cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/r93dfjvvv6.2
title: GoCJ: Google Cloud Jobs Dataset (Enhanced)

description: The GoCJ dataset is comprised of multiple files, where each file contains the sizes of a specified number of jobs expressed in Million Instructions (MI), derived from workload behaviors observed in Google cluster traces. The name of each file indicates the number of jobs it contains; for example, GoCJ_Dataset_1000 includes 1000 jobs along with their associated SLA classes and arrival times.

In this study, a modified version of the GoCJ dataset is employed. Each dataset file consists of three columns: (i) job length in terms of Million Instructions (MI), (ii) Service Level Agreement (SLA: {1, 2, 3}), representing different levels of priorities, and (iii) job arrival time, which captures realistic workload submission behavior.

The experimental evaluation is conducted using the following dataset files: GoCJ_Dataset_1000.csv, GoCJ_Dataset_2000.csv, GoCJ_Dataset_3000.csv, GoCJ_Dataset_4000.csv, GoCJ_Dataset_5000.csv, and GoCJ_Dataset_6000.csv, enabling performance analysis under increasing workload scales.
DOI: 10.57745/uvwb9n
title: README_for_Dataset_from_a_longitudinal_study_measuring_the_effect_of_autobiographical_reasoning_abilities_on_the_clinical_evolution_of_individuals_with_alcohol_use_disorder.txt

description: :unav
DOI: 10.57745/ifswoc
title: Clinical_Data_and_autobiographical_reasoning_by_participants.csv

description: :unav
DOI: 10.57745/ofxlvw
title: Autobiographical_reasoning_in_autobiographical_memories.csv

description: :unav
DOI: 10.57745/owelwo
title: Dataset from a longitudinal study measuring the effect of autobiographical reasoning abilities on the clinical evolution of individuals with alcohol use disorder

description: The present dataset comes from a longitudinal study which aimed at exploring the effect of autobiographical reasoning abilities in the clinical evolution of individuals with alcohol use disorder. These data were collected from 59 individuals with alcohol use disorder and 58 control participants. Participants were invited to recall 2 autobiographical memories from 4 life periods (childhood, adolescence-young adulthood, adulthood, last year) and to respond to questionnaires about sociodemographic information and assessing alcohol consumption (AUDIT), anxiety and depression symptoms (HADS), cognitive functions (MoCA). Participants were recalled 6 months and 12 months after the initial phase to assess anxious and depressive symptoms (HADS) and alcohol consumption (AUDIT-C). Craving intensity was also assessed in individuals with alcohol use disorder (OCDS). We coded autobiographical reasoning according to the coding methods described in the annexes n°2 of the thesis "Sens du Self et Mémoire Autobiographique dans le Trouble de l'Usage de l'Alcool" (De Groote, 2025).
DOI: 10.17632/fpssb7tjbs
title: Digital access as public health infrastructure: Evidence on maternal healthcare utilization from India

description: This is PRISMA checklist data.
DOI: 10.17632/fpssb7tjbs.1
title: Digital access as public health infrastructure: Evidence on maternal healthcare utilization from India

description: This is PRISMA checklist data.
DOI: 10.15468/dl.aze7ad
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria, Carex arenaria)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 24 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012490-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/k5b36ssthj
title: Role of Olfactory Receptor Olfr544 in Neuronal Cells: Insights into Neurite Outgrowth and CREB Signaling

description: Original western blot images
DOI: 10.17632/k5b36ssthj.1
title: Role of Olfactory Receptor Olfr544 in Neuronal Cells: Insights into Neurite Outgrowth and CREB Signaling

description: Original western blot images
DOI: 10.15468/dl.puzxfv
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material citation, Material sample, Specimen, Occurrence evidence, Fossil)",
 "Country is Mexico",
 {
 "or" : [
 "Geometry POLYGON((-99.36 19.04,-98.94 19.04,-98.94 19.59,-99.36 19.59,-99.36 19.04))",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bassariscus astutus",
 "Year 1799-1900"
 ]
}

 The dataset includes 41 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012491-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/z467mjv8j3
title: Lifestyle and metabolic profiles in young and middle-aged adults

description: The metabolic state encompasses the body’s processes of storing and expending biochemical energy and their subsequent effects on biometric indicators and non‑communicable diseases (NCDs). It is closely linked to lifestyle factors such as sleep, nutrition, physical activity, and recreational habits, all of which influence overall health and functional capacity. The rising global prevalence of metabolic syndrome underscores concerns regarding quality of life, disability, and longevity, particularly as metabolic dysregulation increasingly emerges early in adulthood. Evidence suggests that obesity—followed by hypertension and type 2 diabetes—will become leading contributors to disability, highlighting the importance of characterizing lifestyle patterns associated with metabolic risk in young adults.
This dataset includes information from 45 adults across eight domains: general data, biometric indicators, physical activity, feeding patterns, substance use, screen time, sleep quality, and reading habits. These data provide insight into modifiable lifestyle factors relevant to metabolic health and potential areas for intervention. Overall, the sample exhibited generally healthy conditions; however, elevated triglyceride levels and increased body mass index (with 60% classified as overweight or obese) were the primary biometric deviations. Approximately half of the participants reported good sleep quality and adequate physical activity, along with balanced eating and reading habits.
DOI: 10.17632/z467mjv8j3.1
title: Lifestyle and metabolic profiles in young and middle-aged adults

description: The metabolic state encompasses the body’s processes of storing and expending biochemical energy and their subsequent effects on biometric indicators and non‑communicable diseases (NCDs). It is closely linked to lifestyle factors such as sleep, nutrition, physical activity, and recreational habits, all of which influence overall health and functional capacity. The rising global prevalence of metabolic syndrome underscores concerns regarding quality of life, disability, and longevity, particularly as metabolic dysregulation increasingly emerges early in adulthood. Evidence suggests that obesity—followed by hypertension and type 2 diabetes—will become leading contributors to disability, highlighting the importance of characterizing lifestyle patterns associated with metabolic risk in young adults.
This dataset includes information from 45 adults across eight domains: general data, biometric indicators, physical activity, feeding patterns, substance use, screen time, sleep quality, and reading habits. These data provide insight into modifiable lifestyle factors relevant to metabolic health and potential areas for intervention. Overall, the sample exhibited generally healthy conditions; however, elevated triglyceride levels and increased body mass index (with 60% classified as overweight or obese) were the primary biometric deviations. Approximately half of the participants reported good sleep quality and adequate physical activity, along with balanced eating and reading habits.
DOI: 10.17632/5h6n8hxvgt
title: Traditional Costume Travel Photography (TCTP) and Destination Cultural Identity dataset

description: Dataset Title
Traditional Costume Travel Photography (TCTP) and Destination Cultural Identity
Description
This dataset explores how Traditional Costume Travel Photography (TCTP), an emerging form of performative tourism experience, influences tourists’ destination cultural identity. Grounded in the Stimulus–Organism–Response (S-O-R) framework, the dataset integrates qualitative and quantitative data to examine how TCTP experiences evoke cultural immersion and self-expansion, which subsequently foster destination cultural identity. Cultural familiarity is included as a moderating factor capturing individual differences in tourists’ experiential responses. The dataset supports empirical research in tourism, leisure, and cultural identity studies.
Data Sources
The dataset consists of two components:
(1) online user-generated travel narratives related to traditional costume experiences and travel photography;
(2) survey data measuring tourists’ experiential perceptions, psychological mechanisms, and cultural identity outcomes.
Methods
Online Textual Data
User-generated travel narratives were collected via web crawling from major Chinese tourism platforms (e.g., Weibo and Ctrip) using keywords such as “traditional costume experience” and “travel photography.” After cleaning, screening, and de-duplication, 165 valid texts were retained. These texts were analyzed using grounded theory to identify the core experiential dimensions of TCTP.
Survey Data
Survey data were collected through a professional online survey platform. The questionnaire includes established and adapted scales measured on a 5-point Likert scale, as well as demographic and screening questions. Core constructs include TCTP experience elements (costume, landscape, interpersonal interaction), cultural immersion, self-expansion, destination cultural identity, and cultural familiarity. A total of 733 valid responses were obtained. All variables were numerically coded and prepared for statistical analysis.
Data Records
The dataset contains an anonymized textual corpus for qualitative analysis and a numerically coded survey dataset suitable for statistical modeling, including structural equation modeling and moderation–mediation analysis.
Usage Notes
The dataset may be used to (a) replicate or extend the proposed S-O-R-based model; (b) conduct cross-cultural or comparative tourism studies; (c) analyze tourists’ narratives and meaning-making processes; and (d) explore additional mediating or moderating mechanisms related to experiential tourism and cultural identity.
Ethics Statement
Online textual data were collected from publicly available sources in compliance with platform policies. Survey participants provided informed consent prior to participation. All textual data have been anonymized, and the survey dataset contains no personally identifiable information. The dataset is available for non-commercial academic research purposes upon request from the authors.
DOI: 10.17632/5h6n8hxvgt.1
title: Traditional Costume Travel Photography (TCTP) and Destination Cultural Identity dataset

description: Dataset Title
Traditional Costume Travel Photography (TCTP) and Destination Cultural Identity
Description
This dataset explores how Traditional Costume Travel Photography (TCTP), an emerging form of performative tourism experience, influences tourists’ destination cultural identity. Grounded in the Stimulus–Organism–Response (S-O-R) framework, the dataset integrates qualitative and quantitative data to examine how TCTP experiences evoke cultural immersion and self-expansion, which subsequently foster destination cultural identity. Cultural familiarity is included as a moderating factor capturing individual differences in tourists’ experiential responses. The dataset supports empirical research in tourism, leisure, and cultural identity studies.
Data Sources
The dataset consists of two components:
(1) online user-generated travel narratives related to traditional costume experiences and travel photography;
(2) survey data measuring tourists’ experiential perceptions, psychological mechanisms, and cultural identity outcomes.
Methods
Online Textual Data
User-generated travel narratives were collected via web crawling from major Chinese tourism platforms (e.g., Weibo and Ctrip) using keywords such as “traditional costume experience” and “travel photography.” After cleaning, screening, and de-duplication, 165 valid texts were retained. These texts were analyzed using grounded theory to identify the core experiential dimensions of TCTP.
Survey Data
Survey data were collected through a professional online survey platform. The questionnaire includes established and adapted scales measured on a 5-point Likert scale, as well as demographic and screening questions. Core constructs include TCTP experience elements (costume, landscape, interpersonal interaction), cultural immersion, self-expansion, destination cultural identity, and cultural familiarity. A total of 733 valid responses were obtained. All variables were numerically coded and prepared for statistical analysis.
Data Records
The dataset contains an anonymized textual corpus for qualitative analysis and a numerically coded survey dataset suitable for statistical modeling, including structural equation modeling and moderation–mediation analysis.
Usage Notes
The dataset may be used to (a) replicate or extend the proposed S-O-R-based model; (b) conduct cross-cultural or comparative tourism studies; (c) analyze tourists’ narratives and meaning-making processes; and (d) explore additional mediating or moderating mechanisms related to experiential tourism and cultural identity.
Ethics Statement
Online textual data were collected from publicly available sources in compliance with platform policies. Survey participants provided informed consent prior to participation. All textual data have been anonymized, and the survey dataset contains no personally identifiable information. The dataset is available for non-commercial academic research purposes upon request from the authors.
DOI: 10.17632/m3ndfsycng
title: Life Cycle Environmental Performance of Sugarcane Leaf-Derived Activated Carbon

description: This data set contains Life Cycle Environmental Performance of Sugarcane Leaf-Derived Activated Carbon for 
Lead Removal from Textile Wastewater Treatment in Thailand.
DOI: 10.17632/m3ndfsycng.1
title: Life Cycle Environmental Performance of Sugarcane Leaf-Derived Activated Carbon

description: This data set contains Life Cycle Environmental Performance of Sugarcane Leaf-Derived Activated Carbon for 
Lead Removal from Textile Wastewater Treatment in Thailand.
DOI: 10.17632/h4w4g4kdyz
title: Raw Data for doi: 10.1016/j.jnutbio.2013.10.004

description: This dataset contains the original, uncropped, and unprocessed images used in the paper '[doi: 10.1016/j.jnutbio.2013.10.004]'. These files are provided to ensure data transparency and address inquiries on PubPeer regarding Figures 2A, 3B, 4B, and 6B.
DOI: 10.17632/h4w4g4kdyz.1
title: Raw Data for doi: 10.1016/j.jnutbio.2013.10.004

description: This dataset contains the original, uncropped, and unprocessed images used in the paper '[doi: 10.1016/j.jnutbio.2013.10.004]'. These files are provided to ensure data transparency and address inquiries on PubPeer regarding Figures 2A, 3B, 4B, and 6B.
DOI: 10.17632/b8r6rpv8ch
title: Experimental Dataset for Control-Aware Autoscaling of Shared Controllers in Edge Kubernetes Systems

description: This dataset provides a comprehensive experimental benchmark for the study of control-aware autoscaling of shared controllers in edge computing environments based on Kubernetes. It contains aggregated measurements from a large set of controlled experiments in which multiple Industrial Internet of Things (IIoT) devices share a limited number of fuzzy control services deployed as scalable Kubernetes pods. The experiments systematically vary the number of devices, the number of shared controllers, and the operating regime of the controlled systems, enabling the analysis of trade-offs between control performance, temporal stability, and computational resource usage.

The dataset covers three representative classes of control systems with distinct dynamics: a DC motor (fast and delay-sensitive dynamics), a level control tank (slow and naturally damped dynamics), and a vehicle speed control system (high-inertia dynamics). For each plant, multiple operating setpoints were evaluated, and for each setpoint the number of active IIoT devices and shared controllers was varied across a wide range. The dataset includes key metrics such as the integrated absolute error (IAE), the 90th percentile of control loop time and request delay, CPU usage of the control services, and network traffic statistics. Each row in the dataset corresponds to a complete experimental scenario, fully characterized by its control, computational, and networking context.

This dataset is intended to support reproducible research in edge control systems, autoscaling strategies, and data-driven resource management. It can be used for empirical performance evaluation, comparative analysis of autoscaling policies, and as a benchmark for machine learning methods such as risk-aware regression, quantile prediction, and offline reinforcement learning. By providing a unified and well-documented experimental dataset, this work aims to facilitate further research on scalable control architectures and intelligent resource management in edge computing environments.
DOI: 10.17632/b8r6rpv8ch.1
title: Experimental Dataset for Control-Aware Autoscaling of Shared Controllers in Edge Kubernetes Systems

description: This dataset provides a comprehensive experimental benchmark for the study of control-aware autoscaling of shared controllers in edge computing environments based on Kubernetes. It contains aggregated measurements from a large set of controlled experiments in which multiple Industrial Internet of Things (IIoT) devices share a limited number of fuzzy control services deployed as scalable Kubernetes pods. The experiments systematically vary the number of devices, the number of shared controllers, and the operating regime of the controlled systems, enabling the analysis of trade-offs between control performance, temporal stability, and computational resource usage.

The dataset covers three representative classes of control systems with distinct dynamics: a DC motor (fast and delay-sensitive dynamics), a level control tank (slow and naturally damped dynamics), and a vehicle speed control system (high-inertia dynamics). For each plant, multiple operating setpoints were evaluated, and for each setpoint the number of active IIoT devices and shared controllers was varied across a wide range. The dataset includes key metrics such as the integrated absolute error (IAE), the 90th percentile of control loop time and request delay, CPU usage of the control services, and network traffic statistics. Each row in the dataset corresponds to a complete experimental scenario, fully characterized by its control, computational, and networking context.

This dataset is intended to support reproducible research in edge control systems, autoscaling strategies, and data-driven resource management. It can be used for empirical performance evaluation, comparative analysis of autoscaling policies, and as a benchmark for machine learning methods such as risk-aware regression, quantile prediction, and offline reinforcement learning. By providing a unified and well-documented experimental dataset, this work aims to facilitate further research on scalable control architectures and intelligent resource management in edge computing environments.
DOI: 10.17632/y2ybbhztk2
title: Dataset S1-Successive planting changes Soil physical and chemical properties, Enzymatic Activities, and Microbial Communities in Chinese fir(Cunninghamia lanceolata) plantations in China

description: Dataset S1-Successive planting changes Soil physical and chemical properties, Enzymatic Activities, and Microbial Communities in Chinese fir(Cunninghamia lanceolata) plantations in China
DOI: 10.17632/y2ybbhztk2.1
title: Dataset S1-Successive planting changes Soil physical and chemical properties, Enzymatic Activities, and Microbial Communities in Chinese fir(Cunninghamia lanceolata) plantations in China

description: Dataset S1-Successive planting changes Soil physical and chemical properties, Enzymatic Activities, and Microbial Communities in Chinese fir(Cunninghamia lanceolata) plantations in China
DOI: 10.17632/p8cc46znd4
title: Homeland

description: The Homeland dataset is a game-based personalized dialogue dataset built to overcome the limitations of existing counterparts, such as narrow persona types and shallow dialogues. It features self-created global personas (18 structured/unstructured types with unique PIDs) from subjective and objective dimensions, with dialogues collected via random free interaction without mandatory persona use. The dataset includes high-reliability manual local persona annotations (92.02% inter-annotation consistency), plus multi-dimensional labels for emotion, topic and dialogue quality. Comparative analysis shows it has more dialogue turns, a realistic 44.00% local persona occurrence ratio, and tends to in-depth discussion of core persona types like identity, with scalable collection potential for future expansion.
Here we provide some data samples.
DOI: 10.17632/p8cc46znd4.2
title: Homeland

description: The Homeland dataset is a game-based personalized dialogue dataset built to overcome the limitations of existing counterparts, such as narrow persona types and shallow dialogues. It features self-created global personas (18 structured/unstructured types with unique PIDs) from subjective and objective dimensions, with dialogues collected via random free interaction without mandatory persona use. The dataset includes high-reliability manual local persona annotations (92.02% inter-annotation consistency), plus multi-dimensional labels for emotion, topic and dialogue quality. Comparative analysis shows it has more dialogue turns, a realistic 44.00% local persona occurrence ratio, and tends to in-depth discussion of core persona types like identity, with scalable collection potential for future expansion.
Here we provide some data samples.
DOI: 10.17632/zbs38hxpnr
title: Distinct Immuno-Redox Profiles Associated with Heroin and Heroin–Methamphetamine Co-Use: A Pilot Study in Sri Lanka

description: This dataset contains anonymized clinical, biochemical, immunological, and sociodemographic data collected for a comparative cross-sectional study conducted among heroin addicts, heroin–methamphetamine co-users, and non-drug-using control participants in Sri Lanka. The dataset includes questionnaire-based variables covering demographic characteristics, substance use history, treatment history, social support indicators, and DAST-10 scores. Biochemical and immunological measurements include serum interleukin-4 (IL-4) and interleukin-6 (IL-6) levels, nitric oxide metabolites (NOx) as indicators of oxidative stress, and total antioxidant capacity assessed using the ABTS radical scavenging assay. All personal identifiers were removed prior to data deposition to ensure participant confidentiality. The dataset supports the statistical and machine learning analyses reported in the associated research article and may be reused for academic and non-commercial research related to substance abuse, inflammation, oxidative stress, and public health.