cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27t7t4
title: CCDC 2079846: Experimental Crystal Structure Determination

description: Related Article: Aihui Liang, Yao Gao, Reza Asadpour, Zitang Wei, Blake P. Finkenauer, Linrui Jin, Jiaqi Yang, Kang Wang, Ke Chen, Peilin Liao, Chenhui Zhu, Libai Huang, Bryan W. Boudouris, Muhammad Ashraf Alam, Letian Dou|2021|J.Am.Chem.Soc.|143|15215|doi:10.1021/jacs.1c06337
DOI: 10.5517/ccdc.csd.cc27t7y8
title: CCDC 2079850: Experimental Crystal Structure Determination

description: Related Article: Aihui Liang, Yao Gao, Reza Asadpour, Zitang Wei, Blake P. Finkenauer, Linrui Jin, Jiaqi Yang, Kang Wang, Ke Chen, Peilin Liao, Chenhui Zhu, Libai Huang, Bryan W. Boudouris, Muhammad Ashraf Alam, Letian Dou|2021|J.Am.Chem.Soc.|143|15215|doi:10.1021/jacs.1c06337
DOI: 10.6073/pasta/4c9d740edae7f81cb2612426304ba7b7
title: The ecocomDP Annotation Dictionary

description: This data package contains a comprehensive set of semantic annotations (URIs and labels) from datasets in the ecocomDP format published in EDI. The table of annotations, referred to as the ecocomDP Annotation Dictionary, can be viewed in RStudio using the view_annotation_dictionary function of the ecocomDP R package.
DOI: 10.6084/m9.figshare.12860422
title: Identification of Structural Vector Autoregressions by Stochastic Volatility

description: We propose to exploit stochastic volatility for statistical identification of structural vector autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for maximum likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroscedastic SVARs. We apply the model to study the importance of oil supply shocks for driving oil prices. Since shocks identified by heteroscedasticity may not be economically meaningful, we exploit the framework to test instrumental variable restrictions which are overidentifying in the heteroscedastic model. Our findings suggest that conventional supply shocks are negligible, while news shocks about future supply account for almost all the variation in oil prices.
DOI: 10.6084/m9.figshare.12860422.v2
title: Identification of Structural Vector Autoregressions by Stochastic Volatility

description: We propose to exploit stochastic volatility for statistical identification of structural vector autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for maximum likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroscedastic SVARs. We apply the model to study the importance of oil supply shocks for driving oil prices. Since shocks identified by heteroscedasticity may not be economically meaningful, we exploit the framework to test instrumental variable restrictions which are overidentifying in the heteroscedastic model. Our findings suggest that conventional supply shocks are negligible, while news shocks about future supply account for almost all the variation in oil prices.
DOI: 10.6084/m9.figshare.12860422.v1
title: Identification of Structural Vector Autoregressions by Stochastic Volatility

description: We propose to exploit stochastic volatility for statistical identification of Structural Vector Autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for Maximum Likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroskedastic SVARs. We apply the model to study the importance of oil supply shocks for driving oil prices. Since shocks identified by heteroskedasticity may not be economically meaningful, we exploit the framework to test instrumental variable restrictions which are overidentifying in the heteroskedastic model. Our findings suggest that conventional supply shocks are negligible, while news shocks about future supply account for almost all the variation in oil prices.
DOI: 10.6084/m9.figshare.11866179.v1
title: Dynamic Semiparametric Factor Model With Structural Breaks

description: For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. Importantly, our results hold for both large and small breaks in the factor dependency structure. The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16699726
title: Title

description: Description
DOI: 10.6084/m9.figshare.16699726.v1
title: Title

description: Description
DOI: 10.6084/m9.figshare.12849659
title: On the sources of information in the moment structure of dynamic macroeconomic models

description: What features of the data are the key sources of information about the parameters in structural macroeconomic models? As such models grow in size and complexity, the answer to this question has become increasingly difficult. This paper shows how to identify the main sources of parameter information across different parts of the moment structure of macroeconomic models. In particular, we propose a measure of the relative contribution of information by a given subset of moments with respect to any parameter of interest. The measure is trivial to compute even for large-scale models with many free parameters and observed variables. We illustrate our method with an application to a news-driven business cycle model developed by Schmitt-Grohé and Uribe (2012).
DOI: 10.6084/m9.figshare.12849659.v2
title: On the sources of information in the moment structure of dynamic macroeconomic models

description: What features of the data are the key sources of information about the parameters in structural macroeconomic models? As such models grow in size and complexity, the answer to this question has become increasingly difficult. This paper shows how to identify the main sources of parameter information across different parts of the moment structure of macroeconomic models. In particular, we propose a measure of the relative contribution of information by a given subset of moments with respect to any parameter of interest. The measure is trivial to compute even for large-scale models with many free parameters and observed variables. We illustrate our method with an application to a news-driven business cycle model developed by Schmitt-Grohé and Uribe (2012).
DOI: 10.6084/m9.figshare.12849659.v1
title: On the Sources of Information in the Moment Structure of Dynamic Macroeconomic Models

description: What features of the data are the key sources of information about the parameters in structural macroeconomic models? As such models grow in size and complexity, the answer to this question has become increasingly difficult. This article shows how to identify the main sources of parameter information across different parts of the moment structure of macroeconomic models. In particular, we propose a measure of the relative contribution of information by a given subset of moments with respect to any parameter of interest. The measure is trivial to compute even for large-scale models with many free parameters and observed variables. We illustrate our method with an application to a news-driven business cycle model developed by Schmitt-Grohé and Uribe. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.5534925
title: Townsend_et_al_2021_2

description: Mathematica files for Townsend et al. 2021 preprint.
DOI: 10.23636/hwm2-2y73
title: Digitised 19th Century Books - Metadata - 01/09/2021

description: This dataset contains metadata for resources belonging to the British Library’s digitised printed books (18th-19th century) collection (https://www.bl.uk/collection-guides/digitised-printed-books). This metadata has been extracted from British Library catalogue records. The metadata held within our main catalogue is updated regularly. This metadata dataset should be considered a snapshot of this metadata. For a definitive metadata record for an item, you should consult the British Library catalogue at https://explore.bl.uk.
DOI: 10.5281/zenodo.5537101
title: Artifact for a simulation approach to evaluate the effects of memory demands on disaggregated memory

description: This artifact is intended to make available the output files generated during our simulations to analyze the effects of memory demands on disaggregated memory systems. It also includes the scripts necessary to format and reproduce the figures in our paper.
DOI: 10.6084/m9.figshare.16699594
title: ILIAD data - cleaned

description: A secondary data analysis was conducted to using data from the Independent Lexical Instruction and Development (ILIAD) project, a supplementary vocabulary program that taught 377 sophisticated vocabulary words across three years to 350 first, second, and third grade students (Goldstein et al., 2017). Two outcome measures were used to assess word learning, a decontextualized definition task and an expressive labeling task. A word was considered learned when children defined and/or labeled vocabulary targets, respectively. The target vocabulary words were characterized for analysis based on available database estimates of their individual word frequency, age of acquisition, phonotactic probability, neighborhood density, and level of concreteness. First, we compare how well the models fit the data using the full ILIAD dataset (first, second, and third, grades combined). While we want to highlight the differences in modeling techniques for each individual grade level, it would be exhaustive. We randomly selected data from first grade to illustrate the unique differences between MARS, multiple regression, and stepwise regression. This data contains lexical characteristics and decontextualized and expressive learning outcomes for 143 vocabulary words.
DOI: 10.5281/zenodo.5534669
title: Datasets for "Effects of the COVID-19 Pandemic on Authors and Reviewers of American Geophysical Union Journals"

description: These files include summary data on demographics of people submitting journal articles and reviewing manuscripts submitted to American Geophysical Union (AGU) journals. The date range is January 2018 through February 2021, divided by two years before the pandemic and during the pandemic ("year grouping"): March 2018- February 2019; March 2019- February 2020, and March 2020- February 2021 ("year of the COVID-19 pandemic"). Author-related files only include demographics of the submitting, or "corresponding" author. These datasets supplement the under-review manuscript and pre-print submission to ESSOAr (Earth and Space Science Open Archive) "Effects of the COVID-19 Pandemic on Authors and Reviewers of American Geophysical Union Journals."
DOI: 10.6084/m9.figshare.6088073
title: Identifying Demand Shocks From Production Data

description: Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6088073.v3
title: Identifying Demand Shocks From Production Data

description: Standard productivity estimates contain a mixture of cost efficiency and demand conditions. I propose a method to identify the distribution of the demand shock using production data. Identification does not depend on functional form restrictions. It is also robust to dynamic demand considerations and flexible labor. In the parametric case, the ratio of intermediate inputs to the wage bill (input ratio) contains information about the magnitude of the demand shock. The method is tested using data from Spain that contains information on prices and demand conditions. Finally, we generate Monte Carlo simulations to evaluate the method’s performance and sensitivity. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5813793
title: Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries

description: In this article, we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as shape constrained kernel-weighted least squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. In addition, we propose a test to validate whether shape constraints are correctly specified. The competitiveness of SCKLS is shown in a comprehensive simulation study. Finally, we analyze Chilean manufacturing data using the SCKLS estimator and quantify production in the plastics and wood industries. The results show that exporting firms have significantly higher productivity.
DOI: 10.6084/m9.figshare.5813793.v4
title: Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries

description: In this article, we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as shape constrained kernel-weighted least squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. In addition, we propose a test to validate whether shape constraints are correctly specified. The competitiveness of SCKLS is shown in a comprehensive simulation study. Finally, we analyze Chilean manufacturing data using the SCKLS estimator and quantify production in the plastics and wood industries. The results show that exporting firms have significantly higher productivity.
DOI: 10.6084/m9.figshare.6163556
title: Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

description: Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can <i>drastically</i> improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6163556.v3
title: Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

description: Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can <i>drastically</i> improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.
DOI: 10.17632/9tnbjsj6kn.1
title: Lemongrass Leaf Image Dataset: Mobile-Photographed Image Compilation

description: 10,042 images showcasing various Lemongrass leaf conditions. Categories include Dried, Healthy, and Unhealthy leaves. Captured using mobile phones for real-world authenticity. Ideal for machine learning, agricultural research, and plant health analysis. Empowers projects aimed at improving Lemongrass cultivation and disease prevention.
DOI: 10.17632/rcjgm2nz3s.1
title: Data for Fire Dynamics Simulator: fire in a tunnel under construction

description: The data archived here represent a case study chosen for the simulation of a tunnel under construction, where the fire originates from a truck used to transport the spoil resulting from excavation operations. The vehicle was positioned 30 meters from the tunnel entrance and in the centre of the road at the time of the fire outbreak. In the tunnel, there is also a jumbo which is 30 meters from the excavation face as it was decided to simulate a fire not during the actual excavation phase but during the clearing phase. For the ventilation, a duct has been inserted which starts from the outside of the tunnel and introduces fresh air generated by a fan positioned at the beginning of the duct itself. As regards the tunnel and the machinery present, the following dimensions were chosen: • Tunnel – 100 meters long, 10 meters wide and 7 meters high • Truck – 13.6 meters long, 2.6 meters wide and 2.6 meters high • Jumbo – 7 meters long, 4.4 meters wide and 2.6 meters high As regards the air duct, a diameter of 1 meter and a total length of 100 meters was chosen. Simulations can be performed using the Fire Dynamics Simulator (FDS) software. FDS can be downloaded from https://pages.nist.gov/fds-smv/ [Last access 2023, September, 29]
DOI: 10.17632/wj8njsnwz4.1
title: SEC-SAXS data reduced AIF

description: SEC-SAXS data on reduced AIF
DOI: 10.17632/sz223dcb95.1
title: Basic information on biomass and biochar yield

description: The data are all related to biomass pyrolysis, including three pieces of biomass information, ash information and biomass char yield information at different pyrolysis temperatures. The data consists of two parts, including data obtained from one's own experiments and part of the database and other people's experimental data.There are dozens of types of biomass.
DOI: 10.17632/bv3zywjyyj.1
title: Collections of AB20 flow matrix dataset

description: In the problem of Unequal Area Facility Layout Problem (UA_FLP), AB20 flow matrix data used in previous studies are different, making it difficult to accurately compare objective function values. Therefore, we have summarized AB20 flow matrix data used in previous studies. It can be divided into four categories. 1. Data from the original author Armour and Buffa (1963) are asymmetrical matrix. 2. It is Scrabin and Vergin (1975) who created a symmetric matrix using data from Armour and Buffa (1963). 3. Komaruddin and Wong (2010) created a new asymmetric matrix using data from Armour and Buffa (1963). Most values are similar to Armour and Buffa (1963). 4. La Scalia et al. (2019) created a new asymmetric matrix using data from Armour and Buffa (1963). Most values are similar to Armour and Buffa (1963), but different to Komaruddin and Wong (2010). When calculating the objective function value of other layout problems, excluding AB20, the flow matrix data is symmetric, so the sum of the distance and the product of the flow is divided by two. However, the AB20 problem originally uses an asymmetric matrix, so the sum of the distance and the product of the flow must not be divided by two as it is when calculating the objective function value. I used the data from Armour and Buffa (1963). These are various type of flow matrix in AB20. AB20(Armour & Buffa, 1963) : Asymmetric AB20(Scrabin & Vergin, 1975) : Symmetric AB20(Komarudin & Wong, 2010) : Asymmetric AB20(La Scalia et al, 2019) : Asymmetric
DOI: 10.5281/zenodo.8405286
title: Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers

description: This dataset is part of the Smart Tower Project and is intended to accompany academic publications entitled "Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers". It provides valuable data under high wind excitation conditions and is beneficial for researchers in the field of structural dynamics and Structural Health Monitoring (SHM). The dataset includes high-resolution accelerometer data and estimated modal parameters, along with part of the code script used for analysis. Dataset Components: <strong>Raw Accelerometer Data (April Month)</strong>: High-resolution time-series accelerometer data recorded during April, under high wind excitation conditions. The data is sampled at a rate of 250 Hz. The data is organized in dd/YYYYmmdd_HHMMSS.tdms <strong>Format</strong>: TDMS <strong>Modal Parameters</strong>: Resonance frequencies estimated over the entire monitoring period from 29/03/2023 to 25/06/2023. <strong>Format</strong>: Parquet
DOI: 10.5281/zenodo.8405285
title: Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers

description: This dataset is part of the Smart Tower Project and is intended to accompany academic publications entitled "Experimental Validation of Automated OMA and Mode Tracking for Structural Health Monitoring of Transmission Towers". It provides valuable data under high wind excitation conditions and is beneficial for researchers in the field of structural dynamics and Structural Health Monitoring (SHM). The dataset includes high-resolution accelerometer data and estimated modal parameters, along with part of the code script used for analysis. Dataset Components: <strong>Raw Accelerometer Data (April Month)</strong>: High-resolution time-series accelerometer data recorded during April, under high wind excitation conditions. The data is sampled at a rate of 250 Hz. The data is organized in dd/YYYYmmdd_HHMMSS.tdms <strong>Format</strong>: TDMS <strong>Modal Parameters</strong>: Resonance frequencies estimated over the entire monitoring period from 29/03/2023 to 25/06/2023. <strong>Format</strong>: Parquet