cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.2908/tus_20npaydomw
title: Time spent providing unpaid domestic work by sex and age group
title: Zeitverwendung für unbezahlte Haus- und Haushaltsarbeit nach Geschlecht und Altersgruppe
title: Temps consacré au travail domestique non rémunéré par sexe et groupe d'âge

description: Die Tabelle bietet Informationen über unbezahlte unbezahlte Haus- und Haushaltsarbeit (als Hauptaktivität) während der 24 Stunden, unter Verwendung von drei Maßeinheiten: o Aufgewendete Zeit: durchschnittliche Zeit, die alle Personen mit den Aktivitäten verbringen; o Teilnahmedauer: durchschnittliche Zeit, die jene Personen mit den Aktivitäten verbringen, die an der Aktivität teilgenommen haben;o Teilnahmequote: der Anteil der Personen, die einige Zeit mit den Aktivitäten verbracht haben. Alter und Geschlecht können als Untergliederungen verwendet werden. Die Daten wurden zwischen 2020 und 2026 gesammelt.
description: The table provides information on unpaid domestic work (as main activity) individuals do during the 24 hours using three units of measure: o Time spent: mean time spent on the activities by all individuals; o Participation time: mean time spent in the activities by those individuals who took part in the activity; o Participation rate: the proportion of the individuals that spent some time doing the activities. Sex and age can be used as breakdowns. Data have been collected between 2020 and 2026.
description: Le tableau fournit des informations sur le travail domestique non rémunéré (comme activité principale) que les individus réalisent au cours des 24 heures, en utilisant trois unités de mesure : o Temps passé : le temps moyen consacré à l'activité par l'ensemble des individus ; o Temps de participation : le temps moyen passé dans l'activité par les individus qui y ont participé ; o Taux de participation : la proportion d'individus ayant consacré du temps à l'activité. L'âge et le sexe peuvent être utilisés comme critères de ventilation. Les données ont été collectées entre 2020 et 2026.
DOI: 10.2908/tus_20npaywork
title: Time spent in unpaid work by sex and age group
title: Temps consacré au travail non rémunéré par sexe et groupe d'âge
title: Zeitverwendung in unbezahlter Arbeit nach Geschlecht und Altersgruppe

description: Die Tabelle bietet Informationen über unbezahlte Arbeit, die Personen innerhalb von 24 Stunden ausüben, und verwendet folgende Maßeinheiten:o Aufgewendete Zeit: durchschnittliche Zeit, die alle Personen mit den Aktivitäten verbringen;o Teilnahmedauer: durchschnittliche Zeit, die jene Personen mit den Aktivitäten verbringen, die an der Aktivität teilgenommen haben;o Teilnahmequote: der Anteil der Personen, die einige Zeit mit den Aktivitäten verbracht haben. Alter und Geschlecht können als Untergliederungen verwendet werden. Die Daten wurden zwischen 2020 und 2026 gesammelt.
description: The table provides information on unpaid work individuals do during the 24 hours using three units of measure: o Time spent: mean time spent on the activities by all individuals; o Participation time: mean time spent in the activities by those individuals who took part in the activity; o Participation rate: the proportion of the individuals that spent some time doing the activities. Age and sex can be used as breakdowns. Data have been collected between 2020 and 2026.
description: Le tableau fournit des informations sur le travail non rémunéré que les individus réalisent au cours des 24 heures, en utilisant trois unités de mesure : o Temps passé : le temps moyen consacré à l'activité par l'ensemble des individus ; o Temps de participation : le temps moyen passé dans l'activité par les individus qui y ont participé ; o Taux de participation : la proportion d'individus ayant consacré du temps à l'activité. L'âge et le sexe peuvent être utilisés comme critères de ventilation. Les données ont été collectées entre 2020 et 2026.
DOI: 10.2908/tus_20work
title: Time spent in paid and unpaid work by sex and age group
title: Temps consacré au travail total (travail rémunéré ou non) par sexe et et groupe d'âge
title: Zeitverwendung in der Gesamtarbeit (bezahlte und unbezahlte Arbeit) nach Geschlecht und Altersgruppe

description: Die Tabelle bietet Informationen über bezahlte und unbezahlte Arbeit, die Personen innerhalb von 24 Stunden ausüben, und verwendet folgende Maßeinheiten: o Aufgewendete Zeit: durchschnittliche Zeit, die alle Personen mit den Aktivitäten verbringen; o Teilnahmedauer: durchschnittliche Zeit, die jene Personen mit den Aktivitäten verbringen, die an der Aktivität teilgenommen haben; o Teilnahmequote: der Anteil der Personen, die einige Zeit mit den Aktivitäten verbracht haben. Alter und Geschlecht können als Untergliederungen verwendet werden. Die Daten wurden zwischen 2020 und 2026 gesammelt.
description: The table provides information on paid and unpaid work individuals do during the 24 hours using three units of measure: o Time spent: mean time spent on the activities by all individuals; o Participation time: mean time spent in the activities by those individuals who took part in the activity; o Participation rate: the proportion of the individuals that spent some time doing the activities. Age and sex can be used as breakdowns. Data have been collected between 2020 and 2026.
description: Le tableau fournit des informations sur le travail rémunéré et non rémunéré que les individus réalisent au cours des 24 heures, en utilisant trois unités de mesure : o Temps passé : le temps moyen consacré à l'activité par l'ensemble des individus ; o Temps de participation : le temps moyen passé dans l'activité par les individus qui y ont participé ; o Taux de participation : la proportion d'individus ayant consacré du temps à l'activité. L'âge et le sexe peuvent être utilisés comme critères de ventilation. Les données ont été collectées entre 2020 et 2026.
DOI: 10.2908/tus_20npaycarew
title: Time spent providing unpaid care work by sex and age group
title: Temps consacré au travail de soin non rémunéré par sexe et groupe d'âge
title: Zeitverwendung für unbezahlte Pflege- und Betreuungsarbeit nach Geschlecht und Altersgruppe

description: Die Tabelle bietet Informationen über unbezahlte Pflege- und Betreuungsarbeit (als Hauptaktivität) während der 24 Stunden, unter Verwendung von drei Maßeinheiten: o Aufgewendete Zeit: durchschnittliche Zeit, die alle Personen mit den Aktivitäten verbringen; o Teilnahmedauer: durchschnittliche Zeit, die jene Personen mit den Aktivitäten verbringen, die an der Aktivität teilgenommen haben; o Teilnahmequote: der Anteil der Personen, die einige Zeit mit den Aktivitäten verbracht haben. Breite Altersgruppen und Geschlecht können als Untergliederungen verwendet werden. Die Daten wurden zwischen 2020 und 2026 gesammelt.
description: The table provides information on unpaid care work (as main activity) individuals do during the 24 hours using three units of measure: o Time spent: mean time spent on the activities by all individuals; o Participation time: mean time spent in the activities by those individuals who took part in the activity; o Participation rate: the proportion of the individuals that spent some time doing the activities. Sex and broad age groups can be used as breakdowns. Data have been collected between 2020 and 2026.
description: Le tableau fournit des informations sur le travail de soin non rémunéré (comme activité principale) que les individus réalisent au cours des 24 heures, en utilisant trois unités de mesure : o Temps passé : le temps moyen consacré à l'activité par l'ensemble des individus ; o Temps de participation : le temps moyen passé dans l'activité par les individus qui y ont participé ; o Taux de participation : la proportion d'individus ayant consacré du temps à l'activité. Larges catégories d'âge et le sexe peuvent être utilisés comme critères de ventilation. Les données ont été collectées entre 2020 et 2026.
DOI: 10.15468/dl.76zpg6
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Croomia japonica",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 99 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011231-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.19382939
title: Acoustic emission RMS, solar voltage and battery charge percentage time series data for Site 2 Sensor 3 of the Community Slope SAFE trial at Hakha, Chin State, Myanmar

description: Acoustic emission RMS, solar voltage and battery charge percentage time series data for Site 2 Sensor 3 of the Community Slope SAFE trial at Hakha, Chin State, Myanmar
DOI: 10.17028/rd.lboro.19382939.v1
title: Acoustic emission RMS, solar voltage and battery charge percentage time series data for Site 2 Sensor 3 of the Community Slope SAFE trial at Hakha, Chin State, Myanmar

description: Acoustic emission RMS, solar voltage and battery charge percentage time series data for Site 2 Sensor 3 of the Community Slope SAFE trial at Hakha, Chin State, Myanmar
DOI: 10.5281/zenodo.18620263
title: FAKE NEWS DETECTION ON TWITTER PROJECT REPORT.

description: The project is concerned with identifying a solution that could be used to detect and filter out sites containing fake news for purposes of helping users to avoid being lured by clickbaits. It is imperative that such solutions are identified as they will prove to be useful to both readers and tech companies involved in the issue.
DOI: 10.5281/zenodo.18620262
title: FAKE NEWS DETECTION ON TWITTER PROJECT REPORT.

description: The project is concerned with identifying a solution that could be used to detect and filter out sites containing fake news for purposes of helping users to avoid being lured by clickbaits. It is imperative that such solutions are identified as they will prove to be useful to both readers and tech companies involved in the issue.
DOI: 10.5281/zenodo.18619560
title: Simulation Starts and Analyses Scripts for Condensates and MT-Condensate Systems

description: This repository contains simulation starts and analysis scripts for condensates and microtubule (MT)-condensate systems

The dataset includes:





Python scripts for performing molecular dynamics (MD) simulations using OpenMM




Force-field parameters for Calvados3-simulations of MT-Condensate systems




Analysis scripts used for key analyses of the simulation trajectories




Input files necessary to perform the simulations



The scripts are written in Python and require standard/specialised scientific Python libraries and/or OpenMM for execution.
DOI: 10.5281/zenodo.18619561
title: Simulation Starts and Analyses Scripts for Condensates and MT-Condensate Systems

description: This repository contains simulation starts and analysis scripts for condensates and microtubule (MT)-condensate systems

The dataset includes:





Python scripts for performing molecular dynamics (MD) simulations using OpenMM




Force-field parameters for Calvados3-simulations of MT-Condensate systems




Analysis scripts used for key analyses of the simulation trajectories




Input files necessary to perform the simulations



The scripts are written in Python and require standard/specialised scientific Python libraries and/or OpenMM for execution.
DOI: 10.5281/zenodo.17650059
title: Simulation data for "Assessment of Reactive Force Fields for the Failure of Hydrocarbon Chains: Insights from Molecular Dynamics and Density Functional Theory"

description: The upload contains sample input and output files from molecular dynamics simulations of a single linear polyethylene chain (C_202H_406) undergoing elongation and scission. The simulations were conducted using the LAMMPS code with four reactive force fields (AIREBO, MEAM, ReaxFF and ci-ReaxFF).
DOI: 10.5281/zenodo.17650058
title: Simulation data for "Assessment of Reactive Force Fields for the Failure of Hydrocarbon Chains: Insights from Molecular Dynamics and Density Functional Theory"

description: The upload contains sample input and output files from molecular dynamics simulations of a single linear polyethylene chain (C_202H_406) undergoing elongation and scission. The simulations were conducted using the LAMMPS code with four reactive force fields (AIREBO, MEAM, ReaxFF and ci-ReaxFF).
DOI: 10.25345/c5bn9xg9c
title: MassIVE MSV000100815 - Analytical workflow for comprehensive blood metabolomics analysis by GC-MS. Application to children with ventilator associated pneumonia
title: Metabolomics is a widely used approach for analyzing a vast array of low molecular weight compounds such as amino acids, organic acids, vitamins, biogenic amines and carbohydrates in biological samples, with the aim of investigating biomarkers in personalized medicine studies. Advancements in gas chromatography mass spectrometry (GCMS) instrumentation, along with the availability of commercial and public spectral libraries, have highlighted the relevance of GCMS analysis as a valuable tool for metabolomics applications. Stability assessment in derivatization and GCMS analysis is a crucial yet often overlooked aspect of metabolomics studies. In this study, an untargeted GCMS method workflow for large scale metabolomics studies is presented after assessment and optimization of whole blood sample stability. The method consists of a common two-step derivatization procedure including methoximation using methoxyamine hydrochloride, followed by silylation with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA). To ensure the stability of the studied metabolites, extensive stability experiments were performed. The stability of the derivatives was evaluated over 24 h in the autosampler at room temperature, as well as after storage for 24 and 48 h at -20 C for both derivatized and dried extracts. While derivatized samples remained stable for 24-48 h in the freezer, dried extracts exhibited variability after 48 h. Findings support the storage of derivatized samples over dried extracts, ensuring greater stability. To increase confidence in metabolite identification, data from the analysis of 120 standard compounds were utilized. The developed method was applied to analyze blood samples from 32 children with ventilator-associated pneumonia (VAP), collected at four different time points during ICU hospitalization. This analysis led to the identification of 43 metabolites. The results of multivariate and univariate statistical analyses demonstrated several statistically significant metabolites, including aspartic acid, alanine, and pyroglutamic acid, which showed a strong correlation with the disease manifestation and may potentially serve as biomarkers in the diagnosis of ventilator-associated pneumonia (VAP) at the stage of clinical suspicion.
DOI: 10.34740/kaggle/dsv/14817422
title: FAKE NEWS DETECTION ON TWITTER PROJECT REPORT.
DOI: 10.5281/zenodo.18619432
title: Dataset - Self-consistent stabilization of large-scale linear magnetic holes via ion trapping

description: This dataset contains processed magnetic field, density, and ion pressure tensor outputs from two-dimensional hybrid kinetic plasma simulations performed with the MENURA code. The data support the analysis presented in the associated manuscript on magnetic field evolution and pressure anisotropy dynamics under varying guide field strengths and oblique configurations.

Simulation Parameters

Simulations were performed for guide field scales:





L=50 di




L=10 di




L=1 di



Density perturbation amplitudes:





δ=0.20




δ=0.40




δ=0.60



Oblique configurations (where applicable):





α=10∘




α=30∘



All quantities are expressed in normalized simulation units (length in di, time in ω_{c,i}^{−1}, magnetic field, density and pressure normalised to background values).

Temporal Sampling

Data are provided at three normalized times: t=0, 2000, 4000 ω_{c,i}^{−1}, corresponding to the times analysed in the paper.

File Format and Structure

All files are provided in compressed NumPy format (.npz).

How to Load the Data

All files are provided in compressed NumPy format (.npz) and can be loaded using the numpy.load function in Python. Each file behaves as a dictionary containing the magnetic field (B_t*), density (dens_t*), and ion pressure tensor (P_t*) at the three simulation times. Example:





 


import numpy as np


 

# Load file


data = np.load('data_L50_alpha10_d40.npz')



 

# List available variables
print(data.files)



 

# Access fields at t = 2000 ω_ci^{-1}


B = data['B_t2000']        

dens = data['dens_t2000']

P = data['P_t2000']        # shape (6, Nx, Ny)


# Magnetic field components
Bx, By, Bz = B


# Pressure tensor components
Pxx, Pyy, Pzz, Pxy, Pxz, Pyz = P



Each .npz file can also be opened using a context manager (with np.load(...) as data:) to ensure proper file handling. The arrays are directly compatible with NumPy, SciPy, and Matplotlib for further analysis and visualization.

Naming convention

Longitudinal configurations:   data_L{L}_d{delta}.npz 

Oblique configurations:            data_L{L}_alpha{alpha}_d{delta}.npz


Stored variables

Each file contains:





B_t0, B_t2000, B_t4000Magnetic field components with shape (3, Nx, Ny) corresponding to (Bx,By,Bz).




dens_t0, dens_t2000, dens_t4000Plasma density with shape (Nx, Ny).




P_t0, P_t2000, P_t4000Ion pressure tensor with shape (6, Nx, Ny) ordered as (Pxx,Pyy,Pzz,Pxy,Pxz,Pyz).
DOI: 10.5281/zenodo.18619433
title: Dataset - Self-consistent stabilization of large-scale linear magnetic holes via ion trapping

description: This dataset contains processed magnetic field, density, and ion pressure tensor outputs from two-dimensional hybrid kinetic plasma simulations performed with the MENURA code. The data support the analysis presented in the associated manuscript on magnetic field evolution and pressure anisotropy dynamics under varying guide field strengths and oblique configurations.

Simulation Parameters

Simulations were performed for guide field scales:





L=50 di




L=10 di




L=1 di



Density perturbation amplitudes:





δ=0.20




δ=0.40




δ=0.60



Oblique configurations (where applicable):





α=10∘




α=30∘



All quantities are expressed in normalized simulation units (length in di, time in ω_{c,i}^{−1}, magnetic field, density and pressure normalised to background values).

Temporal Sampling

Data are provided at three normalized times: t=0, 2000, 4000 ω_{c,i}^{−1}, corresponding to the times analysed in the paper.

File Format and Structure

All files are provided in compressed NumPy format (.npz).

How to Load the Data

All files are provided in compressed NumPy format (.npz) and can be loaded using the numpy.load function in Python. Each file behaves as a dictionary containing the magnetic field (B_t*), density (dens_t*), and ion pressure tensor (P_t*) at the three simulation times. Example:





 


import numpy as np


 

# Load file


data = np.load('data_L50_alpha10_d40.npz')



 

# List available variables
print(data.files)



 

# Access fields at t = 2000 ω_ci^{-1}


B = data['B_t2000']        

dens = data['dens_t2000']

P = data['P_t2000']        # shape (6, Nx, Ny)


# Magnetic field components
Bx, By, Bz = B


# Pressure tensor components
Pxx, Pyy, Pzz, Pxy, Pxz, Pyz = P



Each .npz file can also be opened using a context manager (with np.load(...) as data:) to ensure proper file handling. The arrays are directly compatible with NumPy, SciPy, and Matplotlib for further analysis and visualization.

Naming convention

Longitudinal configurations:   data_L{L}_d{delta}.npz 

Oblique configurations:            data_L{L}_alpha{alpha}_d{delta}.npz


Stored variables

Each file contains:





B_t0, B_t2000, B_t4000Magnetic field components with shape (3, Nx, Ny) corresponding to (Bx,By,Bz).




dens_t0, dens_t2000, dens_t4000Plasma density with shape (Nx, Ny).




P_t0, P_t2000, P_t4000Ion pressure tensor with shape (6, Nx, Ny) ordered as (Pxx,Pyy,Pzz,Pxy,Pxz,Pyz).
DOI: 10.7910/dvn/z2oe1o
title: The Digital Stress Spiral: A Multilevel Vector Autoregressive (mlVAR) Investigation of Online Pressure, Social Comparison, and Concentration Difficulties

description: Abstract: This dataset comprises intensive longitudinal data collected via the Experience Sampling Method (ESM) from 132 university students over 8 days (3 beeps/day). Containing 2836 valid observations, the study tracks the dynamic interplay of digital stress, online pressure, social comparison, focusing problems, psychological detachment, and happiness.

All momentary variables were assessed using single-item Visual Analog Scales (VAS 0-100). The data structure is optimized for Multilevel Vector Autoregression (mlVAR) and dynamic network analysis to explore temporal relationships between digital connectedness and cognitive well-being.
DOI: 10.25345/c56w96p5m
title: MassIVE MSV000100816 - Proteomic Analysis of EPPIN Interacting Proteins in Human Spermatozoa Identified by Co Immunoprecipitation Followed by Mass Spectrometry
title: Epididymal Protease Inhibitor (EPPIN) is a sperm-associated protein that serves as a docking site for protein protein interactions. Its binding to seminal plasma proteins plays a key role in modulating sperm function. However, the molecular mechanisms underlying these roles remain unclear. This dataset contains proteomic profiles of proteins from human ejaculated spermatozoa that were immunoprecipitated using an anti EPPIN antibody and analyzed by mass spectrometry. This study aimed to characterize proteins associated with EPPIN in human spermatozoa, with a focus on identifying potential partners related to sperm function within intracellular compartments.
DOI: 10.26180/4056279
title: eLearning integration in the library: A case study

description: <b>ABSTRACT</b> <b>Purpose</b> Monash University Library has embraced eLearning as a strategy in its contribution to information research and learning skills development within the university. This paper describes an evaluation of the implementation of the strategy with recommendations for sustaining and improving practice.<br><br><i>Design/Methodology/Approach </i>The evaluation is divided into four categories using a mixed methods methodology for evidence gathering. Quantitative and qualitative data is obtained from both primary and secondary sources for an enriched understanding of practices.<br><br><b>Findings </b>Findings suggest that library staff have gained knowledge and skills indicating a sustainable strategy. However, further work is required to sustain staff development and support staff requirements in the long term. <br><b>Research limitations/implications </b>This research is limited by its wide focus. Although this is mostly resolved through the use of multiple data collection methods, the thoroughness of the evaluation may have suffered while attempting to be comprehensive. <b><br>Practical implications </b>The conclusions of this evaluation as well as methods of its execution can be shared with other institutions wishing to produce eLearning resources in a sustainable and effective manner.<br><br><b>Originality/value </b>Monash University Library develops its eLearning resources in-house, whereas many other institutions outsource. The findings of this case study could be viewed as a positive indication of this in-house practice, which in turn might inform other organisations who are looking for a financially beneficial eLearning alternative.