cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24937155.v1
title: Five *.pdf files with the 13C NMR data for the five dissolved organic matter investigated. N-DOM (DOM in the Negro River), S-DOM (DOM in the Solimões River), A-DOM (DOM in the Amazonas River), T-DOM (DOM in the Tapajós River), B-DOM (DOM in the boreal lakes)

description: These files comprise 13C NMR section integrals for the five DOM, divided into buckets of 0.1 ppm width, and ranging from δC = 0 – 235 ppm. The total integral of these 13C NMR spectra is area normalized to Call = 100% for all five DOM, and to CH0123 according to its proportions. The 13C NMR section integral from δC = 47 - 51 ppm is set to zero for all five 13C NMR spectra of DOM, because of interference from the solvent 12CD3OD, which resonates at δC = 49.0 ppm.A Bruker Avance III spectrometer and TopSpin 3.6/PL6 software were used to acquire 13C nuclear magnetic resonance (NMR) spectra of re-dissolved DOM (10-40 mg solid SPE-DOM in typically 75-135 µL CD3OD (99.95 % 2H; 13C-depleted 12CD3OD, Aldrich, Steinheim, Germany). Briefly, the re-dissolved DOM were transferred to 2.5-3.0 mm Bruker Match tubes and sealed. A cryogenic classical geometry 5 mm z-gradient 13C, 1H probe (B0 = 11.7 T) was used for acquisition of 13C NMR spectra. Transmitter pulses were at ~10 µs for 1H and 13C, and calibrated 90/180-degree pulses were employed for each sample.We used inverse-gated 1H decoupling for 13C NMR spectra to eliminate nOe effects (nuclear Overhauser effects), and (acquisition-time adjusted) linear combinations of the 13C DEPT-45, -135 and -90 NMR spectra (1JCH: 150 Hz) to compute the individual traces of CH (13C DEPT-90 NMR spectrum), CH2 (13C DEPT-45 minus 13C DEPT-135) and CH3 ([13C DEPT-45 plus 13C DEPT-135] minus 13C DEPT-90). We corrected the 13C DEPT-90 NMR spectrum by subtracting an appropriate amount (commonly ~2-3%) of the 13C DEPT-45 NMR spectrum to attenuate leakage of CH3 and CH2 into the 13C DEPT-90 NMR spectrum (methine carbon (CH) in DOM does not show appreciable 13C NMR resonances at δC &lt; 20 ppm) that arises from the unavoidable variance in 1JCH of DOM. Then we determined the relative contributions of the individual spectra (CH3, CH2, CH1) to the sum CH123 as observed in 13C DEPT-45 NMR spectra with recognition of the individual transfer amplitudes which were as follows (CH3 = 1.06; CH2 = 1.0; CH = 0.707). The proportions of quaternary carbon atoms Cq in DOM were computed from comparison of 13C DEPT-45, 13C QUAT, and single pulse 13C NMR spectra.13C NMR overlay figures were computed using the Bruker AMIX software (version 3.9.4) from area normalized spectra with 0.1 ppm buckets and 100% total NMR integral area from δC: 0-235 ppm, with exclusion of the solvent 12CD3OD, which resonates at δC = 49.0 ppm.
DOI: 10.6084/m9.figshare.24937155
title: Five *.pdf files with the 13C NMR data for the five dissolved organic matter investigated. N-DOM (DOM in the Negro River), S-DOM (DOM in the Solimões River), A-DOM (DOM in the Amazonas River), T-DOM (DOM in the Tapajós River), B-DOM (DOM in the boreal lakes)

description: These files comprise 13C NMR section integrals for the five DOM, divided into buckets of 0.1 ppm width, and ranging from δC = 0 – 235 ppm. The total integral of these 13C NMR spectra is area normalized to Call = 100% for all five DOM, and to CH0123 according to its proportions. The 13C NMR section integral from δC = 47 - 51 ppm is set to zero for all five 13C NMR spectra of DOM, because of interference from the solvent 12CD3OD, which resonates at δC = 49.0 ppm.A Bruker Avance III spectrometer and TopSpin 3.6/PL6 software were used to acquire 13C nuclear magnetic resonance (NMR) spectra of re-dissolved DOM (10-40 mg solid SPE-DOM in typically 75-135 µL CD3OD (99.95 % 2H; 13C-depleted 12CD3OD, Aldrich, Steinheim, Germany). Briefly, the re-dissolved DOM were transferred to 2.5-3.0 mm Bruker Match tubes and sealed. A cryogenic classical geometry 5 mm z-gradient 13C, 1H probe (B0 = 11.7 T) was used for acquisition of 13C NMR spectra. Transmitter pulses were at ~10 µs for 1H and 13C, and calibrated 90/180-degree pulses were employed for each sample.We used inverse-gated 1H decoupling for 13C NMR spectra to eliminate nOe effects (nuclear Overhauser effects), and (acquisition-time adjusted) linear combinations of the 13C DEPT-45, -135 and -90 NMR spectra (1JCH: 150 Hz) to compute the individual traces of CH (13C DEPT-90 NMR spectrum), CH2 (13C DEPT-45 minus 13C DEPT-135) and CH3 ([13C DEPT-45 plus 13C DEPT-135] minus 13C DEPT-90). We corrected the 13C DEPT-90 NMR spectrum by subtracting an appropriate amount (commonly ~2-3%) of the 13C DEPT-45 NMR spectrum to attenuate leakage of CH3 and CH2 into the 13C DEPT-90 NMR spectrum (methine carbon (CH) in DOM does not show appreciable 13C NMR resonances at δC &lt; 20 ppm) that arises from the unavoidable variance in 1JCH of DOM. Then we determined the relative contributions of the individual spectra (CH3, CH2, CH1) to the sum CH123 as observed in 13C DEPT-45 NMR spectra with recognition of the individual transfer amplitudes which were as follows (CH3 = 1.06; CH2 = 1.0; CH = 0.707). The proportions of quaternary carbon atoms Cq in DOM were computed from comparison of 13C DEPT-45, 13C QUAT, and single pulse 13C NMR spectra.13C NMR overlay figures were computed using the Bruker AMIX software (version 3.9.4) from area normalized spectra with 0.1 ppm buckets and 100% total NMR integral area from δC: 0-235 ppm, with exclusion of the solvent 12CD3OD, which resonates at δC = 49.0 ppm.
DOI: 10.17632/k4bfxkfytn.1
title: Meals in Malls: Antecedents of Shoppers’ Choice and Behavioural Intentions While Dining at Food Courts

description: A summary of measure of central tendency, skewness, kurtosis, reliability, validity, and model fit of the study titled "Meals in Malls: Antecedents of Shoppers’ Choice and Behavioural Intentions While Dining at Food Courts"
DOI: 10.17632/gk4rhcjgyf.1
title: A Comparative Investigation into the Impact of Directionality on Human Translation and Post-Editing Effort

description: This data supports the study "A Comparative Investigation into the Impact of Directionality on Human Translation and Post-Editing Effort".
DOI: 10.17632/wg99dt6d9k.1
title: settlement of the embankment

description: The database includes 500 analysis results of settlement of the embankment in different designs: the embankment height, the peat thickness, the CMC spacing, and the CMC diameter.
DOI: 10.6084/m9.figshare.24937221
title: Goatpox Vaccine India and Sheeppox Vaccine India Strains

description: The sequences of goatpox vaccine stain and sheeppox vaccine strains were sequenced by the Gujarat Biotechnology Research Centre, Gandhinagar.
DOI: 10.6084/m9.figshare.24937221.v1
title: Goatpox Vaccine India and Sheeppox Vaccine India Strains

description: The sequences of goatpox vaccine stain and sheeppox vaccine strains were sequenced by the Gujarat Biotechnology Research Centre, Gandhinagar.
DOI: 10.17632/5nz98k8c97.1
title: Exploring the Impact of Internship Experiences on Job-Seeking Anxiety Among Undergraduate Students Majoring in Product Design

description: Exploring the Impact of Internship Experiences on Job-Seeking Anxiety Among Undergraduate Students Majoring in Product Design
DOI: 10.17632/9hs9dhrzm3.1
title: Skin color of Chinese women across different regions of China: An analysis based on both individual typology angle and hue angle

description: Original data of the paper "Skin color of Chinese women across different regions of China: An analysis based on both individual typology angle and hue angle"
DOI: 10.17632/r3pwvjdfvh.1
title: Explaining Long-Term Bond Yields Synchronization Dynamics in Europe - Replication Package

description: This repository contains all the data files and codes to reproduce the results of our paper "Explaining Long-Term Bond Yields Synchronization
Dynamics in Europe" by Jesús Crespo Cuaresma and Oscar Fernandez, submitted to the journal Economic Modelling.
DOI: 10.17632/67zv95bzt8.1
title: PRIMITIVE PADDY VARITIES OF ODISHA, INDIA

description: Ten Varieties of Primitive Paddy like Goswami, Jamuna, Jamut, Mamata, Pooja, Rambanbas, Sanghmitra, Sanjeevani, Sadhana and Sannasriya are shared.
Please cite 
N.K. Naik, P.K. Sethy, A.G. Devi, S.K. Behera, Few-shot learning convolutional neural network for primitive indian paddy grain identification using 2D-DWT injection and grey wolf optimizer algorithm, Journal of Agriculture and Food Research (2024), doi: https://doi.org/10.1016/j.jafr.2023.1009
DOI: 10.6084/m9.figshare.24565758
title: Fast Bayesian Record Linkage for Streaming Data Contexts

description: Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive sequentially in time and estimates of links are updated after the arrival of each file. This problem arises in settings such as longitudinal surveys, electronic health records, and online events databases, among others. The challenge in streaming record linkage is to efficiently update parameter estimates as new data arrive. We approach the problem from a Bayesian perspective with estimates calculated from posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that are faster than fitting a joint model with each new data file. In this article, we generalize a two-file Bayesian Fellegi-Sunter model to the multi-file case and propose two methods to perform streaming updates. We examine the effect of prior distribution on the resulting linkage accuracy as well as the computational tradeoffs between the methods when compared to a Gibbs sampler through simulated and real-world survey panel data. We achieve near-equivalent posterior inference at a small fraction of the compute time. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24565758.v1
title: Fast Bayesian Record Linkage for Streaming Data Contexts

description: Record linkage is the task of combining records from multiple files which refer to overlapping sets of entities when there is no unique identifying field. In streaming record linkage, files arrive sequentially in time and estimates of links are updated after the arrival of each file. This problem arises in settings such as longitudinal surveys, electronic health records, and online events databases, among others. The challenge in streaming record linkage is to efficiently update parameter estimates as new data arrive. We approach the problem from a Bayesian perspective with estimates calculated from posterior samples of parameters and present methods for updating link estimates after the arrival of a new file that are faster than fitting a joint model with each new data file. In this article, we generalize a two-file Bayesian Fellegi-Sunter model to the multi-file case and propose two methods to perform streaming updates. We examine the effect of prior distribution on the resulting linkage accuracy as well as the computational tradeoffs between the methods when compared to a Gibbs sampler through simulated and real-world survey panel data. We achieve near-equivalent posterior inference at a small fraction of the compute time. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24645422
title: Extrapolated Cross-Validation for Randomized Ensembles

description: Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This article introduces a cross-validation method, Extrapolated Cross-Validation (ECV), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields <i>δ</i>-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and <i>K</i>-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24645422.v2
title: Extrapolated Cross-Validation for Randomized Ensembles

description: Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This article introduces a cross-validation method, Extrapolated Cross-Validation (ECV), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields <i>δ</i>-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and <i>K</i>-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24645422.v1
title: Extrapolated cross-validation for randomized ensembles

description: Ensemble methods such as bagging and random forests are ubiquitous in various fields, from finance to genomics. Despite their prevalence, the question of the efficient tuning of ensemble parameters has received relatively little attention. This paper introduces a cross-validation method, ECV (Extrapolated Cross-Validation), for tuning the ensemble and subsample sizes in randomized ensembles. Our method builds on two primary ingredients: initial estimators for small ensemble sizes using out-of-bag errors and a novel risk extrapolation technique that leverages the structure of prediction risk decomposition. By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields δ-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk. Our theory accommodates general predictors, only requires mild moment assumptions, and allows for high-dimensional regimes where the feature dimension grows with the sample size. As a practical case study, we employ ECV to predict surface protein abundances from gene expressions in single-cell multiomics using random forests under a computational constraint on the maximum ensemble size. Compared to sample-split and K-fold cross-validation, ECV achieves higher accuracy by avoiding sample splitting. Meanwhile, its computational cost is considerably lower owing to the use of the risk extrapolation technique.
DOI: 10.6084/m9.figshare.24599461
title: An Adaptive Neural Network Regression Method for Structure Identification

description: This article reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function. We develop a novel penalization scheme where a concept of node impurity is introduced in the neural network framework. The node impurity in neural networks represents the homogeneity of the effects of the inputs on the node. We first define the effect of individual input on node and in turn, measure the node impurity based on the effects of inputs on node. We adopt the sum of node impurities as a penalty function whose usage makes the connections from inputs to nodes sparse, which improves estimation accuracy by reducing unnecessary complexity and enables data-adaptive structure identification. Our method takes into account of a large parameter space of the networks ranging from a fully-connected structure to sparsely connected structures. Among possible node connection structures, an optimal model is selected based purely on observed data. Numerical studies based on simulated and real datasets show that the proposed method performs well in identifying the inherent structure of the regression function and produces good estimation accuracy. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24599461.v2
title: An Adaptive Neural Network Regression Method for Structure Identification

description: This article reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function. We develop a novel penalization scheme where a concept of node impurity is introduced in the neural network framework. The node impurity in neural networks represents the homogeneity of the effects of the inputs on the node. We first define the effect of individual input on node and in turn, measure the node impurity based on the effects of inputs on node. We adopt the sum of node impurities as a penalty function whose usage makes the connections from inputs to nodes sparse, which improves estimation accuracy by reducing unnecessary complexity and enables data-adaptive structure identification. Our method takes into account of a large parameter space of the networks ranging from a fully-connected structure to sparsely connected structures. Among possible node connection structures, an optimal model is selected based purely on observed data. Numerical studies based on simulated and real datasets show that the proposed method performs well in identifying the inherent structure of the regression function and produces good estimation accuracy. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24599461.v1
title: An adaptive neural network regression method for structure identification

description: This paper reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function. We develop a novel penalization scheme where a concept of node impurity is introduced in the neural network framework. The node impurity in neural networks represents the homogeneity of the effects of the inputs on the node. We first define the effect of individual input on node and in turn, measure the node impurity based on the effects of inputs on node. We adopt the sum of node impurities as a penalty function whose usage makes the connections from inputs to nodes sparse, which improves estimation accuracy by reducing unnecessary complexity and enables data-adaptive structure identification. Our method takes into account of a large parameter space of the networks ranging from a fully-connected structure to sparsely-connected structures. Among possible node connection structures, an optimal model is selected based purely on observed data. Numerical studies based on simulated and real datasets show that the proposed method performs well in identifying the inherent structure of the regression function and produces good estimation accuracy. Supplementary materials for this paper is available online.
DOI: 10.6084/m9.figshare.24904641.v2
title: Positive training datasets for the SSBlazer model

description: SSBlazer is a pioneering tool that utilizes deep learning to predict Single Strand Break (SSB) sites based on genome-wide single-strand break sequencing data with single-nucleotide resolution.<br>Xu S, Wei J, Li Y. Genome-wide nucleotide-resolution model of single-strand break site reveals species evolutionary hierarchy[J]. arXiv preprint arXiv:2208.09813, 2022.
DOI: 10.6073/pasta/4a9c2ed67ccb6e2dd4b5ece36719d590
title: Hydrogen ion concentrations (pH) in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)

description: As part of the McMurdo Long Term Ecological Research (LTER) project in the Dry Valleys of Antarctica, hydrogen ion concentrations were monitored in various lakes of the region. An Orion portable pH meter was used to record hydrogen ion concentrations at depth specific intervals in perennial ice-covered lakes.
DOI: 10.5281/zenodo.10456097
title: Foliar disease incidence in a tropical seedling community is density dependent and varies along a regional precipitation gradient

description: 1.     Many studies identify fungal and oomycete phytopathogens as natural enemies capable of influencing plant species composition and promoting diversity in plant communities. However, little is known about how plant-pathogen interactions vary along regional abiotic gradients or with tree species characteristics, which limits our understanding of the causes of variation in tree species richness. 

2.     We surveyed 10,756 seedlings from 272 tree species for disease symptoms along a mean annual precipitation gradient in the tropical wet forests of Central Panama for three months in the early wet season (June – August) and two months in the following dry season (March - April). Over 99% of observed disease symptoms were caused by necrotrophic foliar pathogens, while less than 1% of symptoms were attributed to soilborne pathogens. Foliar disease incidence was inversely related to mean annual precipitation, a pattern which may be due to greater disease susceptibility among dry forest species. 

3.     Foliar disease incidence increased with conspecific seedling density but did not respond to the proximity of conspecific adults. Although foliar disease incidence decreased as mean annual precipitation increased, the strength of conspecific density- or distance-dependence was independent of the precipitation gradient.

4.     Seedlings of common tree species and species dispersed by non-flying mammals had a higher risk of foliar pathogen incidence. Increased disease in common species may help reduce their dominance. 

5.     Synthesis: The increases in foliar pathogen incidence with conspecific seedling density, species abundance, and dispersal mechanism indicate that foliar disease incidence is non-random and may contribute to the regulation of tropical plant communities and species coexistence. Furthermore, the relationships between foliar disease incidence, dispersal mechanism, and precipitation suggest plant-pathogen interactions could shift as a response to climate change and disruption of the disperser community.