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Datasets: All

  1. 32-digit values of the first 65 recurrence coefficients for the Krylov-Pal'tsev weight function on [0,Inf] with exponent -1/2

    2017-05-10 18:44:21 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7Q81B3S

    32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*log(1+1/x) on [0,Inf], a=-1/2

    https://purr.purdue.edu/publications/2498

  2. 32-digit values of the first 65 recurrence coefficients for the Krylov-Pal'tsev weight function on [0,Inf] with exponent 1/2

    2017-05-10 19:21:21 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7KH0KBM

    32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*log(1+1/x) on [0,Inf], a=1/2

    https://purr.purdue.edu/publications/2497

  3. 32-digit values of the first 65 recurrence coefficients for the Krylov-Pal'tsev weight function w(x)=exp(-x)*log(1+1/x) on [0,Inf] with exponent 0

    2017-05-10 18:40:29 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7CZ3555

    32-digit values of the first 65 recurrence coefficients for the Krylov-Pal'tsev weight function w(x)=x^a*exp(-x)*log(1+1/x) on [0,Inf], a=0

    https://purr.purdue.edu/publications/2452

  4. 32-digit values of the first 65 recurrence coefficients for the weight function w(x)=exp(-x)*log(1+1/x) on [1,Inf]

    2017-05-09 13:46:36 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7028PJT

    32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*log(1+1/x) on [1,Inf], a=0

    https://purr.purdue.edu/publications/2451

  5. 32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^(-1/2)*exp(-x)*log(1+1/x) on [1,Inf]

    2017-05-09 13:48:54 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7ZW1HX0

    32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*log(1+1/x) on [1,Inf], a=-1/2

    https://purr.purdue.edu/publications/2488

  6. 32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^(1/2)*exp(-x)*log(1+1/x) on [1,Inf]

    2017-05-09 13:49:31 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7PG1PR6

    32-digit values of the first 65 recurrence coefficients for the weight function w(x)=x^a*exp(-x)*log(1+1/x) on [1,Inf], a=1/2

    https://purr.purdue.edu/publications/2489

  7. 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]

    2016-10-21 13:28:16 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R7XS5SC9

    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

    https://purr.purdue.edu/publications/2237

  8. A database of elevational distribution of nonnative plants across 11 mountains in China

    2017-08-24 20:27:08 | Datasets | Contributor(s): Zehao Shen, Qinfeng Guo, Songlin Fei | doi:10.4231/R7610XHR

    A database of elevational distribution of nonnative plants across 11 mountains in China

    https://purr.purdue.edu/publications/2778

  9. A Formal Syntactic Analysis of Complex-Path Motion Predicates in Ghanaian Student Pidgin (GSP)

    2019-07-31 00:00:00 | Datasets | Contributor(s): Kwaku O A Osei-Tutu | doi:10.4231/NTA1-N446

    This is the accompanying data for a dissertation of the same title which formalizes the encoding of motion predicates in GSP and proposes a unified theory which captures the generalizations in the phenomena and accounts for language-specific...

    https://purr.purdue.edu/publications/3177

  10. A Multi-state Model of the CaMKII Holoenzyme using MCell 3.3

    2019-07-29 18:17:29 | Datasets | Contributor(s): Matthew C Pharris, Tamara L Kinzer-Ursem | doi:10.4231/MV0Z-8Z57

    This model uses a specialized rule-based syntax in MCell 3.3 to model the twelve-subunit CaMKII holoenzyme without inducing combinatorial explosion. The model allows us to explore the regulation of CaMKII activation and autophosphorylation.

    https://purr.purdue.edu/publications/3138

  11. A New Perspective on the Tensile Strength of Lap Splices in Reinforced Concrete Members

    2017-09-18 17:18:28 | Datasets | Contributor(s): Brian Richter, Santiago Pujol | doi:10.4231/R7GB2275

    Three series of tests were conducted on specimens with lap lengths varying from 20 to 85 bar diameters. The results indicate that increasing the length of a lap splice beyond 45 bar diameters was an inefficient way to increase the strength of...

    https://purr.purdue.edu/publications/2704

  12. A Non-parametric Bayesian Model for Joint Cell Clustering and Cluster Matching: Identification of Anomalous Sample Phenotypes with Random Effects.

    2014-09-03 20:30:26 | Datasets | Contributor(s): Murat Dundar, Ferit Akova, Halid Ziya Yerebakan, Bartlomiej P. Rajwa | doi:10.4231/R7KK98PG

    The manuscript presents a non-parametric Bayesian algorithm called ASPIRE (Anomalous Sample Phenotype Identification with Random Effects) able to identify phenotypic differences across batches of cytometry samples in the presence of random effects

    https://purr.purdue.edu/publications/1712

  13. A programmable optical stimulator for the Drosophila eye - Supporting biological data for Chen et al. (2017).

    2017-10-27 17:00:32 | Datasets | Contributor(s): Xinping Chen, Donald F Ready, Vikki Weake, Walter Leon-Salas | doi:10.4231/R75H7DFT

    Confocal microscopy images for Chen et al. (2017). HardwareX. "A programmable optical stimulator for the Drosophila eye" 13-33.

    https://purr.purdue.edu/publications/2831

  14. Abel polynomials

    2017-01-20 14:07:11 | Datasets | Contributor(s): Walter Gautschi | doi:10.4231/R72V2D3Z

    Matlab routine for the first N recurrence coefficients of Abel polynomials

    https://purr.purdue.edu/publications/2359

  15. Abundance of major tree species in the eastern United States between 1980 and 2016

    2017-05-02 00:00:00 | Datasets | Contributor(s): Songlin Fei, Christopher Oswalt, Kevin Potter, Jonathon Knott, Insu Jo | doi:10.4231/R7FX77FC

    Abundance of 86 major forest tree species in the eastern United States measured at two times between 1980 and 2016 by size classes.

    https://purr.purdue.edu/publications/2403

  16. ACI 445B Shear Wall Database

    2017-06-28 14:13:04 | Datasets | Contributor(s): Merve Usta, Santiago Pujol, ACI Subcommittee 445B, Aishwarya Puranam, Cheng Song, Ying Wang | doi:10.4231/R7HH6H39

    This structural wall performance database is being compiled and vetted several times per year by ACI (American Concrete Institute) Subcommittee 445B and contained 521 wall tests as of April 24, 2017.

    https://purr.purdue.edu/publications/2434

  17. Active Out-of-Plane Distortion Cracking at a Girder Web Gap

    2015-06-03 13:59:47 | Datasets | Contributor(s): Luke Snyder, Julie Whitehead, Robert J. Connor | doi:10.4231/R7125QKV

    This video supplements Purdue University’s Steel Bridge Research, Inspection, Training, and Engineering (S-BRITE) Center report "Fatigue and Fracture Library for the Inspection, Evaluation, and Repair of Vehicular Steel Bridges."

    https://purr.purdue.edu/publications/1911

  18. ADJUSTING FORCE - Supplementary Materials for the Report: Effects of Realistic Heat Straightening Repair on the Properties and Serviceability of Damaged Steel Beam Bridges

    2013-10-17 12:32:07 | Datasets | Contributor(s): Amit H. Varma, Young Moo Sohn | doi:10.4231/D3SF2MC0W

    Supplementary Materials for the Report: Effects of Realistic Heat Straightening Repair on the Properties and Serviceability of Damaged Steel Bridges

    https://purr.purdue.edu/publications/1132

  19. Advisors' Climate Risk Perceptions and Use of Climate Information: 2016 Survey Data

    2018-11-30 05:00:00 | Datasets | Contributor(s): Vikram Koundinya, Jenna Klink, Amber Mase, Courtney Robinson, Tonya Haigh, Ajay Singh, Sarah Church, Melissa Widhalm, Linda Stalker Prokopy | doi:10.4231/R7G44N9S

    Midwestern agricultural advisors were surveyed in late 2016 to measure their perceptions and attitudes about weather and climate, their awareness of the U2U project, and their use of U2U tools.

    https://purr.purdue.edu/publications/2535

  20. Agricultural Advisors’ Climate Risk Perceptions: 2013 Survey Data

    2016-11-30 00:00:00 | Datasets | Contributor(s): Stuart Carlton, Tonya Haigh, Cody Knutson, Maria Lemos, Amber Mase, Jean McGuire, Lois Morton, Linda S. Prokopy, Melissa J. Widhalm | doi:10.4231/R7707ZDF

    Agricultural advisors in 4 Midwestern states were surveyed in Feb 2013 about their climate risk perceptions, climate change beliefs, and willingness to use climate information.

    https://purr.purdue.edu/publications/2006

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