cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ghz2ghr2d3
title: Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings

description: Study Design and Setting
This single-center retrospective observational cohort study analyzed data from Ibb University Hospitals. We reviewed surgical and radiological records of consecutive patients undergoing laparoscopic cholecystectomy from April 22, 2020, to November 23, 2024.

Ethical Approval
The Ibb University Institutional Research Ethics Committee approved the protocol on January 1, 2025 (Ref: IBBUNI.2025.1.001). Individual consent was waived given the anonymized, retrospective design, following Declaration of Helsinki principles.

Patient Population
We screened adults (≥18 years) with laparoscopic cholecystectomy for benign gallbladder disease (acute/chronic cholecystitis, symptomatic cholelithiasis). Exclusions: gallbladder malignancy, combined hepatobiliary/pancreatic procedures, or incomplete records. Final cohort: 301 patients (STARD Flow Diagram, Supplementary Figure 1).

Data Collection
A trained team extracted preoperative data via standardized electronic forms: demographics (age, sex), BMI, clinical history (prior acute cholecystitis, comorbidities), physical exam (RUQ rigidity), labs (WBC count), and ultrasound (gallbladder wall thickness, impacted stone at neck/cystic duct).

Primary Outcome
"Difficult LC" was a composite: (1) intraoperative conversion to open surgery, or (2) major complication (significant biliary/vascular/visceral injury). Of 384 procedures, 57 (14.8%) were difficult.

Statistical Analysis
Analyses used Python 3.10 (SciPy, pandas, scikit-learn, statsmodels). Continuous data: mean±SD or median (IQR) per Shapiro-Wilk test. Comparisons: t/Mann-Whitney U tests (continuous), χ²/Fisher's exact (categorical).

Model Development
Variables with p&lt;0.10 univariately entered multivariable logistic regression (VIF&lt;2.0, linearity checked). Final model by AIC, p&lt;0.05, clinical relevance. β-coefficients derived integer risk score (divide by smallest β, round). Assessed: AUC (bootstrapped 95% CI, 1000 reps), calibration (Brier score, plot), DCA for utility. Bootstrap internal validation.
DOI: 10.17632/ghz2ghr2d3.1
title: Development and Validation of a Preoperative Risk Score for Difficult Laparoscopic Cholecystectomy: A Retrospective Study in Resource-Limited Settings

description: Study Design and Setting
This single-center retrospective observational cohort study analyzed data from Ibb University Hospitals. We reviewed surgical and radiological records of consecutive patients undergoing laparoscopic cholecystectomy from April 22, 2020, to November 23, 2024.

Ethical Approval
The Ibb University Institutional Research Ethics Committee approved the protocol on January 1, 2025 (Ref: IBBUNI.2025.1.001). Individual consent was waived given the anonymized, retrospective design, following Declaration of Helsinki principles.

Patient Population
We screened adults (≥18 years) with laparoscopic cholecystectomy for benign gallbladder disease (acute/chronic cholecystitis, symptomatic cholelithiasis). Exclusions: gallbladder malignancy, combined hepatobiliary/pancreatic procedures, or incomplete records. Final cohort: 301 patients (STARD Flow Diagram, Supplementary Figure 1).

Data Collection
A trained team extracted preoperative data via standardized electronic forms: demographics (age, sex), BMI, clinical history (prior acute cholecystitis, comorbidities), physical exam (RUQ rigidity), labs (WBC count), and ultrasound (gallbladder wall thickness, impacted stone at neck/cystic duct).

Primary Outcome
"Difficult LC" was a composite: (1) intraoperative conversion to open surgery, or (2) major complication (significant biliary/vascular/visceral injury). Of 384 procedures, 57 (14.8%) were difficult.

Statistical Analysis
Analyses used Python 3.10 (SciPy, pandas, scikit-learn, statsmodels). Continuous data: mean±SD or median (IQR) per Shapiro-Wilk test. Comparisons: t/Mann-Whitney U tests (continuous), χ²/Fisher's exact (categorical).

Model Development
Variables with p&lt;0.10 univariately entered multivariable logistic regression (VIF&lt;2.0, linearity checked). Final model by AIC, p&lt;0.05, clinical relevance. β-coefficients derived integer risk score (divide by smallest β, round). Assessed: AUC (bootstrapped 95% CI, 1000 reps), calibration (Brier score, plot), DCA for utility. Bootstrap internal validation.
DOI: 10.17632/sk6jwy7xmg
title: CONFEX: CONUS and Alaska Fire EXtent database

description: This is a wildfire event database for the contiguous United States (CONUS) and Alaska, based on the VIIRS S-NPP 375 m active fire product for the period 2012–2024. The core product of the database consists of a single GeoPackage (.gpkg) containing polygon perimeters for all the detected events of the entire time span, and a summary CSV file with event-level attributes. The archive also includes: a zipped shapefile for all the events with at least three hotspot detections for the period 2012–2024; yearly zip files, consisting of a single shapefile containing all the events ignited in that year, and a yearly summary CSV for that year. The summary CSV contains key details for each fire event, including the cluster ID, start and end dates, ignition and centroid locations, burned area, along with associated hotspot detections from VIIRS. Each event in the CSV can be linked to its corresponding polygon feature in the GeoPackage or shapefile products using the cluster ID.
DOI: 10.17632/sk6jwy7xmg.3
title: CONFEX: CONUS and Alaska Fire EXtent database

description: This is a wildfire event database for the contiguous United States (CONUS) and Alaska, based on the VIIRS S-NPP 375 m active fire product for the period 2012–2024. The core product of the database consists of a single GeoPackage (.gpkg) containing polygon perimeters for all the detected events of the entire time span, and a summary CSV file with event-level attributes. The archive also includes: a zipped shapefile for all the events with at least three hotspot detections for the period 2012–2024; yearly zip files, consisting of a single shapefile containing all the events ignited in that year, and a yearly summary CSV for that year. The summary CSV contains key details for each fire event, including the cluster ID, start and end dates, ignition and centroid locations, burned area, along with associated hotspot detections from VIIRS. Each event in the CSV can be linked to its corresponding polygon feature in the GeoPackage or shapefile products using the cluster ID.
DOI: 10.17632/38t6twbnzf.1
title: BTC with financial and sentiment indexes

description: This dataset combines bitcoin financial data from cryptocurrency markets and computed sentiment indices. The objective of this combination is to improve the prediction of Bitcoin’s variability.
The data range is from 01/5/2022 until 12/31/2024
DOI: 10.17632/hktsmhfdsj
title: Seagrass Health Monitoring Methods: Meta- Analysis and Expert Survey

description: This dataset was developed to examine how seagrass health is currently monitored and to assess whether commonly used monitoring methods align with expert views on what is most important for evaluating seagrass health. The underlying hypothesis was that, despite the wide range of possible indicators available, seagrass monitoring practice is dominated by a small set of core methods, and that these methods broadly reflect expert judgement, while other potentially informative indicators remain underused.

To test this, we compiled two complementary datasets. The first is a systematic meta-analysis of 500 independent studies that reported seagrass health or condition. Studies were identified through structured literature searches and screening, and for each study we recorded basic metadata (including region, species, and study duration) and the types of monitoring methods used. 

The second dataset consists of responses from a survey of 34 seagrass experts, including researchers and practitioners involved in monitoring, conservation, and management. Survey participants were asked to evaluate the importance and applicability of different seagrass health monitoring methods and to identify approaches expected to become more prominent in the future. Survey responses were annonymised and structured to allow direct comparison with the meta-analysis results.

The data show that most seagrass studies rely on a small number of widely used, cost-effective monitoring methods, particularly environmental parameters and structural indicators, while physiological-level indicators are used relatively infrequently. Expert responses largely mirror this pattern, with high importance assigned to core structural and environmental metrics, but also highlight opportunities to strengthen monitoring by integrating early-warning indicators and emerging approaches such as remote sensing, environmental DNA (eDNA), and automated image analysis.

These datasets can be interpreted and used to explore patterns in seagrass monitoring practice across regions and species, to compare observed monitoring approaches with expert expectations, and to inform the design of more integrated monitoring frameworks. They are suitable for reuse in future meta-analyses, benchmarking exercises, and evidence-based development of seagrass monitoring and management programs.
DOI: 10.17632/hktsmhfdsj.1
title: Seagrass Health Monitoring Methods: Meta- Analysis and Expert Survey

description: This dataset was developed to examine how seagrass health is currently monitored and to assess whether commonly used monitoring methods align with expert views on what is most important for evaluating seagrass health. The underlying hypothesis was that, despite the wide range of possible indicators available, seagrass monitoring practice is dominated by a small set of core methods, and that these methods broadly reflect expert judgement, while other potentially informative indicators remain underused.

To test this, we compiled two complementary datasets. The first is a systematic meta-analysis of 500 independent studies that reported seagrass health or condition. Studies were identified through structured literature searches and screening, and for each study we recorded basic metadata (including region, species, and study duration) and the types of monitoring methods used. 

The second dataset consists of responses from a survey of 34 seagrass experts, including researchers and practitioners involved in monitoring, conservation, and management. Survey participants were asked to evaluate the importance and applicability of different seagrass health monitoring methods and to identify approaches expected to become more prominent in the future. Survey responses were annonymised and structured to allow direct comparison with the meta-analysis results.

The data show that most seagrass studies rely on a small number of widely used, cost-effective monitoring methods, particularly environmental parameters and structural indicators, while physiological-level indicators are used relatively infrequently. Expert responses largely mirror this pattern, with high importance assigned to core structural and environmental metrics, but also highlight opportunities to strengthen monitoring by integrating early-warning indicators and emerging approaches such as remote sensing, environmental DNA (eDNA), and automated image analysis.

These datasets can be interpreted and used to explore patterns in seagrass monitoring practice across regions and species, to compare observed monitoring approaches with expert expectations, and to inform the design of more integrated monitoring frameworks. They are suitable for reuse in future meta-analyses, benchmarking exercises, and evidence-based development of seagrass monitoring and management programs.
DOI: 10.17632/shxpd99dft
title: Soil Organic Carbon and Total Nitrogen Content in the Brazilian Amazon

description: This dataset contains chemical and physical soil properties from 12 sites in the Brazilian Amazon (Pará), including 10 cacao-based agroforestry systems (CAFSs) and 2 secondary vegetation (SV) control areas. Soil samples were collected at four depths (0–10, 10–20, 20–30, and 30–40 cm) and analyzed for soil organic carbon (SOC), soil organic matter (SOM), total nitrogen (TN), C:N ratio, pH in water (1:2.5), and particle-size distribution (sand, silt, and clay), all expressed in g·kg⁻¹ where applicable. Each value represents the average of triplicate laboratory analyses from composite samples derived from five subsamples per depth and site. The dataset is provided in .xlsx and .csv formats, with columns identifying site ID, geographic coordinates, land-use type (CAFS or SV), soil depth, and all measured parameters. These data were used to assess the long-term impacts of cacao-based agroforestry on soil fertility and edaphic properties in highly weathered Amazonian soils.
DOI: 10.17632/shxpd99dft.1
title: Soil Organic Carbon and Total Nitrogen Content in the Brazilian Amazon

description: This dataset contains chemical and physical soil properties from 12 sites in the Brazilian Amazon (Pará), including 10 cacao-based agroforestry systems (CAFSs) and 2 secondary vegetation (SV) control areas. Soil samples were collected at four depths (0–10, 10–20, 20–30, and 30–40 cm) and analyzed for soil organic carbon (SOC), soil organic matter (SOM), total nitrogen (TN), C:N ratio, pH in water (1:2.5), and particle-size distribution (sand, silt, and clay), all expressed in g·kg⁻¹ where applicable. Each value represents the average of triplicate laboratory analyses from composite samples derived from five subsamples per depth and site. The dataset is provided in .xlsx and .csv formats, with columns identifying site ID, geographic coordinates, land-use type (CAFS or SV), soil depth, and all measured parameters. These data were used to assess the long-term impacts of cacao-based agroforestry on soil fertility and edaphic properties in highly weathered Amazonian soils.
DOI: 10.17632/w57wvcxwpr
title: Increasing number of global change disturbance pulses destabilizes soil microbial diversity and functions across land-bridge habitats

description: Increasing number of global change disturbance pulses destabilizes soil microbial diversity and functions across land-bridge habitats
DOI: 10.17632/w57wvcxwpr.1
title: Increasing number of global change disturbance pulses destabilizes soil microbial diversity and functions across land-bridge habitats

description: Increasing number of global change disturbance pulses destabilizes soil microbial diversity and functions across land-bridge habitats
DOI: 10.17632/c2cffxzmm2
title: Multi-temporal photographic dataset of landscape changes in Langtang Valley, Nepal

description: High mountain environments are characterized by massive and often rapid landscape changes due to climatic conditions and sudden geohazards. At the same time these sensitive regions are often prominent and exposed destinations for tourism, regularly accompanied by urbanization processes and socioeconomic changes. This dataset presents multi-temporal landscape photography from the Langtang Valley, a high mountain region located in the central Himalaya that was heavily affected by the April 2015 Nepal Earthquake. The thematic focus of this dataset is laid on changes in glaciers (Section A) and in rural settlements before and after the earthquake (Section B). Relevant historical photographs showing glacier and settlement aspects were taken during exploratory scientific expeditions have been collected and collated from various archives. These images have been repeated from the same viewpoints during several surveys between 2008 and 2025. The elevation of viewpoints of re-photographic surveys in the years 2008, 2012, 2017, 2018 and 2025 ranges between 3400 and 5000 m a.s.l. The dataset is based on the research hypothesis that repeat photography in these mountain landscapes allows for a longer monitoring period than remote sensing data as historical photographs from the years 1949, 1974, and 1980 are available. It also allows for a higher spatial resolution of change detection and is more appropriate for the local scale. Prints and digital versions of selected historical photographs were taken to the field in order to identify the exact viewpoints. This multi-temporal dataset allows for a detailed visual assessment of glacier and settlement changes over a period of several decades and can serve as a baseline for further studies in this prominent Himalayan region.
DOI: 10.17632/c2cffxzmm2.1
title: Multi-temporal photographic dataset of landscape changes in Langtang Valley, Nepal

description: High mountain environments are characterized by massive and often rapid landscape changes due to climatic conditions and sudden geohazards. At the same time these sensitive regions are often prominent and exposed destinations for tourism, regularly accompanied by urbanization processes and socioeconomic changes. This dataset presents multi-temporal landscape photography from the Langtang Valley, a high mountain region located in the central Himalaya that was heavily affected by the April 2015 Nepal Earthquake. The thematic focus of this dataset is laid on changes in glaciers (Section A) and in rural settlements before and after the earthquake (Section B). Relevant historical photographs showing glacier and settlement aspects were taken during exploratory scientific expeditions have been collected and collated from various archives. These images have been repeated from the same viewpoints during several surveys between 2008 and 2025. The elevation of viewpoints of re-photographic surveys in the years 2008, 2012, 2017, 2018 and 2025 ranges between 3400 and 5000 m a.s.l. The dataset is based on the research hypothesis that repeat photography in these mountain landscapes allows for a longer monitoring period than remote sensing data as historical photographs from the years 1949, 1974, and 1980 are available. It also allows for a higher spatial resolution of change detection and is more appropriate for the local scale. Prints and digital versions of selected historical photographs were taken to the field in order to identify the exact viewpoints. This multi-temporal dataset allows for a detailed visual assessment of glacier and settlement changes over a period of several decades and can serve as a baseline for further studies in this prominent Himalayan region.
DOI: 10.17632/xt42c2jfdg
title: Codebook and Annotated Dataset for LLM Career Guidance Analysis Across Ten African Countries

description: This dataset contains the annotated responses, coding schema, and reliability statistics used in a comparative analysis of large language models (LLMs) for computing career guidance across ten African countries. The underlying research examined how different LLMs articulate technical and professional competencies for entry-level computing roles and the extent to which these recommendations reflect local contextual factors.

The dataset includes 60 LLM-generated responses (six models × ten countries) produced using a standardized prompt. Models analyzed include ChatGPT-4, Claude 3.5 Sonnet, Gemini 1.5 Pro, LLaMA 3, DeepSeek-V2, and Mistral. Countries represented are Egypt, South Africa, Senegal, Tunisia, Kenya, Nigeria, Ghana, Benin, Zambia, and Morocco. All responses were collected in English within a fixed time window and without follow-up prompts.

Responses were analyzed using a conceptual content analysis approach informed by the CC2020 Computing Curricula framework. Coding captured both technical competencies (e.g., programming, algorithms, AI/ML, cloud computing) and professional competencies (e.g., adaptability, teamwork, communication, ethics). In addition, a contextual awareness analysis assessed the presence of country-specific references, including local technology ecosystems, language or cultural considerations, national policies, and institutional mentions. Coding reflects the presence of concepts rather than strict keyword matching.

The dataset also includes a rubric-based scoring sheet evaluating response quality across four dimensions (technical coverage, contextual awareness, skills balance, and depth), inter-rater reliability (IRR) data from dual coders, pooled and category-level reliability statistics, and aggregated summaries. Together, these materials enable replication of the reported analyses, secondary analysis of LLM behavior in career guidance contexts, and methodological reuse of the coding framework. The dataset is intended for research and educational purposes and reflects model behavior at the time of data collection.
DOI: 10.17632/xt42c2jfdg.1
title: Codebook and Annotated Dataset for LLM Career Guidance Analysis Across Ten African Countries

description: This dataset contains the annotated responses, coding schema, and reliability statistics used in a comparative analysis of large language models (LLMs) for computing career guidance across ten African countries. The underlying research examined how different LLMs articulate technical and professional competencies for entry-level computing roles and the extent to which these recommendations reflect local contextual factors.

The dataset includes 60 LLM-generated responses (six models × ten countries) produced using a standardized prompt. Models analyzed include ChatGPT-4, Claude 3.5 Sonnet, Gemini 1.5 Pro, LLaMA 3, DeepSeek-V2, and Mistral. Countries represented are Egypt, South Africa, Senegal, Tunisia, Kenya, Nigeria, Ghana, Benin, Zambia, and Morocco. All responses were collected in English within a fixed time window and without follow-up prompts.

Responses were analyzed using a conceptual content analysis approach informed by the CC2020 Computing Curricula framework. Coding captured both technical competencies (e.g., programming, algorithms, AI/ML, cloud computing) and professional competencies (e.g., adaptability, teamwork, communication, ethics). In addition, a contextual awareness analysis assessed the presence of country-specific references, including local technology ecosystems, language or cultural considerations, national policies, and institutional mentions. Coding reflects the presence of concepts rather than strict keyword matching.

The dataset also includes a rubric-based scoring sheet evaluating response quality across four dimensions (technical coverage, contextual awareness, skills balance, and depth), inter-rater reliability (IRR) data from dual coders, pooled and category-level reliability statistics, and aggregated summaries. Together, these materials enable replication of the reported analyses, secondary analysis of LLM behavior in career guidance contexts, and methodological reuse of the coding framework. The dataset is intended for research and educational purposes and reflects model behavior at the time of data collection.
DOI: 10.17632/vsy2gtzh5x
title: CEOs’ Hometown Identity and Corporate Supplier Selection: Evidence from China

description: README: Replication Package
Paper Title
CEOs’ Hometown Identity and Corporate Supplier Selection: Evidence from China
Authors:
Prof. Xianhang Qian (Shandong University)
Kunzhu Zhao (Renmin University of China)

This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
DOI: 10.17632/vsy2gtzh5x.1
title: CEOs’ Hometown Identity and Corporate Supplier Selection: Evidence from China

description: README: Replication Package
Paper Title
CEOs’ Hometown Identity and Corporate Supplier Selection: Evidence from China
Authors:
Prof. Xianhang Qian (Shandong University)
Kunzhu Zhao (Renmin University of China)

This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.