cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e127481v1
title: Web Data Commons Training and Test Sets for Large-Scale Product Matching - Version 2.0

description: Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.
DOI: 10.3886/e114835v1-70904
title: Replication data for: Beyond Work Ethic: Religion, Individual, and Political Preferences

description: We investigate the effect of Reformed Protestantism, relative to Catholicism, on preferences for leisure, and for redistribution and intervention in the economy. We use a Fuzzy Spatial Regression Discontinuity Design to exploit a historical quasi-experiment in Western Switzerland, where in the sixteenth century a hitherto homogeneous region was split and one part assigned to adopt Protestantism. We find that Reformed Protestantism reduces referenda voting for more leisure by 14, redistribution by 5, and government intervention by 7 percentage points. These preferences translate into higher per capita income as well as greater income inequality.
DOI: 10.5281/zenodo.4292751
title: Dataset and analysis for for "Experimental Pauli-frame randomization on a superconducting qubit"

description: Dataset and analysis for "Experimental Pauli-frame randomization on a superconducting qubit" https://arxiv.org/abs/1803.01818
description: This work was funded by LPS/ARO grant W911NF- 14-C-0048. The content of this paper does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
DOI: 10.21227/23s6-ys98
title: Sintel 4D LFVs dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods.
DOI: 10.21227/jdst-xf04
title: Sintel 4D LFVs dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods.
DOI: 10.5281/zenodo.4292756
title: Environmental DNA detection of the giant freshwater crayfish (Astacopsis gouldi)

description: This supplementary datasets contains amplification data for all environmental DNA samples collected as part of publication "Environmental DNA detection of the giant freshwater crayfish (Astacopsis gouldi)" submitted to "Environmental DNA" There are two separate datasets; one with the cT values of A. gouldi amplification by qPCR (amplification metadata) and the other with cT values of eDNA sample amplification during initial quality control for inhibition (Inhibition tests metadata). Each dataset corresponds to separate supplementary material documents. Supplementary S1. Amplification of giant freshwater crayfish: All samples were tested for inhibition by dilution followed by spiked DNA tests. All Samples were run in triplicate (including negative extraction blanks, field and negative controls) using the A. gouldi Taqman assay. We considered samples as positive if amplification crossed a common threshold determined individually within each qPCR run and if detections had CT values ≤ 55 in any of the replicates. If all technical replicates from a sample showed no amplification, then samples were amplified again. If no positive amplification was observed in the re-run qPCR, then a final qPCR was prepared with neat and diluted eDNA samples. If no amplification was observed in any of the qPCR tests then samples were deemed negative. Quantitative PCR reactions were done using the Viia™ 7 Real-Time PCR System (Applied Biosystems, Vic., Australia). Appendix S3. Inhibition tests of environmental DNA samples collected in this study: This supplementary dataset contains amplification data for all environmental DNA samples collected as part of this publication. All samples were tested for inhibition by dilution followed by spiked DNA tests. All Samples were run in triplicate (including negative extraction blanks, field and negative controls) using the A. gouldi Taqman assay. We considered samples as positive if amplification crossed a common threshold determined individually within each qPCR run and if detections had CT values ≤ 55 in any of the replicates. If all technical replicates from a sample showed no amplification, then samples were amplified again. If no positive amplification was observed in the re-run qPCR, then a final qPCR was prepared with neat and diluted eDNA samples. If no amplification was observed in any of the qPCR tests then samples were deemed negative. Quantitative PCR reactions were done using the Viia™ 7 Real-Time PCR System (Applied Biosystems, Vic., Australia).
DOI: 10.5281/zenodo.4292754
title: Dataset of Thermal Features of Lunar Regolith in Mare Humboldtianum

description: The repository includes: 1. dTB values with format .txt 2. dTB of defined units (HU6, HU7, HU9, HU10) with format .xls (some other parameters are also added in this file) 3. The statistical results of BPNN correlation 4. Figures in manuscript
DOI: 10.5281/zenodo.4292753
title: Dataset of Thermal Features of Lunar Regolith in Mare Humboldtianum

description: The repository includes: 1. dTB values with format .txt 2. dTB of defined units (HU6, HU7, HU9, HU10) with format .xls (some other parameters are also added in this file) 3. The statistical results of BPNN correlation 4. Figures in manuscript
DOI: 10.21227/wqek-d453
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.6084/m9.figshare.13239515
title: COVID-19 Wikipedia data

description: COVID-19 related Wikipedia items lists, networks, and views in 11 languages (English, Chinese, Korean, Italian, German, French, Spanish, Portuguese, Russian, Japanese, and Arabic)
DOI: 10.6084/m9.figshare.13239515.v3
title: COVID-19 Wikipedia data

description: COVID-19 related Wikipedia items lists, networks, and views in 11 languages (English, Chinese, Korean, Italian, German, French, Spanish, Portuguese, Russian, Japanese, and Arabic)
DOI: 10.6084/m9.figshare.13293500
title: Wan et al_GEB_2020_0482 data and R code

description: Data and R code for Wan, Chen, Huang & Chen: Global soil microbial biomass decreases with aridity and land-use intensification.
DOI: 10.21227/d004-0w26
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/y4mc-3024
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/6h5a-jp79
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/qcz5-1f64
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/6a0p-2m18
title: Dataset for the meta-heuristics scheduling algorithm

description: Dataset for the meta-heuristics scheduling algorithm
DOI: 10.21227/v7k4-qc14
title: Dataset for the meta-heuristics scheduling algorithm

description: Dataset for the meta-heuristics scheduling algorithm
DOI: 10.21227/mrjz-hq49
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/k5s8-av08
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.21227/s25g-3415
title: the Sintel 4D Light Field Videos dataset

description: The generated dataset consists of 23 synthetic 4D LFVs with 1,204x436 pixels, 9x9 views, and 20--50 frames, and has ground-truth depth values in the central view, so that can be used for training deep learning-based methods. Each scene was rendered with a ``clean'' pass after modifying the production file of ``Sintel'' with reference to the MPI Sintel dataset ~\cite{butler2012naturalistic}. A ``clean'' pass includes complex illumination and reflectance properties including specular reflections, such as smooth shading and specular reflections, while bokeh, motion blur, and semi-transparent objects are excluded.
DOI: 10.5281/zenodo.4292796
title: The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application"

description: A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.
DOI: 10.5281/zenodo.4292795
title: The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application"

description: A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.
DOI: 10.14457/tu.the.2019.772
title: The automated system for glaucoma and diabetic retinopathy pre-screening in mobile phone retinal images

description: Glaucoma and Diabetic Retinopathy (DR) are the leading eye diseases that can cause blindness. Both diseases can be detected by analyzing the abnormalities of pathologies such as the optic disk (OD), optic cup (OC), exudates, or hemorrhages. For glaucoma detection, the ratio of the sizes of OC to OD detection are the significant keys to diagnosis its presence. While the exudates and hemorrhages detection are key indicators of DR. In these past years, technologies of mobile phones, cameras, and lenses are developed rapidly and play an important role in retinal imaging. However, the quality of the mobile phone retinal images drops for some extent compared to those produced by the standard machines. Existing prescreening algorithms perform poorer on the mobile retinal images. In this work, we develop the algorithms for glaucoma and DR screening that can work effectively in the mobile phone retinal images. We develop a novel algorithm so called Alternate Deflation-Inflation Gradient Vector Flow (ADI- GVF) to detect the regions of the OD and the OC for glaucoma prescreening. The sensitivity and specificity of glaucoma prescreening are nearly 90%. For DR screening, we segment the exudates and hemorrhages based on their color features. The sensitivity and specificity of DR prescreening are up to 95% and 79%.
DOI: 10.14457/tu.the.2019.773
title: Developing a workforce scheduling model to reduce occupational heat stress

description: Excessive exposure to high temperature at workplaces is an important cause of heat stress, anxiety, and fatigue in industrial workers. For workplaces where workers are surrounded by heat sources such as furnaces and boilers, the heat exposure levels may need to be administratively controlled to adequately protect workers from excessive heat exposure. This study integrates NIOSH recommended heat stress limits into the design of workforce scheduling models, to mitigate the heat exposure level of workers. An integer programming model is formulated to determine the optimal workforce schedule, where the objectives are to minimize the labor cost and to minimize the temperature difference between the actual exposure levels and the recommended WBGT exposure limits adopted by NIOSH. The heat tolerance of workers and task workloads are taken into consideration. Base on the results, this study demonstrates that the average heat stress level of workers can be reduced significantly by the use of a workforce scheduling approach. In the case study, for the scenario 1, the experienced workers are mainly used. The difference temperature can be reduced as much as 0.74°C per person at the labor cost 14,588 Baht. For the scenario 2, the work-rest ratio are taken into consideration. The 25%work-75%rest experienced workers are mainly used. The difference temperature can be reduced as much as 4.20C per person at the labor cost 10,910 Baht.
DOI: 10.14457/tu.the.2019.774
title: Application of artificial neural networks to automate construction material data classification

description: The applications of Big Data Analytics and Artificial Intelligence (AI) have gained a widespread attention in the construction industry in recent years following the promulgation of Industry 4.0. In the realm of construction research, AI has been widely utilized in areas such as structural design optimization, resource and equipment planning, and project scheduling. The research presented in this work is aimed to utilize AI to assist with the automatic classification of the large volume of construction material orders created by users through an online marketplace website. Such Big Data of material orders contained numerous errors (e.g. typographical errors, incomplete sentences, incorrect units) that were extremely time consuming to correct before the datasets can be used for further business intelligence analysis. In this research, the dataset was obtained from a business-to-business e-commerce company in Thailand, namely BUILK. The data from BUILK was the construction materials purchase orders created by BUILK’s customers through its website, which contained hundreds of thousand unorganized records. Artificial Neural Networks (ANNs) were applied to automate the categorization of approximately 220,000 records of reinforcement steels orders. The ANNs model was developed and trained using over 32,000 records, with approximately 92 percent of prediction accuracy. Also, the predicted value match with the true value as indicated by the intensity of the color was illustrated in Confusion Matrix. The model automatically categorized the steel reinforcement data into 11 groups; deformed bars, round bars, wire mesh (deformed bars), wire mesh (round bars), stirrup, anchor, material and others. Classified the construction materials were able to represent the visualization in each category. Besides, the outcome of this research helped the company to easily analyze the data to generate insights for its business management and development.
DOI: 10.14457/tu.the.2019.775
title: Solar customer detection based on power consumption patterns

description: Nowadays, the solar photovoltaic (PV) systems are rapidly growing worldwide. The utility needs to grasp the changing trends for the planning power system and penalize an illegal installation solar system in order to prevent impacts on the grid. Therefore, this paper aims to detect a solar customer from weekly consumption patterns by three classification algorithms: Logistic regression, cosine similarity and K-nearest neighbors. Furthermore, the clustering methods, K-means, and Density-based spatial clustering of applications with noise (DBSCAN), are utilized for similarity grouping and computational cost reduction by the two-stage clustering technique. The study has been conducted with the non-resident customers in Thailand, the classification results are illustrated and discussed.