
DOI: 10.4231/r7q81b2b
title: 32-digit values of the first 100 recurrence coefficients for the half-range generalized Hermite weight function with exponent -1/2

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^(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 [0,Inf], mu=-1/4, are computed by a moment-based method using the routine sr_hrghermite(dig,32,100,-1/4), where dig=140 has been determined by the routine dig_hrghermite(100,-1/4,132,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary mu &gt; -1/2, 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^(2*mu)*exp(-x^2) on [0,Inf], mu=-1/4
DOI: 10.4231/r7kh0k95
title: 32-digit values of the first 100 recurrence coefficients for the half-range generalized Hermite weight function with exponent 1/2

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=x^(2*mu)*e^(-x^2) on [0,Inf], mu=1/4, are computed by a moment-based method using the routine sr_hrghermite(dig,32,100,1/4), where dig=144 has been determined by the routine dig_hrghermite(100,1/4,136,4,32). The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for an arbitrary mu&gt;-1/2, 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^(2*mu)*exp(-x^2) on [0,Inf], mu=1/4
DOI: 10.15468/dl.7m554h
title: Occurrence Download

description: A dataset containing 216 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 "year",
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 GBIF_WITHIN('POLYGON((92.41069 11.42222,93.24228 11.42222,93.24228 13.53054,92.41069 13.53054,92.41069 11.42222))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND (occurrence.kingdomkey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence."year"

 The dataset's 216 records were derived from 157 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059313-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/r7fq9tkw
title: 32-digit values of the first 100 recurrence coefficients for the Jacobi weight function on [0,1] with exponents -1/2 times a logarithmic factor

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=b=-1/2, are
 computed by a moment-based method using the routine sr_jaclog(dig,32,100,-1/2,-1/2),
 where dig=180 has been determined by the routine dig_jaclog(100,-1/2,-1/2,172,4,32).
 For the moments, see Section 3.1 in Walter Gautschi, "Gauss quadrature routines
 for two classes of logarithmic weight functions", Numerical Algorithms 55 (2010),
 265-277. doi: 10.1007/s11075-010-9366-0. 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.&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=b=-1/2
DOI: 10.4231/r79z92vj
title: 32-digit values of the first 100 recurrence coefficients for the Jacobi weight function on [0,1] with exponents 1/2 times a logarithmic factor

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=b=1/2, are computed by a moment-based method using the routine sr_jaclog(dig,32,100,1/2,1/2), where dig=180 has been determined by the routine dig_jaclog(100,1/2,1/2,172,4,32). For the moments, see Section 3.1 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. 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.&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=b=1/2
DOI: 10.4231/r72j68t4
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=x^(-1/2)*[log(1/x)]^2 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)=x^a*[log(1/x)]^2 on [0,1], a=-1/2, are computed by a modified-moment-based method using the routine sr_jaclogsq(dig,32,100,-1/2), where dig=40 has been determined by the routine dig_jaclogsq(100,-1/2,32,4,32). For the modified moments, see Section 3 in Walter Gautschi, &quot;On certain slowly convergent series occurring in plate contact problems&quot;, Mathematics of Computation 57 (1991), 325-338. It appears that Table 2 in the Appendix of this reference is not entirely accurate: As many as six trailing digits of the twenty digits given are inconsistent with the results (judged more reliable) obtained by the software provided in this dataset. This software allows generating an arbitrary number N of recurrence coefficients for arbitrary a &gt; -1 (not an integer) 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^a*[log(1/x)]^2 on [0,1], a=-1/2
DOI: 10.4231/r7xs5sc9
title: 32-digits values of the first 100 recurrence coefficients for the weight function w(x)=x^(1/2)*[log(1/x)]^2 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)=x^a*[log(1/x)]^2 on [0,1], a=1/2, are computed by a modified-moment-based method using the routine sr_jaclogsq(dig,32,100,1/2), where dig=36 has been determined by the routine dig_jaclogsq(100,1/2,28,4,32). For the modified moments, see Section 3 in Walter Gautschi, &quot;On certain slowly convergent series occurring in plate contact problems&quot;, Mathematics of Computation 57 (1991), 325-338. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary a&gt;-1 (not an integer) 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^a*[log(1/x)]^2 on [0,1], a=1/2
DOI: 10.4231/r7t151n8
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=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=0, are computed by a moment-based method using the routine sr_laglog(dig,32,100,0), where dig=124 has been determined by the routine dig_laglog(100,0,116,4,32). For the moments, see Section 2.1 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.&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/r7dr2sf2
title: 32-digit values of the first 100 recurrence coefficients for the exponential integral weight function E_1 on [0,Inf]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=E_nu(x) on (0,Inf], nu=1, are computed by a moment-based method using the routine sr_Enu(dig,32,100,1), where dig=124 has been determined by the routine dig_Enu(100,1,116,4,32). For the moments, see Exercise 2.26(a) in Walter Gautschi, &quot;Orthogonal polynomials: exercises and solutions&quot;, 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 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)=E_nu(x) on (0,Inf], nu=1
DOI: 10.4231/r7jh3j5s
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_laglog(dig,32,100,1/2), where dig=124 has been determined by the routine dig_laglog(100,1/2,116,4,32). For the moments, see Section 2.1 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.&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/r79021rg
title: 32-digit values of the first 100 recurrence coefficients for the finite-range exponential integral weight function 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)=E_nu(x) on (0,c], nu=1, c=1, are computed by a moment-based method using the routine sr_Enufin(dig,32,100,1), where dig=180 has been determined by the routine dig_Enufin(100,1,1,172,4,32). For the moments, see Exercise 17(c) in Walter Gautschi, &quot;Orthogonal polynomials: exercises and solutions&quot;, 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 nu &gt; 0 and c &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)=E_nu(x) on (0,c], nu=1, c=1
DOI: 10.25934/pr00010846.0
title: Available datapackage for study 'A Randomized, Open-Label, Phase 3 Study of Abemaciclib Combined With Standard Adjuvant Endocrine Therapy Versus Standard Adjuvant Endocrine Therapy Alone in Patients With High Risk, Node Positive, Early Stage, Hormone Receptor Positive, Human Epidermal Receptor 2 Negative, Breast Cancer'

description: The purpose of this study is to evaluate the safety and efficacy of the study drug abemaciclib in participants with high risk, node positive, early stage, hormone receptor positive (HR+), human epidermal receptor 2 negative (HER2-), breast cancer. The data cut to be shared matches the primary publication; https://jamanetwork.com/journals/jamaoncology/article-abstract/2793128
DOI: 10.4231/r75b00d1
title: Wilmeth Active Learning Center Building Construction Time-Lapse, August 2014–September 2016

description: &lt;p&gt;[[Video(http://www.youtube.com/watch?v=JajjmZ6NfUI)]]&lt;/p&gt;

&lt;p&gt;The Thomas S. and Harvey D. Wilmeth Active Learning Center is scheduled to open in the fall of 2017.  The 164,000 SF, $79M design-bid-build project has 26 active learning classrooms and a central library that consolidates 6 campus libraries.  The facility is located in the heart of campus with an atrium and reading room that overlooks the Bell Tower. &lt;/p&gt;

&lt;p&gt;This video was assembled from cameras located on the MATH building for the period of August 2014 to September 2016.&lt;/p&gt;

&lt;p&gt;The full data collection can be found at http://dx.doi.org/10.4231/R70P0WXD.&lt;/p&gt;
description: This video documents the demolition of the Engineering Administration building (ENAD) and the old Heat and Power Plant–North (HPN) building and the construction of the new Wilmeth Active Learning Center.
DOI: 10.4231/r7ws8r7x
title: 32-digit values of the first 100 recurrence coefficients for the finite-range exponential integral weight function on [0,16]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=E_nu(x) on (0,c], nu=1, c=16, are computed by a moment-based method using the routine sr_Enufin(dig,32,100,16), where dig=180 has been determined by the routine dig_Enufin(100,1,16,172,4,32). For the moments, see Exercise 2.26(c) in Walter Gautschi, &quot;Orthogonal polynomials: exercises and solutions&quot;, 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 nu &gt; 0 and c &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)=E_nu(x) on (0,c], nu=1, c=16
DOI: 10.4231/r71j97q6
title: 32-digit values of the first 100 recurrence coefficients for the coerror 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)=erfc(x) on (0,Inf] are computed by a
 moment-based method using the routine sr_erfc(dig,32,100), where dig=140
 has been determined by the routine dig_erfc(100,132,4,32). For the moments,
 see Exercise 2.31(a) in Walter Gautschi, "Orthogonal polynomials: 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 different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=erfc(x) on [0,Inf]
DOI: 10.4231/r7s180gf
title: 32-digit values of the first 100 recurrence coefficients for the reciprocal gamma 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)=1/gamma(x) on [0,Inf] are computed by a multicomponent discretization procedure using the routine sr_recgamma(dig,100), where dig=36 has been determined by the routine dig_recgamma(100,32,4,32). The results are in complete agreement to all twenty digits of the first 40 recurrence coefficients given in Table 1 of Walter Gautschi, &quot;Polynomials orthogonal with respect to the reciprocal gamma function&quot;, BIT 22 (1982), 387-389, doi: 10.1007/BF01934452. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=1/gamma(x) on [0,Inf]
DOI: 10.4231/r7n877rq
title: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=[(1-.999*x^2)*(1-x^2)]^(-1/2) on [-1,1]

description: &lt;p&gt;32-digit values of the first 100 recurrence coefficients for orthogonal polynomials relative to the weight function w(x)=((1-om2*x^2)*(1-x^2))^(-1/2) on [-1,1], om2=.999, are computed by a modified-moment-based method using the routine sr_ellcheb(dig,32,100,.999), where dig=36 has been determined by the routine dig_ellcheb(100,.999,32,4,32), attesting to the high stability of the modified Chebyshev algorithm. (With the parameter om2 very close to 1, this routine will take some extra time to run.) For the modified moments, see Example 4.4 in Walter Gautschi, &quot;On generating orthogonal polynomials&quot;, SIAM Journal on Statistical and Scientific Computing 3 (1982), 289-317. doi: 10.1137/0903018. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary parameter om2, 0 &lt; om2 &lt; 1, and for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=((1-om2*x^2)*(1-x^2))^(-1/2) on [-1,1], om2=.999
DOI: 10.4231/r74b2z9q
title: 32-digit values of the first 100 recurrence coefficients for the second-order cardinal B-spline weight function obtained by discretization

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 multicomponent discretization procedure using the routine sr_cBspline_dis(dig,32,100,2) called with the commands &#39;global dig; dig=34&#39;, where dig=34 has been determined by the routine dig_Bspline_dis(100,2,32,2,32), attesting to the high stability of the multicomponent discretization procedure. The results are in complete agreement with those produced by sr_cBspline_cheb.m (cf. doi:10.4231/R7 . . . ), but are obtained ten times as fast. For details concerning the discretization, see Section 3 in Walter Gautschi, &quot;Polynomials orthogonal with respect to cardinal B-spline weight functions&quot;, Numerical Algorithms, submitted. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary order m and for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=phi_m(x) on [0,m], m=2
DOI: 10.4231/r70k26jx
title: 32-digit values of the first 100 recurrence coefficients for the 10th-order cardinal B-spline 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)=phi_m(x) on [0,m], m=10, are computed by a multicomponent discretization procedure using the routine sr_cBspline_dis(34,32,100,10) called by run_cBspline.m. For details concerning the discretization, see Section 3 in Walter Gautschi, &quot;Polynomials orthogonal with respect to cardinal B-spline weight functions&quot;, Numerical Algorithms, submitted. The results obtained are in complete agreement with the thirty 20-digit recurrence coefficients given in Section 3 of Gradimir V. Milovanović, &quot;Symbolic-numeric computation of orthogonal polynomials and Gaussian quadratures with respect to the cardinal B-spline&quot;, Numerical Algorithms, to appear. The software provided in this dataset allows generating an arbitrary number N of recurrence coefficients for arbitrary order m and for different precisions.&lt;/p&gt;
description: 32-digit values of the first 100 recurrence coefficients for the weight function w(x)=phi_m(x) on [0,m], m=10