cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29071346
title: <b>Shared and distinctive microbial biomarkers enhance precision diagnostics of inflammatory bowel disease and its subtypes</b>

description: Inflammatory Bowel Disease (IBD) is a chronic inflammatory gut disease with major subtypes of Crohn's disease (CD) and ulcerative colitis (UC). They significantly differ in pathology but are similar in clinical manifestations, bringing considerable challenges for accurate non-invasive diagnosis. Recent research highlights the gut microbiome as a promising physiological indicator for diverse diseases, prompting us to investigate shared or distinctive microbial species insights into IBD and its subtypes. For this purpose, we initially established a standardized computational workflow to identify gut microbial biomarkers and their patterns for IBD diagnosis and subtype discrimination. In the process, 1,210 metagenomic samples were analyzed from 6 cohorts; among them, 81 shared species were identified (18 enriched, 63 depleted) across both CD and UC, whereas CD harbored 73 distinct species (37 enriched, 36 depleted) and UC had 32 distinct species (19 enriched, 13 depleted). Functional profiling illustrated shared and distinctive enriched MetaCyc pathways across the two subtypes, offering a comprehensive view of subtype-specific metabolic pathways. Finally, a two-stage machine learning framework was built for diagnosis and subtyping of IBD, making IBD identification in the first stage and subtype classification in the second stage. The framework was well-trained and achieved considerable accuracy, even leading to a novel discovery that shared microbes dominate IBD diagnosis, while distinct microbes drive subtype discrimination.
DOI: 10.18150/hf8rsj
title: Instrukcja edycji tekstu cerkiewnosłowiańskiego na potrzeby rękopisu Mateusza Dziesiątego Fedorovsk Unicode

description: Instrukcja edycji tekstu cerkiewnosłowiańskiego na potrzeby rękopisu Mateusza Dziesiątego Fedorovsk Unicode opisująca przygotowanie środowiska (aplikacje, instalacja czcionki, ustawienia edytora) oraz specyfikę pracy z przygotowaną czcionką.Zbiór danych zawiera także przygotowaną czcionkę.
DOI: 10.6084/m9.figshare.29071451
title: Dog-Owner microbiota in Thailand

description: <b>Title:</b> <b>Pack behavior or personal bonds? Exploring dog-owner microbiota and bacterial </b><b>pathogens in Southeast Asia</b><b>Abstract</b>: Domestic dogs may influence human microbiota and act as reservoirs or sentinels for zoonoticbacteria, yet little is known about these dynamics in Southeast Asia. Using full-length 16S rRNAgene sequencing, we analyzed microbiota from dog-owner pairs in rural Thailand to assessmicrobial diversity, shared taxa, and zoonotic potential. Dogs exhibited higher microbial andpathogen diversity than humans, particularly during the rainy season. While dog-owner pairs sharedmore bacteria than unrelated individuals, overall overlap remained limited —likely driven by theirfree-roaming lifestyles and broader environmental exposures. However, co-occurrence of zoonoticpathogens such as <i>Shigella</i> spp. and <i>Klebsiella pneumoniae</i> suggests dogs could serve as usefulindicators for specific public health risks. Our findings highlight the value of integrating dogs intozoonotic surveillance programs, particularly in regions with free-ranging dog populations and closehuman-animal contact.
DOI: 10.6084/m9.figshare.29071102.v1
title: Job activity and wellbeing of PhD holders beyond academia: a person-centred approach

description: This study explores the job activity profiles and occupational wellbeing of PhD holders pursuing careers beyond academia in Spain. Given the increasing number of PhD holders entering in careers beyond academia, we aim to identify individual variations in job activities – research, interpersonal and managerial, and business activities – and their relationship with work engagement and burnout. Utilising a person-centred approach, we employ latent profile analysis on a sample of 731 PhD holders, revealing four distinct job activity profiles: Research-Managerial, Analyst, Communicator, and Multi-task. Our findings enrich current understanding of PhD holders’ job activities, underscoring that PhD holders have opportunities to conduct PhD-related activities outside academia, but that their expertise remains underutilised. Moreover, our results emphasise the relationship between job activities and occupational wellbeing, suggesting that PhD holders benefit from engaging in a variety of activities beyond research. Ultimately, they indicate that individual differences are more influential than disciplinary backgrounds, with PhD holders pursuing hybrid careers well positioned for research and dissemination. Implications are significant regarding the role of institutions in fostering environments where PhD holders can thrive.
DOI: 10.6084/m9.figshare.29071102
title: Job activity and wellbeing of PhD holders beyond academia: a person-centred approach

description: This study explores the job activity profiles and occupational wellbeing of PhD holders pursuing careers beyond academia in Spain. Given the increasing number of PhD holders entering in careers beyond academia, we aim to identify individual variations in job activities – research, interpersonal and managerial, and business activities – and their relationship with work engagement and burnout. Utilising a person-centred approach, we employ latent profile analysis on a sample of 731 PhD holders, revealing four distinct job activity profiles: Research-Managerial, Analyst, Communicator, and Multi-task. Our findings enrich current understanding of PhD holders’ job activities, underscoring that PhD holders have opportunities to conduct PhD-related activities outside academia, but that their expertise remains underutilised. Moreover, our results emphasise the relationship between job activities and occupational wellbeing, suggesting that PhD holders benefit from engaging in a variety of activities beyond research. Ultimately, they indicate that individual differences are more influential than disciplinary backgrounds, with PhD holders pursuing hybrid careers well positioned for research and dissemination. Implications are significant regarding the role of institutions in fostering environments where PhD holders can thrive.
DOI: 10.6084/m9.figshare.29071111
title: Code.zip from Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

description: When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.
DOI: 10.6084/m9.figshare.28077731
title: Code and data for the manuscript Piironen et al. (2025): Environmental drivers of productivity explain population patterns of an Arctic-nesting bird across a half-century

description: This repository includes the R code and data to reproduce the analysis in the manuscript Piironen et al. (2025): Environmental drivers of productivity explain population patterns of an Arctic-nesting bird across a half-century
DOI: 10.6084/m9.figshare.28077731.v2
title: Code and data for the manuscript Piironen et al. (2025): Environmental drivers of productivity explain population patterns of an Arctic-nesting bird across a half-century

description: This repository includes the R code and data to reproduce the analysis in the manuscript Piironen et al. (2025): Environmental drivers of productivity explain population patterns of an Arctic-nesting bird across a half-century
DOI: 10.7910/dvn/2uqhtp
title: Neuromotion Train Set

description: A large scale simulated dataset for the MUniverse Datasets and Benchmarks paper.
DOI: 10.60717/e65b8263-9a43-4a1f-81ed-43ffee3886b5
title: Pilot Dataset Publication, First DOI from DESY Public Data

description: This dataset is part of an initial test of DESY Public Data integration with DataCite for DOI minting.
DOI: 10.6084/m9.figshare.29071114.v1
title: download_preprocess_python.zip from Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

description: When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.
DOI: 10.6084/m9.figshare.29071114
title: download_preprocess_python.zip from Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

description: When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.
DOI: 10.6084/m9.figshare.29071367
title: Functional groups of ants in a Neotropical rainforest

description: Tropical rainforest ants are species rich, abundant and certain species ecologically dominant. Here, we focus this study on ant species living on the ground and in the leaf-litter, on understory plants and in the canopy in an Amazonian rainforest. We used only two sampling techniques, Winkler extraction for ground-dwelling ants and direct observations for understory and canopy species. This permitted us to identified 494 ant species from 10 subfamilies and 77 genera (Chao1 = 607 species, CI95% 566-670) over ≈3.0 ha.
DOI: 10.6084/m9.figshare.29071111.v1
title: Code.zip from Using spatial extreme-value theory with machine learning to model and understand spatially compounding weather extremes

description: When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel machine learning (ML) framework that integrates spatial extreme-value theory to model weather extremes and to quantify probabilities associated with the occurrence, intensity and spatial extent of these events. Our approach employs new loss functions adapted to extreme values, enabling our model to prioritize the tail rather than the bulk of the data distribution. Applied to a case study of Western European summertime heat extremes, we use daily 500-hPa geopotential height fields and local soil moisture as predictors to capture the complex interplay between local and remote physical processes. Our generative model reveals that different facets of heat extremes are influenced by individual circulation features, such as the relative position of upper-level ridges and troughs that are part of a large-scale wave pattern. This enriches our process understanding from a data-driven perspective. Our approach can extrapolate beyond the range of the data to make risk-related probabilistic statements. It applies more generally to other weather extremes and offers an alternative to traditional physical and ML-based techniques that focus less on the extremal aspects of weather data.
DOI: 10.25345/c5wd3qd5b
title: MassIVE MSV000097905 - Untargeted metabolomics reveals PAMP-associated resistance metabolites in cultivated tomato
title: Plants employ a multi-layered innate immune system to detect and fend off invading fungal pathogens. In one such layer, recognition of Pathogen Associated Molecular Patterns (PAMPs) or elicitors, triggers a signaling cascade that leads to defense against the pathogen. Secondary Metabolites (SMs) are expected to play an important role in this kind of resistance, they are potentially mycotoxic compounds. Tomato plants inoculated with Alternaria solani show clear symptoms of infection 5 days after inoculation. Whereas plants inoculated with Alternaria alternata do not show symptoms. We hypothesized that PAMP-triggered induction of resistance-related metabolites in Solanum lycopersicum maintains the resistance against A. alternata, yet such SMs are suppressed in a susceptible interaction. We compare the metabolomic profile (metabolome) of S. lycopersicum at two time points after treatments with A. alternata, A. solani and the PAMP elicitor? chitin. We subsequently identify SMs that are involved in the early defense response in tomato plants. Our study revealed different metabolomes and shows that indeed A. alternata and chitin-induced metabolomes show larger overlap with each other than with the A. solani metabolome. We identify 62 candidate metabolites associated with PAMP-triggered resistance in tomato plants, including the alkaloid, trigonelline, for which we can confirm that it inhibits fungal growth in vitro when supplied at physiological concentrations. Our findings show that a true, PAMP-triggered, chemical defense is mounted against A. alternata and that it contains mycotoxic compounds previously unidentified in tomato, that could be interesting for future crop protection strategies.
DOI: 10.23664/ahri.lendingahand.2025
title: Lending a hand:Developing a support structure for young migrants in South Africa and Uganda – in-depth interviews from South Africa (uMkhanyakude District, KwaZulu-Natal Province)
DOI: 10.6084/m9.figshare.29066783
title: Data and Figures of Optical Nonlinearities in a Three-Dimensional GaAs/AleGa1-eAs Elliptical Quantum Ring

description: Figures with data of (<b>Optical nonlinearities in a three-dimensional GaAs/Al</b><sub><strong>e</strong></sub><b>Ga</b><sub><strong>1-</strong></sub><sub><strong>e</strong></sub><b>As elliptical quantum ring under a transverse electric field, morphology, temperature, and pressure effects</b>)
DOI: 10.6084/m9.figshare.29071358
title: Othered Futures: World Building and Mini-Design Charette Workshop Guide

description: Othered Futures is a one-day speculative architecture workshop designed for graduating architecture students to re-imagine climate futures related to flooding and extreme heat. This document outlines our workshop design for the world building and mini-design charette portions of the workshop. The workshop was implemented through partnership between the University of the Philippines College of Architecture and the Oscar M Lopez Center, with funding from Humanitarian OpenStreetMaps and Co-Risk Lab.
DOI: 10.60717/17114434-6bb9-4f3c-865f-59395b7c38c3
title: Pilot Dataset Publication, First DOI from DESY Public Data

description: This dataset is part of an initial test of DESY Public Data integration with DataCite for DOI minting.
DOI: 10.34740/kaggle/dsv/11823673
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17909/xr3c-6780
title: Creating test DOI in DOI Form

description: Creating test DOI in DOI Form
DOI: 10.5517/ccdc.csd.cc2hgc9n
title: CCDC 2307711: Experimental Crystal Structure Determination

description: Related Article: Yating Chen, Xueyang Hu, Lingyu Yuan, Shuhua Zhang, Hao Ma|2025|J.Mol.Struct.|1340|142499|doi:10.1016/j.molstruc.2025.142499
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.cc2hgcbp
title: CCDC 2307712: Experimental Crystal Structure Determination

description: Related Article: Yating Chen, Xueyang Hu, Lingyu Yuan, Shuhua Zhang, Hao Ma|2025|J.Mol.Struct.|1340|142499|doi:10.1016/j.molstruc.2025.142499
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.cc2hgccq
title: CCDC 2307713: Experimental Crystal Structure Determination

description: Related Article: Yating Chen, Xueyang Hu, Lingyu Yuan, Shuhua Zhang, Hao Ma|2025|J.Mol.Struct.|1340|142499|doi:10.1016/j.molstruc.2025.142499
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.cc2hgcdr
title: CCDC 2307714: Experimental Crystal Structure Determination

description: Related Article: Yating Chen, Xueyang Hu, Lingyu Yuan, Shuhua Zhang, Hao Ma|2025|J.Mol.Struct.|1340|142499|doi:10.1016/j.molstruc.2025.142499
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.cc2hgcfs
title: CCDC 2307715: Experimental Crystal Structure Determination

description: Related Article: Yating Chen, Xueyang Hu, Lingyu Yuan, Shuhua Zhang, Hao Ma|2025|J.Mol.Struct.|1340|142499|doi:10.1016/j.molstruc.2025.142499
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.cc2kg215
title: CCDC 2367006: Experimental Crystal Structure Determination

description: Related Article: Qinqin Li, Congyu Zuo, Changguo Xue, Jinsong Hu|2025|Surf. Interfaces|65|106497|doi:10.1016/j.surfin.2025.106497
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.cc2ks3m3
title: CCDC 2376665: Experimental Crystal Structure Determination

description: Related Article: Debakanta Tripathy, Soumya Lipsa Rath, Niladri Bihari Debata, Dillip Kumar Chand|2025|Results in Chemistry|15|102310|doi:10.1016/j.rechem.2025.102310
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.cc2kv17q
title: CCDC 2378513: Experimental Crystal Structure Determination

description: Related Article: Peng Jiang, Yu-Meng Luo, Xin-Yuan Li, Shu-Jun Zhang, Wei Guan, Anam-Naseem, Yu-Hang Fan, Qing-Shan Chen, Li-Li Zhang, Hong-Yan Yao, Xiao-Xue He, Hai-Xue Kuang, Bing-You Yang, Yan Liu|2025|Phytochemistry|237|114524|doi:10.1016/j.phytochem.2025.114524
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.cc2kv18r
title: CCDC 2378514: Experimental Crystal Structure Determination

description: Related Article: Peng Jiang, Yu-Meng Luo, Xin-Yuan Li, Shu-Jun Zhang, Wei Guan, Anam-Naseem, Yu-Hang Fan, Qing-Shan Chen, Li-Li Zhang, Hong-Yan Yao, Xiao-Xue He, Hai-Xue Kuang, Bing-You Yang, Yan Liu|2025|Phytochemistry|237|114524|doi:10.1016/j.phytochem.2025.114524
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.