cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.108127
title: Documentation of sediment core PS1820-6
DOI: 10.1594/pangaea.108128
title: Documentation of sediment core PS1821-5
DOI: 10.1594/pangaea.108129
title: Documentation of sediment core PS1821-6
DOI: 10.1594/pangaea.108130
title: Documentation of sediment core PS1822-1
DOI: 10.1594/pangaea.108131
title: Documentation of sediment core PS1822-6
DOI: 10.1594/pangaea.108132
title: Documentation of sediment core PS1823-1
DOI: 10.1594/pangaea.108133
title: Documentation of sediment core PS1823-6
DOI: 10.1594/pangaea.108134
title: Documentation of sediment core PS1824-1
DOI: 10.1594/pangaea.108135
title: Documentation of sediment core PS1824-2
DOI: 10.1594/pangaea.108136
title: Documentation of sediment core PS1825-5
DOI: 10.1594/pangaea.108137
title: Documentation of sediment core PS1826-1
DOI: 10.1594/pangaea.108138
title: Documentation of sediment core PS1826-2
DOI: 10.1594/pangaea.108139
title: Documentation of sediment core PS1827-1
DOI: 10.1594/pangaea.108140
title: Documentation of sediment core PS1828-2
DOI: 10.1594/pangaea.108141
title: Documentation of sediment core PS1828-7
DOI: 10.1594/pangaea.108142
title: Documentation of sediment core PS1829-1
DOI: 10.1594/pangaea.108143
title: Documentation of sediment core PS1831-5
DOI: 10.1594/pangaea.108144
title: Documentation of sediment core PS1833-1
DOI: 10.1594/pangaea.108145
title: Documentation of sediment core PS1834-1
DOI: 10.1594/pangaea.108146
title: Documentation of sediment core PS1836-2
DOI: 10.1594/pangaea.126289
title: Age model of sediment core PS1811-8
DOI: 10.1594/pangaea.124443
title: Documentation of sediment core PS1815-1
DOI: 10.1594/pangaea.124444
title: Documentation of sediment core PS1830-1
DOI: 10.1594/pangaea.126290
title: Age model of sediment core PS1812-6
DOI: 10.1594/pangaea.126291
title: Age model of sediment core PS1813-6
DOI: 10.1594/pangaea.126292
title: Age model of sediment core PS1816-1
DOI: 10.1594/pangaea.126300
title: Sedimentology of core PS1811-8
DOI: 10.1594/pangaea.126301
title: Sedimentology of core PS1812-6
DOI: 10.1594/pangaea.126302
title: Sedimentology of core PS1816-1
DOI: 10.1594/pangaea.126303
title: Sedimentology of core PS1813-6
DOI: 10.1594/pangaea.138406
title: Bulk sedimentological parameters of core PS1821-6
DOI: 10.1594/pangaea.138311
title: Sedimentology of core PS1821-6
DOI: 10.1594/pangaea.218265
title: Age reference and time scale for core PS1824-1
DOI: 10.1594/pangaea.218268
title: Biogenic opal content of sediment core PS1824-1
DOI: 10.1594/pangaea.218270
title: Biogenic opal content of sediment core PS1824-2
DOI: 10.1594/pangaea.219562
title: (Table 1) Major element concentrations in glass shards of marine tephra in core MD90-917

description: Scanning electron microscope (SEM) JEOL coupled with an X-ray energy dispersion spectrometer (EDS) at 15 kV and 0.3 nA
DOI: 10.1594/pangaea.271566
title: Documentation of sediment core PS1806-1
DOI: 10.1594/pangaea.314634
title: Continuous thermosalinograph oceanography along cruise track M24/2
DOI: 10.1594/pangaea.351211
title: Accumulation and flux data calculated for sediment core PS1821-6
DOI: 10.1594/pangaea.351294
title: Age model of sediment core PS1821-6
DOI: 10.1594/pangaea.355323
title: Susceptibility raw data of sediment core PS2473-4
DOI: 10.1594/pangaea.355324
title: Susceptibility raw data of sediment core PS2474-3
DOI: 10.1594/pangaea.422410
title: Sedimentology of core PG1158
DOI: 10.1594/pangaea.422411
title: Sedimentology of core PG1161
DOI: 10.1594/pangaea.422413
title: Sedimentology of core PG1164
DOI: 10.1594/pangaea.422414
title: Sedimentology of core PG1165
DOI: 10.1594/pangaea.422415
title: Sedimentology of core PG1169
DOI: 10.1594/pangaea.422416
title: Sedimentology of core PG1170
DOI: 10.1594/pangaea.422417
title: Sedimentology of core PG1171
DOI: 10.1594/pangaea.422418
title: Sedimentology of core PG1172
DOI: 10.1594/pangaea.422420
title: Sedimentology of core PG1174
DOI: 10.1594/pangaea.422421
title: Sedimentology of core PG1177
DOI: 10.1594/pangaea.422422
title: Sedimentology of core PG1178
DOI: 10.1594/pangaea.422423
title: Sedimentology of core PG1179
DOI: 10.1594/pangaea.422425
title: Sedimentology of core PG1181
DOI: 10.1594/pangaea.422426
title: Sedimentology of core PG1182
DOI: 10.1594/pangaea.422427
title: Sedimentology of core PG1183
DOI: 10.1594/pangaea.422428
title: Sedimentology of core PG1185
DOI: 10.1594/pangaea.377754
title: Radiosonde PS16/05758 during POLARSTERN cruise ANT-VIII/6 on 1990-04-29 04:28h

description: Height of tropopause 11119 m, precipitable water content 1.6187 cm
DOI: 10.1594/pangaea.422429
title: Sedimentology of core PG1186
DOI: 10.1594/pangaea.377755
title: Radiosonde PS16/05759 during POLARSTERN cruise ANT-VIII/6 on 1990-04-28 10:30h

description: Precipitable water content 0.8434 cm
DOI: 10.1594/pangaea.377756
title: Radiosonde PS16/05760 during POLARSTERN cruise ANT-VIII/6 on 1990-04-28 04:42h

description: Precipitable water content 0.9307 cm
DOI: 10.1594/pangaea.377757
title: Radiosonde PS16/05761 during POLARSTERN cruise ANT-VIII/6 on 1990-04-26 10:31h

description: Height of tropopause 10296 m, precipitable water content 0.8851 cm
DOI: 10.1594/pangaea.377758
title: Radiosonde PS16/05762 during POLARSTERN cruise ANT-VIII/6 on 1990-04-26 04:25h

description: Precipitable water content 1.1619 cm
DOI: 10.1594/pangaea.377759
title: Radiosonde PS16/05763 during POLARSTERN cruise ANT-VIII/6 on 1990-04-25 10:31h

description: Height of tropopause 9596 m, precipitable water content 0.8176 cm
DOI: 10.1594/pangaea.377760
title: Radiosonde PS16/05764 during POLARSTERN cruise ANT-VIII/6 on 1990-04-25 04:43h

description: Height of tropopause 10013 m, precipitable water content 0.8133 cm
DOI: 10.1594/pangaea.377761
title: Radiosonde PS16/05765 during POLARSTERN cruise ANT-VIII/6 on 1990-04-24 10:30h

description: Height of tropopause 9805 m, precipitable water content 0.8188 cm
DOI: 10.1594/pangaea.377762
title: Radiosonde PS16/05766 during POLARSTERN cruise ANT-VIII/6 on 1990-04-24 04:41h

description: Precipitable water content 0.7988 cm
DOI: 10.1594/pangaea.377763
title: Radiosonde PS16/05767 during POLARSTERN cruise ANT-VIII/6 on 1990-04-23 10:30h

description: Height of tropopause 9240 m, precipitable water content 0.6177 cm
DOI: 10.1594/pangaea.377764
title: Radiosonde PS16/05768 during POLARSTERN cruise ANT-VIII/6 on 1990-04-23 04:41h

description: Height of tropopause 9080 m, precipitable water content 0.5721 cm
DOI: 10.1594/pangaea.377765
title: Radiosonde PS16/05769 during POLARSTERN cruise ANT-VIII/6 on 1990-04-22 10:30h

description: Height of tropopause 8284 m, precipitable water content 0.663 cm
DOI: 10.1594/pangaea.377766
title: Radiosonde PS16/05770 during POLARSTERN cruise ANT-VIII/6 on 1990-04-22 04:42h

description: Height of tropopause 8422 m, precipitable water content 0.638 cm
DOI: 10.1594/pangaea.377767
title: Radiosonde PS16/05771 during POLARSTERN cruise ANT-VIII/6 on 1990-04-21 10:32h

description: Height of tropopause 8953 m, precipitable water content 0.6755 cm
DOI: 10.1594/pangaea.377768
title: Radiosonde PS16/05772 during POLARSTERN cruise ANT-VIII/6 on 1990-04-21 04:37h

description: Height of tropopause 8378 m, precipitable water content 0.7971 cm
DOI: 10.1594/pangaea.377769
title: Radiosonde PS16/05773 during POLARSTERN cruise ANT-VIII/6 on 1990-04-20 10:30h

description: Height of tropopause 8658 m, precipitable water content 0.6547 cm
DOI: 10.1594/pangaea.377770
title: Radiosonde PS16/05774 during POLARSTERN cruise ANT-VIII/6 on 1990-04-19 10:30h

description: Height of tropopause 10498 m, precipitable water content 1.0188 cm
DOI: 10.1594/pangaea.377771
title: Radiosonde PS16/05775 during POLARSTERN cruise ANT-VIII/6 on 1990-04-19 04:37h

description: Precipitable water content 0.7182 cm
DOI: 10.1594/pangaea.377772
title: Radiosonde PS16/05776 during POLARSTERN cruise ANT-VIII/6 on 1990-04-18 10:30h

description: Height of tropopause 8971 m, precipitable water content 0.4191 cm
DOI: 10.48597/segf-6y8s
title: Measurement of Potassium at Rickleå

description: Measurement of Potassium at Rickleå
DOI: 10.1594/pangaea.377773
title: Radiosonde PS16/05777 during POLARSTERN cruise ANT-VIII/6 on 1990-04-17 10:30h

description: Height of tropopause 8305 m, precipitable water content 0.3762 cm
DOI: 10.1594/pangaea.377774
title: Radiosonde PS16/05778 during POLARSTERN cruise ANT-VIII/6 on 1990-04-17 05:13h

description: Height of tropopause 8392 m, precipitable water content 0.4649 cm
DOI: 10.48597/seva-4zr8
title: Measurement of POPs (persistent organic pollutants) at Koksijde

description: Measurement of POPs (persistent organic pollutants) at Koksijde
DOI: 10.1594/pangaea.377775
title: Radiosonde PS16/05779 during POLARSTERN cruise ANT-VIII/6 on 1990-04-16 10:23h

description: Height of tropopause 8022 m, precipitable water content 0.5104 cm
DOI: 10.1594/pangaea.377776
title: Radiosonde PS16/05780 during POLARSTERN cruise ANT-VIII/6 on 1990-04-16 05:16h

description: Height of tropopause 7460 m, precipitable water content 0.6069 cm
DOI: 10.48597/smem-uj87
title: Measurement of Temperature at Montseny

description: Measurement of Temperature at Montseny
DOI: 10.1594/pangaea.377777
title: Radiosonde PS16/05781 during POLARSTERN cruise ANT-VIII/6 on 1990-04-15 10:31h

description: Height of tropopause 7918 m, precipitable water content 0.712 cm
DOI: 10.1594/pangaea.377778
title: Radiosonde PS16/05782 during POLARSTERN cruise ANT-VIII/6 on 1990-04-14 10:34h

description: Height of tropopause 9329 m, precipitable water content 0.653 cm
DOI: 10.48597/stnn-3bxc
title: Measurement of Particle_number_size_distribution at Melpitz

description: Measurement of Particle_number_size_distribution at Melpitz
DOI: 10.48597/sudh-8st7
title: Measurement of POPs (persistent organic pollutants) at Koksijde

description: Measurement of POPs (persistent organic pollutants) at Koksijde
DOI: 10.1594/pangaea.377779
title: Radiosonde PS16/05783 during POLARSTERN cruise ANT-VIII/6 on 1990-04-13 10:30h

description: Height of tropopause 10095 m, precipitable water content 0.6067 cm
DOI: 10.1594/pangaea.377780
title: Radiosonde PS16/05784 during POLARSTERN cruise ANT-VIII/6 on 1990-04-12 10:30h

description: Height of tropopause 9980 m, precipitable water content 0.7736 cm
DOI: 10.48597/t5rt-pg36
title: Measurement of Aerosol_light_backscattering_coefficient at Trinidad Head

description: Measurement of Aerosol_light_backscattering_coefficient at Trinidad Head
DOI: 10.1594/pangaea.377781
title: Radiosonde PS16/05785 during POLARSTERN cruise ANT-VIII/6 on 1990-04-12 04:46h

description: Precipitable water content 0.3209 cm
DOI: 10.1594/pangaea.377782
title: Radiosonde PS16/05786 during POLARSTERN cruise ANT-VIII/6 on 1990-04-11 10:31h

description: Precipitable water content 0.2575 cm
DOI: 10.48597/tt6k-9wmb
title: Measurement of Aerosol light scattering at Barrow

description: Measurement of Aerosol light scattering at Barrow
DOI: 10.1594/pangaea.377783
title: Radiosonde PS16/05787 during POLARSTERN cruise ANT-VIII/6 on 1990-04-11 04:40h

description: Precipitable water content 0.3211 cm
DOI: 10.1594/pangaea.377784
title: Radiosonde PS16/05788 during POLARSTERN cruise ANT-VIII/6 on 1990-04-10 10:31h

description: Height of tropopause 8907 m, precipitable water content 0.4271 cm
DOI: 10.48597/ttpg-ndum
title: Measurement of Aerosol light scattering at Cabauw Zijdeweg

description: Measurement of Aerosol light scattering at Cabauw Zijdeweg
DOI: 10.34740/kaggle/dsv/5965952
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.13012/b2idb-7486289_v1
title: Datasets for "Phylogenomics of flavobacterial insect nutritional endosymbionts with implications for the phylogeny of their hosts"

description: The file “Fla.fasta”, comprising 10526 positions, is the concatenated amino acid alignments of 51 orthologues of 182 bacterial strains. It was used for the maximum likelihood and maximum parsimony analyses of Flavobacteriales. Bacterial species names and strains were used as the sequence names, host names of insect endosymbionts were shown in brackets. The file “16S.fasta” is the alignment of 233 bacterial 16S rRNA sequences. It contains 1455 positions and was used for the maximum likelihood analysis of flavobacterial insect endosymbionts. The names of endosymbiont strains were replaced by the name of their hosts. In addition to the species names, National Center for Biotechnology Information (NCBI) accession numbers were also indicated in the sequence names (e.g., sequence “Cicadellidae_Deltocephalinae_Macrostelini_Macrosteles_striifrons_AB795320” is the 16S rRNA of Macrosteles striifrons (Cicadellidae: Deltocephalinae: Macrostelini) with a NCBI accession number AB795320). The file “Sulcia_pep.fasta” is the concatenated amino acid alignments of 131 orthologues of “Candidatus Sulcia muelleri” (Sulcia). It contains 41970 positions and presents 101 Sulcia strains and 3 Blattabacterium strains. This file was used for the maximum likelihood analysis of Sulcia. The file “Sulcia_nucleotide.fasta” is the concatenated nucleotide alignment corresponding to the sequences in “Sulcia_pep.fasta” but also comprises the alignment of 16S rRNA. It has 127339 positions and was used for the maximum likelihood and maximum parsimony analyses of Sulcia. Individual gene alignments (16S rRNA and 131 orthologues of Sulcia and Blattabacterium) are deposited in the compressed file “individual_gene_alignments.zip”, which were used to construct gene trees for multispecies coalescent analysis. The names of Sulcia strains were replaced by the name of their hosts in “Sulcia_pep.fasta”, “Sulcia_nucleotide.fasta” and the files in “individual_gene_alignments.zip”. In all the alignment files, gaps are indicated by “-”.
DOI: 10.6084/m9.figshare.19249142
title: Development of the Online Continuing Professional Development Modules in Health Professions Education

description: This table contains the preferred topics and design of the CPD modules
DOI: 10.6084/m9.figshare.19249142.v1
title: Development of the Online Continuing Professional Development Modules in Health Professions Education

description: This table contains the preferred topics and design of the CPD modules
DOI: 10.6084/m9.figshare.17839318
title: Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding

description: Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions. In this article, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding. The <i>auxiliary variables</i> approach leverages variables that are not causally associated with the outcome; in the case of a univariate confounder, our method only requires one auxiliary variable, unlike existing instrumental variable methods that would require as many instruments as there are treatments. An alternative <i>null treatments</i> approach relies on the assumption that at least half of the confounded treatments have no causal effect on the outcome, but does not require a priori knowledge of which treatments are null. Our identification strategies do not impose parametric assumptions on the outcome model and do not rest on estimation of the confounder. This article extends and generalizes existing work on unmeasured confounding with a single treatment and models commonly used in bioinformatics. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17839318.v2
title: Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding

description: Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions. In this article, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding. The <i>auxiliary variables</i> approach leverages variables that are not causally associated with the outcome; in the case of a univariate confounder, our method only requires one auxiliary variable, unlike existing instrumental variable methods that would require as many instruments as there are treatments. An alternative <i>null treatments</i> approach relies on the assumption that at least half of the confounded treatments have no causal effect on the outcome, but does not require a priori knowledge of which treatments are null. Our identification strategies do not impose parametric assumptions on the outcome model and do not rest on estimation of the confounder. This article extends and generalizes existing work on unmeasured confounding with a single treatment and models commonly used in bioinformatics. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17839291
title: Statistical Inference with Local Optima

description: We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic normality and the bootstrap approach. In particular, we analyze the coverage deficiency due to finite number of random initializations. We also investigate the CIs by inverting the likelihood ratio test, the score test, and the Wald test, and we show that the resulting CIs may be very different. We propose a two-sample test procedure even when the maximum likelihood estimator is intractable. In addition, we analyze the performance of the EM algorithm under random initializations and derive the coverage of a CI with a finite number of initializations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17839291.v1
title: Statistical Inference with Local Optima

description: We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood function. We derive the population quantity that is the target of this estimator and study the properties of confidence intervals (CIs) constructed from asymptotic normality and the bootstrap approach. In particular, we analyze the coverage deficiency due to finite number of random initializations. We also investigate the CIs by inverting the likelihood ratio test, the score test, and the Wald test, and we show that the resulting CIs may be very different. We propose a two-sample test procedure even when the maximum likelihood estimator is intractable. In addition, we analyze the performance of the EM algorithm under random initializations and derive the coverage of a CI with a finite number of initializations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.17839318.v1
title: Identifying effects of multiple treatments in the presence of unmeasured confounding

description: Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions. In this paper, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding. The <i>auxiliary variables</i> approach leverages variables that are not causally associated with the outcome; in the case of a univariate confounder, our method only requires one auxiliary variable, unlike existing instrumental variable methods that would require as many instruments as there are treatments. An alternative <i>null treatments</i> approach relies on the assumption that at least half of the confounded treatments have no causal effect on the outcome, but does not require a priori knowledge of which treatments are null. Our identification strategies do not impose parametric assumptions on the outcome model and do not rest on estimation of the confounder. This paper extends and generalizes existing work on unmeasured confounding with a single treatment and models commonly used in bioinformatics.
DOI: 10.6084/m9.figshare.17839324
title: Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration

description: In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time. This random time is used as a link to jointly model the longitudinal and survival data where the unknown time origins are integrated out in the joint likelihood function, which facilitates unbiased and consistent estimation. Since the disease progression pattern naturally predicts time-to-event, we further propose a new functional survival model using the registration function as a predictor of the time-to-event. The asymptotic consistency and semiparametric efficiency of the proposed models are proved. Simulation studies and two real data applications demonstrate the effectiveness of this new approach. Supplementary materials for this article are available online.