cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19251593.v1
title: Pellerin, Lotem et al.

description: Data on sediment porewater, incubation experiments, and bubble samples, retrieved from ‘Big Trail Lake’, a young thermokarst lake 8 km north of Fairbanks, Alaska (64°55'9.47"N, 147°49'20.08"W). The full description of the data, measurement methods, and interpretation will be published in Pellerin, Lotem et al., ‘Methane production controls in a young thermokarst lake formed by abrupt permafrost thaw’, Global Change Biology (accepted). Authors: André Pellerin, Noam Lotem, Katey Walter Anthony, Efrat Eliani Russak, Nicholas Hasson, Hans Røy, Jeffrey P. Chanton, and Orit Sivan
DOI: 10.6084/m9.figshare.19407509
title: Supplementary File S5e Oryza accessions for KWIP

description: <b>Supplementary File S5e</b>. Contains information about the accessions used for estimates of genetic similarity by kWIP, including four accessions sequenced as part of the present study.
DOI: 10.5517/ccdc.csd.cc2bbrfw
title: CCDC 2155288: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbszg
title: CCDC 2155336: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt0j
title: CCDC 2155337: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt1k
title: CCDC 2155338: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt2l
title: CCDC 2155339: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt6q
title: CCDC 2155343: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbt7r
title: CCDC 2155344: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs5n
title: CCDC 2155311: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs6p
title: CCDC 2155312: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs7q
title: CCDC 2155313: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs8r
title: CCDC 2155314: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs9s
title: CCDC 2155315: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbsbt
title: CCDC 2155316: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbscv
title: CCDC 2155317: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbsdw
title: CCDC 2155318: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbsfx
title: CCDC 2155319: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbsgy
title: CCDC 2155320: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbrv9
title: CCDC 2155301: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbrwb
title: CCDC 2155302: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbrxc
title: CCDC 2155303: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbryd
title: CCDC 2155304: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbrzf
title: CCDC 2155305: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs3l
title: CCDC 2155309: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.5517/ccdc.csd.cc2bbs4m
title: CCDC 2155310: Experimental Crystal Structure Determination

description: Related Article: M. Chodkiewicz, S. Pawlędzio, M. Woińska, K. Woźniak|2022|IUCrJ|9|298|doi:10.1107/S2052252522000690
DOI: 10.6084/m9.figshare.18737075
title: Mining ultraconserved elements from transcriptome and genome data to explore the phylogenomics of the free-living lice suborder Psocomorpha (Insecta: Psocodea)

description: Supplementary material resulting from the UCE data mining from WGS data, to explore the phylogenetic relationships within a group of freeliving lice order Psocomorpha.
DOI: 10.6084/m9.figshare.18737075.v3
title: Mining ultraconserved elements from transcriptome and genome data to explore the phylogenomics of the free-living lice suborder Psocomorpha (Insecta: Psocodea)

description: Supplementary material resulting from the UCE data mining from WGS data, to explore the phylogenetic relationships within a group of freeliving lice order Psocomorpha.
DOI: 10.17876/plate/dr.3/plates/102_13606
title: Digital object identifier (DOI) for the plate 13606 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13607
title: Digital object identifier (DOI) for the plate 13607 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13608
title: Digital object identifier (DOI) for the plate 13608 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13609
title: Digital object identifier (DOI) for the plate 13609 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13610
title: Digital object identifier (DOI) for the plate 13610 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13611
title: Digital object identifier (DOI) for the plate 13611 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13612
title: Digital object identifier (DOI) for the plate 13612 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13613
title: Digital object identifier (DOI) for the plate 13613 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13614
title: Digital object identifier (DOI) for the plate 13614 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13615
title: Digital object identifier (DOI) for the plate 13615 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13616
title: Digital object identifier (DOI) for the plate 13616 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13617
title: Digital object identifier (DOI) for the plate 13617 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13618
title: Digital object identifier (DOI) for the plate 13618 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13619
title: Digital object identifier (DOI) for the plate 13619 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13620
title: Digital object identifier (DOI) for the plate 13620 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13621
title: Digital object identifier (DOI) for the plate 13621 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13622
title: Digital object identifier (DOI) for the plate 13622 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13623
title: Digital object identifier (DOI) for the plate 13623 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13624
title: Digital object identifier (DOI) for the plate 13624 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13625
title: Digital object identifier (DOI) for the plate 13625 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13626
title: Digital object identifier (DOI) for the plate 13626 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13627
title: Digital object identifier (DOI) for the plate 13627 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13628
title: Digital object identifier (DOI) for the plate 13628 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13629
title: Digital object identifier (DOI) for the plate 13629 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13630
title: Digital object identifier (DOI) for the plate 13630 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13631
title: Digital object identifier (DOI) for the plate 13631 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13632
title: Digital object identifier (DOI) for the plate 13632 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13633
title: Digital object identifier (DOI) for the plate 13633 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13634
title: Digital object identifier (DOI) for the plate 13634 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13635
title: Digital object identifier (DOI) for the plate 13635 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13636
title: Digital object identifier (DOI) for the plate 13636 in archive 102 from the APPLAUSE DR3
DOI: 10.5517/ccdc.csd.cc2bj6kn
title: CCDC 2160531: Experimental Crystal Structure Determination

description: Related Article: Zhenguo Xi, Xi-Jia Liu, Zhaoquan Guo, Zhiwei Gao, Zhi-Xiang Yu, Hongyin Gao|2023|Nat. Synth.|2|778|doi:10.1038/s44160-023-00293-8
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.cc2bpg7r
title: CCDC 2165574: Experimental Crystal Structure Determination

description: Related Article: Zhenguo Xi, Xi-Jia Liu, Zhaoquan Guo, Zhiwei Gao, Zhi-Xiang Yu, Hongyin Gao|2023|Nat. Synth.|2|778|doi:10.1038/s44160-023-00293-8
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: 10.17876/plate/dr.3/plates/102_13637
title: Digital object identifier (DOI) for the plate 13637 in archive 102 from the APPLAUSE DR3
DOI: 10.5517/ccdc.csd.cc2bd5tr
title: CCDC 2156664: Experimental Crystal Structure Determination

description: Related Article: Zhenguo Xi, Xi-Jia Liu, Zhaoquan Guo, Zhiwei Gao, Zhi-Xiang Yu, Hongyin Gao|2023|Nat. Synth.|2|778|doi:10.1038/s44160-023-00293-8
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: 10.17876/plate/dr.3/plates/102_13638
title: Digital object identifier (DOI) for the plate 13638 in archive 102 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/102_13639
title: Digital object identifier (DOI) for the plate 13639 in archive 102 from the APPLAUSE DR3
DOI: 10.6084/m9.figshare.7874177
title: Smoothing With Couplings of Conditional Particle Filters

description: In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874177.v5
title: Smoothing With Couplings of Conditional Particle Filters

description: In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online.
DOI: 10.17876/plate/dr.3/plates/102_13640
title: Digital object identifier (DOI) for the plate 13640 in archive 102 from the APPLAUSE DR3
DOI: 10.6084/m9.figshare.7869365
title: Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models

description: We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7869365.v5
title: Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models

description: We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is to generate samples from the joint posterior distribution of states and parameters. The key benefit of our approach is the use of ensemble Kalman methods for dimension reduction, which allows inference for high-dimensional state vectors. We compare our methods to existing ones, including ensemble Kalman filters, particle filters, and particle MCMC. Using a real data example of cloud motion and data simulated under a number of nonlinear and non-Gaussian scenarios, we show that our approaches outperform these existing methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7874159
title: Nonnegative Matrix Factorization Via Archetypal Analysis

description: Given a collection of data points, nonnegative matrix factorization (NMF) suggests expressing them as convex combinations of a small set of “archetypes” with nonnegative entries. This decomposition is unique only if the true archetypes are nonnegative and sufficiently sparse (or the weights are sufficiently sparse), a regime that is captured by the separability condition and its generalizations. In this article, we study an approach to NMF that can be traced back to the work of Cutler and Breiman [(1994), “Archetypal Analysis,” <i>Technometrics</i>, 36, 338–347] and does not require the data to be separable, while providing a generally unique decomposition. We optimize a trade-off between two objectives: we minimize the distance of the data points from the convex envelope of the archetypes (which can be interpreted as an empirical risk), while also minimizing the distance of the archetypes from the convex envelope of the data (which can be interpreted as a data-dependent regularization). The archetypal analysis method of Cutler and Breiman is recovered as the limiting case in which the last term is given infinite weight. We introduce a “uniqueness condition” on the data which is necessary for identifiability. We prove that, under uniqueness (plus additional regularity conditions on the geometry of the archetypes), our estimator is robust. While our approach requires solving a nonconvex optimization problem, we find that standard optimization methods succeed in finding good solutions for both real and synthetic data. Supplementary materials for this article are available online
DOI: 10.6084/m9.figshare.7874177.v4
title: Smoothing With Couplings of Conditional Particle Filters

description: In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly informative observations, and for a realistic Lotka–Volterra model with an intractable transition density. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7999325
title: Functional Censored Quantile Regression

description: We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7999325.v2
title: Functional Censored Quantile Regression

description: We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.