cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2jf224
title: CCDC 2336255: Experimental Crystal Structure Determination

description: Related Article: Xuezhi Yu, Jie Tang, Caijin Lei, Chungui Xue, Guangbin Cheng, Chuan Xiao, Hongwei Yang|2024|J.Mater.Chem.A|12|19513|doi:10.1039/D4TA03537B
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.cc2jf235
title: CCDC 2336256: Experimental Crystal Structure Determination

description: Related Article: Xuezhi Yu, Jie Tang, Caijin Lei, Chungui Xue, Guangbin Cheng, Chuan Xiao, Hongwei Yang|2024|J.Mater.Chem.A|12|19513|doi:10.1039/D4TA03537B
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.cc2jf2bd
title: CCDC 2336263: Experimental Crystal Structure Determination

description: Related Article: Xuezhi Yu, Jie Tang, Caijin Lei, Chungui Xue, Guangbin Cheng, Chuan Xiao, Hongwei Yang|2024|J.Mater.Chem.A|12|19513|doi:10.1039/D4TA03537B
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.cc2jf2sv
title: CCDC 2336277: Experimental Crystal Structure Determination

description: Related Article: Xuezhi Yu, Jie Tang, Caijin Lei, Chungui Xue, Guangbin Cheng, Chuan Xiao, Hongwei Yang|2024|J.Mater.Chem.A|12|19513|doi:10.1039/D4TA03537B
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.cc2jk8ky
title: CCDC 2340300: Experimental Crystal Structure Determination

description: Related Article: Constantin G. Daniliuc, Markus Neumann, Roland Fröhlich, Gerhard Erker|2024|CSD Communication|||
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.cc2jljc1
title: CCDC 2341503: Experimental Crystal Structure Determination

description: Related Article: Therese S. S. Kjær, Jakob B. Grinderslev, Lasse N. Skov, Torben R. Jensen|2024|J.Mater.Chem.A|12|19485|doi:10.1039/D4TA02293A
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.cc2jtd70
title: CCDC 2348102: Experimental Crystal Structure Determination

description: Related Article: Roman O. Pankov, Ignatii R. Tarabrin, Alexandra G. Son, Mikhail E. Minyaev, Darya O. Prima, Valentine P. Ananikov|2024|Dalton Trans.|53|12503|doi:10.1039/D4DT01304B
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.cc2jvq7c
title: CCDC 2349373: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Jin, Bao-Nguyen T. Nguyen, Robert P. Davies|2024|Dalton Trans.|53|12554|doi:10.1039/D4DT01418A
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.cc2jvq8d
title: CCDC 2349374: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Jin, Bao-Nguyen T. Nguyen, Robert P. Davies|2024|Dalton Trans.|53|12554|doi:10.1039/D4DT01418A
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.cc2jvq9f
title: CCDC 2349375: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Jin, Bao-Nguyen T. Nguyen, Robert P. Davies|2024|Dalton Trans.|53|12554|doi:10.1039/D4DT01418A
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.cc2jvqbg
title: CCDC 2349376: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Jin, Bao-Nguyen T. Nguyen, Robert P. Davies|2024|Dalton Trans.|53|12554|doi:10.1039/D4DT01418A
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.cc2jvqch
title: CCDC 2349377: Experimental Crystal Structure Determination

description: Related Article: Xiaodong Jin, Bao-Nguyen T. Nguyen, Robert P. Davies|2024|Dalton Trans.|53|12554|doi:10.1039/D4DT01418A
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.cc2jyhhh
title: CCDC 2352047: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2024|CSD Communication|||
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.cc2jyhll
title: CCDC 2352050: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2024|CSD Communication|||
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: 10.6084/m9.figshare.24936474.v2
title: Supplementary code

description: R code for the article "Developing a tool to operationalize seed provenancing for ecosystem restoration: a case study in the Brazilian Cerrado"
DOI: 10.15480/882.9069
title: Dataset of Drop-Tower Experiments with Various Ice Shapes Impacting a Rigid Structure

description: Understanding how various ice shapes affect loads experienced by a ship hull or structure is crucial. Therefore, ice-structure interaction with a focus on the shape of the impacting ice was investigated. In the study drop tower experiments were conducted at the Institute of Ship Structural Design and Analysis of the Hamburg University of Technology to investigate ice impacts on a small scale. Ice specimens with diverse shapes (cylinder, cone, dome, wedge, ellipse, and other variations) were subjected to single impacts against a steel plate. Analysis revealed two main failure modes based on ice geometry, showcasing significant differences in load magnitude. Correlations between contact area, cone angle, specimen length to peak force, pressure, and energy were observed. The experimental setup, test matrix including the dimensions of the tested ice shapes and the data analysis are soon to be published. The experimental data are provided here. The data include the summed up force signal of the load cells and the displacement signal of the drop hammer. The data have been cropped to focus on the relevant portion around the impact load peak. Each file contains a single test run encompassing the time signal in seconds, force signal in kilo Newtons, and displacement signal in millimeters.
DOI: 10.15468/dl.nsacj3
title: Occurrence Download

description: A dataset containing 64449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Canis latrans Say, 1823"
 ]
}

 The dataset includes 64449 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063015-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18117/9svg-9v48
title: Supporting Data for: Segregative Phase Separation of Strong Polyelectrolyte Complexes at High Salt and High Polymer Concentrations

description: This dataset contains photographs, thermogravimetric analysis (TGA) profiles, Fourier transform infrared (FTIR) spectra, and nuclear magnetic resonance (NMR) spectra of samples of poly(styrene sulfonate), poly(diallyldimethylammonium), potassium bromide, and water reported in the authors' manuscript "Segregative Phase Separation of Strong Polyelectrolyte Complexes at High Salt and High Polymer Concentrations."
DOI: 10.15468/dl.x8pfhn
title: Occurrence Download

description: A dataset containing 357 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anaphalis javanica Sch.Bip."
 ]
}

 The dataset includes 357 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063021-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25674049
title: Bayesian Optimal Designs for Multi-Arm Multi-Stage Phase II Randomized Clinical Trials with Multiple Endpoints

description: There is a growing need to evaluate of multiple competing drugs in phase II trials where the number of patients is often limited, and simultaneous assessment of both efficacy and toxicity is crucial. To avoid the waste of research resources, it is indeed more efficient to screen multiple drugs at once in a platform phase II setting. We aim to adapt the Bayesian optimal phase II (BOP2) design to multi-arm trials for both uncontrolled and controlled settings. The binary efficacy and toxicity endpoints are modeled by a Dirichlet distribution as a vector of four outcomes. Posterior marginal distributions at each analysis are used to derive the monitoring threshold that varies during the trial. We control the family-wise Type I error rate for multiple comparison against a common reference value or a shared control. We conduct simulation studies under both uncontrolled and controlled settings to evaluate the operating characteristics of the proposed design. Our simulations demonstrate that the design exhibits better operating characteristics compared to a design using a constant threshold and is less sensitive to changes in accrual rate relative to what was planned. The design had promising operating characteristics and could be used in phase II oncology clinical trials for evaluating multiple drugs at a time.
DOI: 10.6084/m9.figshare.25674049.v2
title: Bayesian Optimal Designs for Multi-Arm Multi-Stage Phase II Randomized Clinical Trials with Multiple Endpoints

description: There is a growing need to evaluate of multiple competing drugs in phase II trials where the number of patients is often limited, and simultaneous assessment of both efficacy and toxicity is crucial. To avoid the waste of research resources, it is indeed more efficient to screen multiple drugs at once in a platform phase II setting. We aim to adapt the Bayesian optimal phase II (BOP2) design to multi-arm trials for both uncontrolled and controlled settings. The binary efficacy and toxicity endpoints are modeled by a Dirichlet distribution as a vector of four outcomes. Posterior marginal distributions at each analysis are used to derive the monitoring threshold that varies during the trial. We control the family-wise Type I error rate for multiple comparison against a common reference value or a shared control. We conduct simulation studies under both uncontrolled and controlled settings to evaluate the operating characteristics of the proposed design. Our simulations demonstrate that the design exhibits better operating characteristics compared to a design using a constant threshold and is less sensitive to changes in accrual rate relative to what was planned. The design had promising operating characteristics and could be used in phase II oncology clinical trials for evaluating multiple drugs at a time.
DOI: 10.6084/m9.figshare.24768805
title: DODII: Bayesian Dose Optimization Design for Randomized Phase II Trials

description: The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improved anti-cancer activity. With the initiation of “Project Optimus” at the FDA, a randomized dose optimization study at the early stage of drug development has become necessary for oncology drug development. We propose a Bayesian Dose Optimization Design for Randomized Phase II trials (DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach in a randomized design, where efficacy and toxicity endpoints are jointly used to inform the dose selection. The adaptive feature of the DODII design will terminate a suboptimal dose at an interim analysis under two circumstances: (a) when efficacy or safety data is unpromising, (b) when the other dose option is clearly better. The critical values of the decision rule for the DODII design can be enumerated prior to the initiation of the clinical study, which makes it practically easy to implement. Through simulation studies, we showed that the DODII design has favorable operating characteristics with controlled selection error, higher power to select the optimal dose option, and reduced total sample size as compared to the other commonly used methods.
DOI: 10.6084/m9.figshare.24768805.v1
title: DODII: Bayesian Dose Optimization Design for Randomized Phase II Trials

description: The traditional MTD-based dose selection paradigm commonly used for cytotoxic chemotherapies might not be optimal for targeted therapies because a higher dose does not necessarily result in improved anti-cancer activity. With the initiation of “Project Optimus” at the FDA, a randomized dose optimization study at the early stage of drug development has become necessary for oncology drug development. We propose a Bayesian Dose Optimization Design for Randomized Phase II trials (DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach in a randomized design, where efficacy and toxicity endpoints are jointly used to inform the dose selection. The adaptive feature of the DODII design will terminate a suboptimal dose at an interim analysis under two circumstances: (a) when efficacy or safety data is unpromising, (b) when the other dose option is clearly better. The critical values of the decision rule for the DODII design can be enumerated prior to the initiation of the clinical study, which makes it practically easy to implement. Through simulation studies, we showed that the DODII design has favorable operating characteristics with controlled selection error, higher power to select the optimal dose option, and reduced total sample size as compared to the other commonly used methods.
DOI: 10.6084/m9.figshare.25674049.v1
title: Bayesian optimal designs for multi-arm multi-stage phase II randomized clinical trials with multiple endpoints

description: There is a growing need to evaluate of multiple competing drugs in phase II trials where the number of patients is often limited, and simultaneous assessment of both efficacy and toxicity is crucial. To avoid the waste of research resources, it is indeed more efficient to screen multiple drugs at once in a platform phase II setting. We aim to adapt the Bayesian optimal phase II (BOP2) design to multi-arm trials for both uncontrolled and controlled settings. The binary efficacy and toxicity endpoints are modelled by a Dirichlet distribution as a vector of four outcomes. Posterior marginal distributions at each analysis are used to derive the monitoring threshold that varies during the trial. We control the family-wise type I error rate for multiple comparison against a common reference value or a shared control. We conduct simulation studies under both uncontrolled and controlled settings to evaluate the operating characteristics of the proposed design. Our simulations demonstrate that the design exhibits better operating characteristics compared to a design using a constant threshold and is less sensitive to changes in accrual rate relative to what was planned. The design had promising operating characteristics and could be used in phase II oncology clinical trials for evaluating multiple drugs at a time.
DOI: 10.6084/m9.figshare.24796171
title: Conditional and Unconditional Treatment Effects in Randomized Clinical Trials: Estimands, Estimation, and Interpretation

description: ICH E9(R1) specifies the importance of precisely defining the treatment effect for clinical trials—to inform patient choices and facilitate evidence-based decision-making. FDA’s guidance on covariate adjustment encourages the judicious use of baseline covariates to enhance efficiency. Careful consideration is required when adjusting for covariates in nonlinear models such as logistic regression and Cox regression. For these nonlinear models, including baseline covariates can change the targeted treatment effect (estimand). It is also crucial that the proposed statistical analyses align with the estimand of interest. Covariate-adjusted estimators used for unconditional treatment effect are typically robust to misspecification of the used regression models. Despite extensive literature and recommendations by the FDA on the statistical theory and properties of these methods, the real-life application of such methodologies is still limited. Moreover, a few open questions require further discussion, especially for time-to-event outcomes. In this article, we present causal estimands definition of conditional and unconditional treatment effects in randomized clinical trials. We also evaluate different estimation methods for estimating conditional and unconditional treatment effects for the binary and time-to-event endpoints. We also discuss practical considerations in choosing the conditional versus unconditional effect, implementing the estimation methods, and interpreting results.
DOI: 10.6084/m9.figshare.24796171.v1
title: Conditional and Unconditional treatment effects in randomized clinical trials: Estimands, Estimation, and Interpretation

description: ICH E9(R1) specifies the importance of precisely defining the treatment effect for clinical trials – to inform patient choices and facilitate evidence-based decision-making. FDA's guidance on covariate adjustment encourages the judicious use of baseline covariates to enhance efficiency. Careful consideration is required when adjusting for covariates in nonlinear models such as logistic regression and Cox regression. For these nonlinear models, including baseline covariates can change the targeted treatment effect (estimand). It is also crucial that the proposed statistical analyses align with the estimand of interest. Covariate-adjusted estimators used for unconditional treatment effect are typically robust to misspecification of the used regression models. Despite extensive literature and recommendations by the FDA on the statistical theory and properties of these methods, the real-life application of such methodologies is still limited. Moreover, a few open questions require further discussion, especially for time-to-event outcomes. In this paper, we present causal estimands definition of conditional and unconditional treatment effects in randomized clinical trials. We also evaluate different estimation methods for estimating conditional and unconditional treatment effects for the binary and time-to-event endpoints. We also discuss practical considerations in choosing the conditional vs. unconditional effect, implementing the estimation methods, and interpreting results.