cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/73kfm8jcch
title: Dataset 2. Methylated osmolyte content of seaweeds and coastal plants from Port Phillip Bay Australia

description: RESEARCH HYPOTHESIS

Methylated osmolytes — DMSP, TMA, TMAO, and choline — are the primary methanogenic substrates in shallow coastal sediments receiving seaweed and plant biomass. We hypothesised that osmolyte concentrations would differ substantially between species and predict methane production potential, providing a simple chemical proxy for estimating methane emissions from different biomass types.

DATA COLLECTION

This dataset contains quantified concentrations of four methylated osmolytes in six species of seaweed and coastal plant biomass collected from Port Phillip Bay, Victoria, Australia. Three seaweeds — Ulva lactuca, Ecklonia radiata (kelp), and Ceramiales (red filamentous algae) — were collected on 2 October 2023 from five sites (Werribee, Altona, St Kilda, Elwood, Green Point). Three coastal plants — Avicennia marina (mangrove leaves), Amphibolis antarctica (seagrass), and Salicornia quinqueflora (saltmarsh) — were collected on 1 August 2024 from Hastings and Flinders.

Biomass samples were frozen immediately after collection, then crushed in liquid nitrogen and 20–50 mg extracted in 2:6:1 chloroform:methanol:water with internal standards (CHAPS, CAPS, PIPES, TRIS at 2 µM). Extracts were analysed in triplicate by HILIC-HRMS on a Vanquish Horizon–Q Exactive Plus Orbitrap, using a seven-point calibration curve. Limits of detection were: choline 0.08 µM, DMSP 1.51 µM, TMA 0.46 µM, TMAO 0.05 µM. Peak integration was performed in Skyline 24.1. All concentrations are normalised to biomass dry weight (nmol/mg).

FINDINGS AND INTERPRETATION

DMSP dominated osmolyte composition in seaweeds, particularly in Ulva, where concentrations were substantially higher than in any other species or osmolyte measured. Coastal plants showed a different composition profile, with choline and TMA as the dominant osmolytes and very low DMSP. When osmolyte data were related to methane production from companion microcosm experiments (Dataset 1), DMSP content correlated strongly with seaweed methane production (adj. R² = 0.81, p &lt; 0.001), and summed methyl group content explained methane production at near 100% stoichiometric efficiency (adj. R² = 0.82, p &lt; 0.001). For coastal plants, particularly mangroves, analysed osmolytes accounted for less than 10% of observed methane production, indicating that other substrates — likely methanol from pectin hydrolysis — are the dominant methanogenic precursors.

This dataset enables comparison of osmolyte profiles across species and can be used alongside Dataset 1 to evaluate osmolyte content as a predictor of methane production potential. It demonstrates that DMSP is a reliable proxy for seaweed methane potential, while osmolyte measurements alone are insufficient for coastal plants.
DOI: 10.17632/73kfm8jcch.1
title: Dataset 2. Methylated osmolyte content of seaweeds and coastal plants from Port Phillip Bay Australia

description: RESEARCH HYPOTHESIS

Methylated osmolytes — DMSP, TMA, TMAO, and choline — are the primary methanogenic substrates in shallow coastal sediments receiving seaweed and plant biomass. We hypothesised that osmolyte concentrations would differ substantially between species and predict methane production potential, providing a simple chemical proxy for estimating methane emissions from different biomass types.

DATA COLLECTION

This dataset contains quantified concentrations of four methylated osmolytes in six species of seaweed and coastal plant biomass collected from Port Phillip Bay, Victoria, Australia. Three seaweeds — Ulva lactuca, Ecklonia radiata (kelp), and Ceramiales (red filamentous algae) — were collected on 2 October 2023 from five sites (Werribee, Altona, St Kilda, Elwood, Green Point). Three coastal plants — Avicennia marina (mangrove leaves), Amphibolis antarctica (seagrass), and Salicornia quinqueflora (saltmarsh) — were collected on 1 August 2024 from Hastings and Flinders.

Biomass samples were frozen immediately after collection, then crushed in liquid nitrogen and 20–50 mg extracted in 2:6:1 chloroform:methanol:water with internal standards (CHAPS, CAPS, PIPES, TRIS at 2 µM). Extracts were analysed in triplicate by HILIC-HRMS on a Vanquish Horizon–Q Exactive Plus Orbitrap, using a seven-point calibration curve. Limits of detection were: choline 0.08 µM, DMSP 1.51 µM, TMA 0.46 µM, TMAO 0.05 µM. Peak integration was performed in Skyline 24.1. All concentrations are normalised to biomass dry weight (nmol/mg).

FINDINGS AND INTERPRETATION

DMSP dominated osmolyte composition in seaweeds, particularly in Ulva, where concentrations were substantially higher than in any other species or osmolyte measured. Coastal plants showed a different composition profile, with choline and TMA as the dominant osmolytes and very low DMSP. When osmolyte data were related to methane production from companion microcosm experiments (Dataset 1), DMSP content correlated strongly with seaweed methane production (adj. R² = 0.81, p &lt; 0.001), and summed methyl group content explained methane production at near 100% stoichiometric efficiency (adj. R² = 0.82, p &lt; 0.001). For coastal plants, particularly mangroves, analysed osmolytes accounted for less than 10% of observed methane production, indicating that other substrates — likely methanol from pectin hydrolysis — are the dominant methanogenic precursors.

This dataset enables comparison of osmolyte profiles across species and can be used alongside Dataset 1 to evaluate osmolyte content as a predictor of methane production potential. It demonstrates that DMSP is a reliable proxy for seaweed methane potential, while osmolyte measurements alone are insufficient for coastal plants.
DOI: 10.17632/9kj6z5h39j
title: Dataset 1. Methane production of seaweeds and coastal plants from Port Phillip Bay, Australia

description: RESEARCH HYPOTHESIS
Methane production potential of different seaweed and coastal plant species is predictable from their methylated osmolyte content (DMSP, TMA, TMAO, choline). High sulfate concentrations in coastal waters suppress competing methanogenic pathways, making methylotrophic methanogenesis — methane production from methylated compounds — dominant in these environments. Osmolytes stored in marine plants are released on cell breakdown and used as substrates by methanogens in the sediment.

DATA COLLECTION
This dataset contains time-series headspace methane concentrations from laboratory sand slurry microcosm experiments. Sand was collected from the intertidal zone at Port Phillip Bay, Victoria, Australia. Three seaweeds — Ulva lactuca, Ecklonia radiata (kelp), and Ceramiales (red filamentous algae) — were collected on 2 October 2023. Three coastal plants — Avicennia marina (mangrove leaves), Amphibolis antarctica (seagrass), and Salicornia quinqueflora (saltmarsh) — were collected on 1 August 2024.

Slurries were prepared in triplicate in 160 mL serum vials with 30 g sand, 70 mL seawater, and ~100 mg wet weight biomass, sealed under anoxic conditions. Headspace gas samples were taken at 0, 50, 120, 220, 320, and 460 h (seaweeds) and 0, 21, 72, 96, 120, 170, and 180 h (coastal plants). Methane was measured by GC-PDHID with NATA-accredited calibration standards and quality-control blanks every 20-30 samples. Controls included no-biomass slurries and pure DMS or TMA additions.

FINDINGS AND INTERPRETATION
Among seaweeds, Ulva produced the most methane (1200 ± 300 ppm at 460 h), followed by red filamentous algae (200 ± 200 ppm) and kelp (100 ± 30 ppm). Among coastal plants, mangrove leaves produced the highest concentrations (5500 ± 200 ppm at 180 h), followed by seagrass (1000 ± 100 ppm) and saltmarsh (400 ± 300 ppm).
Seaweed methane production correlated strongly with DMSP content (adj. R² = 0.81) and total methyl group content (adj. R² = 0.82, p &lt; 0.001), with stoichiometry near 100% conversion of methyl groups to methane. Mangrove leaves, despite producing the highest methane, had osmolytes accounting for less than 10% of observed production; methanol generated through pectin hydrolysis during decomposition is implicated as the primary substrate.

This dataset can be used to compare methane production rates across species and validate stoichiometric osmolyte-to-methane models for seaweeds. Results also show that methane production is a transient phenomenon, plateauing after 7-10 days, consistent with substrate depletion rather than ongoing production. The dataset provides a basis for projecting how eutrophication-driven Ulva blooms, kelp forest decline, and coastal restoration efforts may alter shallow marine methane budgets, and highlights that simple osmolyte measurements are insufficient predictors of methane potential for coastal plants such as mangroves.
DOI: 10.17632/9kj6z5h39j.1
title: Dataset 1. Methane production of seaweeds and coastal plants from Port Phillip Bay, Australia

description: RESEARCH HYPOTHESIS
Methane production potential of different seaweed and coastal plant species is predictable from their methylated osmolyte content (DMSP, TMA, TMAO, choline). High sulfate concentrations in coastal waters suppress competing methanogenic pathways, making methylotrophic methanogenesis — methane production from methylated compounds — dominant in these environments. Osmolytes stored in marine plants are released on cell breakdown and used as substrates by methanogens in the sediment.

DATA COLLECTION
This dataset contains time-series headspace methane concentrations from laboratory sand slurry microcosm experiments. Sand was collected from the intertidal zone at Port Phillip Bay, Victoria, Australia. Three seaweeds — Ulva lactuca, Ecklonia radiata (kelp), and Ceramiales (red filamentous algae) — were collected on 2 October 2023. Three coastal plants — Avicennia marina (mangrove leaves), Amphibolis antarctica (seagrass), and Salicornia quinqueflora (saltmarsh) — were collected on 1 August 2024.

Slurries were prepared in triplicate in 160 mL serum vials with 30 g sand, 70 mL seawater, and ~100 mg wet weight biomass, sealed under anoxic conditions. Headspace gas samples were taken at 0, 50, 120, 220, 320, and 460 h (seaweeds) and 0, 21, 72, 96, 120, 170, and 180 h (coastal plants). Methane was measured by GC-PDHID with NATA-accredited calibration standards and quality-control blanks every 20-30 samples. Controls included no-biomass slurries and pure DMS or TMA additions.

FINDINGS AND INTERPRETATION
Among seaweeds, Ulva produced the most methane (1200 ± 300 ppm at 460 h), followed by red filamentous algae (200 ± 200 ppm) and kelp (100 ± 30 ppm). Among coastal plants, mangrove leaves produced the highest concentrations (5500 ± 200 ppm at 180 h), followed by seagrass (1000 ± 100 ppm) and saltmarsh (400 ± 300 ppm).
Seaweed methane production correlated strongly with DMSP content (adj. R² = 0.81) and total methyl group content (adj. R² = 0.82, p &lt; 0.001), with stoichiometry near 100% conversion of methyl groups to methane. Mangrove leaves, despite producing the highest methane, had osmolytes accounting for less than 10% of observed production; methanol generated through pectin hydrolysis during decomposition is implicated as the primary substrate.

This dataset can be used to compare methane production rates across species and validate stoichiometric osmolyte-to-methane models for seaweeds. Results also show that methane production is a transient phenomenon, plateauing after 7-10 days, consistent with substrate depletion rather than ongoing production. The dataset provides a basis for projecting how eutrophication-driven Ulva blooms, kelp forest decline, and coastal restoration efforts may alter shallow marine methane budgets, and highlights that simple osmolyte measurements are insufficient predictors of methane potential for coastal plants such as mangroves.
DOI: 10.17632/dfzkr3p73v
title: SYNLIP: Synthetic License Plate Dataset for fingerprint, recognition and character detection

description: SYNLIP (SYNthetic LIcense Plate dataset) is a synthetic dataset developed to support research on Automatic License Plate Recognition (ALPR) using computer vision. It integrates three complementary tasks: image matching, LP recognition and character detection. image matching refers to the automatic establishment of correspondences between points, grayscale tones, features, relations, or other entities in images. For image matching the noise added is based on: grayscale, darken and blur images, orientation, occlusion and brightness variations. For LP recognition we use a consistent naming scheme (LP_f#.jpg) with European and US plates format. For character detection we use YOLO format annotations only on license plate characters. 

In total, SYNLIP includes 400 images, manually annotated with an average size of 260x130 and with YOLO detection format. All images were created using several GenAI tools, and manually edited to introduce noise. The dataset was shuffled and a split of 30/70 yielded 280 images for the fingerprint set (KB) and 120 images for queries to image matching. A different shuffle was made and a split 30/70 to annotate for character detection using bounding boxes.

The dataset is distributed as a ZIP archive organized into two main folders:

/detection/ - Includes YOLO-format annotations for character detection;
/detection/classes.txt - classes for character detection;
/detection/images/- Contains images for train and val;
/detection/labels/ - Contains labels for train and val;
/matching/ - 
/matching/kb/- Contains images for knowledge base;
/matching/test/ - Contains images for queries;

This dataset was produced by researchers from the Digital Transformation CoLAB (DTx CoLAB), Guimarães, Portugal.

This work was supported under the base funding project of the DTx CoLAB, under the Missão Interface of the Recovery and Resilience Plan (PRR), integrated in the notice 01/C05-i02/2022, which aims to deepen and consolidate the network of interface institutions between the academic, scientific and technological system and the Portuguese business fabric
DOI: 10.17632/dfzkr3p73v.1
title: SYNLIP: Synthetic License Plate Dataset for fingerprint, recognition and character detection

description: SYNLIP (SYNthetic LIcense Plate dataset) is a synthetic dataset developed to support research on Automatic License Plate Recognition (ALPR) using computer vision. It integrates three complementary tasks: image matching, LP recognition and character detection. image matching refers to the automatic establishment of correspondences between points, grayscale tones, features, relations, or other entities in images. For image matching the noise added is based on: grayscale, darken and blur images, orientation, occlusion and brightness variations. For LP recognition we use a consistent naming scheme (LP_f#.jpg) with European and US plates format. For character detection we use YOLO format annotations only on license plate characters. 

In total, SYNLIP includes 400 images, manually annotated with an average size of 260x130 and with YOLO detection format. All images were created using several GenAI tools, and manually edited to introduce noise. The dataset was shuffled and a split of 30/70 yielded 280 images for the fingerprint set (KB) and 120 images for queries to image matching. A different shuffle was made and a split 30/70 to annotate for character detection using bounding boxes.

The dataset is distributed as a ZIP archive organized into two main folders:

/detection/ - Includes YOLO-format annotations for character detection;
/detection/classes.txt - classes for character detection;
/detection/images/- Contains images for train and val;
/detection/labels/ - Contains labels for train and val;
/matching/ - 
/matching/kb/- Contains images for knowledge base;
/matching/test/ - Contains images for queries;

This dataset was produced by researchers from the Digital Transformation CoLAB (DTx CoLAB), Guimarães, Portugal.

This work was supported under the base funding project of the DTx CoLAB, under the Missão Interface of the Recovery and Resilience Plan (PRR), integrated in the notice 01/C05-i02/2022, which aims to deepen and consolidate the network of interface institutions between the academic, scientific and technological system and the Portuguese business fabric
DOI: 10.17632/wdgbgrp3cx
title: Dataset of ‘Interpreting Emerging Strategies in Soft Power: Model & Modalities of India and South Korea’

description: This study examines the soft power strategies of India and South Korea, focusing on their respective models and methods. Both countries have emerged as significant middle powers, boasting distinct cultural, economic, and technological strengths that shape their global presence. The study compares their approaches, emphasizing how cultural diplomacy, people-to-people exchanges, public diplomacy, and strategic branding enhance their international reputation. The analysis also examines how these soft power strategies influence bilateral relations and regional dynamics, which in turn impact their global standing and diplomatic influence. This dataset acts as a resource for analyzing and interpreting emerging soft power strategies adopted by India and South Korea, respectively, to expand their influence and presence in their soft power efforts. The data was collected through a web-based questionnaire distributed primarily to people aged 18 – 61 and above in India. A total of 511 valid data points were collected from respondents across 28 states and one union territory in India, providing extensive geographic coverage, and were stored in Mendeley. It includes diverse question formats such as single-choice, multiple-choice, and open-ended questions. This data could be analysed from a broad objective such as, to analyze and compare the soft power models of India and South Korea. To identify and evaluate the key modalities of soft power deployment. To assess the impact of soft power on bilateral relations and regional dynamics. And to highlight the limitations and future prospects of soft power engagement. Therefore, there were two main hypotheses: first, India and South Korea follow different models of soft power, with India focusing on its normative initiatives and civilizational legacy, while South Korea’s approach to soft power emphasizes economic development and national progress. Second, the use of soft power strategies is more effective in improving international standing and national branding when actively supported and integrated into national foreign policy frameworks. This data reflects public perception in India regarding the soft power strategies used by both India and South Korea. It highlights specific strategies employed by India and South Korea to increase their reach, shape narratives, and enhance nation branding in the changing landscape of international relations.
DOI: 10.17632/wdgbgrp3cx.2
title: Dataset of ‘Interpreting Emerging Strategies in Soft Power: Model & Modalities of India and South Korea’

description: This study examines the soft power strategies of India and South Korea, focusing on their respective models and methods. Both countries have emerged as significant middle powers, boasting distinct cultural, economic, and technological strengths that shape their global presence. The study compares their approaches, emphasizing how cultural diplomacy, people-to-people exchanges, public diplomacy, and strategic branding enhance their international reputation. The analysis also examines how these soft power strategies influence bilateral relations and regional dynamics, which in turn impact their global standing and diplomatic influence. This dataset acts as a resource for analyzing and interpreting emerging soft power strategies adopted by India and South Korea, respectively, to expand their influence and presence in their soft power efforts. The data was collected through a web-based questionnaire distributed primarily to people aged 18 – 61 and above in India. A total of 511 valid data points were collected from respondents across 28 states and one union territory in India, providing extensive geographic coverage, and were stored in Mendeley. It includes diverse question formats such as single-choice, multiple-choice, and open-ended questions. This data could be analysed from a broad objective such as, to analyze and compare the soft power models of India and South Korea. To identify and evaluate the key modalities of soft power deployment. To assess the impact of soft power on bilateral relations and regional dynamics. And to highlight the limitations and future prospects of soft power engagement. Therefore, there were two main hypotheses: first, India and South Korea follow different models of soft power, with India focusing on its normative initiatives and civilizational legacy, while South Korea’s approach to soft power emphasizes economic development and national progress. Second, the use of soft power strategies is more effective in improving international standing and national branding when actively supported and integrated into national foreign policy frameworks. This data reflects public perception in India regarding the soft power strategies used by both India and South Korea. It highlights specific strategies employed by India and South Korea to increase their reach, shape narratives, and enhance nation branding in the changing landscape of international relations.
DOI: 10.17632/zbmd5x98gp
title: Understanding EFL Teachers’ Affective and Cognitive Responses to ChatGPT in Higher Education

description: The anonymised questionnaire dataset comprises 187 valid responses from higher-education EFL teachers collected via an online survey platform. All responses were anonymous and measured using a five-point Likert scale (1 = strongly disagree; 5 = strongly agree). The instrument was developed based on an extended Technology Acceptance Model (TAM) framework and operationalised nine latent constructs: perceived usefulness (PU), perceived ease of use (PEU), attitude towards use (ATU), behavioural intention (BI), facilitating conditions (FC), technological complexity (TC), anxiety (ANX), self-efficacy (SE), and AI experience (AIE). Each construct was measured with three to four items. The complete questionnaire, including construct definitions and full item wording, has been provided as a supplementary “Item File.” The main dataset contains item-level coded variables (e.g., PU1–PU4, PEU1–PEU3), which correspond directly to the detailed descriptions in the Item File.

Structural equation modelling (SEM), including confirmatory factor analysis (CFA) and structural path analysis, was conducted using AMOS 22.0 to evaluate both the measurement and structural models. Descriptive statistics (means and standard deviations) for all observed variables were calculated in RStudio. Mediation effects were examined in R using bootstrapping procedures with 5,000 resamples to estimate indirect effects and to generate bias-corrected 95% confidence intervals.