cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28861607
title: Adolescent Suicidal Behavior 2024

description: A cross-sectional study was conducted in two government and non-government higher secondary schools in Bangladesh. A total of 351 adolescents aged 11 to 17 years were recruited for the study, with a 98% response rate. Subjects were recruited by cluster sampling. The suicidal Behavior Questionnaire-Revised was used to identify the presence of suicidal behavior. Responses to item 1 were used to assess lifetime suicidal ideation, planning, and attempts. Specifically, a response of “2” in this item indicated lifetime suicidal ideation, while responses “3a” or “3b” indicated suicidal planning, and “4a” or “4b” indicated suicidal attempt. The Depression, Anxiety, Stress-21 (DASS-21) Items were used to determine the presence of depression, anxiety, and stress. The total score for each subscale in the DASS-21 is multiplied by two in the dataset. Another structured questionnaire was used to collect data on sociodemographic, behavioral, institutional, socio-environmental, family-environmental, and psychological data from participants.
DOI: 10.15468/dl.2h3xdd
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abies spectabilis (D.Don) Spach"
 ]
}

 The dataset includes 77 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011119-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cp5uch
title: Occurrence Download

description: A dataset containing 49108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((82.59247 17.44365,160.40753 17.44365,160.40753 48.55635,82.59247 48.55635,82.59247 17.44365))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Zacco platypus (Temminck & Schlegel, 1846)"
 ]
}

 The dataset includes 49108 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011120-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15477962
title: Data supporting Time-Varying Scaled Consensus in Directed Multi-Agent Systems with Delays

description: Dr. Mana Donganont, School of Science, University of Phayao, Thailand
DOI: 10.5281/zenodo.15478006
title: Residual Stress Dataset
DOI: 10.5281/zenodo.15478007
title: Residual Stress Dataset
DOI: 10.15468/dl.rjbcej
title: Occurrence Download

description: A dataset containing 11996 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((151.2123 -10.18881, 153.0218 -10.18881, 153.0218 -8.641833, 151.2123 -8.641833, 151.2123 -10.18881))"
 ]
}

 The dataset includes 11996 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011121-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3p4fgv
title: Occurrence Download

description: A dataset containing 3012002 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is one of (Ghana, Benin, Congo, Democratic Republic of the, Central African Republic, Cameroon, Côte d’Ivoire, Nigeria, South Sudan, Liberia, Sierra Leone, Gabon)",
 "Year 2010-2022"
 ]
}

 The dataset includes 3012002 records from 748 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014775-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/c29xxv9xwv
title: Transformative Impact of Web Series
DOI: 10.17632/c29xxv9xwv.1
title: Transformative Impact of Web Series
DOI: 10.25829/1x09-re46
title: Leaf stomatal traits (density and size) in the MyDiv experiment during the drought year 2022

description: Climate change is expected to increase the frequency and duration of drought events, posing a major threat to the functioning of Central European forests. To investigate how tree growth during the 2018–2020 drought was influenced by species-specific hydro-functional strategies, and whether growth reductions could be mitigated by hydro-functional dissimilarity among neighboring trees, we established this dataset as part of a broader study.

To characterize the water-use strategies of ten temperate tree species, we measured stomatal density and size in 2022 within the MyDiv Experiment. For each species, three top-canopy leaves were sampled from five trees per monoculture plot. Nail-polish imprints of the abaxial leaf surface were prepared and analyzed under a light microscope to count and measure stomata, providing robust species-level estimates of stomatal traits.

These data represent leaf-level hydro-functional traits that reflect stomatal control over transpiration and gas exchange. They were used to assess interspecific variation in water-use strategies and drought responses of tree species in temperate forest ecosystems.
DOI: 10.15468/dl.4xgrzk
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aecidiolum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039414-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13768034
title: Exam Score Prediction Dataset
DOI: 10.15468/dl.aty8qt
title: Occurrence Download

description: A dataset containing 1429 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is one of (Spodoptera frugiperda, Mayetiola destructor, Diuraphis noxia, Cephus cinctus, Sitodiplosis mosellana)",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2025",
 "Geometry POLYGON ((-21.21398 32.72363, 44.12803 33.21309, 43.76094 74.69425, -21.09162 74.32716, -21.21398 32.72363))"
 ]
}

 The dataset includes 1429 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039415-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/cvnc979szv
title: Molecular screening of NEH rice germplasm for N and P

description: Molecular screening of NEH rice germplasm for N and P
DOI: 10.5281/zenodo.17624274
title: Expanded Globular Cluster Database

description: The Expanded Globular Cluster Database (EGCD) is a comprehensive compilation of Milky Way globular cluster properties, combining catalog data from Harris (2010, 2015 update), Angeli et al. (2005), Petralia et al. (2017), Gaia DR3 astrometry, and new observational measurements. Each row corresponds to a single globular cluster, with over 70 variables describing positional, photometric, kinematic, and structural properties. Key variables include Galactic coordinates, heliocentric and galactocentric distances, metallicity ([Fe/H]), horizontal branch magnitude, integrated luminosities and colors, structural parameters (core radius, half-light radius, concentration, ellipticity), velocity dispersions, rotation measurements, relaxation times, age estimates, and derived quantities such as cluster density, dynamic mass, morphochemical indices, and rotation support parameters (V/σ).

The EGCD is intended for reproducible research and analysis of Milky Way globular clusters, including studies of metallicity trends, rotational kinematics, luminosity modeling, and galactic formation history. Derived Cartesian coordinates (x, y, z) and Galactocentric distances were calculated using a solar position of R☉ = 8.2 kpc and z☉ = 20.8 pc, with all positional measurements referenced to J2000.0. Observational data from this study were processed using Deep Sky Stacker, Photopea, Google Sheets, and Python (matplotlib).

The database is curated to ensure clarity and reproducibility. Selection criteria for inclusion were: clusters must have measured metallicity and distance; duplicates and conflicting entries were resolved by prioritizing high-resolution spectroscopic data (Petralia et al.), then Angeli et al., then Harris (2010). Clusters with unresolved conflicts or missing data were excluded. The resulting dataset contains N clusters suitable for statistical and observational analyses of globular cluster properties in relation to Galactic position.
DOI: 10.5281/zenodo.17632982
title: Expanded Globular Cluster Database

description: The Expanded Globular Cluster Database (EGCD) is a comprehensive compilation of Milky Way globular cluster properties, combining catalog data from Harris (2010, 2015 update), Angeli et al. (2005), Petralia et al. (2017), Gaia DR3 astrometry, and new observational measurements. Each row corresponds to a single globular cluster, with over 70 variables describing positional, photometric, kinematic, and structural properties. Key variables include Galactic coordinates, heliocentric and galactocentric distances, metallicity ([Fe/H]), horizontal branch magnitude, integrated luminosities and colors, structural parameters (core radius, half-light radius, concentration, ellipticity), velocity dispersions, rotation measurements, relaxation times, age estimates, and derived quantities such as cluster density, dynamic mass, morphochemical indices, and rotation support parameters (V/σ).

The EGCD is intended for reproducible research and analysis of Milky Way globular clusters, including studies of metallicity trends, rotational kinematics, luminosity modeling, and galactic formation history. Derived Cartesian coordinates (x, y, z) and Galactocentric distances were calculated using a solar position of R☉ = 8.2 kpc and z☉ = 20.8 pc, with all positional measurements referenced to J2000.0. Observational data from this study were processed using Deep Sky Stacker, Photopea, Google Sheets, and Python (matplotlib).

The database is curated to ensure clarity and reproducibility. Selection criteria for inclusion were: clusters must have measured metallicity and distance; duplicates and conflicting entries were resolved by prioritizing high-resolution spectroscopic data (Petralia et al.), then Angeli et al., then Harris (2010). Clusters with unresolved conflicts or missing data were excluded. The resulting dataset contains N clusters suitable for statistical and observational analyses of globular cluster properties in relation to Galactic position.
DOI: 10.17632/cvnc979szv.1
title: Molecular screening of NEH rice germplasm for N and P

description: Molecular screening of NEH rice germplasm for N and P
DOI: 10.17632/stp4hkydh5
title: KLF13-Mediated CES2 Upregulation via p300-Dependent Acetylation Sensitizes Gastric Cancer Cells to Irinotecan

description: Western Blot
DOI: 10.17632/stp4hkydh5.1
title: KLF13-Mediated CES2 Upregulation via p300-Dependent Acetylation Sensitizes Gastric Cancer Cells to Irinotecan

description: Western Blot
DOI: 10.17632/k3w3btvmtt
title: Synthetic Administrative Panel Dataset for Performance-Based Rural Road Maintenance Evaluation

description: This dataset contains a synthetic, simulation-based administrative panel dataset designed to emulate the operational, contractual, and engineering characteristics of a digital performance-based rural road maintenance system. The structure reflects a realistic policy environment in which road maintenance payments are linked to measurable performance thresholds and monitored through a digital inspection platform.

The dataset models 150 rural road assets observed across multiple monthly inspection cycles, creating a balanced panel format suitable for longitudinal, econometric, and machine learning analysis. Each observation represents a road–month inspection record and includes engineering condition scores, payment eligibility status, contractor identity, geographic coordinates, contract duration, and administrative variables.

A digital intervention variable captures staggered adoption of a monitoring platform (e.g., digital scoring or audit system), enabling quasi-experimental evaluation techniques such as:

Difference-in-Differences (DiD)

Event Study Analysis

Regression Discontinuity Design

Instrumental Variables (IV)

Spatial Dependence Models

Dynamic Panel Models

Double Machine Learning (DML)

Performance scoring is based on a composite indicator representing pavement surface condition, drainage quality, shoulders, vegetation clearance, signage, and safety elements. A rule-based incentive mechanism ties payment eligibility to a threshold score of ≥ 80, mimicking real performance-based maintenance contracting (PBMC) environments.

The dataset includes spatial identifiers to support mapping, spillover analysis, treatment diffusion effects, and network-based road governance evaluations. Stochastic variation was introduced to reflect realistic engineering uncertainty, environmental exposure, and measurement noise.

Because the dataset is synthetic, it does not contain personally identifiable information (PII), confidential records, or sensitive administrative content, making it suitable for open publication, reproducibility, benchmarking, teaching, and methodological development in infrastructure policy, econometrics, simulation, and digital governance research.
Key Features
Attribute Details
Type Synthetic, balanced panel dataset
Observations Road × Month structure
Geographic Structure Includes simulated coordinates and spatial adjacency
Intervention Staggered digital monitoring system adoption
Outcome Variables Performance score, payment amount, payment delay
Use Cases Causal inference, econometrics, simulation models, machine learning
DOI: 10.17632/k3w3btvmtt.1
title: Synthetic Administrative Panel Dataset for Performance-Based Rural Road Maintenance Evaluation

description: This dataset contains a synthetic, simulation-based administrative panel dataset designed to emulate the operational, contractual, and engineering characteristics of a digital performance-based rural road maintenance system. The structure reflects a realistic policy environment in which road maintenance payments are linked to measurable performance thresholds and monitored through a digital inspection platform.

The dataset models 150 rural road assets observed across multiple monthly inspection cycles, creating a balanced panel format suitable for longitudinal, econometric, and machine learning analysis. Each observation represents a road–month inspection record and includes engineering condition scores, payment eligibility status, contractor identity, geographic coordinates, contract duration, and administrative variables.

A digital intervention variable captures staggered adoption of a monitoring platform (e.g., digital scoring or audit system), enabling quasi-experimental evaluation techniques such as:

Difference-in-Differences (DiD)

Event Study Analysis

Regression Discontinuity Design

Instrumental Variables (IV)

Spatial Dependence Models

Dynamic Panel Models

Double Machine Learning (DML)

Performance scoring is based on a composite indicator representing pavement surface condition, drainage quality, shoulders, vegetation clearance, signage, and safety elements. A rule-based incentive mechanism ties payment eligibility to a threshold score of ≥ 80, mimicking real performance-based maintenance contracting (PBMC) environments.

The dataset includes spatial identifiers to support mapping, spillover analysis, treatment diffusion effects, and network-based road governance evaluations. Stochastic variation was introduced to reflect realistic engineering uncertainty, environmental exposure, and measurement noise.

Because the dataset is synthetic, it does not contain personally identifiable information (PII), confidential records, or sensitive administrative content, making it suitable for open publication, reproducibility, benchmarking, teaching, and methodological development in infrastructure policy, econometrics, simulation, and digital governance research.
Key Features
Attribute Details
Type Synthetic, balanced panel dataset
Observations Road × Month structure
Geographic Structure Includes simulated coordinates and spatial adjacency
Intervention Staggered digital monitoring system adoption
Outcome Variables Performance score, payment amount, payment delay
Use Cases Causal inference, econometrics, simulation models, machine learning
DOI: 10.17632/g9rgyv53xt.2
title: Original TEM, EELS, and Micromagnetic simulation data for “Source of lunar magnetic anomalies: Magnetic vortex state of α-Fe in Chang’e-5 soil

description: Original TEM, EELS, and Micromagnetic simulation data for “Source of lunar magnetic anomalies: Magnetic vortex state of α-Fe in Chang’e-5 soil”
DOI: 10.15468/dl.m52ch6
title: Occurrence Download

description: A dataset containing 1481 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Reptilia notes, Norsk Feltherpetologisk Forum"
 ]
}

 The dataset includes 1481 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039416-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7929/issda/u1zkg1
title: Quarterly National Household Survey (QNHS) Module on Health, Q3 2001

description: The Quarterly National Household Survey (QNHS) began in September 1997 and ran until Q2 2017 when it was replaced by the Labour Force Survey (LFS). While the main purpose of the QNHS was the production of quarterly labour force estimates, the QHNS also conducted special modules on different social topics each quarter. This special module on Health was run in the third quarter 2001. The module was commissioned by a specially created Health Liaison group. This liaison group comprises of experts in several fields of medical research. The Dept. of Health and the ESRI are also represented on this group. The module has been developped to collect information on: Prevalence of specific health conditions; Length of time on medical waiting lists; Type of health cover; Please note that due to CSO restrictions, these files cannot be matched to their associated microdata files.
DOI: 10.25829/z5m9-4t33
title: Leaf carbon isotopes (δ¹³C) in the MyDiv experiment during the drought year 2022

description: Climate change is expected to increase the frequency and duration of drought events, threatening the functioning of Central European forests. To examine how tree growth during the 2018–2020 drought was influenced by species-specific hydro-functional strategies, and whether growth reductions could be mitigated by hydro-functional dissimilarity among neighboring trees, we established this dataset as part of a broader study.

This dataset contains measurements of the stable carbon isotope ratio (δ¹³C) in leaves collected at the end of the 2022 growing season across all plots of the MyDiv Experiment. For each species and plot, three representative trees were sampled, and five leaves per tree were pooled for analysis. δ¹³C values were determined from dried leaf material using isotope ratio mass spectrometry (IRMS). These measurements provide tree-level information on carbon isotope composition, serving as a proxy for stomatal regulation and water-use efficiency under drought conditions.

Only δ¹³C values from monoculture plots were used as species-level traits in subsequent analyses. The dataset thus represents a subset of leaf-level hydro-functional traits, used to assess interspecific variation in water-use strategies and drought responses of temperate forest tree species.
DOI: 10.48577/jpl.hwdcf3
title: Roman Coronagraph development

description: No abstract available.
DOI: 10.17632/xwvpbfsfzb
title: Gas separation

description: Efficient Separation of CO2/CH4 and H2S/CH4 Gas Mixtures through Three-dimensional graphene
DOI: 10.17632/xwvpbfsfzb.1
title: Gas separation

description: Efficient Separation of CO2/CH4 and H2S/CH4 Gas Mixtures through Three-dimensional graphene
DOI: 10.17632/jbbv466n8j
title: 20251115-DTEHM_research data

description: Title: How Collective-Intelligence Collaboration Influences Resource Evolution in Open Networked Communities: Mechanisms and Evidence
This study was exploratory and was not pre-registered prior to data collection. This study, through a comprehensive literature review, explores the influence of group collaborative interactions on resource content evolution in the process of community-based learning. It focuses on three major categories of characteristics: contextual characteristics (topic type), group structural characteristics (group size and diversity), and interaction characteristics (interaction intensity and interaction depth). The findings from prior studies indicate that the characteristics influencing the evolution of high-quality resource content are diverse and complex. Given the inconsistencies in existing research conclusions, this study proposes the following preliminary hypotheses:
1)In topic-based community learning processes, certain characteristics may serve as key conditions influencing resource content evolution.
2)The quality of resource evolution in community learning is affected by the synergistic effects of multiple characteristics, and different combinations of these characteristics may lead to equivalent evolutionary pathways.
Based on these hypotheses, this study integrates Group Communication Analysis (GCA) and Discrete-Time Event History Analysis (DTEHA) to investigate the mechanisms through which collective collaboration in resource production promotes resource content evolution. Compared with traditional linear or static analytical approaches, this combined method captures both the dynamic communicative features of collaborative processes and the temporal patterns of resource change, thereby providing a more effective means of uncovering the sustained influence of group collaborative behaviors on resource evolution.