cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/00007323
title: Development of the optimal treatment of Nalmefene in patients with alcohol dependence

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.5511662
title: Soil micronutrients & grassland productivity (NutNet dataset)

description: A dataset used in the study: 'Soil properties as key predictors of global grassland production: have we overlooked micronutrients?' The dataset contains information about biomass productivity, climate, soil properties, nutrients and management of 72 non-fertilized NutNet grassland sites.
DOI: 10.5281/zenodo.5510677
title: Manuscript V - Supplementary material

description: <strong>Supplementary material for manuscript V in dissertation:</strong> Selection of rye (<em>Secale cereale</em> L.) for powdery mildew and leaf rust resistance through phenotyping, target sequencing, and association genetics
DOI: 10.5281/zenodo.5510676
title: Manuscript V - Supplementary material

description: <strong>Supplementary material for manuscript V in dissertation:</strong> Selection of rye (<em>Secale cereale</em> L.) for powdery mildew and leaf rust resistance through phenotyping, target sequencing, and association genetics
DOI: 10.6084/m9.figshare.14610678
title: Metadata record for: Geolocated dataset of Chinese overseas development finance

description: This dataset contains key characteristics about the data described in the Data Descriptor Geolocated dataset of Chinese overseas development finance. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.14610678.v1
title: Metadata record for: Geolocated dataset of Chinese overseas development finance

description: This dataset contains key characteristics about the data described in the Data Descriptor Geolocated dataset of Chinese overseas development finance. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.15124524
title: Metadata record for: ValLAI_Crop: A validation dataset for coarse-resolution satellite LAI products over Chinese cropland

description: This dataset contains key characteristics about the data described in the Data Descriptor ValLAI_Crop: A validation dataset for coarse-resolution satellite LAI products over Chinese cropland. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.6084/m9.figshare.15124524.v1
title: Metadata record for: ValLAI_Crop: A validation dataset for coarse-resolution satellite LAI products over Chinese cropland

description: This dataset contains key characteristics about the data described in the Data Descriptor ValLAI_Crop: A validation dataset for coarse-resolution satellite LAI products over Chinese cropland. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
DOI: 10.5281/zenodo.5509678
title: A comparison of particle and fluid models for positive streamer discharges in air

description: This dataset consists of files used to produce the data presented in the article "A comparison of particle and fluid models for positive streamer discharges in air". This dataset includes 1) source code for both particle and fluid simulations 2) input data files, which are actually cross section for particle simulations and transport data for fluid simulations. 3) configuration files for running all simulations; 4) outputted log files of the simulations.
DOI: 10.6073/pasta/f6d3e8aec341ad9a7558b59c0f357db4
title: Biotic and abiotic composition of biological soil crusts in grassland and tarbush ecosystems at the Jornada Basin LTER site, 2017-2018

description: This data package contains data on biotic and abiotic composition of biological soil crusts at grassland and shrubland sites at the Jornada Basin LTER site. Biological soil crusts (BSCs) of three types, cyanolichen, dark algal, and light algal, were collected during the summer 2017, fall 2017, and spring 2018 seasons to discern differences between two vegetation states (grass and tarbush). A total of 51 biocrusts were collected from 3 tarbush sites and 3 grass sites. Phospholipid fatty acid analysis was used to characterize the microfloral community, while a modified extraction method was employed to observe direct counts of microfauna (multicellular microbes and protists). Soil chemical analysis was also used to obtain nutrient concentrations of each biocrust. Data include microfauna counts, microflora biomass, and soil nutrient composition. This study is complete.
DOI: 10.5281/zenodo.5509677
title: A comparison of particle and fluid models for positive streamer discharges in air

description: This dataset consists of files used to produce the data presented in the article "A comparison of particle and fluid models for positive streamer discharges in air". This dataset includes 1) source code for both particle and fluid simulations 2) input data files, which are actually cross section for particle simulations and transport data for fluid simulations. 3) configuration files for running all simulations; 4) outputted log files of the simulations.
DOI: 10.6084/m9.figshare.16632430
title: Appendix C.docx

description: <b>structural model assessment results in tables</b>
DOI: 10.6084/m9.figshare.16632430.v2
title: Appendix C.docx

description: <b>structural model assessment results in tables</b>
DOI: 10.5281/zenodo.5509716
title: Virtual Storage Plant (VSP) preliminary demonstration of Load Frequency Control (LFC)

description: This the dataset of preliminary demostration of using distributed control with consensus algorithm for utilizing a virtual storage plant for automatic load frequency control. The dataset contains raw data from 3 experiments (6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05. The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).
DOI: 10.5281/zenodo.5509715
title: Virtual Storage Plant (VSP) preliminary demonstration of Load Frequency Control (LFC)

description: This the dataset of preliminary demostration of using distributed control with consensus algorithm for utilizing a virtual storage plant for automatic load frequency control. The dataset contains raw data from 3 experiments (6 units in a VSP) for 5 scenarios: TC_5.02.01, TC_5.02.02, TC_5.02.03, TC_5.02.04, TC_5.02.05. The experiments were conducted at the University of Zagreb's Smart Grid Laboratory (SGLab).
DOI: 10.6084/m9.figshare.16632424
title: Appendix B.docx

description: Results
DOI: 10.6084/m9.figshare.16632424.v4
title: Appendix B.docx

description: Results
DOI: 10.6084/m9.figshare.16632424.v3
title: Appendix B.docx

description: Results
DOI: 10.6084/m9.figshare.14527092.v1
title: Mangroves Ecosystems Ecuador 2020

description: The mangrove ecosystem is considered an asset to livelihoods, particularly for ethnic groups in coastal areas. This study explores the perceptions regarding mangrove ecosystem services in two ethnic communities living in “El Morro Port” and “Costa Rica Island” (Ecuador). Seven quantitative variables on the Likert scale were used to conduct an exploratory factor analysis. The total sample includes 94 people. We found 2 main factors namely, tourism (29.07% variance) and food production (25.45% remaining variance). The reliability of the questionnaire was acceptable according to Cronbach's Alpha 0.654. Future interventions should address educating local people on sustainable tourist and food production activities.
DOI: 10.34740/kaggle/dsv/2622053
title: Country Data
DOI: 10.6084/m9.figshare.13519116.v1
title: Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation

description: In this article, we study the problem of multiple change-point detection for a univariate sequence under the epidemic setting, where the behavior of the sequence alternates between a common normal state and different epidemic states. This is a nontrivial generalization of the classical (single) epidemic change-point testing problem. To explicitly incorporate the alternating structure of the problem, we propose a novel model selection based approach for simultaneous inference on both change-points and alternating states. Using the same spirit as profile likelihood, we develop a two-stage alternating pruned dynamic programming algorithm, which conducts efficient and exact optimization of the model selection criteria and has O(n2) as the worst case computational cost. As demonstrated by extensive numerical experiments, compared to classical general-purpose multiple change-point detection procedures, the proposed method improves accuracy for both change-point estimation and model parameter estimation. We further show promising applications of the proposed algorithm to multiple testing with locally clustered signals, and demonstrate its advantages over existing methods in large scale multiple testing, in DNA copy number variation detection, and in oceanographic study. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.13180181.v1
title: Nonparametric Anomaly Detection on Time Series of Graphs

description: Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. One particular objective of anomaly detection from a neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. However, most statistical methods for detecting anomalies have the following unrealistic limitation for brain studies and beyond: that is, network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization via local and global network topological summaries. Second, we adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated and to real data, particularly, two functional magnetic resonance imaging (fMRI) datasets and the Enron communication graph. We find that our new method delivers impressively accurate and realistic results in terms of identifying locations of true change points compared to the results reported by competing approaches. The new method promises to offer a deeper insight into the large-scale characterizations and functional dynamics of the brain and, more generally, into the intrinsic structure of complex dynamic networks. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13275287.v2
title: An Explicit Mean-Covariance Parameterization for Multivariate Response Linear Regression

description: We develop a new method to fit the multivariate response linear regression model that exploits a parametric link between the regression coefficient matrix and the error covariance matrix. Specifically, we assume that the correlations between entries in the multivariate error random vector are proportional to the cosines of the angles between their corresponding regression coefficient matrix columns, so as the angle between two regression coefficient matrix columns decreases, the correlation between the corresponding errors increases. We highlight two models under which this parameterization arises: a latent variable reduced-rank regression model and the errors-in-variables regression model. We propose a novel nonconvex weighted residual sum of squares criterion which exploits this parameterization and admits a new class of penalized estimators. The optimization is solved with an accelerated proximal gradient descent algorithm. Our method is used to study the association between microRNA expression and cancer drug activity measured on the NCI-60 cell lines. An R package implementing our method, MCMVR, is available online.
DOI: 10.6084/m9.figshare.13333849.v2
title: Tensor Canonical Correlation Analysis With Convergence and Statistical Guarantees

description: In many applications, such as classification of images or videos, it is of interest to develop a framework for tensor data instead of an ad-hoc way of transforming data to vectors due to the computational and under-sampling issues. In this article, we study convergence and statistical properties of two-dimensional canonical correlation analysis under an assumption that data come from a probabilistic model. We show that carefully initialized the power method converges to the optimum and provide a finite sample bound. Then we extend this framework to tensor-valued data and propose the higher-order power method, which is commonly used in tensor decomposition, to extract the canonical directions. Our method can be used effectively in a large-scale data setting by solving the inner least squares problem with a stochastic gradient descent, and we justify convergence via the theory of Lojasiewicz’s inequalities without any assumption on data generating process and initialization. For practical applications, we further develop (a) an inexact updating scheme which allows us to use the state-of-the-art stochastic gradient descent algorithm, (b) an effective initialization scheme which alleviates the problem of local optimum in nonconvex optimization, and (c) a deflation procedure for extracting several canonical components. Empirical analyses on challenging data including gene expression and air pollution indexes in Taiwan, show the effectiveness and efficiency of the proposed methodology. Our results fill a missing, but crucial, part in the literature on tensor data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13577937.v2
title: Model Selection With Lasso-Zero: Adding Straw to the Haystack to Better Find Needles

description: The high-dimensional linear model y=Xβ0+ϵ is considered and the focus is put on the problem of recovering the support <i>S</i><sup>0</sup> of the sparse vector β0. We introduce a new l1-based estimator, called <i>Lasso-Zero</i>, whose novelty resides in the repeated use of noise dictionaries concatenated to <i>X</i> for overfitting the response. Lasso-Zero is an extension of thresholded basis pursuit (TBP), for which we prove sign consistency for correlated Gaussian designs. Both theoretical and empirical results motivate the use of noise dictionaries to improve TBP when the coefficients’ amplitude is low. To select the threshold of Lasso-Zero, we suggest to employ a pivotal version of the quantile universal threshold (QUT) that exploits a byproduct of Lasso-Zero to avoid the need to estimate the noise level. Numerical simulations show that Lasso-Zero tuned by QUT performs well in terms of support recovery and provides an excellent trade-off between high power and few false discoveries compared to competitors. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13180187.v2
title: Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling

description: Modeling the joint distribution of extreme events at multiple locations is a challenging task with important applications. In this study, we use max-stable models to study extreme daily precipitation events in Switzerland. The nonstationarity of the spatial process at hand involves important challenges, which are often dealt with by using a stationary model in a so-called climate space, with well-chosen covariates. Here, we instead choose to warp the weather stations under study in a latent space of higher dimension using multidimensional scaling (MDS). Two methods are proposed to define target dissimilarity matrices, based respectively on extremal coefficients and on pairwise likelihoods. Results suggest that the proposed methods allow capturing complex spatial dependences of spatial extreme precipitations, enabling in turn to reliably extrapolate functionals such as extremal coefficients. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.13519115.v2
title: Adaptive Bayesian Spectral Analysis of High-Dimensional Nonstationary Time Series

description: This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk toward zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13143714.v2
title: Particle MCMC With Poisson Resampling: Parallelization and Continuous Time Models

description: We introduce a new version of particle filter in which the number of “children” of a particle at a given time has a Poisson distribution. As a result, the number of particles is random and varies with time. An advantage of this scheme is that descendants of different particles can evolve independently. It makes easy to parallelize computations. Moreover, particle filter with Poisson resampling is readily adapted to the case when a hidden process is a continuous time, piecewise deterministic semi-Markov process. We show that the basic techniques of particle MCMC, namely particle independent Metropolis-Hastings, particle Gibbs sampler and its version with ancestor sampling, work under our Poisson resampling scheme. Our version of particle Gibbs sampler is uniformly ergodic under the same assumptions as its standard counterpart. We present simulation results which indicate that our algorithms can compete with the existing methods. Supplemental materials for this article are available online.
DOI: 10.5281/zenodo.5512900
title: datasets for MARS

description: Here we provide: 1. refdata-GRCh38-2.10/fasta/genome.fa -- reference file 2. Ape_ref -- Reference file for Gorilla_gorill4, pongo_abelii2, pan_troglodytes3.0, and macaca_mulatta_8.0.1 3. gnomAD_hg38_snp -- gnomad vcf for chr21 4. Aquila_results_4samples -- a test input files for MARS
DOI: 10.5281/zenodo.5512899
title: datasets for MARS

description: Here we provide: 1. refdata-GRCh38-2.10/fasta/genome.fa -- reference file 2. Ape_ref -- Reference file for Gorilla_gorill4, pongo_abelii2, pan_troglodytes3.0, and macaca_mulatta_8.0.1 3. gnomAD_hg38_snp -- gnomad vcf for chr21 4. Aquila_results_4samples -- a test input files for MARS
DOI: 10.5519/qd.j48ex8q0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6376 records)
DOI: 10.5281/zenodo.5512953
title: Dataset of RQA-based measures of a minimal perceptual task using sonification

description: Dataset of RQA-based measures of a minimal perceptual task using sonification
DOI: 10.5519/qd.9j4mxb39
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (76 records)
DOI: 10.5519/qd.9vsq233t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7058 records)
DOI: 10.6084/m9.figshare.16629163
title: Dataset COVID-BPM paper.csv

description: This data set can be imported in SPSS and SmartPLS3 for analyzing PLS-SEM results<br>We assign values to five Likert scale from 35 to 39
DOI: 10.6084/m9.figshare.16629163.v1
title: Dataset COVID-BPM paper.csv

description: This data set can be imported in SPSS and SmartPLS3 for analyzing PLS-SEM results<br>We assign values to five Likert scale from 35 to 39
DOI: 10.6084/m9.figshare.14109833
title: The effect of bird exclosures on soybean leaf damage and crop yield.

description: Bird-exclosures were placed over soybean plants at 6 sites in Northern IL, USA. Defoliation measurements were taken at 3 time periods, and crop yield was measured upon harvest. Bird point count data are also included. Bird species are identified by standard alpha code.
DOI: 10.6084/m9.figshare.14109833.v2
title: The effect of bird exclosures on soybean leaf damage and crop yield.

description: Bird-exclosures were placed over soybean plants at 6 sites in Northern IL, USA. Defoliation measurements were taken at 3 time periods, and crop yield was measured upon harvest. Bird point count data are also included. Bird species are identified by standard alpha code.
DOI: 10.5519/qd.ulvrhuln
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5265 records)
DOI: 10.5519/qd.5z614uyu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (9081 records)