cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/lsdvj9
title: Replication data for: Declaração sobre os direitos e propriedade intelectual dos arquivos e obras de Michel Foucault

description: A tese de doutorado “Michel Foucault e a história repensada a partir do espaço”, de Gabriel José Pochapski, defendida em 17 de setembro de 2025, no Programa de Pós-Graduação em História (PPGH-UNICAMP), sob orientação da Profa. Dra. Luana Saturnino Tvardovskas (UNICAMP) e do Prof. Dr. Orazio Irrera (Université Paris 8), é resultado de pesquisas realizadas em arquivos sobre Michel Foucault ainda inéditos, disponíveis apenas para consulta presencial na Bibliothèque nationale de France (BnF) e no Institut Mémoires de l’Édition Contemporaine (IMEC). Em conformidade com as regras de propriedade intelectual previstas na legislação francesa, que assegura aos herdeiros e responsáveis legais o direito de posse, disponibilização e divulgação desses materiais, torna-se impossível incluir tais conteúdos para consulta em novos bancos de dados, salvo aqueles pertencentes à Bibliothèque nationale de France (BnF), conforme atesta a declaração (anexa a este documento) de M. Philippe Chevallier, responsável legal por esses acervos. Diante disso, optamos por incluir apenas a lista dos documentos utilizados neste estudo.
DOI: 10.34894/ipvxmw
title: The Allen Unger Commodities Dataset

description: The Database presents price data, published in the nineteenth and twentieth centuries, in machine-readable form. The original goal was to examine changes in prices and their relation to international trade in early modern Europe. Price series were then expanded from just staple grains and beyond a limited number of years to the entire period ranging from the earliest known series from the High Middle Ages and down to 1914. The geographical range as well as that of commodities continues to expand with continued research and coding of published data. With the larger collection of figures studies of market integration as well as welfare of consumers and other topics has become possible. Continuing support from the Social Sciences and Humanities Research Council of Canada, the National Science Foundation of Britain, the British Academy and NYU Abu Dhabi has made possible the addition of new data and refinement of existing files.
DOI: 10.5281/zenodo.17054204
title: Supplementary Data to Landwehrs et al. 2025 "Topographic Effects of Svalbard on Warm and Moist Air Intrusions into the Central Arctic"

description: This is supplementary data to Landwehrs et al. 2025 "Topographic Effects of Svalbard on Warm and Moist Air Intrusions into the Central Arctic" (https://doi.org/10.5194/egusphere-2025-4535). The dataset contains data and scripts to reproduce the figures from the paper.
DOI: 10.5281/zenodo.17054203
title: Supplementary Data to Landwehrs et al. 2025 "Topographic Effects of Svalbard on Warm and Moist Air Intrusions into the Central Arctic"

description: This is supplementary data to Landwehrs et al. 2025 "Topographic Effects of Svalbard on Warm and Moist Air Intrusions into the Central Arctic" (https://doi.org/10.5194/egusphere-2025-4535). The dataset contains data and scripts to reproduce the figures from the paper.
DOI: 10.6084/m9.figshare.25483138
title: Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data Under Data-Sharing Constraints

description: In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework.
DOI: 10.6084/m9.figshare.25483138.v2
title: Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data Under Data-Sharing Constraints

description: In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework.
DOI: 10.6084/m9.figshare.25270268
title: Image Comparison Based On Local Pixel Clustering

description: Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. This problem, however, is complicated because (i) the observed images often contain noise, (ii) the image intensity functions are discontinuous and have spatial structures. In the literature, a vast majority of the methods are intensity-based. However, such an approach is often questionable in real life situations where small changes in the background may not indicate an actual meaningful change in the images as long as the boundaries of the image objects remain the same. In this article, we propose a flexible and effective image comparison method based on local pixel clustering and construct a test statistic based on the <i>Variation of Information</i> metric. This is a feature based image comparison technique where edges or the jump points are considered as the primary features. Numerical examples and statistical properties show that the proposed image comparison method performs well in various real life scenarios.
DOI: 10.6084/m9.figshare.25270268.v1
title: Image Comparison Based On Local Pixel Clustering

description: Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. This problem, however, is complicated because (i) the observed images often contain noise, (ii) the image intensity functions are discontinuous and have spatial structures. In the literature, a vast majority of the methods are intensity-based. However, such an approach is often questionable in real life situations where small changes in the background may not indicate an actual meaningful change in the images as long as the boundaries of the image objects remain the same. In this article, we propose a flexible and effective image comparison method based on local pixel clustering and construct a test statistic based on the <i>Variation of Information</i> metric. This is a feature based image comparison technique where edges or the jump points are considered as the primary features. Numerical examples and statistical properties show that the proposed image comparison method performs well in various real life scenarios.
DOI: 10.6084/m9.figshare.25483138.v1
title: Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data under Data-Sharing Constraints

description: In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework.
DOI: 10.5518/1536
title: Dataset associated with the paper: A Country-Level Primary-Final-Useful (CL-PFU) Energy and Exergy Database: Overview of its construction and 1971–2020 world-level efficiency results
DOI: 10.6084/m9.figshare.26018126
title: Drift versus Shift: Decoupling Trends and Changepoint Analysis

description: We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth estimates of underlying trends in the presence of complex noise components. However, their inability to shrink exactly to zero inhibits direct changepoint detection. In contrast, penalized likelihood methods are highly effective in locating changepoints. However, they require data with simple patterns in both signal and noise. The proposed decoupling approach combines the strengths of both, that is, the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators, to provide changepoint analysis in complex, modern settings. The proposed framework is outlier robust and can identify a variety of changes, including in mean and slope. It is also easily extended for analysis of parameter shifts in time-varying parameter models like dynamic regressions. We illustrate the flexibility and contrast the performance and robustness of our approach with several alternative methods across a wide range of simulations and application examples.
DOI: 10.6084/m9.figshare.26018126.v2
title: Drift versus Shift: Decoupling Trends and Changepoint Analysis

description: We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth estimates of underlying trends in the presence of complex noise components. However, their inability to shrink exactly to zero inhibits direct changepoint detection. In contrast, penalized likelihood methods are highly effective in locating changepoints. However, they require data with simple patterns in both signal and noise. The proposed decoupling approach combines the strengths of both, that is, the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators, to provide changepoint analysis in complex, modern settings. The proposed framework is outlier robust and can identify a variety of changes, including in mean and slope. It is also easily extended for analysis of parameter shifts in time-varying parameter models like dynamic regressions. We illustrate the flexibility and contrast the performance and robustness of our approach with several alternative methods across a wide range of simulations and application examples.
DOI: 10.6084/m9.figshare.26018126.v1
title: Drift vs Shift: Decoupling Trends and Changepoint Analysis

description: We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth estimates of underlying trends in the presence of complex noise components. However, their inability to shrink exactly to zero inhibits direct changepoint detection. In contrast, penalized likelihood methods are highly effective in locating changepoints. However, they require data with simple patterns in both signal and noise. The proposed decoupling approach combines the strengths of both, i.e. the flexibility of Bayesian DLMs with the hard thresholding property of penalized likelihood estimators, to provide changepoint analysis in complex, modern settings. The proposed framework is outlier robust and can identify a variety of changes, including in mean and slope. It is also easily extended for analysis of parameter shifts in time-varying parameter models like dynamic regressions. We illustrate the flexibility and contrast the performance and robustness of our approach with several alternative methods across a wide range of simulations and application examples.
DOI: 10.6084/m9.figshare.26307627
title: Linear Source Apportionment Using Generalized Least Squares

description: Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.
DOI: 10.6084/m9.figshare.26307627.v1
title: Linear Source Apportionment using Generalized Least Squares

description: Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.
DOI: 10.6084/m9.figshare.26307627.v2
title: Linear Source Apportionment Using Generalized Least Squares

description: Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.
DOI: 10.34810/data2946
title: Responses of Participants from Catalonia in the Research Project “Profesión Docente y Justicia Global (Global Education)”

description: This dataset contains the responses collected within the research project “Profesión Docente y Justicia Global (Global Education),” funded by the Institut de Recerca en Educacióde la Universitat de Barcelona (IREUB). It comprises a total of 538 responses from in-service teachers working in Early Childhood, Primary, Secondary, Vocational Education and Training, and other non-university educational settings. Participants come from public, private, and publicly funded private educational institutions.
DOI: 10.34740/kaggle/dsv/14626043
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.21950/gw7soz
title: Federico-Tena World Population Historical Database : World Population borders 1991 (2026)

description: Project developed by Giovanni Federico (New York University Abu Dhabi) and Antonio Tena Junguito (Universidad Carlos III de Madrid). Dataset: World Population borders 1991 (2026)
description: Microsoft Excel
description: Web site: <strong>https://www.uc3m.es/investigacion/federico-tena-population</strong>
DOI: 10.5517/ccdc.csd.cc2hf5jn
title: CCDC 2306571: Experimental Crystal Structure Determination

description: Related Article: Romina Ocampo, Sandra Mandolesi, Fernando Lorenzo, Diego F. Rodríguez, Fabián Santana-Romo, Antonio Galdamez, Flavia C. Zacconi, Robert A. Burrow, Norma D’Accorso
|2024|Synthesis|56|1429|doi:10.1055/a-2231-4922
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.cc2hk92g
title: CCDC 2310525: Experimental Crystal Structure Determination

description: Related Article: Bram Van Den Bossche, Nicolai Cramer|2026|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c20631
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.cc2hkb1g
title: CCDC 2310555: Experimental Crystal Structure Determination

description: Related Article: Zhengyu Han, Meng Duan, Qianya Wan, Cien Chen, Wen Yang, Zengwei Lai, Peiyuan Yu, Hai Huang, Ming-Liang He, K. N. Houk, Jianwei Sun|2026|ACS Catalysis|||doi:10.1021/acscatal.5c07719
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.cc2hmp72
title: CCDC 2312824: Experimental Crystal Structure Determination

description: Related Article: Chenrui Liu, Ling-Ya Peng, Yumeng Chen, Jingyi An, Zhaoxin Li, Wenshuang Huang, Ganglong Cui, Shaowei Hu|2026|Chem.Commun.|62|616|doi:10.1039/d5cc04879f
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.cc2jw6d1
title: CCDC 2349843: Experimental Crystal Structure Determination

description: Related Article: Guowei Chen, Si-Wei Chen, Yaozu Su, Ru-Jia Wang, Luo Huang, Xiao-Long Liu, Long-Jiang Xing, Bo Liu, Jia-Ming Jin, Yanping Huo, Season Si Chen, Shaomin Ji, Man-Chung Tang, Wen-Cheng Chen|2025|Adv.Funct.Mater.|35|2501039|doi:10.1002/adfm.202501039
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.18350539
title: Dataset for: Hybrid Magnetic Janssen Effect Arising from Percolating Ferromagnetic Grain Networks

description: This repository contains the data underlying the figures in the associated manuscript.





Figure2_Data.csv: Experimental apparent mass measurements.




Figure4_Data.csv & Figure5_Data.csv Numerical simulation results.
DOI: 10.5281/zenodo.18350540
title: Dataset for: Hybrid Magnetic Janssen Effect Arising from Percolating Ferromagnetic Grain Networks

description: This repository contains the data underlying the figures in the associated manuscript.





Figure2_Data.csv: Experimental apparent mass measurements.




Figure4_Data.csv & Figure5_Data.csv Numerical simulation results.
DOI: 10.5517/ccdc.csd.cc2k10pb
title: CCDC 2354471: Experimental Crystal Structure Determination

description: Related Article: Àlex Díaz-Jiménez, Roger Monreal-Corona, Arijit Saha, Andrea Álvarez-Núñez, Anna Company, Teodor Parella, Pedro J. Pérez, Ana Caballero, Anna Roglans, Albert Poater, Anna Pla-Quintana|2026|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c19806
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.