cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18216167
title: NAD Research - Restricted archive

description: Restricted research data associated with the program “Human blood NAD does not decrease during healthy aging.” This archive contains controlled-access datasets required to fully reproduce the analyses reported in the program, including complete epidemiological data and participant-level analysis files.

Access is restricted due to the presence of sensitive human-subject data and bioethical documentation. For access, an official request should be sent to: Diana Romenskaja-Avgul (diana@lifelab1.com) and Dalius Ratautas (dalius.ratautas@gmc.vu.lt).Detailed information about the contents is provided in the accompanying "Readme.txt".

For editors and reviewers: Access to this dataset is available via a private, anonymous deep link provided in the editorial system file titled “Zenodo access links.”
DOI: 10.18150/4ia2bf
title: Research data for manuscript: Pressure-induced self-assembly in 3-phenylpropanal: distinct clustering via π-stacking and distorted H-Bonds

description: This study uncovers how high pressure uniquely manipulates 3-phenyl-1-propanal’s (3P1Pal) dynamics and interactions. Dielectric analysis reveals an anomalous compressed Debye process, signaling a distinct clustering pathway. FTIR and Raman spectroscopy, with simulations, demonstrate high-pressure aggregation is governed by π-stacking and distorted H-bonds, forming larger, denser clusters than those created by cooling. Ultimately, high compression uniquely tailors dynamics and self-assembly, revealing novel assembly principles in viscous liquids.The dataset contains raw high-pressure dielectric, FTIR, and Raman data, collected for 3-phenylpropanal (3PhPal). Calorimetric measurements have also been added.
DOI: 10.6084/m9.figshare.29459642
title: Engineered vesicles enhance oral antibiotic absorption in proximal small intestine and mitigate gut dysbiosis

description: Oral antibiotics are a mainstay for treating bacterial infections, but unabsorbed portions can reach the caecum and colon, leading to gut dysbiosis. Herein, we engineer a biohybrid delivery system through the integration of milk extracellular vesicles with liposomes. The hybrid vesicles employ targeting mechanisms via neonatal Fc receptor and peptide transporter 1, facilitating antibiotic transport across the proximal small intestine. These vesicles exhibit superior drug encapsulation efficiency, stable release behavior, efficient mucus traversal, higher endocytosis, increased basolateral exocytosis, and improved oral absorption, achieving a 3.24-fold increase in oral bioavailability compared to free antibiotics. In lung bacterial infections and bacteremia models, hybrid vesicle-encapsulated cefdinir outperforms free antibiotics in eliminating infections. Notably, this approach also mitigates adverse effects on the intestinal microbiota, safeguarding the animals from dysbiosis-associated metabolic syndromes and opportunistic pathogen infections. This innovative hybrid vesicle system holds great promise for the oral delivery of other drugs that suffer from limited absorption or cause gut dysbiosis.
DOI: 10.6084/m9.figshare.29459642.v1
title: Engineered vesicles enhance oral antibiotic absorption in proximal small intestine and mitigate gut dysbiosis

description: Oral antibiotics are a mainstay for treating bacterial infections, but unabsorbed portions can reach the caecum and colon, leading to gut dysbiosis. Herein, we engineer a biohybrid delivery system through the integration of milk extracellular vesicles with liposomes. The hybrid vesicles employ targeting mechanisms via neonatal Fc receptor and peptide transporter 1, facilitating antibiotic transport across the proximal small intestine. These vesicles exhibit superior drug encapsulation efficiency, stable release behavior, efficient mucus traversal, higher endocytosis, increased basolateral exocytosis, and improved oral absorption, achieving a 3.24-fold increase in oral bioavailability compared to free antibiotics. In lung bacterial infections and bacteremia models, hybrid vesicle-encapsulated cefdinir outperforms free antibiotics in eliminating infections. Notably, this approach also mitigates adverse effects on the intestinal microbiota, safeguarding the animals from dysbiosis-associated metabolic syndromes and opportunistic pathogen infections. This innovative hybrid vesicle system holds great promise for the oral delivery of other drugs that suffer from limited absorption or cause gut dysbiosis.
DOI: 10.6084/m9.figshare.31085758
title: Diketopyrrolopyrrole-Based Two-Dimensional Poly(Arylene Vinylene)s with High Charge Carrier Mobility

description: Raw data for main text
DOI: 10.6084/m9.figshare.31085758.v1
title: Diketopyrrolopyrrole-Based Two-Dimensional Poly(Arylene Vinylene)s with High Charge Carrier Mobility

description: Raw data for main text
DOI: 10.6084/m9.figshare.28852343
title: Hierarchically semi-interpenetrating polymer nanofilms with interconnected hydrated micropores for high-performance seawater desalination

description: All data that support the findings in the current study are available within the Article and its Supplementary Information. The relevant raw data for each figure are provided as source or supplementary data files. Source data are provided with this paper.
DOI: 10.6084/m9.figshare.28852343.v1
title: Hierarchically semi-interpenetrating polymer nanofilms with interconnected hydrated micropores for high-performance seawater desalination

description: All data that support the findings in the current study are available within the Article and its Supplementary Information. The relevant raw data for each figure are provided as source or supplementary data files. Source data are provided with this paper.
DOI: 10.5281/zenodo.18491834
title: COVID-ARE-Konsultationsinzidenz

description: Im Datensatz "COVID-ARE-Konsultationsinzidenz" des Robert Koch-Instituts werden wöchentliche Inzidenzen von ambulanten Konsultationen mit akuter respiratorischer Erkrankung (ARE) und zusätzlicher COVID-19-Diagnose in Deutschland bereitgestellt. Über das syndromische Surveillance-Modul SEED-ARE werden aus Sentinelpraxen Daten von gesetzlich versicherten Patientinnen und Patienten mit einer akuten Atemwegserkrankung inklusive der jeweiligen ICD-10-Diagnosecodes elektronisch übermittelt. Die COVID-ARE-Konsultationsinzidenz wird pro 100.000 Einwohner und nach Altersgruppen ausgewiesen. Der Datensatz ermöglicht eine zeitnahe Einschätzung der Krankheitslast und unterstützt evidenzbasierte Entscheidungen im Infektionsgeschehen mit COVID-19.
DOI: 10.5281/zenodo.18491821
title: KAVNN-Dataset

description: \abstract{Adverse drug reactions pose significant risks to human health; thus, accurate assessment of Drug-Induced Liver and Kidney Injury (DILKI) is critical for drug safety and adhering to the 3R principles (Replacement, Reduction, Refinement). However, current computational strategies often encounter a trade-off: traditional machine learning offers feature-based interpretability but frequently demonstrates constrained predictive capability, whereas deep learning achieves higher performance but typically remains a mechanistic “black box”. To address this challenge, we present the Kolmogorov-Arnold Visible Neural Network (KAVNN), a model designed to accurately predict DILKI and pathological phenotypes while inferring biological mechanisms. KAVNN integrates chemical structures with multi-scale biological priors and employs Fourier series-based Kolmogorov-Arnold Networks (KANs) to replace standard Multi-Layer Perceptrons. This architecture enhances non-linear expressiveness with a compact parameter space. In the drug toxicity prediction task, KAVNN achieves superior average performance over the state-of-the-art baselines, with an approximate 15.5\% improvement observed in the pathological phenotype prediction task. Moreover, it exhibits robust interpretability by identifying toxicity-associated genes, tracing potential causal trajectories from molecular perturbations to tissue injury, and uncovering targets consistent with established toxicology. KAVNN thus bridges the gap between accuracy and interpretability, offering a transparent, mechanism-driven tool for early-stage safety assessment.}\keywords{Drug-Induced Liver and Kidney Injury (DILKI), Kolmogorov-Arnold Networks (KANs), Toxicity prediction, Pathological phenotype prediction}
DOI: 10.5281/zenodo.18491820
title: KAVNN-Dataset

description: \abstract{Adverse drug reactions pose significant risks to human health; thus, accurate assessment of Drug-Induced Liver and Kidney Injury (DILKI) is critical for drug safety and adhering to the 3R principles (Replacement, Reduction, Refinement). However, current computational strategies often encounter a trade-off: traditional machine learning offers feature-based interpretability but frequently demonstrates constrained predictive capability, whereas deep learning achieves higher performance but typically remains a mechanistic “black box”. To address this challenge, we present the Kolmogorov-Arnold Visible Neural Network (KAVNN), a model designed to accurately predict DILKI and pathological phenotypes while inferring biological mechanisms. KAVNN integrates chemical structures with multi-scale biological priors and employs Fourier series-based Kolmogorov-Arnold Networks (KANs) to replace standard Multi-Layer Perceptrons. This architecture enhances non-linear expressiveness with a compact parameter space. In the drug toxicity prediction task, KAVNN achieves superior average performance over the state-of-the-art baselines, with an approximate 15.5\% improvement observed in the pathological phenotype prediction task. Moreover, it exhibits robust interpretability by identifying toxicity-associated genes, tracing potential causal trajectories from molecular perturbations to tissue injury, and uncovering targets consistent with established toxicology. KAVNN thus bridges the gap between accuracy and interpretability, offering a transparent, mechanism-driven tool for early-stage safety assessment.}\keywords{Drug-Induced Liver and Kidney Injury (DILKI), Kolmogorov-Arnold Networks (KANs), Toxicity prediction, Pathological phenotype prediction}
DOI: 10.6084/m9.figshare.28877222
title: Highly reconfigurable neuronlike conductive networks through nanophase structure engineering

description: Source data of figures in the main manuscript
DOI: 10.6084/m9.figshare.28877222.v1
title: Highly reconfigurable neuronlike conductive networks through nanophase structure engineering

description: Source data of figures in the main manuscript
DOI: 10.34740/kaggle/dsv/14739276
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.30060211
title: A single-center 3D reconstruction image dataset for exploring inferior mesenteric artery and vein types

description: The patient's basic information and three-dimensional reconstructed images of the inferior mesenteric artery and vein.
DOI: 10.6084/m9.figshare.30060211.v1
title: A single-center 3D reconstruction image dataset for exploring inferior mesenteric artery and vein types

description: The patient's basic information and three-dimensional reconstructed images of the inferior mesenteric artery and vein.
DOI: 10.5281/zenodo.18187693
title: NAD Research - Open archive

description: Open research data associated with the program “Human blood NAD does not decrease during healthy aging.” The archive includes openly shareable data and analysis materials supporting the published results. 

 

The archive is currently placed under embargo, but it will be made open for public upon publication of the related article. Detailed file descriptions are provided in the accompanying "Readme.txt”.

 

For editors and reviewers: Access to this dataset is available via a private, anonymous deep link provided in the editorial system file titled “Zenodo access links.”
DOI: 10.5281/zenodo.18187692
title: NAD Research - Open archive

description: Open research data associated with the program “Human blood NAD does not decrease during healthy aging.” The archive includes openly shareable data and analysis materials supporting the published results. 

 

The archive is currently placed under embargo, but it will be made open for public upon publication of the related article. Detailed file descriptions are provided in the accompanying "Readme.txt”.

 

For editors and reviewers: Access to this dataset is available via a private, anonymous deep link provided in the editorial system file titled “Zenodo access links.”
DOI: 10.5517/ccxd5q5
title: CCDC 845857: Experimental Crystal Structure Determination

description: Related Article: K.Elasaad, B.Norberg, J.Wouters|2012|Supramol.Chem.|24|312|doi:10.1080/10610278.2012.658392
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.18194589
title: A benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024

description: (1) Half-hourly LE data (2000–2024).This dataset provides seamless half-hourly latent heat flux (LE) data for flux tower sites across China, generated using the AutoML-H2O framework. The half-hourly LE data represent the core product of this dataset and cover the period from 1 January 2000, 00:00 to 31 December 2024, 23:30.Each record is associated with a quality flag indicating the data source: T denotes half-hourly LE derived from original flux tower observations; F represents gap-filled LE within the observation period; and P indicates LE values generated through temporal prolongation beyond the observation period.All timestamps are recorded in local site time and follow the format YYYYMMDD–HHMM (e.g., 20120101–1530). The data provide complete, temporally continuous half-hourly LE time series for all sites.

(2) Daily, monthly, and annual aggregated LE data.Daily, monthly, and annual LE products were directly aggregated from the gap-filled and temporally prolonged half-hourly LE data, ensuring full temporal consistency across all time scales. These aggregated datasets cover the period 2000–2024.For each temporal scale (day, month, and year), quality control information is provided in the form of the proportion of valid half-hourly observations contributing to the aggregated LE value within the corresponding period. This unified definition of data availability allows users to consistently assess data reliability across temporal scales. No distinction is made between gap-filled and prolonged data at aggregated scales, with the aim of providing stable and continuous datasets for long-term hydrological and climate change studies.

(3) Auxiliary driving data.To support reproducibility and extended analyses, auxiliary driving variables used for LE gap-filling and temporal prolongation are also provided. These include hourly meteorological and energy balance variables from ERA5-Land, as well as NDVI and LAI derived from MODIS products. All auxiliary datasets have been spatially and temporally matched to the LE data at the site scale. NDVI and LAI data are available from 18 February 2000, 08:00 (local time) onward; for earlier periods, vegetation indices are not provided.

In addition, the code used for LE gap-filling and temporal prolongation is publicly available and provided alongside the dataset, enabling full reproducibility of the data processing workflow and facilitating reuse for similar applications.

All data files are released in CSV format, with file names clearly indicating site information, temporal scale, and variable type.
DOI: 10.5281/zenodo.18194590
title: A benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024

description: (1) Half-hourly LE data (2000–2024).This dataset provides seamless half-hourly latent heat flux (LE) data for flux tower sites across China, generated using the AutoML-H2O framework. The half-hourly LE data represent the core product of this dataset and cover the period from 1 January 2000, 00:00 to 31 December 2024, 23:30.Each record is associated with a quality flag indicating the data source: T denotes half-hourly LE derived from original flux tower observations; F represents gap-filled LE within the observation period; and P indicates LE values generated through temporal prolongation beyond the observation period.All timestamps are recorded in local site time and follow the format YYYYMMDD–HHMM (e.g., 20120101–1530). The data provide complete, temporally continuous half-hourly LE time series for all sites.

(2) Daily, monthly, and annual aggregated LE data.Daily, monthly, and annual LE products were directly aggregated from the gap-filled and temporally prolonged half-hourly LE data, ensuring full temporal consistency across all time scales. These aggregated datasets cover the period 2000–2024.For each temporal scale (day, month, and year), quality control information is provided in the form of the proportion of valid half-hourly observations contributing to the aggregated LE value within the corresponding period. This unified definition of data availability allows users to consistently assess data reliability across temporal scales. No distinction is made between gap-filled and prolonged data at aggregated scales, with the aim of providing stable and continuous datasets for long-term hydrological and climate change studies.

(3) Auxiliary driving data.To support reproducibility and extended analyses, auxiliary driving variables used for LE gap-filling and temporal prolongation are also provided. These include hourly meteorological and energy balance variables from ERA5-Land, as well as NDVI and LAI derived from MODIS products. All auxiliary datasets have been spatially and temporally matched to the LE data at the site scale. NDVI and LAI data are available from 18 February 2000, 08:00 (local time) onward; for earlier periods, vegetation indices are not provided.

In addition, the code used for LE gap-filling and temporal prolongation is publicly available and provided alongside the dataset, enabling full reproducibility of the data processing workflow and facilitating reuse for similar applications.

All data files are released in CSV format, with file names clearly indicating site information, temporal scale, and variable type.
DOI: 10.6084/m9.figshare.29206643
title: A haplotype-resolved gap-free chromosome assembly of the threatened plant Paradombeya sinensis

description: Gene annotation data of Paradombeya sinensis genome
DOI: 10.6084/m9.figshare.29206643.v1
title: A haplotype-resolved gap-free chromosome assembly of the threatened plant Paradombeya sinensis

description: Gene annotation data of Paradombeya sinensis genome
DOI: 10.6084/m9.figshare.29856323
title: Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks

description: This dataset addresses the need for multimodal biomechanical recordings during over-ground walking, ramps, and stairs by synchronously capturing electromyographic (EMG), inertial (IMU), and plantar pressure data. We collected data from 45 adults (15 with unilateral transtibial amputation and 30 without amputation) who completed five standardized locomotor tasks: level walking, ramp ascent/descent, and stair ascent/descent. Each participant performed 50 supervised trials. Wireless EMG and IMU sensors (Delsys Trigno Avanti) measured muscle activation and kinematics, while intelligent insoles (XSENSOR) captured plantar pressure distribution. Raw data were saved in .hpf (EMG/IMU) and .XSN (pressure) formats, with processed outputs in .csv files. All data are organized by task and sensor type, including complete participant metadata. Key dataset outputs include time-normalized EMG amplitudes, segment kinematics, and pressure maps across terrains and populations. The dataset was validated technically and experimentally during the acquisition. This resource enables quantitative analysis of gait adaptation and supports machine learning for locomotion classification. Data are provided in accessible formats to foster reuse in biomechanics, rehabilitation engineering, robotics, and clinical gait research.
DOI: 10.6084/m9.figshare.29856323.v1
title: Multimodal biomechanical dataset from transtibial amputees and able-bodied adults across five locomotion tasks

description: This dataset addresses the need for multimodal biomechanical recordings during over-ground walking, ramps, and stairs by synchronously capturing electromyographic (EMG), inertial (IMU), and plantar pressure data. We collected data from 45 adults (15 with unilateral transtibial amputation and 30 without amputation) who completed five standardized locomotor tasks: level walking, ramp ascent/descent, and stair ascent/descent. Each participant performed 50 supervised trials. Wireless EMG and IMU sensors (Delsys Trigno Avanti) measured muscle activation and kinematics, while intelligent insoles (XSENSOR) captured plantar pressure distribution. Raw data were saved in .hpf (EMG/IMU) and .XSN (pressure) formats, with processed outputs in .csv files. All data are organized by task and sensor type, including complete participant metadata. Key dataset outputs include time-normalized EMG amplitudes, segment kinematics, and pressure maps across terrains and populations. The dataset was validated technically and experimentally during the acquisition. This resource enables quantitative analysis of gait adaptation and supports machine learning for locomotion classification. Data are provided in accessible formats to foster reuse in biomechanics, rehabilitation engineering, robotics, and clinical gait research.
DOI: 10.5281/zenodo.18491782
title: Data set of fish trawl in the Pearl River Estuary in 23 years
DOI: 10.5281/zenodo.18491783
title: Data set of fish trawl in the Pearl River Estuary in 23 years
DOI: 10.5281/zenodo.18491813
title: Datasets for EffiRadNet – Radiology Binary Classification Tasks

description: This Zenodo repository contains the data and extended data supporting the results of the article “EffiRadNet: Lightweight and User-Friendly Open-Source EfficientNet-Based Model for Radiology Image Binary Classification Tasks”.
DOI: 10.5281/zenodo.18491814
title: Datasets for EffiRadNet – Radiology Binary Classification Tasks

description: This Zenodo repository contains the data and extended data supporting the results of the article “EffiRadNet: Lightweight and User-Friendly Open-Source EfficientNet-Based Model for Radiology Image Binary Classification Tasks”.
DOI: 10.6084/m9.figshare.30359713
title: Fatigue Characterization Multimodal Datasets for Stereo Vision in Mixed Reality

description: A study on the multimodal characterization of stereoscopic visual fatigue induced by mixed reality environments.
DOI: 10.6084/m9.figshare.30359713.v1
title: Fatigue Characterization Multimodal Datasets for Stereo Vision in Mixed Reality

description: A study on the multimodal characterization of stereoscopic visual fatigue induced by mixed reality environments.
DOI: 10.5281/zenodo.18491860
title: SARI-Hospitalisierungsinzidenz

description: Im Datensatz "SARI-Hospitalisierungsinzidenz" des Robert Koch-Instituts werden die wöchentlichen Inzidenzen schwerer akuter respiratorischer Infektionen (SARI) auf Bundesebene in Deutschland bereitgestellt. Die Daten stammen aus der syndromischen Krankenhaussurveillance ICOSARI, die auf ICD-10-Diagnosecodes aus etwa 70 Sentinelkliniken basiert. Der Datensatz umfasst Hospitalisierungsinzidenzen pro 100.000 Einwohner, aufgeschlüsselt nach Altersgruppen, Saison und Kalenderwoche. Neben der Gesamtheit aller SARI-Fälle werden auch Fälle mit zusätzlicher COVID-19-, Influenza- oder RSV-Diagnose separat erfasst. Die Surveillance liefert entscheidende Informationen zur Krankheitslast akuter respiratorischer Erkrankungen und dient als Grundlage für epidemiologische Bewertungen und Empfehlungen für gesundheitspolitische Maßnahmen.