cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16632580
title: Matching One Sample According to Two Criteria in Observational Studies

description: Multivariate matching has two goals (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneous in a few key covariates. When there are only a few binary covariates, both goals may be achieved by matching exactly for these few covariates. Commonly, however, there are many covariates, so goals (i) and (ii) come apart, and must be achieved by different means. As is also true in a randomized experiment, similar distributions can be achieved for a high-dimensional covariate, but close pairs can be achieved for only a few covariates. We introduce a new polynomial-time method for achieving both goals that substantially generalizes several existing methods; in particular, it can minimize the earthmover distance between two marginal distributions. The method involves minimum cost flow optimization in a network built around a tripartite graph, unlike the usual network built around a bipartite graph. In the tripartite graph, treated subjects appear twice, on the far left and the far right, with controls sandwiched between them, and efforts to balance covariates are represented on the right, while efforts to find close individual pairs are represented on the left. In this way, the two efforts may be pursued simultaneously without conflict. The method is applied to our on-going study in the Medicare population of the relationship between superior nursing and sepsis mortality. The match2C package in R implements the method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16649146.v1
title: Saddlepoint approximations for spatial panel data models

description: We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. Our saddlepoint density and tail area approximation feature <i>relative error</i> of order O(1/(n(T−1))) with <i>n</i> being the cross-sectional dimension and <i>T</i> the time-series dimension. The main theoretical tool is the tilted-Edgeworth technique in a non-identically distributed setting. The density approximation is always non-negative, does not need resampling, and is accurate in the tails. Monte Carlo experiments on density approximation and testing in the presence of nuisance parameters illustrate the good performance of our approximation over first-order asymptotics and Edgeworth expansion. An empirical application to the investment-saving relationship in OECD (Organisation for Economic Co-operation and Development) countries shows disagreement between testing results based on first-order asymptotics and saddlepoint techniques.
DOI: 10.6084/m9.figshare.17033074
title: GEE-Assisted Variable Selection for Latent Variable Models with Multivariate Binary Data

description: Multivariate data are commonly analyzed using one of two approaches: a conditional approach based on generalized linear latent variable models (GLLVMs) or some variation thereof, and a marginal approach based on generalized estimating equations (GEEs). With research on mixed models and GEEs having gone down separate paths, there is a common mindset to treat the two approaches as mutually exclusive, with which to use driven by the question of interest. In this article, focusing on multivariate binary responses, we study the connections between the parameters from conditional and marginal models, with the aim of using GEEs for fast variable selection in GLLVMs. This is accomplished through two main contributions. First, we show that GEEs are zero consistent for GLLVMs fitted to multivariate binary data. That is, if the true model is a GLLVM but we misspecify and fit GEEs, then the latter is able to asymptotically differentiate between truly zero versus nonzero coefficients in the former. Building on this result, we propose GEE-assisted variable selection for GLLVMs using score- and Wald-based information criteria to construct a fast forward selection path followed by pruning. We demonstrate GEE-assisted variable selection is selection consistent for the underlying GLLVM, with simulation studies demonstrating its strong finite sample performance and computational efficiency.
DOI: 10.6084/m9.figshare.16632580.v2
title: Matching One Sample According to Two Criteria in Observational Studies

description: Multivariate matching has two goals (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneous in a few key covariates. When there are only a few binary covariates, both goals may be achieved by matching exactly for these few covariates. Commonly, however, there are many covariates, so goals (i) and (ii) come apart, and must be achieved by different means. As is also true in a randomized experiment, similar distributions can be achieved for a high-dimensional covariate, but close pairs can be achieved for only a few covariates. We introduce a new polynomial-time method for achieving both goals that substantially generalizes several existing methods; in particular, it can minimize the earthmover distance between two marginal distributions. The method involves minimum cost flow optimization in a network built around a tripartite graph, unlike the usual network built around a bipartite graph. In the tripartite graph, treated subjects appear twice, on the far left and the far right, with controls sandwiched between them, and efforts to balance covariates are represented on the right, while efforts to find close individual pairs are represented on the left. In this way, the two efforts may be pursued simultaneously without conflict. The method is applied to our on-going study in the Medicare population of the relationship between superior nursing and sepsis mortality. The match2C package in R implements the method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16632580.v1
title: Matching One Sample According to Two Criteria in Observational Studies

description: Multivariate matching has two goals: (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneous in a few key covariates. When there are only a few binary covariates, both goals may be achieved by matching exactly for these few covariates. Commonly, however, there are many covariates, so goals (i) and (ii) come apart, and must be achieved by different means. As is also true in a randomized experiment, similar distributions can be achieved for a high-dimensional covariate, but close pairs can be achieved for only a few covariates. We introduce a new polynomial-time method for achieving both goals that substantially generalizes several existing methods; in particular, it can minimize the earthmover distance between two marginal distributions. The method involves minimum cost flow optimization in a network built around a tripartite graph, unlike the usual network built around a bipartite graph. In the tripartite graph, treated subjects appear twice, on the far left and the far right, with controls sandwiched between them, and efforts to balance covariates are represented on the right, while efforts to find close individual pairs are represented on the left. In this way, the two efforts may be pursued simultaneously without conflict. The method is applied to our on-going study in the Medicare population of the relationship between superior nursing and sepsis mortality. The match2C package in R implements the method.
DOI: 10.5281/zenodo.5707672
title: Irregular data for testing non-tabular data reading and parsing

description: key1#value1,value2,...,valueN key2#value1,value2,...,valueM Generated with gen_confus.jl from https://github.com/abelsiqueira/call-julia-from-python-experiments
DOI: 10.5281/zenodo.5707671
title: Irregular data for testing non-tabular data reading and parsing

description: key1#value1,value2,...,valueN key2#value1,value2,...,valueM Generated with gen_confus.jl from https://github.com/abelsiqueira/call-julia-from-python-experiments
DOI: 10.5281/zenodo.5703876
title: Impact of Community Masking on COVID-19: A Cluster-Randomized Trial in Bangladesh

description: We ran a randomized trial of mask promotion in Bangladesh; the intervention increased mask-use and reduced symptomatic SARS-CoV-2 infections.
DOI: 10.5281/zenodo.5703875
title: Impact of Community Masking on COVID-19: A Cluster-Randomized Trial in Bangladesh

description: We ran a randomized trial of mask promotion in Bangladesh; the intervention increased mask-use and reduced symptomatic SARS-CoV-2 infections.
DOI: 10.18430/m37rhe
title: X-ray diffraction data for the crystal Structure of ROK family protein from Burkholderia vietnamiensis G4 (7RHE)
DOI: 10.5281/zenodo.5708235
title: msd-errors - Data for diffusion coefficient and offset figures

description: Data for a figure that shows the distributions of the mean and varance of the diffusion coefficient and offset that are found from the analysis of 1024 unique random walks using kinisi. This is performed for both a truely random walk and a temporally anti-correlated random walk.<br><br>Created using showyourwork from this GitHub repo.
DOI: 10.5281/zenodo.5680385
title: Behavioral account of attended stream enhancement for neural tracking

description: This dataset provides material and data associated with Huet, Micheyl, Parizet, Gaudrain (<strong>2021</strong>) "Behavioral account of attended stream enhances neural tracking", <em>Frontiers in Neuroscience</em>, doi: 10.3389/fnins.2021.674112. The current version of the repository contains only the audio data (temporal envelopes) and behavioral data. The EEG data will be added as it is curated.
DOI: 10.5281/zenodo.5680384
title: Behavioral account of attended stream enhancement for neural tracking

description: This dataset provides material and data associated with Huet, Micheyl, Parizet, Gaudrain (<strong>2021</strong>) "Behavioral account of attended stream enhances neural tracking", <em>Frontiers in Neuroscience</em>, doi: 10.3389/fnins.2021.674112. The current version of the repository contains only the audio data (temporal envelopes) and behavioral data. The EEG data will be added as it is curated.
DOI: 10.5281/zenodo.5708240
title: msd-errors - Data for scaling figures

description: Data for a figure that shows the scaling of uncertainty as a function of simulation length and number of atoms and step size, for both true and anitcorrelated random walks. This compares the kinisi approach (on just 32 unique walks) with the numerical values from (1024 unique walks). <br><br>Created using showyourwork from this GitHub repo.
DOI: 10.5287/bodleian:gazqvapa4
title: Dataset of confirmed Hospital for Tropical Diseases, HCMC Dengue patients
DOI: 10.5281/zenodo.5708190
title: Sisor rabdophorus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5708189
title: Sisor rabdophorus SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.13413581.v1
title: Rice yields and the effect of weed management in an organic production system with winter flooding

description: Winter flooding of paddy rice production is characterized by flooding of paddy fields during the winter fallow season. It has attracted attention in terms of its ecological function, supporting the biodiversity of paddy field ecosystems. Therefore, recent winter flooding in Japan has been conducted by organic farming. However, it is not well documented how organic farming with winter flooding affect the productivity of rice. We conducted field experiments to examine changes in rice yield in organic farming with winter flooding compared with those in conventional and organic farming over the period of 5 and 9 years. The yields in organic farming with winter flooding were lower than those in conventional farming but were almost the same as those in organic farming. We found a trend that shows the relative yield of organic farming with winter flooding compared to that of conventional farming increased during the first several years after the conversion from conventional farming to organic farming with winter flooding, but that it decreased after several years. This trend may be caused by the flourishing of weeds during continuous organic farming with winter flooding. Therefore, we tested four weed control methods: rice bran application, mechanical weeding, repeated puddling, and side-row fertilizer application. Although each method suppressed weeds to certain extents, some combinations of different weeding methods more effectively suppressed weed growth, thereby resulting in enhanced rice yields. We found a trend in rice yield and a possibility of combining several weed control methods in organic farming with winter flooding.
DOI: 10.6084/m9.figshare.14045819
title: Genetic control of root architectural traits in KDML105 chromosome segment substitution lines under well-watered and drought stress conditions

description: Drought is a major constraint in rainfed rice production and root architectural traits are important breeding targets for improving productivity under drought stress. A set of chromosome segment substitution lines (KDML105-CSSLs) and KDML105 were grown in the wet season at two sites (Rice Gene Discovery (RGD) and Ubon Ratchatani Rice Research Center (URRC)) in Thailand under well-watered (WW) and drought stress (DS) treatments. RGD is characterized by having a heavy clay soil type while URRC’s soil has a high percentage of sand and characterized by infertility. Root architecture traits varied within the population at both sites and exhibited plasticity in response to drought as affected by location by water regime interaction. Lateral root density increased by 77% with drought at RGD but decreased by 18% at URRC. The proportion of nodal roots that elongated more vertically increased under drought stress by 21%, at RGD. Root number per tiller was negatively associated with tiller number and biomass at RGD under drought, while lateral root density was negatively associated with biomass under drought at URRC. Eight QTL were identified for the number of nodal roots per tiller, lateral root density, and nodal root growth angle. Several candidate genes were identified by annotating the genes within the QTL regions. Our study presented genetic insights into root architectural traits with potential use in rice breeding programs for drought tolerance.
DOI: 10.6084/m9.figshare.13685787
title: Evaluation of APSIM-wheat to simulate the response of yield and grain protein content to nitrogen application on an Andosol in Japan

description: The self-sufficiency ratio and national average yield of wheat are low in Japan. Reducing the yield gap and receiving the government subsidy for grain quality are vital strategies for profitability. Elucidating optimum nitrogen application scheme is awaited to attain both higher yield and appropriate grain protein content (GPC) for wheat cultivation in Japan. Such decision support can be realized by integrating field experimental knowledge to crop growth models, although they have scarcely been utilized for wheat production in Japan. Therefore, the purposes of this study were to apply a widely used crop growth model (APSIM) to wheat growth on an Andosol in the Kanto region in Japan by calibration and validation. Selected model parameters of APSIM-wheat for phenology, leaf growth, and grain formation were readjusted based on the phenology and growth data of soft and hard wheat cultivars. Then the model was validated by using similar variables obtained in an independent experiment. For the simulation of the optimum sowing for winter wheat in the Kanto area (November), the root mean square error for grain yield was 23 and 48 g m<sup>−2</sup> for Ayahikari and Yumeshiho varieties, respectively, and that for GPC was 1.9 and 1.4%. Thus, the overall model performance was acceptable for optimum sowing. However, grain yield and dry matter production were significantly overestimated when the data of late sowing groups were included. Therefore, further model improvement was suggested to add an algorithm to reduce the number of emerged plants under cold temperature in late sowing conditions.
DOI: 10.6084/m9.figshare.13685787.v2
title: Evaluation of APSIM-wheat to simulate the response of yield and grain protein content to nitrogen application on an Andosol in Japan

description: The self-sufficiency ratio and national average yield of wheat are low in Japan. Reducing the yield gap and receiving the government subsidy for grain quality are vital strategies for profitability. Elucidating optimum nitrogen application scheme is awaited to attain both higher yield and appropriate grain protein content (GPC) for wheat cultivation in Japan. Such decision support can be realized by integrating field experimental knowledge to crop growth models, although they have scarcely been utilized for wheat production in Japan. Therefore, the purposes of this study were to apply a widely used crop growth model (APSIM) to wheat growth on an Andosol in the Kanto region in Japan by calibration and validation. Selected model parameters of APSIM-wheat for phenology, leaf growth, and grain formation were readjusted based on the phenology and growth data of soft and hard wheat cultivars. Then the model was validated by using similar variables obtained in an independent experiment. For the simulation of the optimum sowing for winter wheat in the Kanto area (November), the root mean square error for grain yield was 23 and 48 g m<sup>−2</sup> for Ayahikari and Yumeshiho varieties, respectively, and that for GPC was 1.9 and 1.4%. Thus, the overall model performance was acceptable for optimum sowing. However, grain yield and dry matter production were significantly overestimated when the data of late sowing groups were included. Therefore, further model improvement was suggested to add an algorithm to reduce the number of emerged plants under cold temperature in late sowing conditions.
DOI: 10.6084/m9.figshare.13413581
title: Rice yields and the effect of weed management in an organic production system with winter flooding

description: Winter flooding of paddy rice production is characterized by flooding of paddy fields during the winter fallow season. It has attracted attention in terms of its ecological function, supporting the biodiversity of paddy field ecosystems. Therefore, recent winter flooding in Japan has been conducted by organic farming. However, it is not well documented how organic farming with winter flooding affect the productivity of rice. We conducted field experiments to examine changes in rice yield in organic farming with winter flooding compared with those in conventional and organic farming over the period of 5 and 9 years. The yields in organic farming with winter flooding were lower than those in conventional farming but were almost the same as those in organic farming. We found a trend that shows the relative yield of organic farming with winter flooding compared to that of conventional farming increased during the first several years after the conversion from conventional farming to organic farming with winter flooding, but that it decreased after several years. This trend may be caused by the flourishing of weeds during continuous organic farming with winter flooding. Therefore, we tested four weed control methods: rice bran application, mechanical weeding, repeated puddling, and side-row fertilizer application. Although each method suppressed weeds to certain extents, some combinations of different weeding methods more effectively suppressed weed growth, thereby resulting in enhanced rice yields. We found a trend in rice yield and a possibility of combining several weed control methods in organic farming with winter flooding.
DOI: 10.6084/m9.figshare.13553359.v1
title: Accelerated senescence and nitrogen remobilization in flag leaves enhance nitrogen use efficiency in soft red winter wheat

description: Wheat (<i>Triticum aestivum</i> L.) production requires a large amount of nitrogen (N) supply because growers aim to achieve high grain yield and appropriate grain protein content simultaneously. A comprehensive understanding of the mechanisms that underpin efficient N usage at limited N in wheat can facilitate the development of new N-saving varieties in this major crop. Here, we performed comparative analysis of flag leaf responses to N availability in soft red winter wheat with contrasting N use efficiency (NUE); VA08MAS-369 (high NUE) and VA07W-415 (low NUE). This study demonstrated that accelerated senescence along with enhanced breakdown of protein and starch in flag leaves was correlated with higher grain yield, NUE for grain yield, and NUE for grain protein under N limitation. The more dramatic reductions in flag leaf N compounds and carbohydrate reserves in VA08MAS-369 were linked with significantly elevated expression of genes and enzymes associated with these metabolic pathways in this high NUE genotype. Consistent with the gene expression data, nitrate reductase, glutamine synthetase, and NAD-dependent glutamate dehydrogenase activities were highly induced under limited N in VA08MAS-369. It was previously reported that accelerated senescence contributes to increased grain protein content in wheat under regular N supply. This study provides molecular and physiological evidence that vigorous senescence and N remobilization also benefit grain yield under N deprivation.
DOI: 10.6084/m9.figshare.14045819.v1
title: Genetic control of root architectural traits in KDML105 chromosome segment substitution lines under well-watered and drought stress conditions

description: Drought is a major constraint in rainfed rice production and root architectural traits are important breeding targets for improving productivity under drought stress. A set of chromosome segment substitution lines (KDML105-CSSLs) and KDML105 were grown in the wet season at two sites (Rice Gene Discovery (RGD) and Ubon Ratchatani Rice Research Center (URRC)) in Thailand under well-watered (WW) and drought stress (DS) treatments. RGD is characterized by having a heavy clay soil type while URRC’s soil has a high percentage of sand and characterized by infertility. Root architecture traits varied within the population at both sites and exhibited plasticity in response to drought as affected by location by water regime interaction. Lateral root density increased by 77% with drought at RGD but decreased by 18% at URRC. The proportion of nodal roots that elongated more vertically increased under drought stress by 21%, at RGD. Root number per tiller was negatively associated with tiller number and biomass at RGD under drought, while lateral root density was negatively associated with biomass under drought at URRC. Eight QTL were identified for the number of nodal roots per tiller, lateral root density, and nodal root growth angle. Several candidate genes were identified by annotating the genes within the QTL regions. Our study presented genetic insights into root architectural traits with potential use in rice breeding programs for drought tolerance.