cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/p82ksjmb7j.1
title: Channel centerline temperature data pertaining to catalytically supported thermal combustion systems with thermally conductive materials

description: The channel centerline temperature data are obtained for the catalytically supported thermal combustion systems with thermally conductive materials. The system is modeled as two channels and a catalyst layer with very small dimensions. These dimensions could be increased or decreased as desired for particular applications. This design analysis treats some of these dimensions as variables, and some as constrained by combustion stability considerations. In order for flame holding to occur, methane and air must be premixed and provided in an appropriate velocity region to reside. Methane and air are perfectly premixed with a temperature of 300 degrees Kelvin before they enter the system prior to combustion. The strength of the mixture composition is typically expressed in terms of its equivalence ratio. Platinum is catalytically-active towards promoting the heterogeneous surface reaction. Complex modeling methods and algorithms are required for the system due not only to the complex geometry of the system but also the complex physicochemical processes involved. Steady-state analyses are performed, variations in system pressure and temperature are determined in accordance with the ideal gas law, and the system operates in the laminar flow regime due to the small Reynolds numbers. The maximum Reynolds number is less than 360 at the flow inlet and 960 when the velocity of the flow of the fluid is highest in the channels. The model is implemented in commercially available software FLUENT to obtain the solution of the problem. Detailed chemistry is included in the model. Detailed chemical mechanisms are playing an increasingly important role in developing chemical kinetics models for combustion. Detailed chemical mechanisms are incorporated into the reacting flow for the system. The homogeneous combustion is modeled with the detailed chemical mechanism for methane oxidation in CHEMKIN format. Detailed heterogeneous chemistry in SURFACE-CHEMKIN format is included in the model. The rates of the elementary reactions involved in the combustion process are determined by Arrhenius kinetic expressions. Numerical simulations with the detailed chemical mechanism are typically computationally expensive. The detailed chemical mechanism is invariably stiff and therefore its numerical integration is computationally costly.
Contributor: Junjie Chen, E-mail address: koncjj@gmail.com, ORCID: 0000-0002-5022-6863, Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China
DOI: 10.17632/rh4zjz8vt3.1
title: NGS- IL-12 treated primary murine neuronal cultures 

description: We performed next generation sequencing of primary murine neurons stimulated with recombinant IL-12 to test the isolated effect of IL-12 on neurons, independent from a heavily inflamed CNS microenvironment. Principal component analysis (PCA) revealed a dose-dependent response to IL-12 in the neuronal transcriptome. Our computational analysis identified 1,878 significantly DEGs (p≤0.01, log ratio ≥0.5). Several of the upregulated genes have been linked to promoting neurogenesis (Fpr1, Mbdi1), neuroprotection (Timp1, Angptl4) and enhancing remyelination (Timp1, Matn2), thereby suggesting a direct neuroprotective and neurotrophic effect of IL-12. 

Sequencing raw data and processed gene expression data will be deposited into the GEO repository upon publication. Code for analyses will be available upon contacting the corresponding authors: becher@immunology.uzh.ch and mundt@immunology.uzh.ch

Sequencing raw data and processed gene expression data will be deposited into the GEO repository upon publication.
DOI: 10.6084/m9.figshare.20732619
title: Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs

description: We establish conditions under which Metropolis-Hastings (MH) algorithms with a position-dependent proposal covariance matrix will or will not have the geometric rate of convergence. Some of the diffusions based MH algorithms like the Metropolis adjusted Langevin algorithm (MALA) and the pre-conditioned MALA (PCMALA) have a position-independent proposal variance. Whereas, for other modern variants of MALA like the manifold MALA (MMALA) that adapt to the geometry of the target distributions, the proposal covariance matrix changes in every iteration. Thus, we provide conditions for geometric ergodicity of different variations of the Langevin algorithms. These results have important practical implications as these provide crucial justification for the use of asymptotically valid Monte Carlo standard errors for Markov chain based estimates. The general conditions are verified in the context of conditional simulation from the two most popular generalized linear mixed models (GLMMs), namely the binomial GLMM with the logit link and the Poisson GLMM with the log link. Empirical comparison in the framework of some spatial GLMMs shows that the computationally less expensive PCMALA with an appropriately chosen pre-conditioning matrix may outperform the MMALA.
DOI: 10.6084/m9.figshare.20732619.v2
title: Convergence of Position-Dependent MALA with Application to Conditional Simulation in GLMMs

description: We establish conditions under which Metropolis-Hastings (MH) algorithms with a position-dependent proposal covariance matrix will or will not have the geometric rate of convergence. Some of the diffusions based MH algorithms like the Metropolis adjusted Langevin algorithm (MALA) and the pre-conditioned MALA (PCMALA) have a position-independent proposal variance. Whereas, for other modern variants of MALA like the manifold MALA (MMALA) that adapt to the geometry of the target distributions, the proposal covariance matrix changes in every iteration. Thus, we provide conditions for geometric ergodicity of different variations of the Langevin algorithms. These results have important practical implications as these provide crucial justification for the use of asymptotically valid Monte Carlo standard errors for Markov chain based estimates. The general conditions are verified in the context of conditional simulation from the two most popular generalized linear mixed models (GLMMs), namely the binomial GLMM with the logit link and the Poisson GLMM with the log link. Empirical comparison in the framework of some spatial GLMMs shows that the computationally less expensive PCMALA with an appropriately chosen pre-conditioning matrix may outperform the MMALA.
DOI: 10.6084/m9.figshare.20732619.v1
title: Convergence of position-dependent MALA with application to conditional simulation in GLMMs

description: We establish conditions under which Metropolis-Hastings (MH) algorithms with a position-dependent proposal covariance matrix will or will not have the geometric rate of convergence. Some of the diffusions based MH algorithms like the Metropolis adjusted Langevin algorithm (MALA) and the pre-conditioned MALA (PCMALA) have a position-independent proposal variance. Whereas, for other modern variants of MALA like the manifold MALA (MMALA) that adapt to the geometry of the target distributions, the proposal covariance matrix changes in every iteration. Thus, we provide conditions for geometric ergodicity of different variations of the Langevin algorithms. These results have important practical implications as these provide crucial justification for the use of asymptotically valid Monte Carlo standard errors for Markov chain based estimates. The general conditions are verified in the context of conditional simulation from the two most popular generalized linear mixed models (GLMMs), namely the binomial GLMM with the logit link and the Poisson GLMM with the log link. Empirical comparison in the framework of some spatial GLMMs shows that the computationally less expensive PCMALA with an appropriately chosen pre-conditioning matrix may outperform the MMALA.
DOI: 10.6084/m9.figshare.21183271
title: Essays on structured additive regression models applied to data in development economics (Electronic material)

description: This file contains the code and datasets required for reproducibility and replicability purposes of the three cases developed in the PhD thesis "Essays on structured additive regression models applied to data in development economics".
DOI: 10.6084/m9.figshare.21183271.v1
title: Essays on structured additive regression models applied to data in development economics (Electronic material)

description: This file contains the code and datasets required for reproducibility and replicability purposes of the three cases developed in the PhD thesis "Essays on structured additive regression models applied to data in development economics".
DOI: 10.6084/m9.figshare.20618496
title: 3T BOLD S12

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618460
title: 3T BOLD S11

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618460.v1
title: 3T BOLD S11

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618595
title: 3T BOLD S13

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618595.v1
title: 3T BOLD S13

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618622
title: 3T BOLD S14

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618622.v1
title: 3T BOLD S14

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617383
title: 3T BOLD S7

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617383.v1
title: 3T BOLD S7

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617407.v1
title: 3T BOLD S8

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617590.v2
title: 3T BOLD S10

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617338
title: 3T BOLD S6

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617494
title: 3T BOLD S9

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617494.v3
title: 3T BOLD S9

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617590
title: 3T BOLD S10

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617287.v1
title: 3T BOLD S5

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617338.v1
title: 3T BOLD S6

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617407
title: 3T BOLD S8

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617494.v2
title: 3T BOLD S9

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617590.v1
title: 3T BOLD S10

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617203
title: 3T BOLD S2

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617203.v1
title: 3T BOLD S2

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617227
title: 3T BOLD S3

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617227.v1
title: 3T BOLD S3

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617260.v2
title: 3T BOLD S4

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20618496.v1
title: 3T BOLD S12

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20616975.v1
title: 7T BOLD S13

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20617086
title: 7T BOLD S14

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20617086.v1
title: 7T BOLD S14

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20617185
title: 3T BOLD S1

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617260
title: 3T BOLD S4

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20616678
title: 7T BOLD S11

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616975
title: 7T BOLD S13

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20617185.v1
title: 3T BOLD S1

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20617260.v1
title: 3T BOLD S4

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20616186
title: 7T BOLD S9

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616186.v1
title: 7T BOLD S9

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616309
title: 7T BOLD S10

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616309.v1
title: 7T BOLD S10

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616678.v1
title: 7T BOLD S11

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616813
title: 7T BOLD S12

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616813.v1
title: 7T BOLD S12

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615970
title: 7T BOLD S7

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615970.v1
title: 7T BOLD S7

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616084
title: 7T BOLD S8

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615709
title: 7T BOLD S5

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615709.v1
title: 7T BOLD S5

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615835
title: 7T BOLD S6

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615835.v1
title: 7T BOLD S6

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20616084.v1
title: 7T BOLD S8

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20614908.v1
title: 7T BOLD S1

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615253
title: 7T BOLD S2

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615253.v1
title: 7T BOLD S2

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615451
title: 7T BOLD S3

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615451.v1
title: 7T BOLD S3

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20615586.v1
title: 7T BOLD S4

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20612190.v1
title: 7T VASO S8 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20614704
title: 7T VASO S10

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20614704.v1
title: 7T VASO S10

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20614908
title: 7T BOLD S1

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.20617287
title: 3T BOLD S5

description: Stimfiles and preprocessed fMRI data (.nii format) for one participant scanned at 3T BOLD.
DOI: 10.6084/m9.figshare.20611005
title: 7T VASO S6

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20611005.v1
title: 7T VASO S6

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20611134
title: 7T VASO S7

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20612190
title: 7T VASO S8 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20613519
title: 7T VASO S9 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20613519.v1
title: 7T VASO S9 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609592.v1
title: 7T VASO S2 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609901
title: 7T VASO S4 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20610138
title: 7T VASO S5 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20610138.v1
title: 7T VASO S5 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20611134.v1
title: 7T VASO S7

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20615586
title: 7T BOLD S4

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T BOLD.
DOI: 10.6084/m9.figshare.14519127
title: readme

description: The dataset for "layer-specific, retinotopically-diffuse modulation in human visual cortex by emotional faces":A total of 43 scan sessions from 25 participants (average age 25.9 years, 12 females), consisting of 14 scan sessions from 14 unique participants at 3T BOLD, 14 scan sessions from 14 unique participants at 7T BOLD, and 15 scan sessions from 10 unique participants at 7T VASO, among whom 5 were scanned twice to evaluate test-retest reliability of VASO
DOI: 10.6084/m9.figshare.14519127.v1
title: readme

description: The dataset for "layer-specific, retinotopically-diffuse modulation in human visual cortex by emotional faces":A total of 43 scan sessions from 25 participants (average age 25.9 years, 12 females), consisting of 14 scan sessions from 14 unique participants at 3T BOLD, 14 scan sessions from 14 unique participants at 7T BOLD, and 15 scan sessions from 10 unique participants at 7T VASO, among whom 5 were scanned twice to evaluate test-retest reliability of VASO
DOI: 10.6084/m9.figshare.20609013
title: 7T VASO S1

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609013.v1
title: 7T VASO S1

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609424
title: 7T VASO S3

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609592
title: 7T VASO S2 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609424.v1
title: 7T VASO S3

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20609901.v1
title: 7T VASO S4 (two scan sessions)

description: Stimfiles and raw fMRI data (.nii format) for one participant scanned at 7T VASO.
DOI: 10.6084/m9.figshare.20736594
title: Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements

description: We develop a communication-efficient distributed estimation for the 1-bit compressive sensing where unknown sparse signals are coded into binary measurements with noises and sign flips. We allow for distinctive sign-flipped probabilities and intensities of noises for measurements collected at different nodes, which raises a heterogeneity issue. We suggest a distributed algorithm through penalized least squares to recover sparse signals. This algorithm is computationally very efficient with only gradient information communicated. The resulting distributed estimate converges after a single iteration even when a lousy initial estimate is provided, and achieves a nearly oracle rate after a constant number of iterations. We prove that, under some mild conditions, with high probability, the distributed estimate approximates the underlying true sparse signal with precision <i>δ</i> after a finite number of iterations, as long as the total sample size <i>N</i> satisfies (s log p)/(δ2N)=O(1), where <i>p</i> is the dimension and <i>s</i> is the number of nonzero elements of the underlying true sparse signals. We establish statistical guarantee for support recovery. Extensive experiments are provided to illustrate the effectiveness of our proposed distributed algorithm. Supplementary materials for this article are available online.