cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25745/gkvk-7a52
title: Primary Water Level Time Series - FM 521

description: FM 521 primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2012-02-29 18:00 GMT through 2012-07-03 10:48 GMT.
DOI: 10.14469/hpc/10988
title: The rational design of helium bonds
DOI: 10.14469/hpc/10989
title: Design, Synthesis, and Evaluation of a Helicenoidal DMAP Lewis Base Catalyst
DOI: 10.14469/hpc/10990
title: The past, present and future of Scientific discourse
DOI: 10.14469/hpc/10991
title: A Computational Evaluation of the Evidence for the Synthesis of 1,3-Dimethylcyclobutadiene in the Solid State and Aqueous Solution
DOI: 10.14469/hpc/10993
title: Silver-Catalysed Enantioselective Addition of OH and NH Bonds to Allenes: A New Model for Stereoselectivity Based on Noncovalent Interactions
DOI: 10.14469/hpc/10994
title: Silver-Catalysed Enantioselective Addition of OH and NH Bonds to Allenes: A New Model for Stereoselectivity Based on Noncovalent Interactions
DOI: 10.14469/hpc/10995
title: Silver-Catalysed Enantioselective Addition of OH and NH Bonds to Allenes: A New Model for Stereoselectivity Based on Noncovalent Interactions
DOI: 10.14469/hpc/10997
title: Chemical datuments as scientific enablers
DOI: 10.14469/hpc/10998
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 2a
DOI: 10.14469/hpc/10999
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 3a
DOI: 10.14469/hpc/11000
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 4a
DOI: 10.14469/hpc/11001
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 5a
DOI: 10.14469/hpc/11002
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 6a
DOI: 10.14469/hpc/11003
title: Experimental and Computational Investigation of the Mechanism of Carbon Dioxide/Cyclohexene Oxide Copolymerization Using A Dizinc Catalyst. Table 7a
DOI: 10.14469/hpc/11005
title: The Vinylcarbene – Cyclopropene Equilibrium of Silicon: an Isolable Disilenyl Silylene
DOI: 10.18112/openneuro.ds003004.v1.1.1
title: Imagined Emotion Study
DOI: 10.14469/hpc/11006
title: N-heteroatom substitution effect in 3-aza-cope rearrangements
DOI: 10.14469/hpc/11007
title: Mechanistic Diversity in Thermal Fragmentation Reactions: A Computational Exploration of CO and CO2 Extrusions from Five-Membered Rings
DOI: 10.14469/hpc/11008
title: Mechanistic and Chiroptical Studies on the Desulfurization of Epidithiodioxopiperazines Reveal Universal Retention of Configuration at the Bridgehead Carbon Atoms
DOI: 10.18112/openneuro.ds004173.v1.0.1
title: Movement-related artefacts (MR-ART) dataset
DOI: 10.25745/bwzh-gh97
title: Primary Water Level Time Series - Pelican Point

description: Pelican Point primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2012-04-29 19:06 GMT through 2012-08-01 16:00 GMT.
DOI: 10.6084/m9.figshare.20560443
title: Dataset to reproduce results in IsoNet paper

description: This dataset contains 3 tomograms of HIV particles, one IsoNet corrected HIV tomogram, one tomogram of a neuronal synapse with its IsoNet reconstruction, and 10 simulated apoferritin subtomograms. Users should be able to reproduce result in the article: https://www.biorxiv.org/content/10.1101/2021.07.17.452128v1.full
DOI: 10.5517/ccdc.csd.cc2b7ngq
title: CCDC 2152313: Experimental Crystal Structure Determination

description: Related Article: Daisuke Yokose, Yuki Nagashima, Suzuka Kinoshita, Juntaro Nogami, Ken Tanaka|2022|Angew.Chem.,Int.Ed.|61|e202202542|doi:10.1002/anie.202202542
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.cc2b7nhr
title: CCDC 2152314: Experimental Crystal Structure Determination

description: Related Article: Daisuke Yokose, Yuki Nagashima, Suzuka Kinoshita, Juntaro Nogami, Ken Tanaka|2022|Angew.Chem.,Int.Ed.|61|e202202542|doi:10.1002/anie.202202542
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.cc2b7njs
title: CCDC 2152315: Experimental Crystal Structure Determination

description: Related Article: Daisuke Yokose, Yuki Nagashima, Suzuka Kinoshita, Juntaro Nogami, Ken Tanaka|2022|Angew.Chem.,Int.Ed.|61|e202202542|doi:10.1002/anie.202202542
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: 10.25745/3fft-3x46
title: Primary Water Level Time Series - Oyster Lake

description: Oyster Lake primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2012-08-14 12:18 GMT through 2012-12-04 10:06 GMT.
DOI: 10.25745/sbym-5y91
title: Primary Water Level Time Series - Shoal Water Flats

description: Shoal Water Flats primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2012-01-26 12:24 GMT through 2012-05-03 11:48 GMT.
DOI: 10.25745/5b1j-4661
title: Primary Water Level Time Series - Arroyo City

description: Arroyo City primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 2012-01-04 18:06 GMT through 2012-05-08 10:36 GMT.
DOI: 10.25810/s7n-vh24.31
title: African Newspaper Coverage of Climate Change or Global Warming, 2004-2022 - July 2022

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.20559912
title: Resultados de simulacion numerica.xlsx

description: <br> Tables and data resulting from the numerical simulation
DOI: 10.6084/m9.figshare.20559912.v1
title: Resultados de simulacion numerica.xlsx

description: <br> Tables and data resulting from the numerical simulation
DOI: 10.6084/m9.figshare.20559912.v2
title: Resultados de simulacion numerica.xlsx

description: <br> Tables and data resulting from the numerical simulation
DOI: 10.6084/m9.figshare.20560722
title: Supplementary tables

description: Supplementary tables for the article
DOI: 10.6084/m9.figshare.20560722.v1
title: Supplementary tables

description: Supplementary tables for the article
DOI: 10.25745/hjcc-ad06
title: Primary Water Level Time Series - Christmas Bay

description: Christmas Bay primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 1991-10-29 04:00 GMT through 1995-08-22 12:54 GMT.
DOI: 10.6084/m9.figshare.9762731
title: Valid Inference Corrected for Outlier Removal

description: Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and <i>p</i>-values on the remaining data as if this were the original data collected. This standard “detect-and-forget” approach has been shown to be problematic, and in this article we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real datasets to illustrate how our inferential results can differ from the traditional <i>detect-and-forget</i> strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9762731.v4
title: Valid Inference Corrected for Outlier Removal

description: Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and <i>p</i>-values on the remaining data as if this were the original data collected. This standard “detect-and-forget” approach has been shown to be problematic, and in this article we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real datasets to illustrate how our inferential results can differ from the traditional <i>detect-and-forget</i> strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9762731.v3
title: Valid Inference Corrected for Outlier Removal

description: Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and <i>p</i>-values on the remaining data as if this were the original data collected. This standard “detect-and-forget” approach has been shown to be problematic, and in this article we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real datasets to illustrate how our inferential results can differ from the traditional <i>detect-and-forget</i> strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10261058
title: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming

description: Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker and Mizera shows that modern convex optimization techniques—in particular, interior point methods—are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem; second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations; third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems; fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of the SQP approach in experiments on synthetic datasets and a large genetic association dataset. In large datasets (n≈106 observations, m≈103 mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia and in an R package available on CRAN (https://CRAN.R-project.org/package=mixsqp). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10261058.v4
title: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming

description: Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker and Mizera shows that modern convex optimization techniques—in particular, interior point methods—are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem; second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations; third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems; fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of the SQP approach in experiments on synthetic datasets and a large genetic association dataset. In large datasets (n≈106 observations, m≈103 mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia and in an R package available on CRAN (https://CRAN.R-project.org/package=mixsqp). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10261058.v3
title: A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming

description: Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric empirical Bayes methods. Maximum likelihood of mixture proportions has traditionally been solved using the expectation maximization (EM) algorithm, but recent work by Koenker and Mizera shows that modern convex optimization techniques—in particular, interior point methods—are substantially faster and more accurate than EM. Here, we develop a new solution based on sequential quadratic programming (SQP). It is substantially faster than the interior point method, and just as accurate. Our approach combines several ideas: first, it solves a reformulation of the original problem; second, it uses an SQP approach to make the best use of the expensive gradient and Hessian computations; third, the SQP iterations are implemented using an active set method to exploit the sparse nature of the quadratic subproblems; fourth, it uses accurate low-rank approximations for more efficient gradient and Hessian computations. We illustrate the benefits of the SQP approach in experiments on synthetic datasets and a large genetic association dataset. In large datasets (n≈106 observations, m≈103 mixture components), our implementation achieves at least 100-fold reduction in runtime compared with a state-of-the-art interior point solver. Our methods are implemented in Julia and in an R package available on CRAN (https://CRAN.R-project.org/package=mixsqp). Supplementary materials for this article are available online.
DOI: 10.25745/ke30-gg90
title: Primary Water Level Time Series - Clear Lake

description: Clear Lake primary water level measurements (PWL). Raw data as received at CBI in CSV format. Time stamp following ISO 8601. PWL in meters referenced to station datum. Time series span: 1991-11-11 23:00 GMT through 2008-09-13 02:00 GMT
DOI: 10.5517/ccdc.csd.cc20t3fd
title: CCDC 1871173: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc20t3hg
title: CCDC 1871175: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnn7
title: CCDC 2104269: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnp8
title: CCDC 2104270: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnq9
title: CCDC 2104271: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnrb
title: CCDC 2104272: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnsc
title: CCDC 2104273: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mntd
title: CCDC 2104274: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc28mnvf
title: CCDC 2104275: Experimental Crystal Structure Determination

description: Related Article: Sandip Naskar, Rajib Moi, Indrajit Das, Kumar Biradha|2022|Angew.Chem.,Int.Ed.|61|e202204141|doi:10.1002/anie.202204141
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.cc29281q
title: CCDC 2117332: Experimental Crystal Structure Determination

description: Related Article: Mengfu Dai, Zhimin Sun, Liang-An Chen|2022|Angew.Chem.,Int.Ed.|61|e202203835|doi:10.1002/anie.202203835
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.