cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27115357
title: Ethical Standards Survey Data #1

description: This dataset corresponds to the data received from the first survey
DOI: 10.15468/dl.xjka8b
title: Occurrence Download

description: A dataset containing 354 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Marlierea spruceana O.Berg"
 ]
}

 The dataset includes 354 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032203-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27115333
title: lusc_reference_regressions.rds

description: This dataset contains the extended reference regressions determined from the TCGA-LUSC dataset.
DOI: 10.6084/m9.figshare.27115333.v1
title: lusc_reference_regressions.rds

description: This dataset contains the extended reference regressions determined from the TCGA-LUSC dataset.
DOI: 10.21227/08zt-dj91
title: UNL UDE Dictionary for Indian Cooking

description: The Universal Networking Language (UNL) UDE Dictionary for Indian Cooking is a pioneering framework aimed at facilitating seamless communication and knowledge sharing across diverse languages and cultures, with a special focus on the rich culinary traditions of India. This dictionary provides a comprehensive and structured representation of essential culinary terms, ingredients, cooking techniques, and descriptors in Hindi, paired with their corresponding UNL equivalents. Each entry includes a UNL term, a definition and examples.
DOI: 10.5281/zenodo.13844569
title: Cleat Positioning Cycling Dataset
DOI: 10.5281/zenodo.13844570
title: Cleat Positioning Cycling Dataset
DOI: 10.15468/dl.w8jdrg
title: Occurrence Download

description: A dataset containing 7405 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acartia clausi Giesbrecht, 1889"
 ]
}

 The dataset includes 7405 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032205-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27115363
title: Ethical Standards Survey Data Survey#2

description: This dataset corresponds to the second survey administered.
DOI: 10.6084/m9.figshare.27115330
title: luad_reference_regressions.rds

description: This dataset contains the extended reference regressions determined from the TCGA-LUAD dataset.
DOI: 10.15468/dl.apkw2j
title: Occurrence Download

description: A dataset listing the 19 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-81.19167 46.5022,-81.19274 46.49954,-81.19375 46.49806,-81.19382 46.49795,-81.20412 46.47097,-81.20181 46.44311,-81.18714 46.41712,-81.16156 46.39555,-81.1276 46.38052,-81.0886 46.37349,-81.04837 46.37515,-81.04465 46.37572,-81.0406 46.37633,-81.03842 46.37666,-81.03348 46.37746,-81.01981 46.38023,-81.01837 46.38058,-80.99506 46.38985,-80.98411 46.39218,-80.9824 46.39273,-80.96699 46.39866,-80.96577 46.39921,-80.95546 46.40519,-80.94896 46.40734,-80.94792 46.40783,-80.91209 46.433,-80.91114 46.43401,-80.89468 46.45945,-80.89446 46.46002,-80.89002 46.48302,-80.89001 46.48626,-80.89636 46.51072,-80.89687 46.5147,-80.89784 46.51641,-80.89811 46.51744,-80.89891 46.51893,-80.90028 46.52072,-80.91015 46.53817,-80.93246 46.55813,-80.93296 46.55847,-80.95253 46.56925,-80.9557 46.57067,-80.95696 46.57125,-80.95925 46.57229,-80.99311 46.58325,-81.0301 46.58704,-81.06712 46.58334,-81.08175 46.57865,-81.08828 46.57711,-81.11983 46.56613,-81.12113 46.56551,-81.1241 46.56417,-81.14219 46.55329,-81.15181 46.54851,-81.1728 46.53094,-81.17335 46.53033,-81.18223 46.51877,-81.1828 46.51786,-81.1815 46.51746,-81.19026 46.50365,-81.19167 46.5022)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EN, VU, NT)",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "Year 2014-2024"
 ]
}

 The dataset's 19 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032201-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26808975
title: Natural Gradient Variational Bayes Without Fisher Matrix Analytic Calculation and Its Inversion

description: This article introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our approach is the avoidance of analytically computing the Fisher information matrix and its explicit inversion. Instead, we introduce an iterative procedure for generating a sequence of matrices that converge to the inverse of Fisher information. The natural gradient variational Bayes algorithm without analytic expression of the Fisher matrix and its inversion is provably convergent and achieves a convergence rate of order O( log s/s), with <i>s</i> the number of iterations. We also obtain a central limit theorem for the iterates. Implementation of our method does not require storage of large matrices, and achieves a linear complexity in the number of variational parameters. Our algorithm exhibits versatility, making it applicable across a diverse array of variational Bayes domains, including Gaussian approximation and normalizing flow Variational Bayes. We offer a range of numerical examples to demonstrate the efficiency and reliability of the proposed variational Bayes method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.26808975.v2
title: Natural Gradient Variational Bayes Without Fisher Matrix Analytic Calculation and Its Inversion

description: This article introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our approach is the avoidance of analytically computing the Fisher information matrix and its explicit inversion. Instead, we introduce an iterative procedure for generating a sequence of matrices that converge to the inverse of Fisher information. The natural gradient variational Bayes algorithm without analytic expression of the Fisher matrix and its inversion is provably convergent and achieves a convergence rate of order O( log s/s), with <i>s</i> the number of iterations. We also obtain a central limit theorem for the iterates. Implementation of our method does not require storage of large matrices, and achieves a linear complexity in the number of variational parameters. Our algorithm exhibits versatility, making it applicable across a diverse array of variational Bayes domains, including Gaussian approximation and normalizing flow Variational Bayes. We offer a range of numerical examples to demonstrate the efficiency and reliability of the proposed variational Bayes method. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.26808975.v1
title: Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion

description: This paper introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our approach is the avoidance of analytically computing the Fisher information matrix and its explicit inversion. Instead, we introduce an iterative procedure for generating a sequence of matrices that converge to the inverse of Fisher information. The natural gradient variational Bayes algorithm without analytic expression of the Fisher matrix and its inversion is provably convergent and achieves a convergence rate of order O( log s/s), with <i>s</i> the number of iterations. We also obtain a central limit theorem for the iterates. Implementation of our method does not require storage of large matrices, and achieves a linear complexity in the number of variational parameters. Our algorithm exhibits versatility, making it applicable across a diverse array of variational Bayes domains, including Gaussian approximation and normalizing flow Variational Bayes. We offer a range of numerical examples to demonstrate the efficiency and reliability of the proposed variational Bayes method.
DOI: 10.6084/m9.figshare.26617182
title: Optimal Network Membership Estimation under Severe Degree Heterogeneity

description: Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This article examines the impact of degree heterogeneity on statistical limits of network data analysis. Introducing the heterogeneity distribution (HD) under a degree-corrected mixed membership model, we show that the optimal rate of mixed membership estimation is an explicit functional of the HD. This result confirms that severe degree heterogeneity decelerates the error rate, even when the overall sparsity remains unchanged. To obtain a rate-optimal method, we modify an existing spectral algorithm, Mixed-SCORE, by adding a pre-PCA normalization step. This step normalizes the adjacency matrix by a diagonal matrix consisting of the <i>b</i>th power of node degrees, for some b∈R. We discover that <i>b</i> = 1/2 is universally favorable. The resulting spectral algorithm is rate-optimal for networks with arbitrary degree heterogeneity. A technical component in our proofs is entry-wise eigenvector analysis of the normalized graph Laplacian. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27115342
title: Supplementary Data, Table 1

description: This is data analyzing concentrations of cytokines and growth factor in specialized media as well as two sources of PRP. This dataset also examines relative concentration changes in the media after incubation with endometrial organoids
DOI: 10.6084/m9.figshare.26617182.v2
title: Optimal Network Membership Estimation under Severe Degree Heterogeneity

description: Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This article examines the impact of degree heterogeneity on statistical limits of network data analysis. Introducing the heterogeneity distribution (HD) under a degree-corrected mixed membership model, we show that the optimal rate of mixed membership estimation is an explicit functional of the HD. This result confirms that severe degree heterogeneity decelerates the error rate, even when the overall sparsity remains unchanged. To obtain a rate-optimal method, we modify an existing spectral algorithm, Mixed-SCORE, by adding a pre-PCA normalization step. This step normalizes the adjacency matrix by a diagonal matrix consisting of the <i>b</i>th power of node degrees, for some b∈R. We discover that <i>b</i> = 1/2 is universally favorable. The resulting spectral algorithm is rate-optimal for networks with arbitrary degree heterogeneity. A technical component in our proofs is entry-wise eigenvector analysis of the normalized graph Laplacian. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27115342.v2
title: Supplementary Data, Table 1

description: This is data analyzing concentrations of cytokines and growth factor in specialized media as well as two sources of PRP. This dataset also examines relative concentration changes in the media after incubation with endometrial organoids
DOI: 10.6084/m9.figshare.26617182.v1
title: Optimal Network Membership Estimation under Severe Degree Heterogeneity

description: Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This paper examines the impact of degree heterogeneity on statistical limits of network data analysis. Introducing the empirical heterogeneity distribution (EHD) under a degree-corrected mixed membership model, we show that the optimal rate of mixed membership estimation is an explicit functional of the EHD. This result confirms that severe degree heterogeneity decelerates the error rate, even when the overall sparsity remains unchanged. To obtain a rate-optimal method, we modify an existing spectral algorithm, Mixed-SCORE, by adding a pre-PCA normalization step. This step normalizes the adjacency matrix by a diagonal matrix consisting of the <i>b</i>th power of node degrees, for some b∈ℝ. We discover that <i>b</i> = 1/2 is universally favorable. The resulting spectral algorithm is rate-optimal for networks with arbitrary degree heterogeneity. A technical component in our proofs is entry-wise eigenvector analysis of the normalized graph Laplacian.
DOI: 10.6084/m9.figshare.27115342.v1
title: Supplementary Data

description: This is data analyzing concentrations of cytokines and growth factor in specialized media as well as two sources of PRP. This dataset also examines relative concentration changes in the media after incubation with endometrial organoids
DOI: 10.6084/m9.figshare.26663027
title: Parallel Sampling of Decomposable Graphs Using Markov Chains on Junction Trees

description: Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary conditions for when multi-edge perturbations maintain decomposability of the graph. Using these, we characterize a simple class of partitions that efficiently classify all edge perturbations by whether they maintain decomposability. Second, we propose a novel parallel nonreversible Markov chain Monte Carlo sampler for distributions over junction tree representations of the graph. At every step, the parallel sampler executes simultaneously all edge perturbations within a partition. Through simulations, we demonstrate the efficiency of our new edge perturbation conditions and class of partitions. We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.26663027.v1
title: Parallel sampling of decomposable graphs using Markov chains on junction trees

description: Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary conditions for when multi-edge perturbations maintain decomposability of the graph. Using these, we characterize a simple class of partitions that efficiently classify all edge perturbations by whether they maintain decomposability. Second, we propose a novel parallel non-reversible Markov chain Monte Carlo sampler for distributions over junction tree representations of the graph. At every step, the parallel sampler executes simultaneously all edge perturbations within a partition. Through simulations, we demonstrate the efficiency of our new edge perturbation conditions and class of partitions. We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG.
DOI: 10.6084/m9.figshare.26663027.v2
title: Parallel Sampling of Decomposable Graphs Using Markov Chains on Junction Trees

description: Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems. While parameter inference is straightforward in this setup, inferring the underlying graph is a challenge driven by the computational difficulty in exploring the space of decomposable graphs. This work makes two contributions to address this problem. First, we provide sufficient and necessary conditions for when multi-edge perturbations maintain decomposability of the graph. Using these, we characterize a simple class of partitions that efficiently classify all edge perturbations by whether they maintain decomposability. Second, we propose a novel parallel nonreversible Markov chain Monte Carlo sampler for distributions over junction tree representations of the graph. At every step, the parallel sampler executes simultaneously all edge perturbations within a partition. Through simulations, we demonstrate the efficiency of our new edge perturbation conditions and class of partitions. We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.y6acrm
title: Occurrence Download

description: A dataset listing the 19 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-81.18993 46.50895,-81.20815 46.48642,-81.21612 46.46036,-81.21257 46.43385,-81.19784 46.40925,-81.17326 46.38877,-81.15245 46.37938,-81.14627 46.37565,-81.14105 46.37423,-81.14104 46.37423,-81.14102 46.37423,-81.10535 46.36455,-81.06143 46.36362,-81.01962 46.37299,-80.98479 46.39154,-80.98306 46.39285,-80.96081 46.41603,-80.9604 46.41665,-80.95131 46.43598,-80.95117 46.43647,-80.95079 46.44136,-80.94891 46.44523,-80.94847 46.44672,-80.94589 46.4684,-80.951 46.48987,-80.95115 46.49022,-80.96763 46.51455,-80.99391 46.53437,-81.02758 46.54786,-81.06556 46.55378,-81.06592 46.5538,-81.12329 46.54753,-81.12549 46.54692,-81.16705 46.52817,-81.16861 46.52713,-81.18373 46.51491,-81.18489 46.51378,-81.18628 46.51249,-81.18787 46.51098,-81.18792 46.51093,-81.18875 46.5102,-81.18935 46.50967,-81.18985 46.50906,-81.18995 46.50896,-81.18993 46.50895)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EN, VU, NT)",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "Year 2014-2024"
 ]
}

 The dataset's 19 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032204-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7geneu
title: Occurrence Download

description: A dataset containing 16944 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Aedes albopictus (Skuse, 1894)"
 ]
}

 The dataset includes 16944 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032208-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13845336
title: Bioactive compounds, antioxidant activity, and sensory profile of sorghum tisane

description: This dataset provides the result of bioactive compounds, antioxidant activity, sensory acceptance, and sensory profile measurements towards sorghum tisane with different pericarp colors.
DOI: 10.5281/zenodo.13845337
title: Bioactive compounds, antioxidant activity, and sensory profile of sorghum tisane

description: This dataset provides the result of bioactive compounds, antioxidant activity, sensory acceptance, and sensory profile measurements towards sorghum tisane with different pericarp colors.
DOI: 10.15468/dl.ymk338
title: Occurrence Download

description: A dataset listing the 5 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-81.48755 46.42636,-81.48755 46.42614,-81.48673 46.41893,-81.48695 46.41892,-81.48684 46.41799,-81.48652 46.41711,-81.48616 46.41393,-81.48598 46.41311,-81.4851 46.4132,-81.48456 46.4117,-81.48399 46.40892,-81.48377 46.40835,-81.48279 46.40684,-81.47739 46.39194,-81.47778 46.39183,-81.477 46.39045,-81.47671 46.39007,-81.47661 46.38979,-81.47584 46.38857,-81.47559 46.38864,-81.46178 46.37094,-81.44006 46.35464,-81.43919 46.35413,-81.42061 46.34653,-81.41575 46.3435,-81.40906 46.3418,-81.40235 46.33905,-81.39326 46.33778,-81.37018 46.33191,-81.36906 46.33178,-81.31293 46.33405,-81.31209 46.33422,-81.27696 46.34548,-81.26456 46.34763,-81.26118 46.34864,-81.22598 46.36422,-81.19983 46.38679,-81.18553 46.41398,-81.18459 46.4429,-81.19714 46.47051,-81.19883 46.47281,-81.22263 46.49552,-81.22542 46.49692,-81.22727 46.49821,-81.23238 46.50138,-81.23369 46.50226,-81.23401 46.50239,-81.23861 46.50524,-81.24096 46.50653,-81.24194 46.50567,-81.24744 46.50795,-81.2555 46.51198,-81.25709 46.51253,-81.25743 46.51208,-81.2842 46.52314,-81.28511 46.52334,-81.31762 46.52742,-81.35056 46.52564,-81.35157 46.5255,-81.36534 46.52208,-81.40174 46.51764,-81.43847 46.5037,-81.46676 46.48236,-81.4836 46.45587,-81.48383 46.45524,-81.48479 46.45248,-81.48525 46.45105,-81.48828 46.42993,-81.48755 46.42636)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (VU, NT)",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "Year 2014-2024"
 ]
}

 The dataset's 5 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032206-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.