cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6941697
title: Raw images corresponding to article "Relative assessment of cloth mask protection against ballistic droplets: a frugal approach"

description: The images correspond to scans used to obtain the data reported on V. Márquez-Alvarez, J. Amigó-Vega, A. Rivera, A. J. Batista-Leyva, and E. Altshuler, <em>Relative assessment of cloth mask protection against ballistic droplets: A frugal approach</em>. PLOS ONE 17, e0275376 (2022). doi: 10.1371/journal.pone.0275376.
description: If you use this dataset, please cite both the dataset and the journal article mentioned above.
DOI: 10.5281/zenodo.6941696
title: Raw images corresponding to article "Relative assessment of cloth mask protection against ballistic droplets: a frugal approach"

description: The images correspond to scans used to obtain the data reported on V. Márquez-Alvarez, J. Amigó-Vega, A. Rivera, A. J. Batista-Leyva, and E. Altshuler, <em>Relative assessment of cloth mask protection against ballistic droplets: A frugal approach</em>. PLOS ONE 17, e0275376 (2022). doi: 10.1371/journal.pone.0275376.
description: If you use this dataset, please cite both the dataset and the journal article mentioned above.
DOI: 10.3886/e113786v1-128860
title: Replication data for: The Wrong Side(s) of the Tracks: The Causal Effects of Racial Segregation on Urban Poverty and Inequality

description: A striking negative correlation exists between an area's residential racial segregation and its population characteristics, but it is recognized that this relationship may not be causal. I present a novel test of causality from segregation to population characteristics by exploiting the arrangements of railroad tracks in the nineteenth century to isolate plausibly exogenous variation in areas' susceptibility to segregation. I show that this variation satisfies the requirements for a valid instrument. Instrumental variables estimates demonstrate that segregation increases metropolitan rates of black poverty and overall black-white income disparities, while decreasing rates of white poverty and inequality within the white population. (JEL I32, J15, N31, N32, N91, N92, R23)
DOI: 10.6084/m9.figshare.20405677
title: A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters

description: Inference for high-dimensional, large scale and long series dynamic systems is a challenging task in modern data science. The existing algorithms, such as particle filter or sequential importance sampler, do not scale well to the dimension of the system and the sample size of the dataset, and often suffers from the sample degeneracy issue for long series data. The recently proposed Langevinized ensemble Kalman filter (LEnKF) addresses these difficulties in a coherent way. However, it cannot be applied to the case that the dynamic system contains unknown parameters. This article proposes the so-called stochastic approximation-LEnKF for jointly estimating the states and unknown parameters of the dynamic system, where the parameters are estimated on the fly based on the state variables simulated by the LEnKF under the framework of stochastic approximation Markov chain Monte Carlo (MCMC). Under mild conditions, we prove its consistency in parameter estimation and ergodicity in state variable simulations. The proposed algorithm can be used in uncertainty quantification for long series, large scale, and high-dimensional dynamic systems. Numerical results indicate its superiority over the existing algorithms. We employ the proposed algorithm in state-space modeling of the sea surface temperature with a long short term memory (LSTM) network, which indicates its great potential in statistical analysis of complex dynamic systems encountered in modern data science. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20405677.v1
title: A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters

description: Inference for high-dimensional, large scale and long series dynamic systems is a challenging task in modern data science. The existing algorithms, such as particle filter or sequential importance sampler, do not scale well to the dimension of the system and the sample size of the dataset, and often suffers from the sample degeneracy issue for long series data. The recently proposed Langevinized ensemble Kalman filter (LEnKF) addresses these difficulties in a coherent way. However, it cannot be applied to the case that the dynamic system contains unknown parameters. This article proposes the so-called stochastic approximation-LEnKF for jointly estimating the states and unknown parameters of the dynamic system, where the parameters are estimated on the fly based on the state variables simulated by the LEnKF under the framework of stochastic approximation Markov chain Monte Carlo (MCMC). Under mild conditions, we prove its consistency in parameter estimation and ergodicity in state variable simulations. The proposed algorithm can be used in uncertainty quantification for long series, large scale, and high-dimensional dynamic systems. Numerical results indicate its superiority over the existing algorithms. We employ the proposed algorithm in state-space modeling of the sea surface temperature with a long short term memory (LSTM) network, which indicates its great potential in statistical analysis of complex dynamic systems encountered in modern data science. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20457233.v2
title: Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions

description: The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selection procedure. We propose a general class of loss functions for covariance matrix estimation and establish accompanying finite-sample risk bounds and conditions for the asymptotic optimality of the cross-validation selector. In simulation studies, we demonstrate the optimality of our proposed selector in moderate sample sizes and across diverse data-generating processes. The practical benefits of our procedure are highlighted in a dimensionality reduction application to single-cell transcriptome sequencing data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20457233
title: Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions

description: The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selection procedure. We propose a general class of loss functions for covariance matrix estimation and establish accompanying finite-sample risk bounds and conditions for the asymptotic optimality of the cross-validation selector. In simulation studies, we demonstrate the optimality of our proposed selector in moderate sample sizes and across diverse data-generating processes. The practical benefits of our procedure are highlighted in a dimensionality reduction application to single-cell transcriptome sequencing data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20457233.v1
title: Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions

description: The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selection procedure. We propose a general class of loss functions for covariance matrix estimation and establish accompanying finite-sample risk bounds and conditions for the asymptotic optimality of the cross-validation selector. In numerical experiments, we demonstrate the optimality of our proposed selector in moderate sample sizes and across diverse data-generating processes. The practical benefits of our procedure are highlighted in a dimension reduction application to single-cell transcriptome sequencing data.
DOI: 10.6084/m9.figshare.20539462
title: Fast Multilevel Functional Principal Component Analysis

description: We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA. Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10, 000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20539462.v1
title: Fast Multilevel Functional Principal Component Analysis

description: We introduce fast multilevel functional principal component analysis (fast MFPCA), which scales up to high dimensional functional data measured at multiple visits. The new approach is orders of magnitude faster than and achieves comparable estimation accuracy with the original MFPCA. Methods are motivated by the National Health and Nutritional Examination Survey (NHANES), which contains minute-level physical activity information of more than 10, 000 participants over multiple days and 1440 observations per day. While MFPCA takes more than five days to analyze these data, fast MFPCA takes less than five minutes. A theoretical study of the proposed method is also provided. The associated function mfpca.face() is available in the R package refund. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.7158937
title: mPCNL youtube data

description: mPCNL youtube videos data were collected by authors.
DOI: 10.5281/zenodo.7158936
title: mPCNL youtube data

description: mPCNL youtube videos data were collected by authors.
DOI: 10.5281/zenodo.7158963
title: quercus_bicolor_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7158962
title: quercus_bicolor_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.21298764
title: Topographic Profiles of Ariel's Yangoor crater

description: This .csv file contains four topographic profiles of Ariel's Yangoor crater. Each profile is given a letter that corresponds to the location shown in the accompanying figure. For each profile, the x-column is the horizontal distance in meters, and y-column is the vertical heigth in meteres. Heights are relative to an arbitrary "0" datum, and are thus only relative.
DOI: 10.6084/m9.figshare.21298764.v1
title: Topographic Profiles of Ariel's Yangoor crater

description: This .csv file contains four topographic profiles of Ariel's Yangoor crater. Each profile is given a letter that corresponds to the location shown in the accompanying figure. For each profile, the x-column is the horizontal distance in meters, and y-column is the vertical heigth in meteres. Heights are relative to an arbitrary "0" datum, and are thus only relative.
DOI: 10.5281/zenodo.7158961
title: Subjects who have developed SARS-CoV-2 specific IgM-S after vaccination show a longer humoral immunity and a lower frequency of infection

description: We analysed anti-SARS-CoV-2 spike (S) protein (IgG-S), anti-nucleocapsid IgG (IgG-N) and anti-Spike IgM (IgM-S) in 1872 vaccinees at the first dose (D1; w0), after three weeks at the second dose (D2; w3) at three (w6) and 23 weeks (w29) after D2; 109 subjects where further tested at the booster dose (D3, w44), at three weeks (w47) and six months (w70) after D3. Two-level linear regression models were used to evaluate the differences in IgG-S levels.
DOI: 10.5281/zenodo.7158960
title: Subjects who have developed SARS-CoV-2 specific IgM-S after vaccination show a longer humoral immunity and a lower frequency of infection

description: We analysed anti-SARS-CoV-2 spike (S) protein (IgG-S), anti-nucleocapsid IgG (IgG-N) and anti-Spike IgM (IgM-S) in 1872 vaccinees at the first dose (D1; w0), after three weeks at the second dose (D2; w3) at three (w6) and 23 weeks (w29) after D2; 109 subjects where further tested at the booster dose (D3, w44), at three weeks (w47) and six months (w70) after D3. Two-level linear regression models were used to evaluate the differences in IgG-S levels.
DOI: 10.5517/ccdc.csd.cc289wgy
title: CCDC 2094870: Experimental Crystal Structure Determination

description: Related Article: Yunlong Li, Wenchao Jiang, Jie Lin, Juan Ma, Bao-Hua Xu, Yong-Gui Zhou, Zhengkun Yu|2022|Org.Lett.|24|7123|doi:10.1021/acs.orglett.2c02758
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28rcn2
title: CCDC 2107834: Experimental Crystal Structure Determination

description: Related Article: Yunlong Li, Wenchao Jiang, Jie Lin, Juan Ma, Bao-Hua Xu, Yong-Gui Zhou, Zhengkun Yu|2022|Org.Lett.|24|7123|doi:10.1021/acs.orglett.2c02758
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b71bz
title: CCDC 2151720: Experimental Crystal Structure Determination

description: Related Article: Hui Sun, Yi Cheng, Houyun Teng, Xiaoqi Chen, Xiaokang Niu, Hao Yang, Yu-Ming Cui, Li-Wen Xu, Lei Yang|2022|J.Org.Chem.|87|13346|doi:10.1021/acs.joc.2c01385
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7094807
title: HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru

description: The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.
DOI: 10.5281/zenodo.7094808
title: HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru

description: The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.
DOI: 10.6073/pasta/939f9e5e3b6ba9c724de36090583c93b
title: Sediment pigment concentrations from lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing

description: The Beaufort Lagoon Ecosystems Long Term Ecological Research (BLE LTER) project seasonally collects undisturbed surface sediments during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods from lagoon sites along the Beaufort Sea (Elson, Simpson, Jago, and Kaktovik lagoons, plus Stefansson Sound) to quantify algal pigment concentrations. Pigments reported are chlorophyll a, pheophorbide, pheophytin, chlorophyllide, fucoxanthin, zeaxanthin, alloxanthin, and peridinin. Pigment concentrations are measured using high-precision liquid chromatography (HPLC). Concentrations are represented both as an areal basis (mg/m2 of surface sediment) and a mass basis (μg/g of dry sediment). In 2022 we found that a post-analysis mathematical error generated incorrect concentration values for pigments. This error was identified in subsequent QA/QC and immediately corrected since the raw data were not in error. The entire dataset to date (2018-2021) was revised in spring 2022 with the corrected data (revision knb-lter-ble.12.2).
DOI: 10.6073/pasta/33148f09ef3e5854ed285d9603f53dc4
title: Circulation dynamics: currents, waves, temperature measurements from moorings in lagoon sites along the Alaska Beaufort Sea coast, 2018-ongoing

description: Starting August 2018, five moorings deployed on the seafloor of multiple lagoons in the Beaufort Sea will record currents, waves, temperature, and pressure. Moorings are retrieved and re-deployed each August. This data is being collected to better understand the multi-seasonal circulation dynamics between the Beaufort Sea and coastal lagoons. Two moorings are deployed in Elson Lagoon, one in Stefansson Sound, one in Jago Lagoon, and one in Kaktovik Lagoon. Each mooring contains two data loggers: RBRduo3 T.D wave loggers and Lowell Instruments LLC TCM-1 tilt current meters. The RBR instruments measure temperature, pressure, and derived wave energy, average wave period, average wave height, maximum wave period, maximum wave height, 1/10 wave period, 1/10 wave height, significant wave period, significant wave height, tidal slope, depth, and sea pressure. The Lowell LLC TML-1 tilt current meters measure water velocity, heading, and temperature.
DOI: 10.7303/syn30991637
title: GENIE BPC CRC 2.0-public
DOI: 10.5281/zenodo.7109010
title: Single-cell resolution of the plant response to Pseudomonas syringae infection

description: Original images for figures and quantifications.
DOI: 10.5281/zenodo.7109009
title: Single-cell resolution of the plant response to Pseudomonas syringae infection

description: Original images for figures and quantifications.
DOI: 10.5517/ccdc.csd.cc2b88hd
title: CCDC 2152903: Experimental Crystal Structure Determination

description: Related Article: Wei Cheng, Jin-Bu Xu, Neng Wang, Peng Wen, Xian-Li Zhou, Feng Gao|2022|J.Org.Chem.|87|13411|doi:10.1021/acs.joc.2c01831
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b9hpv
title: CCDC 2154087: Experimental Crystal Structure Determination

description: Related Article: Yunlong Li, Wenchao Jiang, Jie Lin, Juan Ma, Bao-Hua Xu, Yong-Gui Zhou, Zhengkun Yu|2022|Org.Lett.|24|7123|doi:10.1021/acs.orglett.2c02758
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b9hqw
title: CCDC 2154088: Experimental Crystal Structure Determination

description: Related Article: Yunlong Li, Wenchao Jiang, Jie Lin, Juan Ma, Bao-Hua Xu, Yong-Gui Zhou, Zhengkun Yu|2022|Org.Lett.|24|7123|doi:10.1021/acs.orglett.2c02758
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bb090
title: CCDC 2154571: Experimental Crystal Structure Determination

description: Related Article: Yaowen Liu, Martin C. Dietl, Chunyu Han, Matthias Rudolph, Frank Rominger, Petra Krämer, A. Stephen K. Hashmi|2022|Org.Lett.|24|7101|doi:10.1021/acs.orglett.2c02161
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.