cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3jnr655h2w
title: Sedimentary and Geochemical Data of Short Cores in Coastal Wetlands of Sansha Bay, Southeast China

description: Coastal wetlands are essential yet vulnerable carbon sinks; however, the impacts of vegetation replacement on carbon dynamics remain less understood. This study investigates the sources, burial rates, and historical trends of sedimentary organic carbon (OC) across various wetland types, including mangroves, Spartina alterniflora (S. A.), and barren tidal flats in Sansha Bay, Fujian Province, Southeast China. We conducted all experiments in the State Key Laboratory of Marine Geology, Tongji University.
Six sediment cores were retrieved from diverse vegetation types in the intertidal wetlands of Sansha Bay between July 27 and August 2, 2018 . A 1-meter-long PVC pipe was vertically driven into the sediment to obtain core samples. Due to variations in sediment composition, the lengths of the cores ranged from 72 to 92 cm. Sampling locations included mangrove areas (SD-3, SD-8), S. A. dominated zones (SD-4, SD-7), and barren mudflats (SD-1, SD-2). Following longitudinal splitting and photographic documentation, cores were subsampled at 1 cm intervals, with one half archived at 4°C for future analysis.
Grain size analysis: Sediment samples were analyzed for grain size at 2 cm intervals. A 0.5 g subsample was treated with 15 ml of 30% hydrogen peroxide and heated at 60°C for 4 hours to remove organic matter. This was followed by treatment with 15 ml of 10% hydrochloric acid at 60°C for additional 4 hours to eliminate carbonates. The residual sample was rinsed to achieve a neutral pH, then dispersed using sodium hexametaphosphate and sonicated for 5 minutes. Grain size analysis was conducted using a Beckman Coulter LS230 laser particle size analyzer, which has a measurement range of 0.04-2000 μm and a relative error of less than1%. 
Total organic carbon (TOC), total nitrogen (TN) and δ13C Analysis: For the organic geochemical analysis, sediments were subsampled at 1–2 cm sections. Carbonates were removed using 10% hydrochloric acid, followed by neutralization and grinding to a particle size of less than 200 mesh. The concentrations of TOC and TN were determined using a Vario EL cube elemental analyzer, while δ¹³C values were measured with a MAT253 isotope ratio mass spectrometer.
Radionuclide Analysis: To correct for sediment compaction effects, water content was measured by drying samples at 60°C. After sealing, samples sat for at least 20 days to allow for 222Rn ingrowth, this allowed us to determine supported 210Pb activity by measuring daughter products of 226Ra. Gamma-ray measurements were conducted by using an ORTEC low-background well-type HPGe gamma-ray detector (GWL-120-15-LB-AWT). Analytical precision and methodological adherence were consistent with Wang et al. (2019). This analysis yielded activity measurements for supported 210Pb (based on photo peaks at 295.0 and 351.9 keV), total 210Pb (at 46.5 keV) and 137Cs (at 661.6 keV). Excess 210Pb (210Pbex) activity was determined by subtracting the supported 210Pb from total 210Pb.
DOI: 10.17632/3jnr655h2w.2
title: Sedimentary and Geochemical Data of Short Cores in Coastal Wetlands of Sansha Bay, Southeast China

description: Coastal wetlands are essential yet vulnerable carbon sinks; however, the impacts of vegetation replacement on carbon dynamics remain less understood. This study investigates the sources, burial rates, and historical trends of sedimentary organic carbon (OC) across various wetland types, including mangroves, Spartina alterniflora (S. A.), and barren tidal flats in Sansha Bay, Fujian Province, Southeast China. We conducted all experiments in the State Key Laboratory of Marine Geology, Tongji University.
Six sediment cores were retrieved from diverse vegetation types in the intertidal wetlands of Sansha Bay between July 27 and August 2, 2018 . A 1-meter-long PVC pipe was vertically driven into the sediment to obtain core samples. Due to variations in sediment composition, the lengths of the cores ranged from 72 to 92 cm. Sampling locations included mangrove areas (SD-3, SD-8), S. A. dominated zones (SD-4, SD-7), and barren mudflats (SD-1, SD-2). Following longitudinal splitting and photographic documentation, cores were subsampled at 1 cm intervals, with one half archived at 4°C for future analysis.
Grain size analysis: Sediment samples were analyzed for grain size at 2 cm intervals. A 0.5 g subsample was treated with 15 ml of 30% hydrogen peroxide and heated at 60°C for 4 hours to remove organic matter. This was followed by treatment with 15 ml of 10% hydrochloric acid at 60°C for additional 4 hours to eliminate carbonates. The residual sample was rinsed to achieve a neutral pH, then dispersed using sodium hexametaphosphate and sonicated for 5 minutes. Grain size analysis was conducted using a Beckman Coulter LS230 laser particle size analyzer, which has a measurement range of 0.04-2000 μm and a relative error of less than1%. 
Total organic carbon (TOC), total nitrogen (TN) and δ13C Analysis: For the organic geochemical analysis, sediments were subsampled at 1–2 cm sections. Carbonates were removed using 10% hydrochloric acid, followed by neutralization and grinding to a particle size of less than 200 mesh. The concentrations of TOC and TN were determined using a Vario EL cube elemental analyzer, while δ¹³C values were measured with a MAT253 isotope ratio mass spectrometer.
Radionuclide Analysis: To correct for sediment compaction effects, water content was measured by drying samples at 60°C. After sealing, samples sat for at least 20 days to allow for 222Rn ingrowth, this allowed us to determine supported 210Pb activity by measuring daughter products of 226Ra. Gamma-ray measurements were conducted by using an ORTEC low-background well-type HPGe gamma-ray detector (GWL-120-15-LB-AWT). Analytical precision and methodological adherence were consistent with Wang et al. (2019). This analysis yielded activity measurements for supported 210Pb (based on photo peaks at 295.0 and 351.9 keV), total 210Pb (at 46.5 keV) and 137Cs (at 661.6 keV). Excess 210Pb (210Pbex) activity was determined by subtracting the supported 210Pb from total 210Pb.
DOI: 10.17632/bycvvnmkrt
title: Mapping the metabolic perturbations associated with palmitate-induced oxidative stress and development of insulin resistance in skeletal muscle cells

description: All the raw NMR data used for the publication has been shared here.
DOI: 10.15468/dl.a89yph
title: Occurrence Download

description: A dataset containing 261 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cardamine tenuirostris"
 ]
}

 The dataset includes 261 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018910-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bycvvnmkrt.1
title: Mapping the metabolic perturbations associated with palmitate-induced oxidative stress and development of insulin resistance in skeletal muscle cells

description: All the raw NMR data used for the publication has been shared here.
DOI: 10.17632/p59x63mpkp
title: The effect of polysaccharides on gastric mucosal injury - multi omics analysis

description: The gastroprotective effect of Hericium erinaceus polysaccharides on alcohol induced acute gastric mucosal injury in mice was investigated using proteomics, metabolomics, and 16 rRNA omics analysis strategies. It provides scientific basis for utilizing β - glucan from Hericium erinaceus as a functional food ingredient with gastric protective potential.
DOI: 10.15468/dl.944d7w
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Myanmar",
 "TaxonKey is Abisara abnormis"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018911-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/p59x63mpkp.1
title: The effect of polysaccharides on gastric mucosal injury - multi omics analysis

description: The gastroprotective effect of Hericium erinaceus polysaccharides on alcohol induced acute gastric mucosal injury in mice was investigated using proteomics, metabolomics, and 16 rRNA omics analysis strategies. It provides scientific basis for utilizing β - glucan from Hericium erinaceus as a functional food ingredient with gastric protective potential.
DOI: 10.17632/2fn82cd72d
title: Resources - Design of a WLAN network for information transfer in a university, 2025.

description: The documents shown are a complement to the research called Design of a WLAN network for information transfer in a university, 2025..
DOI: 10.17632/2fn82cd72d.1
title: Resources - Design of a WLAN network for information transfer in a university, 2025.

description: The documents shown are a complement to the research called Design of a WLAN network for information transfer in a university, 2025..
DOI: 10.17632/kg9fnpn65y.2
title: Controlled study on the use of Mixed Reality for teaching PC and smartphone hardware: results with Meta Quest 3

description: Teaching PC and smartphone hardware assembly faces a significant challenge in educational contexts with limited access to physical laboratories or real equipment. This limitation hinders hands-on learning, which is essential for developing technical skills in engineering and related field students. Although digital simulators and tutorials have been implemented as a solution, many of these resources fail to offer immersive experiences or foster deep understanding due to their low interactivity or lack of immediate feedback. In response to this problem, we propose a Mixed Reality (MR)-based educational application, called Build_3D, designed for use with the Meta Quest 3 headset. This tool allows students to interact in a three-dimensional environment with virtual hardware components, experiencing the assembly process visually, tactilely, and autonomously. To evaluate its effectiveness, a quasi-experimental study was conducted with a control group that used traditional methods and an experimental group that worked with the application. The results indicated a significant improvement in the academic performance of the experimental group, attributed to the immersive and interactive nature of the MR experience. Furthermore, students reported high satisfaction, especially in terms of the interface, clarity of information, and ease of use. These findings suggest that integrating MR into educational environments can overcome physical barriers, promote active learning, and strengthen students' technical understanding. The proposed solution not only improves academic performance but also represents a viable and scalable model for modernizing practical teaching in higher education, especially in technological disciplines.
DOI: 10.17632/f8vfp2v6cr
title: "Graduate Students' ChatGPT Use in Educational Research: Action Research Project Data"

description: Dataset Description: ChatGPT Use in Graduate Action Research Projects
Research Hypothesis: This study hypothesized that ChatGPT significantly influences graduate students' research competencies, improving speed, writing quality, reflection, and time management during action research projects, with positive correlations between perceived usefulness and research outcomes.
Data Overview: Dataset contains responses from 19 graduate education students (82.6% response rate) from Universidad del Desarrollo, Chile, during 2023-2024. Data captures ChatGPT usage experiences across two critical action research phases: design and implementation.
Data Collection
Multi-moment approach using three online questionnaires:
Moment 1: Design phase completion - project planning usage
Moment 2: Implementation phase end - execution utilization
Moment 3: Project completion - satisfaction survey (expert-validated)

Questionnaires examined usage frequency, purposes, perceived benefits, challenges, and efficiency perceptions.
Key Findings
Usage Patterns: 61% used ChatGPT in design phase, 83% in implementation. Primary applications included objective formulation, feasibility analysis, monitoring instruments, and decision evaluation.
Benefits: 94.4% reported improved text quality, 88.9% enhanced reflection capacity, 88.9% faster task completion. Idea generation was most valued support (86.67% design, 57.89% implementation).
Challenges: 72% found prompt design required substantial information input; 44% considered it time-consuming.
Statistical Correlations: Strong positive relationships between speed-writing (r=0.969), speed-reflection (r=0.937), time investment-result quality (r=0.745), and information usefulness-utility (r=0.976).
Data Interpretation: No significant differences between research phases (p=0.798) indicate consistent tool value. Strong correlations suggest students perceiving speed improvements also report enhanced writing and reflection. Positive correlation between prompt design effort and result quality indicates initial investment yields better outcomes.
Applications: Data supports AI integration strategies in graduate education, training protocol development, research competency assessment, and investigation of AI assistance versus independent skill development balance.
Limitations: Non-probabilistic convenience sampling limits generalizability. Findings specific to education graduate students using ChatGPT 3.5 during 2023-2024. Self-reported perceptions may not reflect actual performance improvements.
Data Structure contains questionnaire responses from both design and implementation stages, statistical calculations for correlation coefficients, and accompanying graphs visualizing usage patterns and perception data.
DOI: 10.17632/f8vfp2v6cr.1
title: "Graduate Students' ChatGPT Use in Educational Research: Action Research Project Data"

description: Dataset Description: ChatGPT Use in Graduate Action Research Projects
Research Hypothesis: This study hypothesized that ChatGPT significantly influences graduate students' research competencies, improving speed, writing quality, reflection, and time management during action research projects, with positive correlations between perceived usefulness and research outcomes.
Data Overview: Dataset contains responses from 19 graduate education students (82.6% response rate) from Universidad del Desarrollo, Chile, during 2023-2024. Data captures ChatGPT usage experiences across two critical action research phases: design and implementation.
Data Collection
Multi-moment approach using three online questionnaires:
Moment 1: Design phase completion - project planning usage
Moment 2: Implementation phase end - execution utilization
Moment 3: Project completion - satisfaction survey (expert-validated)

Questionnaires examined usage frequency, purposes, perceived benefits, challenges, and efficiency perceptions.
Key Findings
Usage Patterns: 61% used ChatGPT in design phase, 83% in implementation. Primary applications included objective formulation, feasibility analysis, monitoring instruments, and decision evaluation.
Benefits: 94.4% reported improved text quality, 88.9% enhanced reflection capacity, 88.9% faster task completion. Idea generation was most valued support (86.67% design, 57.89% implementation).
Challenges: 72% found prompt design required substantial information input; 44% considered it time-consuming.
Statistical Correlations: Strong positive relationships between speed-writing (r=0.969), speed-reflection (r=0.937), time investment-result quality (r=0.745), and information usefulness-utility (r=0.976).
Data Interpretation: No significant differences between research phases (p=0.798) indicate consistent tool value. Strong correlations suggest students perceiving speed improvements also report enhanced writing and reflection. Positive correlation between prompt design effort and result quality indicates initial investment yields better outcomes.
Applications: Data supports AI integration strategies in graduate education, training protocol development, research competency assessment, and investigation of AI assistance versus independent skill development balance.
Limitations: Non-probabilistic convenience sampling limits generalizability. Findings specific to education graduate students using ChatGPT 3.5 during 2023-2024. Self-reported perceptions may not reflect actual performance improvements.
Data Structure contains questionnaire responses from both design and implementation stages, statistical calculations for correlation coefficients, and accompanying graphs visualizing usage patterns and perception data.
DOI: 10.17632/5zgc7752y7
title: Gondar City Water Supply

description: This dataset underpins the research article titled “Integrated Analysis of Reservoir Storage Fluctuations and Distribution Loss in Gondar City's Water Supply,” focusing on the Angereb Reservoir in Gondar, Ethiopia. It encompasses spatial and tabular data capturing water surface area dynamics and volume variations from 2016 to 2024, across four seasonal snapshots annually: mid-January (winter), April (spring), July (summer), and October (autumn).

Dataset Components:

Water Indices (NDWI & MNDWI):
Raster layers derived from Sentinel-2 Level-1C surface reflectance images, stored in GeoTIFF format.

NDWI (Normalized Difference Water Index): (Green - NIR) / (Green + NIR)
MNDWI (Modified NDWI): (Green - SWIR1) / (Green + SWIR1) (for only July)
These indices facilitate water body delineation and are suitable for GIS-based analysis.
Reservoir Polygons:
Digitized shapefiles delineate the reservoir surface at each time point. Polygons were created via thresholding of NDWI/MNDWI layers, with manual refinements.
Projection: WGS 84 / UTM Zone 37N.
These polygons form the basis for surface area and volume calculations.

Digital Elevation Models (DEMs):
ALOS PALSAR DEMs (12.5m resolution), clipped to the reservoir extent for each season. They enable terrain analysis, elevation profiling, and volume estimation via elevation difference methods
DOI: 10.17632/5zgc7752y7.1
title: Gondar City Water Supply

description: This dataset underpins the research article titled “Integrated Analysis of Reservoir Storage Fluctuations and Distribution Loss in Gondar City's Water Supply,” focusing on the Angereb Reservoir in Gondar, Ethiopia. It encompasses spatial and tabular data capturing water surface area dynamics and volume variations from 2016 to 2024, across four seasonal snapshots annually: mid-January (winter), April (spring), July (summer), and October (autumn).

Dataset Components:

Water Indices (NDWI & MNDWI):
Raster layers derived from Sentinel-2 Level-1C surface reflectance images, stored in GeoTIFF format.

NDWI (Normalized Difference Water Index): (Green - NIR) / (Green + NIR)
MNDWI (Modified NDWI): (Green - SWIR1) / (Green + SWIR1) (for only July)
These indices facilitate water body delineation and are suitable for GIS-based analysis.
Reservoir Polygons:
Digitized shapefiles delineate the reservoir surface at each time point. Polygons were created via thresholding of NDWI/MNDWI layers, with manual refinements.
Projection: WGS 84 / UTM Zone 37N.
These polygons form the basis for surface area and volume calculations.

Digital Elevation Models (DEMs):
ALOS PALSAR DEMs (12.5m resolution), clipped to the reservoir extent for each season. They enable terrain analysis, elevation profiling, and volume estimation via elevation difference methods
DOI: 10.17632/c82t7wd3x2
title: AIoT in Electronic Health Records and Hospital Management Enhancing Clinical Efficiency, Patient Safety, and Decision-Making

description: This paper explores the transformative role of the Artificial Intelligence of Things (AIoT) in optimizing Electronic Health Records (EHR) and hospital management systems. By integrating smart IoT sensors with AI algorithms, hospitals can monitor patients in real time, predict clinical risks, and automate administrative tasks—all from a centralized EHR interface. This seamless integration leads to increased operational efficiency, reduced human error, and improved patient safety.
DOI: 10.17632/c82t7wd3x2.1
title: AIoT in Electronic Health Records and Hospital Management Enhancing Clinical Efficiency, Patient Safety, and Decision-Making

description: This paper explores the transformative role of the Artificial Intelligence of Things (AIoT) in optimizing Electronic Health Records (EHR) and hospital management systems. By integrating smart IoT sensors with AI algorithms, hospitals can monitor patients in real time, predict clinical risks, and automate administrative tasks—all from a centralized EHR interface. This seamless integration leads to increased operational efficiency, reduced human error, and improved patient safety.
DOI: 10.15468/dl.49rjum
title: Occurrence Download

description: A dataset containing 15867 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus rubra",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15867 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018914-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/zjhymwjtxv.3
title: A dataset of histopathology images of paratuberculosis disease.

description: Images of histopathology slides of normal and positive tissues (infected with paratuberculosis). The total number of images is 366 (157 negative + 209 positive). The "ImagesS" folder contains the same images but with a scale bar overlaid on each image to remove any ambiguity about the magnification.
DOI: 10.17632/gf3wy57jd8.1
title: Lexicon Dictionary

description: To enhance accuracy, particularly for rare words, a lexicon-based dataset was curated by marketing experts, in which only positive sentiments were labeled as "1" and negative sentiments as "2".
DOI: 10.17632/3g77sjdnp2
title: The global wooden furniture trade network

description: The global wooden furniture trade network has undergone significant changes in the past few decades. In order to identify and avoid trade risks and understand the market development, it is crucial to analyze the evolutionary and risk propagation mechanism of the trade network. This study established the global wooden furniture trade network during 2000-2023 using complex network theory, and then analyzed the structure characteristics and spatial patterns. Random attack and targeted attack simulations were conducted to analyze the robustness of the network, while a cascading failure model was utilized to simulate the avalanche effects to explore the risk propagation characteristics.
DOI: 10.17632/3g77sjdnp2.1
title: The global wooden furniture trade network

description: The global wooden furniture trade network has undergone significant changes in the past few decades. In order to identify and avoid trade risks and understand the market development, it is crucial to analyze the evolutionary and risk propagation mechanism of the trade network. This study established the global wooden furniture trade network during 2000-2023 using complex network theory, and then analyzed the structure characteristics and spatial patterns. Random attack and targeted attack simulations were conducted to analyze the robustness of the network, while a cascading failure model was utilized to simulate the avalanche effects to explore the risk propagation characteristics.
DOI: 10.17632/nbpybx34sc.1
title: Related Query

description: Related search queries extracted monthly for the keywords "Hamrah-e-Aval " and "Irancell" from 2014 to 2024. (Related Query data after tagging)
DOI: 10.17632/dsf4jk285p.1
title: Timeline

description: Keyword search volume data from Google Trends over the past decade. This data was obtained directly from Google Trends in CSV format without using scrapers. (monthly for the keywords "Hamrah-e-Aval " and "Irancell" from 2010–2025)