cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bwckvktwdh.5
title: Data for: Theory and Empirical Evidence from Solow Model under the Constant Elasticity of Substitution: Income and Factor Substitution

description: This document includes dataset and codes used in the paper "Theory and Empirical Evidence from Solow Model under the Constant Elasticity of Substitution: Income and Factor Substitution". We use two data files: 1-) data_cross is related to the cross sectional analysis 2-) data_panel is related to the panel data analysis. There are six different stata command text files. 1-) code_cross_6085 replicates cross sectional analysis reported in Table 2 (panel a). 2-) code_cross_6095 is for cross sectional analysis reported in Table 2 (panel b). 3-) code_cross_6010 produces results for cross sectional analysis over the period 1960-2010, which is not reported in the main text. 4-) code_panel_6085 replicates the results reported in Tables 3 and 4 (panel a). 5-) code_panel_6095 produces the results reported in Tables 3 and 4 (panel b). 6-) code_panel_6010 is for panel data analysis reported in Table 5 (panels a, b, c and d). All these stata codes also produce unrestricted model results of Eqs. (8-15) and unrestricted and restricted versions of Solow-CD equations.
DOI: 10.17632/bwckvktwdh.8
title: Data for: Income and Factor Substitution: An Investigation on the Solow Growth Model under the Constant Elasticity of Substitution

description: This document includes dataset and codes used in the paper "Income and Factor Substitution: An Investigation on the Solow Growth Model under the Constant Elasticity of Substitution". 

We use two data files: 

1-) data_cross is for the cross sectional analysis
2-) data_panel is for the panel data analysis.
DOI: 10.34740/kaggle/dsv/9072855
title: AutoXGB
DOI: 10.34740/kaggle/dsv/9072856
title: OnlineFree
DOI: 10.15468/dl.ez9udh
title: Occurrence Download

description: A dataset containing 208675 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Egypt",
 "Geometry POLYGON((31.93391 25.09622,37.61161 25.09622,37.61161 29.88035,31.93391 29.88035,31.93391 25.09622))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset includes 208675 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043148-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66f4vw
title: Occurrence Download

description: A dataset containing 1877 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euproctus montanus (Savi, 1838)"
 ]
}

 The dataset includes 1877 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043154-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7hs4z3fdnj.1
title: POMFRET

description: **Project Title:** Good and Bad Classification of Pomfret Fish (Pampus argenteus) Using Samsung F62 Mobile Camera

**Description:**

The project "Good and Bad Classification of Pomfret Fish (Pampus argenteus)" aims to develop an automated system for distinguishing between healthy (good) and unhealthy (bad) pomfret fish using image classification techniques. The dataset consists of over 600 images, equally divided between good and bad samples. All images were captured using a Samsung F62 mobile camera against a black background in daylight conditions, ensuring consistent and high-quality visual data.

**Dataset Composition:**
- **Good Samples (Healthy):** The dataset includes over 300 images of healthy pomfret fish, exhibiting normal body structure, clear eyes, and vibrant coloration. These images serve as positive samples for training the classification model.
 
- **Bad Samples (Unhealthy):** The dataset also contains over 300 images of unhealthy pomfret fish, showing signs of disease, physical damage, discoloration, or other factors indicating poor health. These images represent the negative class for model training and evaluation.

**Data Collection Setup:**
All images were captured using the Samsung F62 mobile camera to ensure high resolution and consistent image quality. The use of a black background helps to highlight the fish's features and reduce environmental distractions, while daylight conditions provide natural lighting, enhancing the visibility of details necessary for accurate classification.

**Image Characteristics:**
The dataset includes images of pomfret fish with various physical conditions and features. This diversity ensures that the classification model can generalize well and accurately identify the health status of pomfret fish under different conditions.

**Data Annotation:**
Each image is carefully annotated to indicate whether the fish is in a good or bad condition. These annotations serve as the ground truth, essential for training, validating, and testing the machine learning model.

**Data Preprocessing:**
Preprocessing steps include resizing images to a standard resolution, normalizing pixel values, and performing data augmentation techniques such as rotation, flipping, and scaling to enhance the model's robustness. These preprocessing steps help the model learn relevant features and improve its performance on unseen data.

**Applications:**
- **Fisheries Management:** The classification system can assist in monitoring the health of pomfret fish populations, enabling timely interventions to maintain fish health and quality.
- **Seafood Quality Control:** In seafood markets, the model can help in ensuring that only healthy fish are sold to consumers, improving food safety and customer satisfaction.
- **Research:** The dataset and model can be used in research to further explore the health indicators of pomfret fish and improve classification techniques.
DOI: 10.17632/2yy4j7rgvg.1
title: Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis

description: Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. 
Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. 
Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by support vector machine with SMOTE-ENN (AUC = .942; balanced accuracy = .868, sensitivity = .898; specificity = .837). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. 
Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.
DOI: 10.17632/2yy4j7rgvg.2
title: Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis

description: Background: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. 
Methods: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 5-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. 
Results: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = .811; balanced accuracy = .745, sensitivity = .765; specificity = .726). The best predictors of burnout were Beck’s Depression Inventory scores, Athen’s Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. 
Conclusions: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.
DOI: 10.17632/n8bx8d8j2k.1
title: Data and R code for Olk & Miebs: "Re-Centralized Finance: How the cryptocurrency sphere turned into a shadow banking system"

description: The R code downloads the data used for Figure 1 from FRED. It also reads and plots the market cap data for Figure 2. The excel file is the basis of Figure 3.
DOI: 10.17632/n8bx8d8j2k.2
title: Data and R code for Olk & Miebs: "Re-Centralized Finance: How the cryptocurrency sphere turned into a shadow banking system"

description: The R code downloads the data used for Figure 1 from FRED and plots all other Figures.
DOI: 10.17632/4w7sxpg3yy.1
title: Data for gut microbiome and fecal metabolome

description: Data for gut microbiome and fecal metabolome among individuals with overweight or obesity
DOI: 10.17632/tv6w6yyfy8.2
title: Dataset assessing three samples of French university students to 5 personality tests: IPO, PDQ-4+, PANAS, AQ and HADS.

description: This dataset provides the results of three samples of French university students to 5 personality tests: IPO, PDQ-4+, PANAS, AQ and HADS.
The first part of this dataset (Study 1) comprise 602 first- and second-year psychology students (University of Angers, France): 112 men, 486 women, and 4 participants of unspecified gender. This gender ratio is typical of first- and second years psychology students in France. The mean age of the sample is 19.40 years (SD = 1.95). 
This first part of dataset is composed of two separate samples: 269 first-year students (Sample 1 = Study 1 Sample 1.xlsx) and 333 second-year (Sample 2 = Study 1 Sample 2.xlsx).
This first part of this dataset gives the scores obtained by this sample of students to the French version of the Inventory of Personality Organization (Kernberg). In IPO, the level of personality organization is established by assessing three main components of personality, namely primitive Psychological defenses (PD = 16 items), Reality testing (RT= 21 items), and identity diffusion (ID = 20 items). Personality disorders manifest themselves in high levels of one or more of these three dimensions (scales). This data set gives the results obtained by this group of students on these 57 items.
The second part of this dataset (Study 2 = Study 2.xlsx) is consisted of 305 students from different departments (Arts and humanities) at the University of Angers. It is composed of 63 men and 242 women, with a mean age of 19.83 years (SD = 2.12). 
The second part of this dataset offers the results of this sample of students to the three scales (PD/RT/ID) of the French version of the IPO and to the French versions of the following tests: Personality Diagnostic Questionnaire-4+ (PDQ-4+), Positive and Negative Affect Schedule (PANAS), Aggression Questionnaire (AQ), and Hospital Anxiety and Depression Scale (HADS).
These two studies were approved by the research ethics committee of Angers University (November 2021). Data were collected from 26 November 2021 to 19 February 2022. 
The third part of this dataset contains data also collected from November 26, 2021. This study is also approved by the research ethics committee of Angers University (November 2021). Data collection ceased on September 31, 2022. This third study contains (1) responses from 607 French young adults to the French version of the Inventory of Personality Organization (IPO-fr), (2) Responses obtained by the same 607 respondents to the French version of the Personality Diagnostic Questionnaire - Fourth edition + (PDQ-4+): self-report questionnaire and Clinical significance scale and (3) Basic demographic information (gender and age) collected for all respondents. 
Data were collected from men and women aged 18-26 years (mean age 19.38±1.93 SD). 138 of the 170 subjects were women (mean age 19.34±1.88 SD, range 18-26 years) and 32 were men (mean age 19.53±2.17 SD, range 18-25 years).
DOI: 10.17632/tv6w6yyfy8.3
title: Dataset assessing three samples of French university students to 5 personality tests: IPO, PDQ-4+, PANAS, AQ and HADS.

description: This dataset provides the results of three samples of French university students to 5 personality tests: IPO, PDQ-4+, PANAS, AQ and HADS. These three studies were approved by the research ethics committee of Angers University (November 2021). Data were collected from November 26, 2021 to February 19, 2022 for Studies 1 and 2. Data collection for Study 3 ended on September 31, 2022.
1) The first part of this dataset (Study 1) comprise 602 first- and second-year psychology students (University of Angers, France): 112 men, 486 women, and 4 participants of unspecified gender (Mean age = 19.40 years / SD = 1.95). This first part of dataset is composed of two separate samples: 269 first-year students (Sample 1 = Study 1 Sample 1.xlsx) and 333 second-year (Sample 2 = Study 1 Sample 2.xlsx). It gives the scores obtained by this sample of students to the French translation of the Inventory of Personality Organization (Kernberg). In IPO, the level of personality organization is established by assessing three main components of personality, namely Primitive psychological Defenses (PD = 16 items), Identity Diffusion (ID = 20 items), and Reality Testing (RT= 21 items). Personality disorders manifest themselves in high levels of one or more of these three dimensions (scales). This data set gives the results obtained by this group of students on these 57 items. 
The second part of this dataset (Study 2 = Study 2.xlsx) is consisted of 305 students from different departments (Arts and humanities) at the University of Angers. It is composed of 63 men and 242 women, with a mean age of 19.83 years (SD = 2.12). It offers the results of this sample to the three scales (PD/ID/RT) of the French translation of the IPO and to the French versions of the following tests: Personality Diagnostic Questionnaire-4+ (PDQ-4+), Positive and Negative Affect Schedule (PANAS), Aggression Questionnaire (AQ), and Hospital Anxiety and Depression Scale (HADS).
Studies 1 and 2 were used to develop and validate the French version of the IPO in 40 items and 2 scales (PD-ID/RT): the IPO-fr.
In the third part (= Study 3), respondents are 182 first- and second-year students from the Department of Psychology and 425 first- and second-year students from the Departments of History, Language Studies and Physical and Social Geography (Mage = 19.38, SD = 1.93). Study 3 contains (1) Responses from 607 French young adults to each item of the IPO-fr, (2) Responses obtained by the same 607 respondents to the French PDQ-4+ (self-report questionnaire and Clinical significance scale), and (3) Basic demographic information (gender and age) collected for all respondents. For the IPO-fr, scores on the PD/ID and RT scales were calculated by summing participants' scores on the items in each scale. The total score consists of the sum of participants' scores on the PD/ID and RT scales. A codebook containing the French version of the PDQ-4+ and the IPO-fr is included in this dataset.
DOI: 10.6084/m9.figshare.26362416
title: Targeted drug delivery to the thrombus by fusing streptokinase with a fibrin-binding peptide (CREKA): an <i>in silico</i> study

description: <b>Aim:</b> Streptokinase has poor selectivity and provokes the immune response. In this study, we used <i>in silico</i> studies to design a fusion protein to achieve targeted delivery to the thrombus. <b>Materials & methods:</b> Streptokinase was analyzed computationally for mapping. The fusion protein modeling and quality assessment were carried out on several servers. The enzymatic activity and the stability of the fusion protein and its complex with plasminogen were assessed through molecular docking analysis and molecular dynamics simulation respectively. <b>Results:</b> Physicochemical properties analysis, protein quality assessments, protein–protein docking and molecular dynamics simulations predicted that the designed fusion protein is functionally active. <b>Conclusion:</b> Our results showed that this fusion protein might be a prospective candidate as a novel thrombolytic agent with better selectivity. Thrombolytic agents are categorized into two classes based on their mechanisms: eukaryotic serine proteases and prokaryotic proteins like streptokinase (SK), which forms binary complexes with plasmin(ogen). Current SK versions lack selectivity, leading to non-specific activation of fibrin and systemic hemorrhage, highlighting the need for targeted drug delivery to thrombus sites. Peptides offer promise for fibrin targeting due to their small size, ease of manufacturing and decreased immunogenicity, with CREKA (Cys-Arg-Glu-Lys-Ala) specifically identifying fibrin–fibronectin complexes. <i>In silico</i> research presents a valuable strategy for molecular design and optimization, allowing for the evaluation of fusion proteins like CREKA-SK for targeted thrombolysis. This study aims to design and assess the efficacy of a fusion protein comprising CREKA and SK for targeted thrombolysis, leveraging <i>in silico</i> techniques for modeling and quality assessment of SK's 3D structure, docking simulations with its natural target, and molecular dynamics (MD) simulations to assess stability and flexibility. Extensive mapping of SK was conducted, incorporating experimental data on its structural domains and interaction sites. This mapping provided critical insights into identifying regions suitable for fusion with the fibrin-binding peptide (CREKA). Molecular modeling and analysis revealed the successful fusion of CREKA peptide with SK at region 302, with computational assessments confirming the stability and integrity of the fused protein. The development of targeted thrombolytic agents like CREKA-SK holds promise for improving clinical responses in thrombosis management, potentially reducing adverse effects associated with non-specific fibrinolysis.
DOI: 10.6084/m9.figshare.26362416.v1
title: Targeted drug delivery to the thrombus by fusing streptokinase with a fibrin-binding peptide (CREKA): an <i>in silico</i> study

description: <b>Aim:</b> Streptokinase has poor selectivity and provokes the immune response. In this study, we used <i>in silico</i> studies to design a fusion protein to achieve targeted delivery to the thrombus. <b>Materials & methods:</b> Streptokinase was analyzed computationally for mapping. The fusion protein modeling and quality assessment were carried out on several servers. The enzymatic activity and the stability of the fusion protein and its complex with plasminogen were assessed through molecular docking analysis and molecular dynamics simulation respectively. <b>Results:</b> Physicochemical properties analysis, protein quality assessments, protein–protein docking and molecular dynamics simulations predicted that the designed fusion protein is functionally active. <b>Conclusion:</b> Our results showed that this fusion protein might be a prospective candidate as a novel thrombolytic agent with better selectivity. Thrombolytic agents are categorized into two classes based on their mechanisms: eukaryotic serine proteases and prokaryotic proteins like streptokinase (SK), which forms binary complexes with plasmin(ogen). Current SK versions lack selectivity, leading to non-specific activation of fibrin and systemic hemorrhage, highlighting the need for targeted drug delivery to thrombus sites. Peptides offer promise for fibrin targeting due to their small size, ease of manufacturing and decreased immunogenicity, with CREKA (Cys-Arg-Glu-Lys-Ala) specifically identifying fibrin–fibronectin complexes. <i>In silico</i> research presents a valuable strategy for molecular design and optimization, allowing for the evaluation of fusion proteins like CREKA-SK for targeted thrombolysis. This study aims to design and assess the efficacy of a fusion protein comprising CREKA and SK for targeted thrombolysis, leveraging <i>in silico</i> techniques for modeling and quality assessment of SK's 3D structure, docking simulations with its natural target, and molecular dynamics (MD) simulations to assess stability and flexibility. Extensive mapping of SK was conducted, incorporating experimental data on its structural domains and interaction sites. This mapping provided critical insights into identifying regions suitable for fusion with the fibrin-binding peptide (CREKA). Molecular modeling and analysis revealed the successful fusion of CREKA peptide with SK at region 302, with computational assessments confirming the stability and integrity of the fused protein. The development of targeted thrombolytic agents like CREKA-SK holds promise for improving clinical responses in thrombosis management, potentially reducing adverse effects associated with non-specific fibrinolysis.