cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7482191.v1
title: Effect of Hatching Time on Yolk Sac Percentage and Broiler Live Performance

description: ABSTRACT This study investigated the effects of broiler chick hatching time on the percentage of the yolk sac and subsequent broiler live performance. Broiler hatching eggs were obtained from a commercial flock at 55 wk of age and were stored for 2 d at 18°C and 75% relative humidity (RH) prior to incubation. Chicks were identified as hatching Early (471-477 h), Middle (480-486 h), and Late (494-510 h). All chicks were removed from the trays at 510 h of incubation. Body weight (BW) and yolk weight (YW) were determined at emergence from the shell (initial hatch time) and at placement on feed. Chicks were permanently identified by hatch time with neck tags, feather sexed, weighed, and introduced to feed and water in litter floor pens. Broiler BW and feed consumption (FC) were then determined 7, 21, and 35 d of age. Mortality was recorded daily. Although the percentage of the yolk was similar at hatch time, it was less in Early compared to Middle and Late chicks at placement (p≤0.05). Broiler chick BW was greater at placement in Late chicks compared to Early and Middle chicks (p≤0.05) but this advantage disappeared by 7 d because Late chicks consumed less feed to 7 d (p≤0.05). Early hatched broilers exhibited greater BW than Late with Middle broilers intermediate at 35 d (p≤0.05). There were no significant differences in feed consumption and feed conversion ratio (FCR) at 35 d of age. Additionally, late hatch chicks exhibited greater mortality. Overall, live performance of Late hatching chicks, judged by mortality and BW at 35 d, was reduced compared to Early hatching chicks.
DOI: 10.7284/105124
title: Gravimeter data collected during research cruise MGL1204 using a Bell BGM-3 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7482185
title: paper：two tapered bodies

description: The data of a paper.
DOI: 10.6084/m9.figshare.7482185.v1
title: paper：two tapered bodies

description: The data of a paper.
DOI: 10.6084/m9.figshare.7482188.v1
title: Reinforcement design of concrete sections based on the arc-length method

description: Abstract The reinforcement design of concrete cross-sections with the parabola-rectangle diagram is a well-established model. A global limit analysis, considering geometrical and material nonlinear behavior, demands a constitutive relationship that better describes concrete behavior. The Sargin curve from the CEB-FIP model code, which is defined from the modulus of elasticity at the origin and the peak point, represents the descending branch of the stress-strain relationship. This research presents a numerical method for the reinforcement design of concrete cross-sections based on the arc length process. This method is numerically efficient in the descending branch of the Sargin curve, where other processes present convergence problems. The examples discuss the reinforcement design of concrete sections based on the parabola-rectangle diagram and the Sargin curve using the design parameters of the local and global models, respectively.
DOI: 10.6084/m9.figshare.7482179
title: Table S1.xlsx

description: Major elements data of Site U1456 since 3.8 Ma
DOI: 10.6084/m9.figshare.7482182
title: data1

description: test
DOI: 10.6084/m9.figshare.7482182.v1
title: data1

description: test
DOI: 10.7284/105117
title: Gyrocompass data collected during research cruise MGL1204 using a Kongsberg Simrad GC80 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105116
title: INS (Inertial Navigation System) data collected during research cruise MGL1204 using a Kongsberg Seapath 200 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105125
title: INS (Inertial Navigation System) data collected during research cruise MGL1204 using a Applanix POS/MV-320 V3 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105113
title: Magnetometer data collected during research cruise MGL1204 using a Geometrics G-882 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7482128
title: CRONICAS Cohort Study | Baseline | Multimorbidity

description: The <b>CRONICAS Cohort Study</b> is a longitudinal study performed in four Peruvian settings that differ by degree of urbanisation, level of outdoor and indoor pollution, and altitude.<br>The <b>CRONICAS Cohort Study protocol and variables</b> are described in full in:<br>Miranda JJ, Bernabe-Ortiz A, Smeeth L, Gilman RH, Checkley W; CRONICAS Cohort Study Group. Addressing geographical variation in the progression of non-communicable diseases in Peru: the CRONICAS cohort study protocol. <i>BMJ Open</i> 2012;2(1):e000610.<br>In this collection we make available the <b>dataset</b> and <b>code</b> for the <b>analysis of multimorbidity</b> using the baseline data.
DOI: 10.7284/105114
title: Meteorological Station data collected during research cruise MGL1204 using a RM Young 26700 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105112
title: Multibeam Echosounder data collected during research cruise MGL1204 using a Kongsberg EM122 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7482128.v1
title: CRONICAS Cohort Study | Baseline | Multimorbidity

description: The <b>CRONICAS Cohort Study</b> is a longitudinal study performed in four Peruvian settings that differ by degree of urbanisation, level of outdoor and indoor pollution, and altitude.<br>The <b>CRONICAS Cohort Study protocol and variables</b> are described in full in:<br>Miranda JJ, Bernabe-Ortiz A, Smeeth L, Gilman RH, Checkley W; CRONICAS Cohort Study Group. Addressing geographical variation in the progression of non-communicable diseases in Peru: the CRONICAS cohort study protocol. <i>BMJ Open</i> 2012;2(1):e000610.<br>In this collection we make available the <b>dataset</b> and <b>code</b> for the <b>analysis of multimorbidity</b> using the baseline data.
DOI: 10.7284/105118
title: pCO2 (Partial Pressure of Carbon Dioxide) data collected during research cruise MGL1204 using a LDEO pCO2 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105120
title: Singlebeam Echosounder data collected during research cruise MGL1204 using a Knudsen 3260 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105119
title: TSG (Thermosalinograph) data collected during research cruise MGL1204 using a Sea-Bird SBE-21 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/107714
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise MGL1205 using a Hawaii UHDAS instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7481789
title: Supplemental table.pdf

description: Antibody validation table
DOI: 10.7284/105130
title: Expendable Probe data collected during research cruise MGL1205 using a Sippican MK21 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7481789.v1
title: Supplemental table.pdf

description: Antibody validation table
DOI: 10.6084/m9.figshare.7481171
title: Unified Noncrossing Multiple Quantile Regressions Tree

description: In this article, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the generalized, unbiased interaction detection and estimation algorithm, the quantile regression tree (QRT) method has been developed to construct a tree model for an individual quantile function. However, QRT produces different tree models across quantile levels because it estimates several QRT models separately. Furthermore, the estimated quantile functions from QRT often cross each other and consequently violate the basic properties of quantiles. This undesirable phenomenon reduces prediction accuracy and makes it difficult to interpret the resulting tree models. To overcome such limitations, we propose the unified noncrossing multiple quantile regressions tree (UNQRT) method, which constructs a common tree structure across all interesting quantile levels for better data visualization and model interpretation. Furthermore, the UNQRT estimates noncrossing multiple quantile functions simultaneously by enforcing noncrossing constraints, resulting in the improvement of prediction accuracy. The numerical results are presented to demonstrate the competitive performance of the proposed UNQRT over QRT. Supplementary materials for this article are available online.
DOI: 10.7284/105140
title: GNSS (Global Navigation Satellite System) data collected during research cruise MGL1205 using a C&C C-Nav 3050 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7480892
title: Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication

description: We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic datasets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7480892.v2
title: Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication

description: We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic datasets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.7481459.v1
title: Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models<sup>*</sup>

description: A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood by matching summary statistics for the observed data with statistics computed from data simulated from the true process, based on parameter draws from the prior. Draws that produce a ‘match’ between observed and simulated summaries are retained, and used to estimate the inaccessible posterior. With no reduction to a low-dimensional set ofsufficient statistics being possible in the state space setting, we define the summaries as the maximum of an auxiliary likelihood function, and thereby exploit the asymptotic sufficiency of this estimator for the auxiliary parameter vector. We derive conditions under which this approach - including a computationally efficient version based on the auxiliary score - achieves Bayesian consistency. To reduce the well-documented inaccuracy of ABC in multi-parameter settings, we propose the separate treatment of each parameter dimension using an integrated likelihood technique. Three stochastic volatility models for which exact Bayesian inference is either computationally challenging, or infeasible, are used for illustration. We demonstrate that our approach compares favorably against an extensive set of approximate and exact comparators. An empirical illustration completes the paper.
DOI: 10.6084/m9.figshare.7480892.v1
title: Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication

description: We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information.
DOI: 10.6084/m9.figshare.7481171.v1
title: Unified Non-crossing Multiple Quantile Regressions Tree

description: In this paper, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the GUIDE algorithm, the quantile regression tree (QRT) method has been developed to construct a tree model for an individual quantile function. However, QRT produces different tree models across quantile levels because it estimates several quantile regression tree models separately. Furthermore, the estimated quantile functions from QRT often cross each other and consequently violate the basic properties of quantiles. This undesirable phenomenon reduces prediction accuracy and makes it difficult to interpret the resulting tree models. To overcome such limitations, we propose the unified non-crossing multiple quantile regressions tree (UNQRT) method, which constructs a common tree structure across all interesting quantile levels for better data visualization and model interpretation. Furthermore, the UNQRT estimates non-crossing multiple quantile functions simultaneously by enforcing non-crossing constraints, resulting in the improvement of prediction accuracy. The numerical results are presented to demonstrate the competitive performance of the proposed UNQRT over QRT.
DOI: 10.7284/105142
title: GNSS (Global Navigation Satellite System) data collected during research cruise MGL1205 using a C&C C-Nav 2000 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7481171.v2
title: Unified Noncrossing Multiple Quantile Regressions Tree

description: In this article, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the generalized, unbiased interaction detection and estimation algorithm, the quantile regression tree (QRT) method has been developed to construct a tree model for an individual quantile function. However, QRT produces different tree models across quantile levels because it estimates several QRT models separately. Furthermore, the estimated quantile functions from QRT often cross each other and consequently violate the basic properties of quantiles. This undesirable phenomenon reduces prediction accuracy and makes it difficult to interpret the resulting tree models. To overcome such limitations, we propose the unified noncrossing multiple quantile regressions tree (UNQRT) method, which constructs a common tree structure across all interesting quantile levels for better data visualization and model interpretation. Furthermore, the UNQRT estimates noncrossing multiple quantile functions simultaneously by enforcing noncrossing constraints, resulting in the improvement of prediction accuracy. The numerical results are presented to demonstrate the competitive performance of the proposed UNQRT over QRT. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7481171.v3
title: Unified Noncrossing Multiple Quantile Regressions Tree

description: In this article, we consider the estimation problem of a tree model for multiple conditional quantile functions of the response. Using the generalized, unbiased interaction detection and estimation algorithm, the quantile regression tree (QRT) method has been developed to construct a tree model for an individual quantile function. However, QRT produces different tree models across quantile levels because it estimates several QRT models separately. Furthermore, the estimated quantile functions from QRT often cross each other and consequently violate the basic properties of quantiles. This undesirable phenomenon reduces prediction accuracy and makes it difficult to interpret the resulting tree models. To overcome such limitations, we propose the unified noncrossing multiple quantile regressions tree (UNQRT) method, which constructs a common tree structure across all interesting quantile levels for better data visualization and model interpretation. Furthermore, the UNQRT estimates noncrossing multiple quantile functions simultaneously by enforcing noncrossing constraints, resulting in the improvement of prediction accuracy. The numerical results are presented to demonstrate the competitive performance of the proposed UNQRT over QRT. Supplementary materials for this article are available online.
DOI: 10.7284/105143
title: Gravimeter data collected during research cruise MGL1205 using a Bell BGM-3 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/105136
title: Gyrocompass data collected during research cruise MGL1205 using a Kongsberg Simrad GC80 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7480232
title: Magnetic Resonance Imaging of Mouse Brain Networks Plasticity Following Motor Learning

description: <i>In vivo MRI was performed twice (time interval ~25 days) on a 9.4 Tesla vertical bore NMR spectrometer (Bruker Biospin, Billerica, MA) using a 20 mm diameter volume coil as the radiofrequency transmitter and receiver. Images were acquired using a three-dimensional (3D) T2-weighted fast spin echo sequence (FSE) with: echo time (TE)/repetition time (TR) = 40/700 ms, 16 mm x 16 mm x 16 mm field of view, and 128 x 128 x 80 matrix (resolution = 0.125 mm × 0.125 mm × 0.2 mm, echo train length = 4, number of average = 2, flip angle = 40°, bandwidth (BW)=100 kHz).</i> <i>Ex vivo MRI-DTI of brain specimens was performed on the same 9.4 T NMR spectrometer and a 15 mm diameter volume coil. Images were acquired using a diffusion weighted 3D gradient and spin echo sequence with: TE = 33 ms, TR = 900 ms, BW= 100 kHz, and 4 averages. The FOV and matrix size were 13.0 mm x 10.0 mm x 18.4 mm and 128 x 96 x 180, resulting in a native resolution of 0.1 mm x 0.1 mm x 0.1 mm. 6 diffusion-weighted images (b value =1700 s/mm2) and 2 non-diffusion-weighted images were acquired.</i>
DOI: 10.6084/m9.figshare.7480232.v1
title: Magnetic Resonance Imaging of Mouse Brain Networks Plasticity Following Motor Learning

description: <i>In vivo MRI was performed twice (time interval ~25 days) on a 9.4 Tesla vertical bore NMR spectrometer (Bruker Biospin, Billerica, MA) using a 20 mm diameter volume coil as the radiofrequency transmitter and receiver. Images were acquired using a three-dimensional (3D) T2-weighted fast spin echo sequence (FSE) with: echo time (TE)/repetition time (TR) = 40/700 ms, 16 mm x 16 mm x 16 mm field of view, and 128 x 128 x 80 matrix (resolution = 0.125 mm × 0.125 mm × 0.2 mm, echo train length = 4, number of average = 2, flip angle = 40°, bandwidth (BW)=100 kHz).</i> <i>Ex vivo MRI-DTI of brain specimens was performed on the same 9.4 T NMR spectrometer and a 15 mm diameter volume coil. Images were acquired using a diffusion weighted 3D gradient and spin echo sequence with: TE = 33 ms, TR = 900 ms, BW= 100 kHz, and 4 averages. The FOV and matrix size were 13.0 mm x 10.0 mm x 18.4 mm and 128 x 96 x 180, resulting in a native resolution of 0.1 mm x 0.1 mm x 0.1 mm. 6 diffusion-weighted images (b value =1700 s/mm2) and 2 non-diffusion-weighted images were acquired.</i>
DOI: 10.7284/105135
title: INS (Inertial Navigation System) data collected during research cruise MGL1205 using a Kongsberg Seapath 200 instrument system onboard the platform RV Marcus G. Langseth

description: Original (unprocessed) data collected during a Field_expedition