cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/46htwnp833
title: Mango DMC and NIR spectra

description: These datasets contain Near-infrared (NIR) absorbance spectra of the wavelength range 309-149 nm of mango mesocarp with corresponding Dry Matter Content (DMC) values. 

The intent of publishing this dataset is to provide a consistent benchmark for researchers to compare modelling techniques against published results using the same training and test sets. In its original form, the dataset was presented as a real-world case study, where several years of data were used to develop a model and performance was then evaluated using data from the final year, explicitly accepting differences in population variance.

Researchers should not combine all data and perform a random split into training and test sets. A random split would allow replicate spectra to appear in both sets, leading to overly optimistic performance estimates. As described in the associated publications: “Each fruit was scanned TWICE on the widest section of each cheek (approximately the middle of the fruit), orthogonal to the endocarp plane. This scan location represents fruit tissue that approximates the average mesocarp composition (data not shown). A subset of 744 samples was scanned at THREE fruit temperatures (approximately 15, 25, and 35 °C).”

If researchers choose to define their own training and test splits, this must be clearly stated and the resulting outcomes should not be compared directly with those reported in the original publications.
The file "MangoDMC_NIR_Data_v3.csv" contains data as used in the publication "Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content" (Postharvest Biology and Technology, 2020, 168:111202; https://www.sciencedirect.com/science/article/pii/S0925521420301629), with addition of data from an additional harvest season, as used in the publication "Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content" (Spectrochimica Acta Part A 2024 311: 124003; https://www.sciencedirect.com/science/article/pii/S1386142524001690).

The current version (v4) has an additional file ".csv". This file augments the data of version 3 with data from additional instruments and seasons as used in the submitted thesis of Jeremy Walsh, 2024, Central Queensland University, "Deep Learning in Estimation of Fruit Attributes Using Near Infrared Spectroscopy".
DOI: 10.17632/46htwnp833.6
title: Mango DMC and NIR spectra

description: These datasets contain Near-infrared (NIR) absorbance spectra of the wavelength range 309-149 nm of mango mesocarp with corresponding Dry Matter Content (DMC) values. 

The intent of publishing this dataset is to provide a consistent benchmark for researchers to compare modelling techniques against published results using the same training and test sets. In its original form, the dataset was presented as a real-world case study, where several years of data were used to develop a model and performance was then evaluated using data from the final year, explicitly accepting differences in population variance.

Researchers should not combine all data and perform a random split into training and test sets. A random split would allow replicate spectra to appear in both sets, leading to overly optimistic performance estimates. As described in the associated publications: “Each fruit was scanned TWICE on the widest section of each cheek (approximately the middle of the fruit), orthogonal to the endocarp plane. This scan location represents fruit tissue that approximates the average mesocarp composition (data not shown). A subset of 744 samples was scanned at THREE fruit temperatures (approximately 15, 25, and 35 °C).”

If researchers choose to define their own training and test splits, this must be clearly stated and the resulting outcomes should not be compared directly with those reported in the original publications.
The file "MangoDMC_NIR_Data_v3.csv" contains data as used in the publication "Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content" (Postharvest Biology and Technology, 2020, 168:111202; https://www.sciencedirect.com/science/article/pii/S0925521420301629), with addition of data from an additional harvest season, as used in the publication "Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content" (Spectrochimica Acta Part A 2024 311: 124003; https://www.sciencedirect.com/science/article/pii/S1386142524001690).

The current version (v4) has an additional file ".csv". This file augments the data of version 3 with data from additional instruments and seasons as used in the submitted thesis of Jeremy Walsh, 2024, Central Queensland University, "Deep Learning in Estimation of Fruit Attributes Using Near Infrared Spectroscopy".
DOI: 10.17632/jzxr7zbv7d
title: Data and code for machine learning analysis of aeolian transport processes

description: This repository provides representative datasets and analysis codes supporting a study on the application of machine learning methods to aeolian transport processes. 
The dataset includes example and processed data illustrating the structure, variables, and feature representations used in the analysis, such as wind-related parameters and derived predictors relevant to aeolian transport. These data are representative subsets of larger field measurement datasets.
The code includes core scripts for data preprocessing, feature construction, model training, evaluation, and interpretability analysis. The machine learning workflow encompasses supervised classification and regression models, together with explainable machine learning techniques used to assess feature contributions.
DOI: 10.17632/jzxr7zbv7d.2
title: Data and code for machine learning analysis of aeolian transport processes

description: This repository provides representative datasets and analysis codes supporting a study on the application of machine learning methods to aeolian transport processes. 
The dataset includes example and processed data illustrating the structure, variables, and feature representations used in the analysis, such as wind-related parameters and derived predictors relevant to aeolian transport. These data are representative subsets of larger field measurement datasets.
The code includes core scripts for data preprocessing, feature construction, model training, evaluation, and interpretability analysis. The machine learning workflow encompasses supervised classification and regression models, together with explainable machine learning techniques used to assess feature contributions.
DOI: 10.17632/gb3g3ztgpb.1
title: Berambadi_Fracture_FEFLOW

description: Shape / xlsx / csv files of subdomain (Bounding Box) within Berambadi (Peninsular India) - nitrate concentration, Darcy flux, hydraulic head etc. FEFLOW model also provided, needs license to run.
DOI: 10.17632/d9h56y6tz7
title: SAMR study

description: dataset for SMAR model
DOI: 10.17632/d9h56y6tz7.1
title: SAMR study

description: dataset for SMAR model
DOI: 10.17632/fsj4vgwx4v
title: Text Analysis LLM_ Return to work Interviews

description: This repository provides full methodological transparency materials for the LLM-assisted qualitative component of the study:

Procedural Justice in AI-Mediated Return-to-Work Decisions: Privacy, Human Oversight, and Trust among Injured Workers.

The package documents how a Large Language Model (LLM) was used as a structured analytic support tool within a human-led, theory-driven qualitative coding process. It includes coding scripts, prompts, validation procedures, example datasets, overlap metrics, and reproducibility materials.

The LLM was used exclusively to assist structured extraction under predefined theoretical constructs. It did not generate new theory, replace human coding, or override human interpretive judgment.

Purpose of the Repository

The repository was created to ensure:

Methodological transparency

Replicability

Parameter disclosure

Prompt disclosure

Validation transparency

Stability testing documentation

Compliance with emerging best practices in AI-assisted qualitative research

It directly addresses editorial recommendations to open-source AI coding scripts and provide a clear step-by-step protocol.
DOI: 10.17632/fsj4vgwx4v.1
title: Text Analysis LLM_ Return to work Interviews

description: This repository provides full methodological transparency materials for the LLM-assisted qualitative component of the study:

Procedural Justice in AI-Mediated Return-to-Work Decisions: Privacy, Human Oversight, and Trust among Injured Workers.

The package documents how a Large Language Model (LLM) was used as a structured analytic support tool within a human-led, theory-driven qualitative coding process. It includes coding scripts, prompts, validation procedures, example datasets, overlap metrics, and reproducibility materials.

The LLM was used exclusively to assist structured extraction under predefined theoretical constructs. It did not generate new theory, replace human coding, or override human interpretive judgment.

Purpose of the Repository

The repository was created to ensure:

Methodological transparency

Replicability

Parameter disclosure

Prompt disclosure

Validation transparency

Stability testing documentation

Compliance with emerging best practices in AI-assisted qualitative research

It directly addresses editorial recommendations to open-source AI coding scripts and provide a clear step-by-step protocol.
DOI: 10.17632/xrpshxvt8w
title: Ceilometer and Radiosonde Data - November 2017

description: Ceilometer and Radiosonde data measured during November 2017. The ceilometer's data files are named according to the measurements' time of acquisition, where for example A7112616.DAT means that the file contains measurements acquired on the 26 of November 2017 starting at 4 PM. The structure of the file is explained in the CL51 manual. The radiosonde data files are named according to their time of flight, where for example 261117_1300.txt means that the radiosonde was floated on the 26 of November 2017 at 1 PM. The files are ASCII formatted in table form, where the column names are explained in the Meteomodem M10's manual.
DOI: 10.17632/xrpshxvt8w.1
title: Ceilometer and Radiosonde Data - November 2017

description: Ceilometer and Radiosonde data measured during November 2017. The ceilometer's data files are named according to the measurements' time of acquisition, where for example A7112616.DAT means that the file contains measurements acquired on the 26 of November 2017 starting at 4 PM. The structure of the file is explained in the CL51 manual. The radiosonde data files are named according to their time of flight, where for example 261117_1300.txt means that the radiosonde was floated on the 26 of November 2017 at 1 PM. The files are ASCII formatted in table form, where the column names are explained in the Meteomodem M10's manual.
DOI: 10.17632/cj2bwfxrg4
title: A Low-Cost Three-Wavelength Illumination System for Camera-Based Photoplethysmography Imaging

description: This paper presents the design and implementation of a low-cost, reliable three-wavelength (3-λ) illumination device intended for photoplethysmography imaging (PPGI) measurements. The proposed system employs high-power light-emitting diodes operating in the green (520 nm), red (625 nm), and near-infrared (855 nm) spectral regions, enabling non-contact cardiovascular monitoring with flexible spectral configuration. The illumination module integrates seven green LEDs, eleven red LEDs, and fifteen infrared LEDs, a distribution selected to compensate for wavelength-dependent camera sensitivity while providing sufficient radiant intensity and spatial uniformity at low power consumption and cost. Each wavelength channel is independently driven using dedicated single-channel linear LED drivers with pulse-width modulation control, allowing precise adjustment of illumination intensity. System operation is managed by a microcontroller-based control platform, which ensures accurate timing, synchronization, and stable operation required for PPGI applications. The compact and modular design supports straightforward integration with camera-based imaging systems, while optional wireless control enhances usability in laboratory environments. Overall, the proposed illumination device represents a robust and cost-effective solution for experimental and applied PPGI measurements in research-oriented non-contact biosensing systems.
All essential files for replication and further development are included:
PCB_control_unit CAD Files
PCB_illumination_unit CAD Files
Protective_cover_top & bottom.stl CAD Files
Illumination_device_app.m Matlab Files
Illumination_device_firmware.rar Microchip studio Files
DOI: 10.25919/2g6n-vq97
title: Leaf and stripe rust field data for OzWheat diversity panel (V1 and V2)

description: This collection contains rust resistance phenotype data for two rust diseases, leaf (LR) and stripe rust or yellow rust (YR) collected from four different field sites within Australia of the OzWheat panel 1 and 2 in 2023 and 2024. This data was collected as part of GRDC project: ACRCP Phase 5: Optimising genetic control of wheat rusts through development of RustHapSelect (ref:CSP2304-013RTX). In this project we aim to validate a new approach to bring effective and durable rust resistance loci into elite high-yielding backgrounds much faster. This will ensure breeder take-up of this material free of deleterious background effects and increase adult plant resistance in Australian wheat varieties. Applying novel genomic selection techniques to identify resistance sources in two wheat diversity panels, Vavilov diversity panel (295 lines) and the OzWheat diversity panel (589 lines) and optimise crossing strategies to introgress into new cultivars.
DOI: 10.17632/cj2bwfxrg4.1
title: A Low-Cost Three-Wavelength Illumination System for Camera-Based Photoplethysmography Imaging

description: This paper presents the design and implementation of a low-cost, reliable three-wavelength (3-λ) illumination device intended for photoplethysmography imaging (PPGI) measurements. The proposed system employs high-power light-emitting diodes operating in the green (520 nm), red (625 nm), and near-infrared (855 nm) spectral regions, enabling non-contact cardiovascular monitoring with flexible spectral configuration. The illumination module integrates seven green LEDs, eleven red LEDs, and fifteen infrared LEDs, a distribution selected to compensate for wavelength-dependent camera sensitivity while providing sufficient radiant intensity and spatial uniformity at low power consumption and cost. Each wavelength channel is independently driven using dedicated single-channel linear LED drivers with pulse-width modulation control, allowing precise adjustment of illumination intensity. System operation is managed by a microcontroller-based control platform, which ensures accurate timing, synchronization, and stable operation required for PPGI applications. The compact and modular design supports straightforward integration with camera-based imaging systems, while optional wireless control enhances usability in laboratory environments. Overall, the proposed illumination device represents a robust and cost-effective solution for experimental and applied PPGI measurements in research-oriented non-contact biosensing systems.
All essential files for replication and further development are included:
PCB_control_unit CAD Files
PCB_illumination_unit CAD Files
Protective_cover_top & bottom.stl CAD Files
Illumination_device_app.m Matlab Files
Illumination_device_firmware.rar Microchip studio Files
DOI: 10.17632/hzhg95752g.2
title: Probabilistic Seismic Demand Modeling of RC Grid-Like Frames: Assessing Response Sensitivity Across Parametric Structural Configurations

description: The research hypothesis of this study is that the seismic response sensitivity of RC grid-like frames is primarily governed by structural geometry and member stiffness ratios rather than material strength properties. Specifically, it is hypothesized that higher-order geometric configurations, such as increased story height and specific bay aspect ratios, lead to a higher "Vulnerability Index" (β), where the rate of increase in structural drift relative to seismic intensity approaches unity.
The data, generated through approximately 4,974 non-linear time history analyses using the ETABS API across 90 structural archetypes, supports this hypothesis. The primary dataset (ETABS_D.csv) includes variables such as story height (2 to 10 stories), bay aspect ratios (0.33 to 3.0), concrete strength (20 to 35 MPa), and various member dimensions. By relating the Peak Ground Acceleration (PGA) to the Maximum Interstory Drift Ratio (MaxIDR) using logarithmic power-law fits (EDP=a(IM)^b ), the data shows a clear divergence in seismic behavior based on height.
Notable findings indicate that taller structures (9–10 stories) exhibit high sensitivity, with β values ranging from 0.93 to 0.96. Conversely, shorter buildings (2–5 stories) display a "hardening" response, with lower sensitivity indices between 0.57 and 0.65. Interestingly, the data demonstrates that concrete compressive strength has a negligible impact on response sensitivity, with β remaining relatively constant between 0.51 and 0.63 despite strength variations. The most critical factor identified is geometric stiffness; certain combinations of column and beam sizes achieved the highest vulnerability index of 0.98, suggesting that the "Strong-Column/Weak-Beam" principle is the most vital design consideration for these frames.
To interpret this data, researchers should focus on the slope (β) of the Probabilistic Seismic Demand Model (PSDM). A β value closer to 1.0 indicates a linear relationship where seismic demand increases proportionately with intensity, representing higher vulnerability. Values significantly lower than 1.0 indicate a structure that possesses greater inherent reserve capacity or "hardening" against increased seismic loads.
This data can be used to develop simplified fragility curves and to inform performance-based design strategies. By understanding which parametric configurations lead to higher β values, engineers can optimize member sizing to ensure that grid-like frames remain resilient under varying seismic intensities without relying solely on higher-grade materials. The dataset serves as a benchmark for validating simplified analytical models against rigorous non-linear simulation results. This enables a more nuanced understanding of how global structural parameters—rather than localized material upgrades—dictate the safety of grid-like reinforced concrete systems in seismically active regions.
DOI: 10.17632/8x32msjdx8.1
title: Replication package for "Working longer, feeling worse? How job quality shapes the mental health toll of delayed retirement"

description: Replication package for "Working longer, feeling worse? How job quality shapes the mental health toll of delayed retirement" by Alexandra Lugova, Michele Belloni, Berangere Legendre, Jeremy Tanguy
DOI: 10.17632/3s6nbtx52p
title: Supplemental Tables for HMDP Hotspot Paper

description: These are supplemental tables for a manuscript on using the Hybrid Mouse Diversity Panel's genomic, transcriptomic, and epigenomic layers to better understand the progression of heart failure
DOI: 10.17632/3s6nbtx52p.3
title: Supplemental Tables for HMDP Hotspot Paper

description: These are supplemental tables for a manuscript on using the Hybrid Mouse Diversity Panel's genomic, transcriptomic, and epigenomic layers to better understand the progression of heart failure
DOI: 10.17632/kw7xfrrk43
title: More Than Coding: A Mixed-Methods Systematic Review of the Multidimensional Landscape of Programming and AI in Primary Education

description: This dataset contains the structured data extracted for a systematic literature review examining the multidimensional impact of programming and artificial intelligence (AI) education in primary school (ages 6–12). The dataset includes detailed information on included studies, screening and eligibility criteria, and analytical coding across four dimensions: cognitive, social, socioemotional, and technical outcomes.
Studies were identified through systematic searches in multiple academic databases and selected according to predefined inclusion and exclusion criteria. Data were extracted and synthesised using a mixed-methods approach to identify pedagogical practices, reported impacts, methodological patterns, and persistent research gaps.
This dataset supports transparency and replicability and may be useful for researchers, educators, and policymakers interested in digital competence development, AI literacy, and curriculum integration in primary education.
DOI: 10.17632/kw7xfrrk43.1
title: More Than Coding: A Mixed-Methods Systematic Review of the Multidimensional Landscape of Programming and AI in Primary Education

description: This dataset contains the structured data extracted for a systematic literature review examining the multidimensional impact of programming and artificial intelligence (AI) education in primary school (ages 6–12). The dataset includes detailed information on included studies, screening and eligibility criteria, and analytical coding across four dimensions: cognitive, social, socioemotional, and technical outcomes.
Studies were identified through systematic searches in multiple academic databases and selected according to predefined inclusion and exclusion criteria. Data were extracted and synthesised using a mixed-methods approach to identify pedagogical practices, reported impacts, methodological patterns, and persistent research gaps.
This dataset supports transparency and replicability and may be useful for researchers, educators, and policymakers interested in digital competence development, AI literacy, and curriculum integration in primary education.
DOI: 10.17632/rg4tyrxv2j
title: Experimental dataset and processing scripts for ultra-lean methane and biogas combustion in an optically accessible single-cylinder engine

description: This dataset contains the experimental data and processing scripts supporting the thermo-optical investigation of ultra-lean methane and CO2-diluted methane combustion in an optically accessible AVL 5402 single-cylinder research engine.
The experiments were conducted under spark-ignition and ultra-lean conditions (λ ≈ 5.5). The dataset includes crank-angle-resolved in-cylinder pressure data, cylinder volume traces, calculated apparent heat release rate (HRR), mass fraction burned (MFB) metrics (CA10, CA50, CA90), IMEP values, and exhaust gas emissions (dry basis, ppm and vol.%).
Python scripts used for signal filtering and combustion analysis are provided to ensure reproducibility. The dataset is made available for peer review and will be publicly accessible upon article acceptance.
DOI: 10.17632/rg4tyrxv2j.1
title: Experimental dataset and processing scripts for ultra-lean methane and biogas combustion in an optically accessible single-cylinder engine

description: This dataset contains the experimental data and processing scripts supporting the thermo-optical investigation of ultra-lean methane and CO2-diluted methane combustion in an optically accessible AVL 5402 single-cylinder research engine.
The experiments were conducted under spark-ignition and ultra-lean conditions (λ ≈ 5.5). The dataset includes crank-angle-resolved in-cylinder pressure data, cylinder volume traces, calculated apparent heat release rate (HRR), mass fraction burned (MFB) metrics (CA10, CA50, CA90), IMEP values, and exhaust gas emissions (dry basis, ppm and vol.%).
Python scripts used for signal filtering and combustion analysis are provided to ensure reproducibility. The dataset is made available for peer review and will be publicly accessible upon article acceptance.
DOI: 10.5281/zenodo.18652961
title: 25Mg_an_ND

description: Experimental data found in https://doi.org/10.1103/rzb1-t6bt
DOI: 10.5281/zenodo.18652960
title: 25Mg_an_ND

description: Experimental data found in https://doi.org/10.1103/rzb1-t6bt