cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28212973
title: Bivariate DeepKriging for Large-Scale Spatial Interpolation of Wind Fields

description: High spatial resolution wind data play a crucial role in various fields such as climate, oceanography, and meteorology. However, spatial interpolation or downscaling of bivariate wind fields, characterized by velocity in two dimensions, poses a challenge due to their non-Gaussian nature, high spatial variability, and heterogeneity. While cokriging is commonly employed in spatial statistics for predicting bivariate spatial fields, it is suboptimal for non-Gaussian processes and computationally prohibitive for large datasets. In this article, we introduce bivariate DeepKriging, a novel method using a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions for predicting bivariate spatial data. Additionally, we devise a distribution-free uncertainty quantification technique based on bootstrap and ensemble DNN. We establish the theoretical basis for bivariate DeepKriging by linking it with the Linear Model of Coregionalization (LMC). Our proposed approach surpasses traditional cokriging predictors, including those using commonly used covariance functions like the linear model of co-regionalization and parsimonious bivariate Matérn covariance. We demonstrate the computational efficiency and scalability of the proposed DNN model, achieving computation speeds approximately 20 times faster than conventional techniques. Furthermore, we apply the bivariate DeepKriging method to wind data across the Middle East region at 506,771 locations, showcasing superior prediction performance over cokriging predictors while significantly reducing computation time.
DOI: 10.6084/m9.figshare.28212973.v1
title: Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields

description: High spatial resolution wind data play a crucial role in various fields such as climate, oceanography, and meteorology. However, spatial interpolation or downscaling of bivariate wind fields, characterized by velocity in two dimensions, poses a challenge due to their non-Gaussian nature, high spatial variability, and heterogeneity. While cokriging is commonly employed in spatial statistics for predicting bivariate spatial fields, it is suboptimal for non-Gaussian processes and computationally prohibitive for large datasets. In this paper, we introduce bivariate DeepKriging, a novel method utilizing a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions for predicting bivariate spatial data. Additionally, we devise a distribution-free uncertainty quantification technique based on bootstrap and ensemble DNN. We establish the theoretical basis for bivariate DeepKriging by linking it with the Linear Model of Coregionalization (LMC). Our proposed approach surpasses traditional cokriging predictors, including those utilizing commonly used covariance functions like the linear model of co-regionalization and parsimonious bivariate Matérn covariance. We demonstrate the computational efficiency and scalability of the proposed DNN model, achieving computation speeds approximately 20 times faster than conventional techniques. Furthermore, we apply the bivariate DeepKriging method to wind data across the Middle East region at 506,771 locations, showcasing superior prediction performance over cokriging predictors while significantly reducing computation time.
DOI: 10.6084/m9.figshare.28212973.v2
title: Bivariate DeepKriging for Large-Scale Spatial Interpolation of Wind Fields

description: High spatial resolution wind data play a crucial role in various fields such as climate, oceanography, and meteorology. However, spatial interpolation or downscaling of bivariate wind fields, characterized by velocity in two dimensions, poses a challenge due to their non-Gaussian nature, high spatial variability, and heterogeneity. While cokriging is commonly employed in spatial statistics for predicting bivariate spatial fields, it is suboptimal for non-Gaussian processes and computationally prohibitive for large datasets. In this article, we introduce bivariate DeepKriging, a novel method using a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions for predicting bivariate spatial data. Additionally, we devise a distribution-free uncertainty quantification technique based on bootstrap and ensemble DNN. We establish the theoretical basis for bivariate DeepKriging by linking it with the Linear Model of Coregionalization (LMC). Our proposed approach surpasses traditional cokriging predictors, including those using commonly used covariance functions like the linear model of co-regionalization and parsimonious bivariate Matérn covariance. We demonstrate the computational efficiency and scalability of the proposed DNN model, achieving computation speeds approximately 20 times faster than conventional techniques. Furthermore, we apply the bivariate DeepKriging method to wind data across the Middle East region at 506,771 locations, showcasing superior prediction performance over cokriging predictors while significantly reducing computation time.
DOI: 10.6084/m9.figshare.28599380.v2
title: Assessment of Case Influence in the Lasso with a Case-weight Adjusted Solution Path

description: We study case influence in the Lasso regression using Cook’s distance which measures overall change in the fitted values when one observation is deleted. Unlike in ordinary least squares regression, the estimated coefficients in the Lasso do not have a closed form due to the nondifferentiability of the l1 penalty, and neither does Cook’s distance. To find the case-deleted Lasso solution without refitting the model, we approach it from the full data solution by introducing a weight parameter ranging from 1 to 0 and generating a solution path indexed by this parameter. We show that the solution path is piecewise linear with respect to a simple function of the weight parameter under a fixed penalty. The resulting case influence is a function of the penalty and weight, and it becomes Cook’s distance when the weight is 0. As the penalty parameter changes, selected variables change, and the magnitude of Cook’s distance for the same data point may vary with the subset of variables selected. In addition, we introduce a case influence graph to visualize how the contribution of each data point changes with the penalty parameter. From the graph, we can identify influential points at different penalty levels and make modeling decisions accordingly. Moreover, we find that case influence graphs exhibit different patterns between underfitting and overfitting phases, which can provide additional information for model selection.
DOI: 10.25824/redu/w4swtv
title: Código para geração de dados e otimização por DE no domínio de MCDA - voltado para equidade

description: Códigos gerados durante a pesquisa de doutorado que gerou a tese cujo título é "Uma Abordagem de Ajuste de Parâmetros Orientada à Equidade em Métodos de Apoio à Decisão Multicritério". O objetivo da pesquisa foi a obtenção de um ordenamento equânime a partir da proposição de um novo método de ajuste de parâmetros de dois modelos de agregação em MCDA, quantificado por uma métrica dita de equidade entre dois subgrupos de alternativas. Em nossa proposta, consideramos a otimização da medida de equidade NDKL por meio de uma meta-heurística conhecida como Evolução Diferencial para os modelos de agregação SAW e Integral de Choquet (especificamente a sub-classe de modelos conhecida como 2-aditiva) e trabalhamos com dados sintéticos e dados reais. Portanto, nos códigos gerados, dispomos dos algoritmos utilizados para gerar dados, do algoritmo para cálculo dos valores das alternativas a partir dos dois modelos de agregação utilizados, do algoritmo que calcula a equidade a partir da métrica NDKL e do processo de otimização a partir da Evolução Diferencial disponível no Scipy.
DOI: 10.6084/m9.figshare.29649677
title: The genome assembly of Phyllospadix iwatensis along with its predicted coding sequences and protein sequences

description: The chromosomal-scale genome was constructed by integrating Illumina, PacBio HiFi, and high-throughput chromosome conformation capture (Hi-C) sequencing techniques.
DOI: 10.6084/m9.figshare.29649677.v1
title: The genome assembly of Phyllospadix iwatensis along with its predicted coding sequences and protein sequences

description: The chromosomal-scale genome was constructed by integrating Illumina, PacBio HiFi, and high-throughput chromosome conformation capture (Hi-C) sequencing techniques.
DOI: 10.7910/dvn/xpkhc0
title: Replication Data for: Three Is a Crowd: Information and Electoral Coordination in Argentina

description: Successful coordination around a Duvergerian equilibrium requires accurate and consistent information about parties’ expected electoral support. In practice, such information is often unreliable and rarely available at the local level, thus hindering voters’ coordination. In this paper we leverage Argentina’s Open, Mandatory and Simultaneous Primary Elections as a large-scale survey of voter preferences. Using data from 135 municipalities in the province of Buenos Aires (2011-2023), we show that a narrower margin between the top-two placed parties in the primary increases both turnout and the proportion of positive votes in the general election, while decreasing electoral fragmentation. In line with Cox’s (1997) expectations, we further show that the second-placed party in the primary is substantially more likely to win the election than the third-placed one. Consistent with theoretical predictions, these effects are more pronounced (a) in concurrent elections; (b) in smaller municipalities; and (c) when the second-placed party is closer to the first-placed one.
DOI: 10.5281/zenodo.18376615
title: Infrared Brightness Temperature Evolution Modulated by Pressure-stimulated Rock Potential
DOI: 10.5281/zenodo.18376614
title: Infrared Brightness Temperature Evolution Modulated by Pressure-stimulated Rock Potential
DOI: 10.5281/zenodo.18330487
title: Unicamp-NAMSS: A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS

description: We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
DOI: 10.5281/zenodo.18330486
title: Unicamp-NAMSS: A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS

description: We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
DOI: 10.5281/zenodo.15867070
title: MetalliCan

description: MetalliCan

MetalliCan is a dataset on metal-related production activities in Canada and their relation to environmental and social impacts. An open-access article presenting the dataset is available here and a web application to explore its content is available here.

This release introduces a new table capturing changes in vegetation and soil carbon, based on data from Ganzenmüller et al (2025). This version constitutes the primary data foundation for the development of site-specific life-cycle inventories.

It includes:



The relational database in SQL format

SQL schema file

Entity–Relationship Diagram (ERD)

Individual tables in CSV format
DOI: 10.5281/zenodo.18376523
title: MetalliCan

description: MetalliCan

MetalliCan is a dataset on metal-related production activities in Canada and their relation to environmental and social impacts. An open-access article presenting the dataset is available here and a web application to explore its content is available here.

This release introduces a new table capturing changes in vegetation and soil carbon, based on data from Ganzenmüller et al (2025). This version constitutes the primary data foundation for the development of site-specific life-cycle inventories.

It includes:



The relational database in SQL format

SQL schema file

Entity–Relationship Diagram (ERD)

Individual tables in CSV format
DOI: 10.25824/redu/bgikmn
title: Module with an virtual flux method for multi-regions constraint algorithm

description: The Virtual Flux Method (VFM) constructs an auxiliary (virtual) thermal system using a finite element mesh and solves it, creating a heat flux through the original structure. Although this thermal system isn't part of the core problem, its element flux values serve to enforce connection constraints via element penalization. This dataset contains a Python script and a PDF document explaining the organization of multiple regions.
DOI: 10.6084/m9.figshare.30483716
title: <b>Discovery of triacylglycerol-binding proteins in </b><b><i>Cereus jamacaru</i></b><b> DC. through integrative </b><b><i>in silico </i></b><b>methods</b>

description: <b>Transcriptome dataset from C. jamacaru</b>
DOI: 10.6084/m9.figshare.30483716.v1
title: <b>Discovery of triacylglycerol-binding proteins in </b><b><i>Cereus jamacaru</i></b><b> DC. through integrative </b><b><i>in silico </i></b><b>methods</b>

description: <b>Transcriptome dataset from C. jamacaru</b>
DOI: 10.17632/369cwyjv27
title: data-manuscript

description: Compressing experimental data
DOI: 10.17632/369cwyjv27.1
title: data-manuscript

description: Compressing experimental data
DOI: 10.17632/4r9tc9tc39
title: SZT1.0 (Global Subduction Zone Temperature 1.0)

description: The database SZT1.0 includes 25 subduction zones and 240,768 lines of temperature and subduction induced velocity data. The data is calculated based on a multi-physical parametric and multi-temporal scale 3D numerical model. Based on the Stag3D model (Tackley and Xie, 2003) and the finite difference method using a three-dimensionalization of a given plate shape (Slab2; Hayes et al., 2018) and the subducted plate subduction velocity (MORVEL; Argus et al., 2011) following the law of conservation of mass of matter, Stokes' equations and the law of conservation of kinetic energy (Gerya, 2011; Yoshioka and Murakami, 2007) and the intrinsic relations of viscoplastic rheological properties and viscosity coefficients (Ranalli, 1995), we constructed three-dimensional numerical models for the 25 subduction zones, and calculated the three-dimensional temperature field and the induced mantle/crustal flow velocity fields. The slab velocity (isite=3) is kinematically prescribed in slab guide which is increasing in trench-perpendicular length according to MORVEL plate motion along 3D slab geometry. In this database, we focus on the induced mantle/crustal flow velocity, so the velocity at the grids within the slab (isite=3) is shown to be zero value. For the new data of six regions updated after SZT1.0 (New-Guinea, Java, Pamir, Puysegur, South_Mexico, and Sulawesi), the prescribed 3D slab velocity data is provided. Suduction zone numbers and names are as follows: 1 Alaska, 2 Aleutians, 3 Cascadia, 4 Chile, 5 Columbia-Ecuador, 6 Costa_Rica, 7 Hokkaido, 8 Izu-Bonin, 9 Kamchatka, 10 Kermadec-Tonga, 11 Mariana, 12 Myanmar, 13 Peru, 14 Philippines, 15 Ryukyu, 16 Santa_Cruz_Islands, 17 Scotia, 18 Solomon_Islands, 19 Sumatra, 20 New-Guinea, 21 Java, 22 Pamir, 23 Puysegur, 24 South_Mexico, 25 Sulawesi.

File name: stored in text format (a file), the name of the file is "SZT1.0.dat", including 11 columns, from left to right, longitude (deg), latitude (deg), depth (km), temperature (Celsius degree), position (1-5), eastward velocity component (cm/y), northward velocity component (cm/y), vertically downward velocity component (cm/y), subduction zone name, and subduction zone number (1-25). The position number (1-5) meaning is as follows: 1 means the overriding upper crust (0-15 km in depth); 2 means the overriding lower crust (15-30 km in depth); 3 means the subducting plate; 4 means the mantle; 5 means the accretionary prism. Data reading method: All ASCII files in this data file can be opened directly with a text program (e.g., Notepad). 
Please cite:
Ji, Y., Yan, R., Zeng, D., Xie, C., Zhu, W., Qu, R., Yoshioka, S. (2021). Slab dehydration in Sumatra: Implications for fast and slow earthquakes and arc magmatism, Geophysical Research Letters, 48(3), e2020GL090576, https://doi.org/10.1029/2020GL090576
DOI: 10.17632/4r9tc9tc39.2
title: SZT1.0 (Global Subduction Zone Temperature 1.0)

description: The database SZT1.0 includes 25 subduction zones and 240,768 lines of temperature and subduction induced velocity data. The data is calculated based on a multi-physical parametric and multi-temporal scale 3D numerical model. Based on the Stag3D model (Tackley and Xie, 2003) and the finite difference method using a three-dimensionalization of a given plate shape (Slab2; Hayes et al., 2018) and the subducted plate subduction velocity (MORVEL; Argus et al., 2011) following the law of conservation of mass of matter, Stokes' equations and the law of conservation of kinetic energy (Gerya, 2011; Yoshioka and Murakami, 2007) and the intrinsic relations of viscoplastic rheological properties and viscosity coefficients (Ranalli, 1995), we constructed three-dimensional numerical models for the 25 subduction zones, and calculated the three-dimensional temperature field and the induced mantle/crustal flow velocity fields. The slab velocity (isite=3) is kinematically prescribed in slab guide which is increasing in trench-perpendicular length according to MORVEL plate motion along 3D slab geometry. In this database, we focus on the induced mantle/crustal flow velocity, so the velocity at the grids within the slab (isite=3) is shown to be zero value. For the new data of six regions updated after SZT1.0 (New-Guinea, Java, Pamir, Puysegur, South_Mexico, and Sulawesi), the prescribed 3D slab velocity data is provided. Suduction zone numbers and names are as follows: 1 Alaska, 2 Aleutians, 3 Cascadia, 4 Chile, 5 Columbia-Ecuador, 6 Costa_Rica, 7 Hokkaido, 8 Izu-Bonin, 9 Kamchatka, 10 Kermadec-Tonga, 11 Mariana, 12 Myanmar, 13 Peru, 14 Philippines, 15 Ryukyu, 16 Santa_Cruz_Islands, 17 Scotia, 18 Solomon_Islands, 19 Sumatra, 20 New-Guinea, 21 Java, 22 Pamir, 23 Puysegur, 24 South_Mexico, 25 Sulawesi.

File name: stored in text format (a file), the name of the file is "SZT1.0.dat", including 11 columns, from left to right, longitude (deg), latitude (deg), depth (km), temperature (Celsius degree), position (1-5), eastward velocity component (cm/y), northward velocity component (cm/y), vertically downward velocity component (cm/y), subduction zone name, and subduction zone number (1-25). The position number (1-5) meaning is as follows: 1 means the overriding upper crust (0-15 km in depth); 2 means the overriding lower crust (15-30 km in depth); 3 means the subducting plate; 4 means the mantle; 5 means the accretionary prism. Data reading method: All ASCII files in this data file can be opened directly with a text program (e.g., Notepad). 
Please cite:
Ji, Y., Yan, R., Zeng, D., Xie, C., Zhu, W., Qu, R., Yoshioka, S. (2021). Slab dehydration in Sumatra: Implications for fast and slow earthquakes and arc magmatism, Geophysical Research Letters, 48(3), e2020GL090576, https://doi.org/10.1029/2020GL090576
DOI: 10.17632/dn9djfbj4r
title: Math anxiety data respon

description: This dataset contains response data from 302 primary school students who participated in a study on the Math Anxiety Scale for Primary School Students. The data were collected to assess students’ levels of mathematics anxiety using a series of items representing emotional, cognitive, and behavioral aspects related to mathematics learning. Each respondent provided answers based on a predefined rating scale. This dataset can be used for analyzing levels of math anxiety, instrument validation, and further research related to psychological factors influencing mathematics learning among primary school students.
DOI: 10.17632/dn9djfbj4r.1
title: Math anxiety data respon

description: This dataset contains response data from 302 primary school students who participated in a study on the Math Anxiety Scale for Primary School Students. The data were collected to assess students’ levels of mathematics anxiety using a series of items representing emotional, cognitive, and behavioral aspects related to mathematics learning. Each respondent provided answers based on a predefined rating scale. This dataset can be used for analyzing levels of math anxiety, instrument validation, and further research related to psychological factors influencing mathematics learning among primary school students.