cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/455t348yxk.1
title: Data for Doctoral Thesis (J. HORŇAS): A Machine Learning Approach for Fatigue Life Prediction of Additively Manufactured Metal Parts

description: Title of Doctoral Thesis: A Machine Learning Approach for Fatigue Life Prediction of Additively Manufactured Metal Parts
Author: Jan HORŇAS

Affiliation:
Czech Technical University in Prague
Faculty of Nuclear Sciences and Physical Engineering
Trojanova 13, 12000 Prague, Czechia

The root directory contains a README.pdf file describing the datasets, along with several Comma-Separated Values (CSV) files corresponding to the experimental raw dataset, training (baseline or augmented) and test sets for each permutation (P1, ..., P5), see Tab. 1 in README.pdf. The structure is as follows:

 /
 ├── README.pdf
 ├── 00_Experimental_Raw_Dataset/
 ├── 01_Baseline_Dataset/
 ├── 02_Augmented_Dataset_8_instances/
 ├── 03_Augmented_Dataset_23_instances/
 └── 04_Augmented_Dataset_69_instances/
 ├── X_Scaled_Augmented_Training_Set_P1_69_instances.csv
 ├── X_Scaled_Experimental_Test_Set_P1.csv
 ├── X_Scaled_Experimental_Training_Set_P1.csv
 ├── y_Augmented_Training_Set_P1_69_instances.csv
 ├── y_Experimental_Test_Set_P1.csv
 ├── y_Experimental_Training_Set_P1.csv
 ├── ...
 ├── X_Scaled_Augmented_Training_Set_P5_69_instances.csv
 ├── X_Scaled_Experimental_Test_Set_P5.csv
 ├── X_Scaled_Experimental_Training_Set_P5.csv
 ├── y_Augmented_Training_Set_P5_69_instances.csv
 ├── y_Experimental_Test_Set_P5.csv
 └── y_Experimental_Training_Set_P5.csv

The final training datasets used in doctoral thesis are formed by concatenating the experimental and augmented data (generated by variational autoencoder) as described by (Eq. 1) and (Eq. 2) in the README.pdf file.

The labels of individual columns—representing features (X) and target(y)—in the CSV files are denoted as listed in Tab. 2 in README.pdf file.


---------- IF ANY DATASET IS USED, PLEASE CITE ALL RELATED REFERENCES LISTED BELOW ----------

[1] Jan Horňas. “A Machine Learning Approach for Fatigue Life Prediction of Additively Manufactured
Metal Parts”. Doctoral thesis. Prague: Czech Technical University in Prague, 2025. 
URL: https://hdl.handle.net/10467/177291.

[2] Jan Horňas et al. “Multivariate interpolation and machine learning models for extreme defects-based
fatigue life prediction of Ti6Al4V specimens fabricated by SLM”. In: Engineering Fracture Mechanics
314 (2025). ISSN: 0013-7944. DOI: https://doi.org/10.1016/j.engfracmech.2024.110756.

[3] Jan Horňas et al. “A machine learning based approach with an augmented dataset for fatigue life
prediction of additively manufactured Ti-6Al-4V samples”. In: Engineering Fracture Mechanics 293
(2023). ISSN: 0013-7944. DOI: https://doi.org/10.1016/j.engfracmech.2023.109709.

[4] Jan Horňas et al. “Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples
using machine learning approach”. In: International Journal of Fatigue 169 (2023). ISSN: 0142-1123.
DOI: https://doi.org/10.1016/j.ijfatigue.2022.107483.
DOI: 10.17632/4d4j3cgxv9.2
title: Material for "Compassion Over Warmth: AI Communication Style and Advice Utilization in High-Stakes Decision-Making"

description: This dataset contains experimental data from two studies examining how AI communication style (Control/Neutral, Warm, Compassionate) influences advice utilization in high-stakes decision-making contexts.
Study 1 (N = 566) employed a medical treatment decision paradigm where participants chose between cancer treatment options after receiving recommendations from "CareAI," an AI assistant. The study used a between-subjects design with three communication style conditions.
Study 2 (N = 563) employed a financial advisory paradigm where participants estimated retirement portfolio survival probabilities for clients after receiving analysis from "WealthAI." The study used a 3 (Communication Style: between-subjects) × 2 (Task Stakes: within-subjects) mixed design with 16 client scenarios (8 high-stakes, 8 low-stakes).
Variables include:
Demographics (age, gender, education)
Pre-task measures: AI experience, health/financial literacy, trust propensity, risk perception, algorithm aversion, need for cognition
Trial-level behavioral data: initial choices/estimates, final decisions, AI recommendations
Post-task measures: perceived warmth, competence, compassion, perceived risk, affective trust
Computed dependent variable: Weight of Advice (WOA)
DOI: 10.13140/rg.2.2.23514.20162
title: Moluscos ibéricos. Material docente: Modiolus barbatus
DOI: 10.17632/5kz4j52y8w
title: Foreign Affiliate Performance: Independence, Location and Parental control

description: Replication synthetic_data and do-file
DOI: 10.17632/5kz4j52y8w.2
title: Foreign Affiliate Performance: Independence, Location and Parental control

description: Replication synthetic_data and do-file
DOI: 10.17632/mg5d29g5gs
title: DPU-ALDOSKI V2

description: The fundamental hypothesis of this research is that educational buildings, particularly departments with high laboratory density, exhibit a "large idle baseline," where laboratory equipment and systems remain powered to stay in standby mode even during periods of inactivity. Traditional Energy Management Systems (EMS), based on fixed timers or simple rule-based control, lack the proactive thinking necessary to address irregular academic schedules and fluctuating occupancy. Therefore, this study proposes a binary intelligent scheduler based on the Chimpanzee Optimization Algorithm (ChOA) to optimize on/off schedules. The main objective is to demonstrate that AI-driven optimization can reduce avoidable energy waste without compromising established laboratory processes or equipment safety.
The DPU-ALDOSKI dataset provides sub-minute telemetry measurements (at a rate of 30 seconds) capturing voltage, current, active power, and frequency from the Duhok Polytechnic University (DPU) laboratories. Raw fluxes include electrical logs from two institutes: Duhok and Sheikhan. The results demonstrate that the scheduler based on the ChOA algorithm achieved a significant positive reduction in energy consumption across all time horizons of the test. Specifically:
Device 1 (Sheikhan): showed a weekly decrease of 17.21%, bringing the total decrease to 24.01% over an 11-month monitoring period. Device 3 (Duhok): Achieved a weekly decrease of 13.16% and a similar long-term decrease of 24.01%. Operational efficiency: The system successfully reduced "working hours" (power supply time) by 26% for device 1 and by 33% for device 3 by converting idle periods into planned downtime. Statistical significance: Verification using t-tests and the Wilcoxon signed-rank test confirmed that these savings are highly statistically significant, with p-values significantly lower than 10-4.
The data highlights that the savings are time-driven, not load-driven, meaning they result from intelligent scheduling rather than reduced equipment performance or impact on power quality. The 3-sigma control scheme verified that voltage and frequency remained stable throughout the study period, ensuring that the energy savings were not due to external fluctuations. This derived dataset (DPU-ALDOSKI-After) records optimized hourly on/off paths aligned with the original timestamps. It serves as a reusable evaluation framework for building control research, enabling other researchers to benchmark algorithms such as PSO or GA, or test short-term load prediction (STLF) models under the same policy (8:00–21:00) and institutional constraints.
DOI: 10.17632/mg5d29g5gs.1
title: DPU-ALDOSKI V2

description: The fundamental hypothesis of this research is that educational buildings, particularly departments with high laboratory density, exhibit a "large idle baseline," where laboratory equipment and systems remain powered to stay in standby mode even during periods of inactivity. Traditional Energy Management Systems (EMS), based on fixed timers or simple rule-based control, lack the proactive thinking necessary to address irregular academic schedules and fluctuating occupancy. Therefore, this study proposes a binary intelligent scheduler based on the Chimpanzee Optimization Algorithm (ChOA) to optimize on/off schedules. The main objective is to demonstrate that AI-driven optimization can reduce avoidable energy waste without compromising established laboratory processes or equipment safety.
The DPU-ALDOSKI dataset provides sub-minute telemetry measurements (at a rate of 30 seconds) capturing voltage, current, active power, and frequency from the Duhok Polytechnic University (DPU) laboratories. Raw fluxes include electrical logs from two institutes: Duhok and Sheikhan. The results demonstrate that the scheduler based on the ChOA algorithm achieved a significant positive reduction in energy consumption across all time horizons of the test. Specifically:
Device 1 (Sheikhan): showed a weekly decrease of 17.21%, bringing the total decrease to 24.01% over an 11-month monitoring period. Device 3 (Duhok): Achieved a weekly decrease of 13.16% and a similar long-term decrease of 24.01%. Operational efficiency: The system successfully reduced "working hours" (power supply time) by 26% for device 1 and by 33% for device 3 by converting idle periods into planned downtime. Statistical significance: Verification using t-tests and the Wilcoxon signed-rank test confirmed that these savings are highly statistically significant, with p-values significantly lower than 10-4.
The data highlights that the savings are time-driven, not load-driven, meaning they result from intelligent scheduling rather than reduced equipment performance or impact on power quality. The 3-sigma control scheme verified that voltage and frequency remained stable throughout the study period, ensuring that the energy savings were not due to external fluctuations. This derived dataset (DPU-ALDOSKI-After) records optimized hourly on/off paths aligned with the original timestamps. It serves as a reusable evaluation framework for building control research, enabling other researchers to benchmark algorithms such as PSO or GA, or test short-term load prediction (STLF) models under the same policy (8:00–21:00) and institutional constraints.
DOI: 10.17632/djw8bmnd7y
title: De-identified Clinical Dataset of Pediatric PANS and PANDAS: Comorbidities and Symptom Severity (2015–2025)

description: This study examined whether children diagnosed with Pediatric Acute-onset Neuropsychiatric Syndrome (PANS) or Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) commonly have additional psychiatric conditions and whether these conditions are linked to greater symptom severity at diagnosis. We also explored whether age or sex was related to symptom burden.
The dataset includes cleaned, fully de-identified information from 265 unique children aged 3–18 years who were diagnosed with PANDAS (n = 149) or PANS-only (n = 116) at a private outpatient pediatric clinic in Utah between 2015 and 2025. Data were gathered retrospectively from electronic medical records using standardized review procedures. The shared dataset contains demographic information, diagnostic classification, documented psychiatric comorbidities (including attention-deficit/hyperactivity disorder, depression, and autism spectrum disorder), and baseline symptom severity measures.
Obsessive–compulsive symptom severity at presentation was measured using the Children’s Yale–Brown Obsessive-Compulsive Scale (CY-BOCS). Overall neuropsychiatric symptom burden was assessed using a standardized PANS/PANDAS symptom severity scale. All symptom severity values are based on parent-reported assessments recorded at the initial clinical visit.
The data show that psychiatric comorbidities are common among children with PANS and PANDAS, occurring at rates higher than those typically reported in the general pediatric population. Attention-deficit/hyperactivity disorder (ADHD) was frequently observed but was not strongly associated with greater obsessive–compulsive symptom severity at onset. Older age at diagnosis was modestly associated with higher symptom severity, while sex was not meaningfully related to clinical presentation.
These findings suggest that PANS and PANDAS are complex conditions involving broad neuropsychiatric symptoms rather than isolated obsessive–compulsive features. The dataset may be used to explore patterns of comorbidity, relationships between demographic factors and symptom severity, and to generate hypotheses for future longitudinal or mechanistic studies. Because the data come from a referred clinical population, results should be interpreted as descriptive and not representative of all children with these conditions.
DOI: 10.17632/djw8bmnd7y.1
title: De-identified Clinical Dataset of Pediatric PANS and PANDAS: Comorbidities and Symptom Severity (2015–2025)

description: This study examined whether children diagnosed with Pediatric Acute-onset Neuropsychiatric Syndrome (PANS) or Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) commonly have additional psychiatric conditions and whether these conditions are linked to greater symptom severity at diagnosis. We also explored whether age or sex was related to symptom burden.
The dataset includes cleaned, fully de-identified information from 265 unique children aged 3–18 years who were diagnosed with PANDAS (n = 149) or PANS-only (n = 116) at a private outpatient pediatric clinic in Utah between 2015 and 2025. Data were gathered retrospectively from electronic medical records using standardized review procedures. The shared dataset contains demographic information, diagnostic classification, documented psychiatric comorbidities (including attention-deficit/hyperactivity disorder, depression, and autism spectrum disorder), and baseline symptom severity measures.
Obsessive–compulsive symptom severity at presentation was measured using the Children’s Yale–Brown Obsessive-Compulsive Scale (CY-BOCS). Overall neuropsychiatric symptom burden was assessed using a standardized PANS/PANDAS symptom severity scale. All symptom severity values are based on parent-reported assessments recorded at the initial clinical visit.
The data show that psychiatric comorbidities are common among children with PANS and PANDAS, occurring at rates higher than those typically reported in the general pediatric population. Attention-deficit/hyperactivity disorder (ADHD) was frequently observed but was not strongly associated with greater obsessive–compulsive symptom severity at onset. Older age at diagnosis was modestly associated with higher symptom severity, while sex was not meaningfully related to clinical presentation.
These findings suggest that PANS and PANDAS are complex conditions involving broad neuropsychiatric symptoms rather than isolated obsessive–compulsive features. The dataset may be used to explore patterns of comorbidity, relationships between demographic factors and symptom severity, and to generate hypotheses for future longitudinal or mechanistic studies. Because the data come from a referred clinical population, results should be interpreted as descriptive and not representative of all children with these conditions.
DOI: 10.17632/79f2mzwyck
title: gold10ypayments

description: This file contains raw macroeconomic time series used for empirical analysis, including LBMA gold prices and U.S. interest payment data.
The dataset is provided as input data and does not include any transformation, estimation, or model-based output.
DOI: 10.17632/79f2mzwyck.1
title: gold10ypayments

description: This file contains raw macroeconomic time series used for empirical analysis, including LBMA gold prices and U.S. interest payment data.
The dataset is provided as input data and does not include any transformation, estimation, or model-based output.
DOI: 10.17632/ztjj6y34wd
title: Dataset and R Code: Behavioral Mediators of Effective Learning Perception in Multi-generational Contexts.

description: DataSet for Behavioral Mediators of Effective Learning Perception in Multi-generational Contexts article.
DOI: 10.17632/ztjj6y34wd.1
title: Dataset and R Code: Behavioral Mediators of Effective Learning Perception in Multi-generational Contexts.

description: DataSet for Behavioral Mediators of Effective Learning Perception in Multi-generational Contexts article.
DOI: 10.17632/m678cnygwm.1
title: Replication files for -- AI and Perception Biases in Investments: An Experimental Study

description: This file contains code and data for the full replication of "AI and Perception Biases in Investments: An Experimental Study". The readme describes the R and python packages needed to run the code, and some additional information about the tables included in the R and Python code files.
DOI: 10.17632/yn9ptx7mnd
title: Dataset on Suicide Risk, Substance Abuse, and Family Functioning Among University Students in a Health Faculty

description: This dataset derives from a cross-sectional public health study aimed at assessing family functioning, substance use, and suicide risk and protective factors among university students.

File structure

The data file is provided in tabular format (rows = participants; columns = variables) and contains approximately 50 columns, organized into four main sections:

Informed consent: a dichotomous variable documenting voluntary agreement to participate in the study.

Mental and social health assessment instruments: items from the Family APGAR (Apgar_1–Apgar_9), the Drug Abuse Screening Test (DAST-10) (DAST_1–DAST_10), and the Positive and Negative Suicide Ideation Inventory (PANSI) (Pansi_1–Pansi_13).

Sociodemographic, academic, and health insurance variables: gender identity, biological sex, age group, relationship status, health insurance scheme, and academic program.

Derived and classification variables: total scores (Family APGAR score and DAST-10 score) and categorical variables for clinical classification and interpretation (Clas_apgar, Inter_apgar, Clas_DAST10, Inter_DAST10).

Variable naming scheme

Variable names follow a standardized structure to facilitate reuse and interpretation:

Instrument-specific prefixes (Apgar_, DAST_, Pansi_) followed by sequential numbering.

Derived variables identified by descriptive prefixes (Puntaje, Clas_, Inter_).

Sociodemographic variables use concise and consistent naming conventions (genero, sexo, rango_edad, sit_sent, reg_salud, prog_acade).

Variable types and encoding

The dataset includes dichotomous categorical, polytomous ordinal categorical, and discrete quantitative variables. Most instrument items use a five-point ordinal scale numerically coded from 0 to 4, with higher values indicating greater frequency or intensity of the assessed construct.

The file uses UTF-8 character encoding, ensuring proper representation of Spanish-language characters. Missing values are represented by empty cells or instrument-specific codes.
DOI: 10.17632/yn9ptx7mnd.2
title: Dataset on Suicide Risk, Substance Abuse, and Family Functioning Among University Students in a Health Faculty

description: This dataset derives from a cross-sectional public health study aimed at assessing family functioning, substance use, and suicide risk and protective factors among university students.

File structure

The data file is provided in tabular format (rows = participants; columns = variables) and contains approximately 50 columns, organized into four main sections:

Informed consent: a dichotomous variable documenting voluntary agreement to participate in the study.

Mental and social health assessment instruments: items from the Family APGAR (Apgar_1–Apgar_9), the Drug Abuse Screening Test (DAST-10) (DAST_1–DAST_10), and the Positive and Negative Suicide Ideation Inventory (PANSI) (Pansi_1–Pansi_13).

Sociodemographic, academic, and health insurance variables: gender identity, biological sex, age group, relationship status, health insurance scheme, and academic program.

Derived and classification variables: total scores (Family APGAR score and DAST-10 score) and categorical variables for clinical classification and interpretation (Clas_apgar, Inter_apgar, Clas_DAST10, Inter_DAST10).

Variable naming scheme

Variable names follow a standardized structure to facilitate reuse and interpretation:

Instrument-specific prefixes (Apgar_, DAST_, Pansi_) followed by sequential numbering.

Derived variables identified by descriptive prefixes (Puntaje, Clas_, Inter_).

Sociodemographic variables use concise and consistent naming conventions (genero, sexo, rango_edad, sit_sent, reg_salud, prog_acade).

Variable types and encoding

The dataset includes dichotomous categorical, polytomous ordinal categorical, and discrete quantitative variables. Most instrument items use a five-point ordinal scale numerically coded from 0 to 4, with higher values indicating greater frequency or intensity of the assessed construct.

The file uses UTF-8 character encoding, ensuring proper representation of Spanish-language characters. Missing values are represented by empty cells or instrument-specific codes.
DOI: 10.5281/zenodo.18328488
title: CGE model output datasets

description: A collection of results generated by a Computable General Equilibrium (CGE) model, providing macro-economic, sectoral, and distributional insights into water-related policies. These outputs help assess the broader economic impacts of governance actions, tariff adjustments, and resource-management scenarios.

For clarification read notes.txt

Data in each file:

scen01_01     = Water scarcityscen02_01     = Reduced Leakage (result data not displayed on the platform)scen03_01     = Water Price Increasescen04_01     = Sewage Disposalscen05_01     = Oil Price increase
DOI: 10.5281/zenodo.18328489
title: CGE model output datasets

description: A collection of results generated by a Computable General Equilibrium (CGE) model, providing macro-economic, sectoral, and distributional insights into water-related policies. These outputs help assess the broader economic impacts of governance actions, tariff adjustments, and resource-management scenarios.

For clarification read notes.txt

Data in each file:

scen01_01     = Water scarcityscen02_01     = Reduced Leakage (result data not displayed on the platform)scen03_01     = Water Price Increasescen04_01     = Sewage Disposalscen05_01     = Oil Price increase
DOI: 10.17632/czrw8kbvw7
title: Effect of Two Intradialytic Exercise Programs on Heart Rate Variability in Hemodialysis Patients

description: Objective: To determine the effects of a non-immersive virtual reality (NIVR) program versus a conventional intradialytic exercise (CIE) program on heart rate variability (HRV) in individuals with chronic kidney disease (CKD) undergoing hemodialysis (HD).

Materials and Methods: A quasi-experimental study was conducted with 24 ambulatory patients from the HD unit of the Consorci Sanitari de Terrassa, Barcelona, Spain, between August and December 2024. Patients were assigned by convenience to the NIVR or CIE groups. A pre–post assessment was performed for variables related to the time, frequency, and nonlinear domains of HRV using the Polar H10 sensor.

Results: The CIE group demonstrated clinically significant improvements in variables such as the stress index (SI; d = 0.80) and SD2 (d = 0.63), reaching marginal statistical significance for both outcomes (P = 0.07 and P = 0.08, respectively). In contrast, the NIVR group showed small-to-moderate clinical significance in LF (d = 0.77), HF (d = 0.77), and SD2 (d = 0.43), with P &gt; 0.05. A 40% reduction in cardiovascular risk was observed in participants initially classified as high risk in the CIE group (P = 0.02). NIVR was not superior to CIE in improving cardiac autonomic modulation.

Conclusion: CIE yielded more favorable outcomes in cardiac autonomic modulation compared with NIVR. However, given the study limitations, it is not possible to conclusively determine which program provides greater benefits. This research serves as a foundation for future studies designed to address the identified limitations and enable more robust comparisons.