cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gpt33
title: CCDC 2285075: Experimental Crystal Structure Determination

description: Related Article: Yanyao Liu, M. Kevin Brown|2023|J.Am.Chem.Soc.|145|25061|doi:10.1021/jacs.3c08105
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.cc2gx66t
title: CCDC 2291216: Experimental Crystal Structure Determination

description: Related Article: Yuji Mikata, Mizuho Uchida, Hinata Koike, Sunao Shoji, Yutaka Ohsedo, Yasushi Kawai, Takashi Matsuo|2023|Dalton Trans.|52|17375|doi:10.1039/D3DT02814C
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.cc2gx67v
title: CCDC 2291217: Experimental Crystal Structure Determination

description: Related Article: Yuji Mikata, Mizuho Uchida, Hinata Koike, Sunao Shoji, Yutaka Ohsedo, Yasushi Kawai, Takashi Matsuo|2023|Dalton Trans.|52|17375|doi:10.1039/D3DT02814C
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.cc2gx68w
title: CCDC 2291218: Experimental Crystal Structure Determination

description: Related Article: Yuji Mikata, Mizuho Uchida, Hinata Koike, Sunao Shoji, Yutaka Ohsedo, Yasushi Kawai, Takashi Matsuo|2023|Dalton Trans.|52|17375|doi:10.1039/D3DT02814C
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.cc2gx69x
title: CCDC 2291219: Experimental Crystal Structure Determination

description: Related Article: Yuji Mikata, Mizuho Uchida, Hinata Koike, Sunao Shoji, Yutaka Ohsedo, Yasushi Kawai, Takashi Matsuo|2023|Dalton Trans.|52|17375|doi:10.1039/D3DT02814C
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.cc2gytgq
title: CCDC 2292774: Experimental Crystal Structure Determination

description: Related Article: D. Marchetti, A. Pedrini, C. Massera, M.F. Diame, C. Jandl, G. Steinfeld, M. Gemmi|2023|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|79|432|doi:10.1107/S2052520623007680
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: 10.5281/zenodo.10199587
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2060-2079

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199588
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2060-2079

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199544
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2040-2059

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199543
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2040-2059

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.6084/m9.figshare.24650373
title: Challenging Assumptions of Normality in AES s-Box Configurations Under Side-Channel Analysis -- Experimental Data

description: Article Abstract:Power-based Side-Channel Analysis (SCA) began with visual-based examinations and has progressed to utilize data-driven statistical analysis. Two distinct classifications of these methods have emerged over the years; those focused on leakage exploitation and those dedicated to leakage detection. This work primarily focuses on a leakage detection-based schema that utilizes Welch's t-test, known as Test Vector Leakage Assessment (TVLA). Both classes of methods process collected data using statistical frameworks that result in the successful exfiltration of information via SCA. Often, statistical testing used during analysis requires the assumption that collected power consumption data originate from a normal distribution. To date, this assumption has remained largely uncontested. This work seeks to demonstrate that while past studies have assumed the normality of collected power traces, this assumption should be properly evaluated. In order to evaluate this assumption, an implementation of Tiny-AES-c with nine unique substitution-box (s-box) configurations is conducted using TVLA to guide experimental design. By leveraging the complexity of the AES algorithm, a sufficiently diverse and complex dataset was developed. Under this dataset, statistical tests for normality such as the Shapiro-Wilk test and the Kolmogorov-Smirnov test provide significant evidence to reject the null hypothesis that the power consumption data are normally distributed. To address this observation, existing non-parametric equivalents such as the Wilcoxon Signed-Rank Test and the Kruskal-Wallis Test are discussed in relation to currently used parametric tests such as Welch's t-test.
DOI: 10.6084/m9.figshare.24650373.v1
title: Challenging Assumptions of Normality in AES s-Box Configurations Under Side-Channel Analysis -- Experimental Data

description: Article Abstract:Power-based Side-Channel Analysis (SCA) began with visual-based examinations and has progressed to utilize data-driven statistical analysis. Two distinct classifications of these methods have emerged over the years; those focused on leakage exploitation and those dedicated to leakage detection. This work primarily focuses on a leakage detection-based schema that utilizes Welch's t-test, known as Test Vector Leakage Assessment (TVLA). Both classes of methods process collected data using statistical frameworks that result in the successful exfiltration of information via SCA. Often, statistical testing used during analysis requires the assumption that collected power consumption data originate from a normal distribution. To date, this assumption has remained largely uncontested. This work seeks to demonstrate that while past studies have assumed the normality of collected power traces, this assumption should be properly evaluated. In order to evaluate this assumption, an implementation of Tiny-AES-c with nine unique substitution-box (s-box) configurations is conducted using TVLA to guide experimental design. By leveraging the complexity of the AES algorithm, a sufficiently diverse and complex dataset was developed. Under this dataset, statistical tests for normality such as the Shapiro-Wilk test and the Kolmogorov-Smirnov test provide significant evidence to reject the null hypothesis that the power consumption data are normally distributed. To address this observation, existing non-parametric equivalents such as the Wilcoxon Signed-Rank Test and the Kruskal-Wallis Test are discussed in relation to currently used parametric tests such as Welch's t-test.
DOI: 10.5281/zenodo.10214398
title: Charcoal Dataset

description: The wood charcoal database presented in this work contains 44 forest species which were cataloged by the Laboratory of Wood Anatomy at the Federal University of Parana in Curitiba, Brazil. The trees were cut in a natural forest, and disks were extracted from the diameter at breast height (DBH), with a thickness of about 8 cm. Twelve samples were obtained from each species, with dimensions of 2 X 2 X 5 cm. Each sample was wrapped in aluminum foil and carbonized in a muffle furnace (Q318S, Quimis), with a final temperature of 450 Celsius and a heating rate of 1.66 Celsius min−1. The carbonized material remained at the final temperature for two hours. The images were obtained directly from the carbonized material, without coating, with a tabletop microscope (TM-1000, Hitachi).
The images were stored in TIFF (Tagged Image File) format with no compression and resolution of 1280 × 1040 pixels. Figure 1 shows some samples of the database.
The Charcoal Database may be used for non-commercial research provided you acknowledge the source of the image by citing the following paper in publications about your research:

Maruyama, T., Oliveira, L. S., Britto Jr, Nisgoski, S., Automatic Classification of Native Wood Charcoal, Ecological Informatics, 48:1-7, 2018.
DOI: 10.5281/zenodo.10214399
title: Charcoal Dataset

description: The wood charcoal database presented in this work contains 44 forest species which were cataloged by the Laboratory of Wood Anatomy at the Federal University of Parana in Curitiba, Brazil. The trees were cut in a natural forest, and disks were extracted from the diameter at breast height (DBH), with a thickness of about 8 cm. Twelve samples were obtained from each species, with dimensions of 2 X 2 X 5 cm. Each sample was wrapped in aluminum foil and carbonized in a muffle furnace (Q318S, Quimis), with a final temperature of 450 Celsius and a heating rate of 1.66 Celsius min−1. The carbonized material remained at the final temperature for two hours. The images were obtained directly from the carbonized material, without coating, with a tabletop microscope (TM-1000, Hitachi).
The images were stored in TIFF (Tagged Image File) format with no compression and resolution of 1280 × 1040 pixels. Figure 1 shows some samples of the database.
The Charcoal Database may be used for non-commercial research provided you acknowledge the source of the image by citing the following paper in publications about your research:

Maruyama, T., Oliveira, L. S., Britto Jr, Nisgoski, S., Automatic Classification of Native Wood Charcoal, Ecological Informatics, 48:1-7, 2018.
DOI: 10.17909/3s1j-gp52
title: NGC 6822 JWST Imaging

description: These are NIRCam and MIRI observations of the central stellar bar of the nearby dwarf galaxy NGC 6822. This dataset includes observations in the F115W, F200W, F356W, and F444W filters with NIRCam, and in the F770W, F1000W, F1500W, and F2100W filters with MIRI.
DOI: 10.5281/zenodo.10199444
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2020-2039

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199445
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85hotter 2020-2039

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199411
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2080-2099

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199412
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2080-2099

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.7853536
title: Thai Word Embeddings (word2vec) Trained on Oscar Corpus

description: A large Thai word2vec model trained on Oscar corpus and tokenized and normalized with PyThaiNLP. The model can be loaded using gensim, it is saved in binary format.
DOI: 10.5281/zenodo.7853537
title: Thai Word Embeddings (word2vec) Trained on Oscar Corpus

description: A large Thai word2vec model trained on Oscar corpus and tokenized and normalized with PyThaiNLP. The model can be loaded using gensim, it is saved in binary format.
DOI: 10.5281/zenodo.10199362
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2060-2079

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199363
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2060-2079

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5519/qd.g5p972j0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (82 records)
DOI: 10.6084/m9.figshare.14599925
title: Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data

description: Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning methods (e.g., artificial neural networks and support vector machines) would have some difficulties. For instance, a training dataset containing both in-control and out-of-control (OC) process observations is required by a supervised machine learning method, but it is rarely available in SPC applications. Furthermore, many machine learning methods work like black boxes. It is often difficult to interpret their learning mechanisms and the resulting decision rules in the context of an application. In the SPC literature, there have been some existing discussions on how to handle the lack of OC observations in the training data, using the one-class classification, artificial contrast, real-time contrast, and some other novel ideas. However, these approaches have their own limitations to handle SPC problems. In this article, we extend the self-starting process monitoring idea that has been employed widely in modern SPC research to a general learning framework for monitoring processes with serially correlated data. Under the new framework, process characteristics to learn are well specified in advance, and process learning is sequential in the sense that the learned process characteristics keep being updated during process monitoring. The learned process characteristics are then incorporated into a control chart for detecting process distributional shift based on all available data by the current observation time. Numerical studies show that process monitoring based on the new learning framework is more reliable and effective than some representative existing machine learning SPC approaches.
DOI: 10.6084/m9.figshare.14599925.v1
title: Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data

description: Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning methods (e.g., artificial neural networks and support vector machines) would have some difficulties. For instance, a training dataset containing both in-control and out-of-control (OC) process observations is required by a supervised machine learning method, but it is rarely available in SPC applications. Furthermore, many machine learning methods work like black boxes. It is often difficult to interpret their learning mechanisms and the resulting decision rules in the context of an application. In the SPC literature, there have been some existing discussions on how to handle the lack of OC observations in the training data, using the one-class classification, artificial contrast, real-time contrast, and some other novel ideas. However, these approaches have their own limitations to handle SPC problems. In this article, we extend the self-starting process monitoring idea that has been employed widely in modern SPC research to a general learning framework for monitoring processes with serially correlated data. Under the new framework, process characteristics to learn are well specified in advance, and process learning is sequential in the sense that the learned process characteristics keep being updated during process monitoring. The learned process characteristics are then incorporated into a control chart for detecting process distributional shift based on all available data by the current observation time. Numerical studies show that process monitoring based on the new learning framework is more reliable and effective than some representative existing machine learning SPC approaches.
DOI: 10.6084/m9.figshare.19230093
title: Sensitivity Prewarping for Local Surrogate Modeling

description: In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input–output relationships. However, narrowing focus to local scale means that global trends must be relearned over and over again. In this article, we propose a framework for incorporating information from a global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the “warped simulator” is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.
DOI: 10.6084/m9.figshare.19230093.v4
title: Sensitivity Prewarping for Local Surrogate Modeling

description: In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input–output relationships. However, narrowing focus to local scale means that global trends must be relearned over and over again. In this article, we propose a framework for incorporating information from a global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the “warped simulator” is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.
DOI: 10.6084/m9.figshare.19230093.v3
title: Sensitivity Prewarping for Local Surrogate Modeling

description: In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer experiments via local models, which induce sparsity by only considering short range interactions, can tackle huge analyses of complicated input–output relationships. However, narrowing focus to local scale means that global trends must be relearned over and over again. In this article, we propose a framework for incorporating information from a global sensitivity analysis into the surrogate model as an input rotation and rescaling preprocessing step. We discuss the relationship between several sensitivity analysis methods based on kernel regression before describing how they give rise to a transformation of the input variables. Specifically, we perform an input warping such that the “warped simulator” is equally sensitive to all input directions, freeing local models to focus on local dynamics. Numerical experiments on observational data and benchmark test functions, including a high-dimensional computer simulator from the automotive industry, provide empirical validation.
DOI: 10.6084/m9.figshare.21131405
title: TSEC: A Framework for Online Experimentation under Experimental Constraints

description: Thompson sampling is a popular algorithm for tackling multi-armed bandit problems, and has been applied in a wide range of applications, from website design to portfolio optimization. In such applications, however, the number of choices (or arms) <i>N</i> can be large, and the data needed to make adaptive decisions require expensive experimentation. One is then faced with the constraint of experimenting on only a small subset of K≪N arms within each time period, which poses a problem for traditional Thompson sampling. We propose a new Thompson Sampling under Experimental Constraints (TSEC) method, which addresses this so-called “arm budget constraint.” TSEC makes use of a Bayesian interaction model with effect hierarchy priors, to model correlations between rewards on different arms. This fitted model is then integrated within Thompson sampling, to jointly identify a good subset of arms for experimentation and to allocate resources over these arms. We demonstrate the effectiveness of TSEC in two applications with arm budget constraints. The first is a simulated website optimization study, where TSEC shows considerable improvements over industry benchmarks. The second is a portfolio optimization application on industry-based exchange-traded funds, where TSEC provides more consistent and greater wealth accumulation over standard investment strategies.