cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/c4h3zk69p5.1
title: Embryonic In Ovo Stimulation-Supplementary Tables and Supplementary Figure

description: Supplementary Tables (Table S)
Table S1A: MultiQC_FastQC Analysis of Taxonomic Composition in the Microbial Communities of Chicken Caecum from in Ovo Simulation.
Table S1B: Sequencing Data Summary: Raw, Trimmed, and Final Paired Reads for Microbial Composition Analysis.
Table S1C: All statistics are based on contigs of size &gt;= 500 bp, unless otherwise noted (e.g., "# contigs (&gt;= 0 bp)" and "Total length (&gt;= 0 bp)" include all contigs).
Table S2: Taxonomic Composition Identified from Sequencing Data: Raw, Trimmed, and Final Paired Reads of chicken guts obtained from the in ovo simulation with the five treatments.
Table S3: Transcriptome sequencing statistics among the 30 ileum samples (5 groups × 6 repeats; groups comprised negative control (NC), positive control (PC), Quercetin (Q), Vitamin E (VE) and Combined use of Q and VE (Q+VE)) obtained from in ovo embryonic injection chickens with the NC, PC, Q, VE and Q+VE. 
Table S4A: Differential expression genes (DEGs) between Positive control group (PC) and Negative control group (NC), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). Those of |log2 (Fold Change) with - and + values were down- and up-regulated in PC and NC, respectively.
Table S4B: Differential expression genes (DEGs) between Positive control group (PC) and Quercetin+Vitamin E (Q+VE), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). Those of |log2 (Fold Change) with - and + values were down- and up-regulated in Q+VE and PC, respectively.
Table S4C: Differential expression genes (DEGs) between Negative control group (NC) and Quercetin+Vitamin E (Q+VE), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). Those of |log2 (Fold Change) with - and + values were down- and up-regulated in NC and Q+VE, respectively.
Table S4D: Differential expression genes (DEGs) between Positive control group (PC) and Quercetin group (Q), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). Those of |log2 (Fold Change) with - and + values were down- and up-regulated in PC and Q, respectively.
Table S4E: Differential expression genes (DEGs) between Positive control group (NC) and Vitamin E group (VE), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). Those of |log2 (Fold Change) with - and + values were down- and up-regulated in VE and PC, respectively.
Table S4F: Differential expression genes (DEGs) between Negative control group (NC) and Quercetin (Q), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs). 
Table S4G: Differential expression genes (DEGs) between Negative control group (NC) and Vitamin E (VE), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs).
Table S4H: Differential expression genes (DEGs) between Quercetin (Q) and Vitamin E (VE), genes with|log2 (Fold Change) | &gt; 1 & padj &lt; 0.05 were considered (DEGs).
DOI: 10.17632/64cy9j8tbc
title: Dados_Simulados_Pausa_Ativa_Estresse_Fadiga_Concentracao_UFPA.csv

description: Efeitos da Pausa Ativa em Ambiente Verde nos Níveis de Estresse, Fadiga e Concentração de Servidores Técnicos da Universidade Federal do Pará: Uma Abordagem Multimétodo.
 Objetivos: Avaliar os efeitos de uma pausa ativa realizada em ambiente verde sobre os níveis de estresse, fadiga e concentração de servidores técnicos da UFPA, utilizando medidas subjetivas (questionários) e objetivas (análise pupilar e testes cognitivos).
DOI: 10.17632/64cy9j8tbc.1
title: Dados_Simulados_Pausa_Ativa_Estresse_Fadiga_Concentracao_UFPA.csv

description: Efeitos da Pausa Ativa em Ambiente Verde nos Níveis de Estresse, Fadiga e Concentração de Servidores Técnicos da Universidade Federal do Pará: Uma Abordagem Multimétodo.
 Objetivos: Avaliar os efeitos de uma pausa ativa realizada em ambiente verde sobre os níveis de estresse, fadiga e concentração de servidores técnicos da UFPA, utilizando medidas subjetivas (questionários) e objetivas (análise pupilar e testes cognitivos).
DOI: 10.17632/pwr5zzw64n.3
title: Survey Data to Evaluate Faculty Motivation, Management Support and Faculty Engagement on the Performance of Higher Educational Institutions

description: A survey was conducted to capture the antecedents of the performance of Higher Educational Institutions in Bhubaneswar, Cuttack, Ganjam, Puri and Sambalpur in the Odisha state of India. 436 faculty members from 55 higher educational institutions were contacted to elicit survey data on three exogenous variables namely, managerial support, faculty motivation, faculty engagement and the exogenous variable HEI performance. Data from the paper-based questionnaire survey was analyzed to evaluate direct and indirect causal relationships using SmartPLS 4.0 software.
DOI: 10.17632/kk49s4zjgz.3
title: Betting against the odds? The effect of mental health on problem gambling behavior

description: This study examines mental health as a risk factor for problem gambling using a longitudinal data from Australia. We hypothesize that poor mental health increases the probability of engaging in problem gambling. We find that poor mental health can increase the likelihood of problem gambling. Our findings highlight the importance of prioritizing mental health alongside, or even above, changes to gambling regulations.

This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The findings and views reported in this paper, however, are those of the authors and should not be attributed to either FaHCSIA or the Melbourne Institute. 

Please note that, according to the user agreement, it is strictly prohibited to match, share, link, integrate, or merge HILDA data with other datasets without explicit permission from the Australian Government Department of Social Services (DSS). Therefore, the dataset cannot be shared under any circumstances. Researchers can request access to the HILDA data through the Australian Data Archive Dataverse platform. The authors provide STATA do-files for analysis: (1) a do-file to clean the data and generate the variables used in the analysis; and (2) a do-file to reproduce the tables presented in the paper.
DOI: 10.17632/5nvsv7ypg4.1
title: Student Performance Bangladesh

description: This dataset has been collected to support research on predicting the academic performance of Secondary School Certificate (SSC) and Higher Secondary Certificate (HSC) students in Bangladesh. It comprises responses from many students across various institutions in the country.

The dataset includes a diverse set of features that are believed to influence academic outcomes. These features cover a wide range of domains such as:

Demographic Information: Age, gender, parental education, and occupation.

Academic History: Previous grades, subject preferences, study time, tutoring, etc.

Socioeconomic Factors: Family income, number of siblings, living location (urban/rural).

Institutional Factors: Type of school/college (public/private), distance from home, teacher-student ratio, etc.

Lifestyle and Behavioral Aspects: Sleep habits, screen time, daily routines, mental health indicators, and parental support.

The dataset is labeled with the actual academic performance (grades or GPA) of students in SSC and HSC examinations. The goal is to facilitate the development of predictive models and interpretability studies, with a focus on early intervention and academic counseling.

The dataset is anonymized and free from personally identifiable information. It is intended for academic research, education policy analysis, and machine learning experimentation
DOI: 10.17632/3r8hw8wmmk
title: Alzheimer MRI Preprocessed Dataset

description: The data is collected from several websites, hospitals, and public repositories. The datasetconsists of preprocessed MRI (magnetic resonance imaging) images. All the images are resized to 128 x 128 pixels. The dataset has four classes of images.
The Dataset is consists of a total of 6400 MRI images. 
Class - 1: Mild Demented (896 images)
Class - 2: Moderate Demented (64 images)
Class - 3: Non Demented (3200 images)
Class - 4: Very Mild Demented (2240 images)

Motive The main motive behind sharing this dataset is to design/develop an accurate framework or architecture for the classification of Alzheimer's disease.
DOI: 10.17632/3r8hw8wmmk.1
title: Alzheimer MRI Preprocessed Dataset

description: The data is collected from several websites, hospitals, and public repositories. The datasetconsists of preprocessed MRI (magnetic resonance imaging) images. All the images are resized to 128 x 128 pixels. The dataset has four classes of images.
The Dataset is consists of a total of 6400 MRI images. 
Class - 1: Mild Demented (896 images)
Class - 2: Moderate Demented (64 images)
Class - 3: Non Demented (3200 images)
Class - 4: Very Mild Demented (2240 images)

Motive The main motive behind sharing this dataset is to design/develop an accurate framework or architecture for the classification of Alzheimer's disease.
DOI: 10.17632/fpxkskt86z
title: PGAE-ICA: A simplified digital system for intellectual measurement-assessment in children and adolescents using cognitive testing and machine learning techniques

description: A total of 103 participants aged 9 to 17, were recruited for this study, including 39 with abnormal intelligence and 64 with normal intelligence. Participants completed the Chinese Wechsler Intelligence Scale for Children and primary cognitive ability tests in a counterbalanced order.
DOI: 10.17632/fpxkskt86z.1
title: PGAE-ICA: A simplified digital system for intellectual measurement-assessment in children and adolescents using cognitive testing and machine learning techniques

description: A total of 103 participants aged 9 to 17, were recruited for this study, including 39 with abnormal intelligence and 64 with normal intelligence. Participants completed the Chinese Wechsler Intelligence Scale for Children and primary cognitive ability tests in a counterbalanced order.
DOI: 10.17632/64gdkh8t2t.1
title: Supplementary material

description: This dataset supports the study on the development of a robust and eco-friendly biocatalyst based on thermostable β-glucosidase immobilized on linen fabric. The β-glucosidase enzyme was immobilized using a covalent periodate-based method, with ethylenediamine-glutaraldehyde as a spacer to enhance performance. Two biocatalyst systems were developed and tested (Lf-β-glucosidase and LfEG-β-glucosidase). This dataset supports the study on the characterization of thermostable β-glucosidase immobilized on linen fabric for sustainable lactose hydrolysis and whey valorization. The supplementary materials include figures and tables related to enzyme purification and kinetic analysis. Supplementary Figure 1 presents the results of nickel affinity chromatography used for enzyme purification. Supplementary Figures 2 and 3 show the kinetic analysis of Lf-β-glucosidase. Supplementary Table 1 provides detailed kinetic and inhibitory parameters for both the free and immobilized forms of the enzyme. These data demonstrate the functional properties of the immobilized biocatalyst and support its potential for repeated use in dairy bioprocessing applications. All supplementary content corresponds to data referenced in the main manuscript.
DOI: 10.17632/yyswmw648t
title: Supplementary material for the research paper titled "Congo red dye removal using green synthesized titanium oxide nanoparticles using saponin biosurfactant derived from Sapindus mukorossi"

description: The data presents the Congo Red dye removal efficiency using synthesized biogenic TiO₂ nanoparticles derived from saponin biosurfactant of Sapindus mukorossi. The study investigates the effect of varying adsorbent dosages (1, 2, 2.5, and 5 g/L) and different initial dye concentrations ranging from 50 ppm to 150 ppm. The experiments were designed to evaluate the adsorption capacity and efficiency of biogenic TiO₂ under these varying conditions. The results are compiled and presented in tabulated form.
DOI: 10.17632/yyswmw648t.4
title: Supplementary material for the research paper titled "Congo red dye removal using green synthesized titanium oxide nanoparticles using saponin biosurfactant derived from Sapindus mukorossi"

description: The data presents the Congo Red dye removal efficiency using synthesized biogenic TiO₂ nanoparticles derived from saponin biosurfactant of Sapindus mukorossi. The study investigates the effect of varying adsorbent dosages (1, 2, 2.5, and 5 g/L) and different initial dye concentrations ranging from 50 ppm to 150 ppm. The experiments were designed to evaluate the adsorption capacity and efficiency of biogenic TiO₂ under these varying conditions. The results are compiled and presented in tabulated form.
DOI: 10.17632/3hnjp4kwmc
title: Artificial Intelligence and the Future of Supply Chain Entrepreneurship: A Bibliometric Analysis of Business Trends and Innovations

description: Data Artificial Intelligence and the Future of Supply Chain Entrepreneurship: A Bibliometric Analysis of Business Trends and Innovations
DOI: 10.17632/3hnjp4kwmc.1
title: Artificial Intelligence and the Future of Supply Chain Entrepreneurship: A Bibliometric Analysis of Business Trends and Innovations

description: Data Artificial Intelligence and the Future of Supply Chain Entrepreneurship: A Bibliometric Analysis of Business Trends and Innovations
DOI: 10.17632/mckpt7gfd5
title: Spectroscopic data of the 4-Amino-3-chloro-coumarin and 4-Amino-3-bromo-coumarin

description: This article presents the ¹H NMR, ¹³C NMR, and LC–MS data for 4-amino-3-bromo-coumarin (Figures 1,2 and 3) and 4-amino-3-chloro-coumarin (Figures 4, 5 and 6). The ¹H and ¹³C NMR spectra were obtained by dissolving the compounds in deuterated DMSO-d₆ and analysing them using a Bruker 400 MHz instrument equipped with Topspin software. LC–MS analysis was conducted by dissolving the compounds in acetonitrile and analysing them with a Shimadzu LCMS instrument. These spectral data are valuable for comparison with structurally related molecules containing a coumarin backbone.
DOI: 10.17632/mckpt7gfd5.3
title: Spectroscopic data of the 4-Amino-3-chloro-coumarin and 4-Amino-3-bromo-coumarin

description: This article presents the ¹H NMR, ¹³C NMR, and LC–MS data for 4-amino-3-bromo-coumarin (Figures 1,2 and 3) and 4-amino-3-chloro-coumarin (Figures 4, 5 and 6). The ¹H and ¹³C NMR spectra were obtained by dissolving the compounds in deuterated DMSO-d₆ and analysing them using a Bruker 400 MHz instrument equipped with Topspin software. LC–MS analysis was conducted by dissolving the compounds in acetonitrile and analysing them with a Shimadzu LCMS instrument. These spectral data are valuable for comparison with structurally related molecules containing a coumarin backbone.
DOI: 10.17632/gsn389zzws
title: Synthesis and characterization of Gelatin-Hydroxyapatite nanohybrid composites

description: Abstract
Gelatin-based nanocomposites have gained significant attention as sustainable and multifunctional biomaterials due to their natural origin, biocompatibility, and tuneable properties, making them strong candidates for advanced structural applications, such as matrix fillers and functional coatings in porous substrates like leather. But, pure gelatin suffers from limited mechanical strength, thermal instability, and rapid solubility in aqueous environments, necessitating structural reinforcement. Nano-calcium hydroxyapatite (CaP) is a well-established inorganic additive that enhances the mechanical, thermal, and structural performance of gelatin matrices; however, its tendency to agglomerate in gelatin limits uniform dispersion, and consequently restricts its reinforcement capabilities. This study focuses on the synthesis and characterization of a novel nanohybrid composite, integrating gelatin nano-emulsion as the biopolymer matrix with different weight ratios of nano-CaP colloidal suspension (CaP10% and CaP20%) as the reinforcing phase. Various characterization techniques were used to evaluate the influence of nano-CaP on interfacial interaction and effect on microstructure. Fourier transform infrared spectroscopy and X-ray diffraction results show calcium hydroxyapatite as the main phase (75%) in the nanohybrid composite while scanning electron microscope confirmed its intimate contact and homogenous dispersion. The 10% CaP loading provided a better physical and thermo response, including a higher glass transition temperature (220 °C), and increased hydrophobicity (45% increase in water contact angle). These enhancements in properties are attributed to the homogenous dispersion of nano-CaP in the nanogelatin matrix and the strong interfacial interaction between the particle surfaces and the nanogelatin molecule. Overall, the results demonstrate the potential of colloidal nano-CaP suspension as a dual-function agent for cross-linking and thermo-mechanical reinforcement in gelatin-based nanocomposites.
DOI: 10.17632/gsn389zzws.1
title: Synthesis and characterization of Gelatin-Hydroxyapatite nanohybrid composites

description: Abstract
Gelatin-based nanocomposites have gained significant attention as sustainable and multifunctional biomaterials due to their natural origin, biocompatibility, and tuneable properties, making them strong candidates for advanced structural applications, such as matrix fillers and functional coatings in porous substrates like leather. But, pure gelatin suffers from limited mechanical strength, thermal instability, and rapid solubility in aqueous environments, necessitating structural reinforcement. Nano-calcium hydroxyapatite (CaP) is a well-established inorganic additive that enhances the mechanical, thermal, and structural performance of gelatin matrices; however, its tendency to agglomerate in gelatin limits uniform dispersion, and consequently restricts its reinforcement capabilities. This study focuses on the synthesis and characterization of a novel nanohybrid composite, integrating gelatin nano-emulsion as the biopolymer matrix with different weight ratios of nano-CaP colloidal suspension (CaP10% and CaP20%) as the reinforcing phase. Various characterization techniques were used to evaluate the influence of nano-CaP on interfacial interaction and effect on microstructure. Fourier transform infrared spectroscopy and X-ray diffraction results show calcium hydroxyapatite as the main phase (75%) in the nanohybrid composite while scanning electron microscope confirmed its intimate contact and homogenous dispersion. The 10% CaP loading provided a better physical and thermo response, including a higher glass transition temperature (220 °C), and increased hydrophobicity (45% increase in water contact angle). These enhancements in properties are attributed to the homogenous dispersion of nano-CaP in the nanogelatin matrix and the strong interfacial interaction between the particle surfaces and the nanogelatin molecule. Overall, the results demonstrate the potential of colloidal nano-CaP suspension as a dual-function agent for cross-linking and thermo-mechanical reinforcement in gelatin-based nanocomposites.
DOI: 10.17632/6p3mhnwmgm.1
title: Nigeria's AfT-Export Dataset

description: This dataset contains panel data used to analyse the impact of Aid for Trade (AfT) on Nigeria’s agricultural export performance from 2005 to 2018. The data is structured at the bilateral level, capturing Nigeria’s trade with multiple partner countries over time. It includes detailed information on bilateral exports, disaggregated Aid for Trade flows (total, economic infrastructure, productive capacity, and trade policy), control variables such as GDP, distance, real and agricultural production, as well as dummy variables capturing trade agreements, linguistic ties, and regional integration (e.g. ECOWAS membership).

The dataset supports fixed effects gravity model estimations and includes relevant economic and institutional indicators drawn from a range of sources including UN COMTRADE, OECD CRS, World Bank World Development Indicators (WDI), CEPII, and the WTO.

This dataset is made available for academic and research purposes only. While every effort has been made to ensure the accuracy and consistency of the data, the compilers make no warranties regarding its completeness, reliability, or fitness for a particular purpose. Users are advised to consult original data sources (e.g. UN COMTRADE, OECD CRS, World Bank WDI, CEPII, WTO) for official statistics and definitions.
DOI: 10.17632/9p8jb3zrnb
title: Surrogate Model-Assisted Machine Learning Optimization of Chemical Vapor Deposition for Enhanced Film Deposition Rate and Uniformity

description: Research Hypothesis:
This research hypothesizes that a machine learning–assisted optimization framework can significantly enhance the efficiency and accuracy of optimizing key performance indicators—film deposition rate and thickness uniformity—in a Chemical Vapor Deposition (CVD) system. By leveraging XGBoost-predicted outputs and constructing surrogate models, it is possible to identify optimal process conditions with substantially reduced computational cost compared to direct CFD-based optimization.

What the Data Shows:
The dataset contains the predicted results from a pre-trained XGBoost regression model applied to a set of test cases derived from CFD simulations of a vertical CVD reactor. Each data point includes the input parameters (susceptor temperature and inlet gas velocity) and the corresponding model-predicted deposition rate and uniformity. These predictions were used to develop second-order polynomial surrogate models that accurately represent the relationship between the process inputs and outputs. The dataset supports three optimization scenarios: (i) maximizing deposition rate under a uniformity constraint, (ii) minimizing non-uniformity under a minimum deposition rate constraint, and (iii) maximizing the deposition-to-uniformity ratio.

Notable Findings:

XGBoost provided the most accurate predictions among tested ML models when compared with CFD simulation results, demonstrating high fidelity in capturing nonlinear behaviors of the CVD process.

The polynomial surrogate models achieved high R² scores and enabled rapid optimization using the Sequential Least Squares Programming (SLSQP) algorithm.

Optimal process windows were identified with improved uniformity and deposition performance, outperforming traditional optimization approaches in both accuracy and computational speed.

Description and Interpretation:
The dataset includes input parameters (temperature and velocity), XGBoost-predicted outputs (deposition rate and uniformity), and results from surrogate model-based optimization for each scenario. The data is structured in a CSV/Excel file format, with clearly labeled columns for inputs, outputs, and optimization metrics. The surrogate models were derived using polynomial regression and validated against the original XGBoost predictions.

This dataset can be interpreted to understand how changes in reactor operating conditions affect deposition characteristics and how machine learning can streamline optimization in CVD applications. It is especially useful for researchers developing intelligent control systems, digital twin frameworks, or data-driven design tools for thin-film fabrication. The surrogate models included enable rapid response surface analysis and can be integrated into real-time optimization pipelines for smart manufacturing.