cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.pck4gz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
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 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075381-240626123714530/datasets/export for details.
DOI: 10.17632/mf82zr45pt.1
title: Data for: An improved discrete Harris Hawks optimization algorithm for the no-wait job shop problem to minimize total weighted tardiness

description: Data for: An improved discrete Harris Hawks optimization algorithm for the no-wait job shop problem to minimize total weighted tardiness
DOI: 10.17632/8w2mmk5smf.1
title: Parabacteroides johnsonii Inhibits the Onset and Progression of Colorectal Cancer by Modulating the Gut Microbiota

description: Colorectal cancer (CRC) is the third most prevalent malignant tumor and the second leading cause of cancer-related deaths globally. The genus Parabacteroides is an important component of the gut microbiota. P. distasonis and P. goldsteinii are reported probiotics, and their roles in CRC have been investigated in related studies. However, the association between P. johnsonii and CRC remains unknown. The purpose of the present study aimed to investigate the association of Parabacteroides johnsonii with colorectal cancer (CRC) symptom alleviation in azoxymethane (AOM)/dextran sulfate sodium (DSS)-treated C57BL/6 mice and to determine its underlying mechanisms. The results revealed that P. johnsonii intervention reduced the number and slowed the growth of colonic tumors, improved tumor histological scores, and decreased microenvironmental inflammation levels. P. johnsonii reversed dysbiosis in the gut microbiota of CRC mice, increased gut microbial species diversity, and maintained gut microbiota stability. Furthermore, P. johnsonii intervention increased the abundance of Bifidobacterium pseudolongum and Lactobacillus, which play a role in ameliorating AOM/DSS-induced gut microbiota dysbiosis. P. johnsonii intervention affected the metabolic pathways, including amino sugar degradation and galactose metabolism, sphingolipid synthesis, amino acid synthesis, and polyphenol synthesis pathways, with the tryptophan metabolism pathway as the primary pathway being affected.
DOI: 10.17632/w5562c5jys.1
title: Kannada Scene Text dataset

description: Kannada Scene Images dataset for Language and Image processing related tasks. Feel Free to use
Reference Research Paper: https://ieeexplore.ieee.org/abstract/document/10499106
Github: https://github.com/Anoshor/KannadaSceneTextDataset
DOI: 10.17632/gnhgh77svw.1
title: Urban Investment Bonds and the Value Relevance of Earnings

description: This study uses data from local Chinese state-owned enterprises (SOEs) for 2008–2019 to explore the impact of municipal bonds on the value relevance of their earnings.
DOI: 10.17632/hdhn4djbbr.1
title: original computer code, algorithms, or computational models

description: After unzipping the file, the code is the original code of the thesis algorithm, the original code of the algorithm is written in c++, and needs to be run in the visual studio operating environment.
DOI: 10.17632/t9jcdjdg76.1
title: Replication file for "Are Women Better Stewards of Foreign Aid? Health Aid, Women Ministers and Infant Mortality"

description: Data and R codes for replication
DOI: 10.6084/m9.figshare.26787523
title: Supplementary Material for: Impact of diabetes mellitus and obesity comorbidities on survival outcomes after hepatocellular carcinoma resection: A multicenter retrospective study

description: Introduction: This study aimed to evaluate the association of obesity and diabetes mellitus (DM) comorbidity with hepatocellular carcinoma (HCC) recurrence and survival.
Methods: We investigated 1,644 patients who underwent hepatic resection for solitary HCC without vascular invasion using computed tomography. Patients were categorized into four groups according to the combination of obesity and DM comorbidities: OB (+) or (−) and DM (+) or (−).Postoperative cumulative recurrence rates within and beyond 2 years and beyond 5 years were assessed.
Results: Multivariate Cox proportional hazard regression analysis revealed that the adjusted hazard ratios (HRs) of reduced recurrence-free survival was 1.10 (95% confidence interval [CI]: 0.91–1.33; p = 0.31), 0.94 (95% CI: 0.78–1.12; p = 0.48), and 1.24 (95% CI: 1.01–1.54; p = 0.045) in the OB(+)DM(−), OB(−)DM(+), and OB(+)DM(+) groups compared with the OB(−)DM(−) group, respectively. Additionally, the corresponding adjusted HRs of reduced overall survival were 0.93 (p = 0.57), 0.97 (p = 0.76), and 1.38 (p = 0.013) for OB(+)DM(−), OB(−)DM(+), and OB(+)DM(+) groups, respectively. No significant difference in the early recurrence rate was determined among the four groups. The OB(+)DM(+) group demonstrated an increased risk for late recurrence beyond 2 years and 5 years postoperatively compared with the OB(−)DM(−) group (HR: 1.51; p = 0.024 and HR: 2.53; p = 0.046, respectively). The OB(+)DM(−) and OB(−)DM(+) groups demonstrated an increased risk for late recurrence beyond 5 years postoperatively (HR: 3.83; p &lt; 0.001 and HR: 1.95; p = 0.037, respectively).
Conclusions: Obesity and DM coexistence increased late recurrence and worsened prognosis in patients with HCC undergoing hepatic resection. The results help surgeons develop possible different surveillance protocol and need to focus on diabetes/obesity control during life-long surveillance for patients with HCC.
DOI: 10.17632/c6z323wky2.2
title: Data on herbal consumption in Indonesia during the COVID-19 pandemic

description: This dataset captures the patterns and trends of herbal consumption in Indonesia during the COVID-19 pandemic. It includes detailed information on the types of herbs consumed, the frequency and duration of their usage, the demographics of the consumers (such as age, gender, and region), and the motivations behind their consumption (e.g., immunity boosting, respiratory health). Additionally, the dataset records the sources of information that influenced consumers' decisions, such as social media, traditional knowledge, or medical advice. This comprehensive dataset aims to provide insights into how the pandemic influenced health behaviors and the role of traditional medicine in public health responses.

There are three types of herbal classification in Indonesia:
1. Herbs for jamu (herbal medicine)
2. FF for Ftitofarmaka (Phytopharmaceuticals)
3. OHT for Obat Herbal Terstandar (Standardized Herbal Medicine)
DOI: 10.17632/c6z323wky2.3
title: Data on herbal consumption in Indonesia during the COVID-19 pandemic

description: This dataset captures the patterns and trends of herbal consumption in Indonesia during the COVID-19 pandemic. It includes detailed information on the types of herbs consumed, the frequency and duration of their usage, the demographics of the consumers (such as age, gender, and region), and the motivations behind their consumption (e.g., immunity boosting, respiratory health). Additionally, the dataset records the sources of information that influenced consumers' decisions, such as social media, traditional knowledge, or medical advice. This comprehensive dataset aims to provide insights into how the pandemic influenced health behaviors and the role of traditional medicine in public health responses.

There are three types of herbal classification in Indonesia:
1. Herbs for jamu (herbal medicine)
2. FF for Ftitofarmaka (Phytopharmaceuticals)
3. OHT for Obat Herbal Terstandar (Standardized Herbal Medicine)
DOI: 10.17632/c6z323wky2.4
title: Data on herbal consumption in Indonesia during the COVID-19 pandemic

description: This dataset captures the patterns and trends of herbal consumption in Indonesia during the COVID-19 pandemic. It includes detailed information on the types of herbs consumed, the frequency and duration of their usage, the demographics of the consumers (such as age, gender, and region), and the motivations behind their consumption (e.g., immunity boosting, respiratory health). Additionally, the dataset records the sources of information that influenced consumers' decisions, such as social media, traditional knowledge, or medical advice. This comprehensive dataset aims to provide insights into how the pandemic influenced health behaviors and the role of traditional medicine in public health responses.

There are three types of herbal classification in Indonesia:
1. Herbs for jamu (herbal medicine)
2. FF for Ftitofarmaka (Phytopharmaceuticals)
3. OHT for Obat Herbal Terstandar (Standardized Herbal Medicine)
DOI: 10.17632/ts8rgv6xx4.1
title: Organizational Sustainability in Industry 4.0

description: The advent of the Fourth Industrial Revolution has motivated manufacturing companies across the globe to pursue organizational sustainability agendas. However, the obsoleteness of current organizational sustainability standards in the light of IR 4.0 has posed a challenge to companies in implementing relevant strategies to sustain a competitive edge. Although Total Quality Management (TQM) practices help organizations sustain global competitiveness, there is disagreement in the literature regarding practices that influences organizational sustainability in the context of IR 4.0. This study investigates the influential TQM soft practices on organizational sustainability in the context of IR 4.0 by exploring the relationship between TQM soft practices (i.e., leadership, customer focus, continuous improvement, employee empowerment, information and analysis) and innovative performance on sustainability in the context of Industry 4.0. It further investigates the mediating role of innovative performance on the relationship between TQM practices and organizational sustainability. The Systems theory and the European Foundation of Quality Model (EFQM) of excellence are the guiding theories employed to explain the relationship between the variables in the research model. A total of 281 owners/managers of ISO manufacturing firms, representing a response rate of 71.5%, participated in this study. Data were collected via self-administered questionnaires. PLS-SEM was used to analyze the data and test the hypotheses. Statistical results reveal that, except for leadership, TQM practices (i.e., customer focus, continuous improvement, employee empowerment, information and analysis) significantly influence sustainability. Furthermore, while innovative performance was found to be significantly related to organizational sustainability, it was also found to mediate the relationship between TQM practices (i.e., customer focus, continuous improvement, employee empowerment, information and analysis) and organizational sustainability in the context of Industry 4.0. Finally, theoretical and methodological contributions, managerial implications and recommendations for future research are discussed
DOI: 10.17632/c6z323wky2.1
title: Data on herbal consumption in Indonesia during the COVID-19 pandemic

description: This dataset captures the patterns and trends of herbal consumption in Indonesia during the COVID-19 pandemic. It includes detailed information on the types of herbs consumed, the frequency and duration of their usage, the demographics of the consumers (such as age, gender, and region), and the motivations behind their consumption (e.g., immunity boosting, respiratory health). Additionally, the dataset records the sources of information that influenced consumers' decisions, such as social media, traditional knowledge, or medical advice. This comprehensive dataset aims to provide insights into how the pandemic influenced health behaviors and the role of traditional medicine in public health responses.

There are three types of herbal classification in Indonesia:
1. Herbs for jamu (herbal medicine)
2. FF for Ftitofarmaka (Phytopharmaceuticals)
3. OHT for Obat Herbal Terstandar (Standardized Herbal Medicine)
DOI: 10.17632/cf8wsrzd37.1
title: Data for: Children’s self-reported discomfort of different restorative treatments for deep caries lesions in primary teeth: Results from a randomized clinical trial (CEPECO 1)

description: Data of discomfort from a clinical trial - CEPECO 1.
DOI: 10.17632/55bkprcr4t.1
title: Alternate-Day Modified Fasting Shifts Metabolically Unhealthy Obesity to Metabolically Healthy Obesity in Obese Rats

description: Alternate-day fasting is an emerging strategy for caloric restriction and treatment of obesity. We aimed to assess the effects of alternate-day modified fasting (ADMF) on adipose tissue dysfunction and hepatic insulin signaling pathway, inflammation, and oxidative stress in diet-induced obesity rats. Wistar male rats (n=18) were divided equally into three groups: control (regular chow), high-fat diet (HFD), and HFD+ADMF groups. Animals underwent assessments of weekly body weight, glucose tolerance tests, serum biochemical markers, and adiponectin, insulin, and leptin were assessed. Hepatic tissue was assessed for 1) insulin receptors and insulin signaling pathway, 2) inflammatory and oxidative profiles, and 3) histopathological changes, and pancreatic tissue was assessed for histopathological changes. In HFD-fed animals, ADMF decreased body weight and improved glucose tolerance. ADMF restored hepatic and adipose tissue insulin sensitivity and ameliorated inflammatory and oxidative stress profiles and histopathological changes in hepatic tissue. ADMF also restored the normal function of adipose tissue. In conclusion, ADMF changes metabolically unhealthy obesity to metabolically healthy obesity despite maintaining a high-fat diet feeding in rats through improving adipose tissue dysfunction and modulation of the hepatic insulin signaling pathway, inflammation, and oxidative stress.
DOI: 10.17632/c26vbcxhcx.1
title: NACI for HNSCC

description: Radiomics features to predict pCR to NACI for HNSCC.
DOI: 10.17632/c26vbcxhcx.2
title: NACI for HNSCC

description: Radiomics features to predict pCR to NACI for HNSCC.
DOI: 10.17632/sjjkf36rkv.1
title: Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design

description: This dataset contains simulation results and information to reproduce the findings for the publication "Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design" submitted to Renewable Energy.
DOI: 10.17632/nj5t3p9mgy.1
title: Machine Learning Model Performance in Dry and Wet Seasons

description: These codes implements machine learning models such as Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) with geospatial and non-geospatial data to predict volume of water consumed by poor urban households in dry and wet seasons
DOI: 10.17632/nj5t3p9mgy.2
title: Machine Learning Model Performance in Dry and Wet Seasons

description: These codes implements machine learning models such as Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) with geospatial and non-geospatial data to predict volume of water consumed by poor urban households in dry and wet seasons
DOI: 10.17632/nj5t3p9mgy.3
title: Machine Learning Model Performance in Dry and Wet Seasons

description: This study examines the performance of predictive machine learning models implemented with an integrated tool of geospatial technology (GST) and machine learning techniques (MLT). Historical data of daily volume of water consumed in dry and wet seasons was gathered through questionnaires, and integrated with socioeconomic data, weather data, property data and geospatial data. The datasets were passed through Principal Component Analysis algorithm to select few features that explain the variability in all the original variables. The selected features were inputted into four predictive models – Multilinear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). Three error metrics, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R squared (R2) score were used to measure the model performances. All four models performed very well in predicting volume of water consumed by poor urban households as they produced RMSE of 57 litres, 46 litres, 63 litres and 52 litres respectively during training, and R2 score of 88%, 92%, 88% and 90% respectively. Significance test performed at 95% confidence level shows that there is significant difference between volume of water consumed during dry and wet seasons.
DOI: 10.17632/67m9hwhrbg.1
title: 110 years of LSU Rice Breeding Program: scripts and datasets

description: This research aimed to understand the critical role of adopting advanced breeding tools and optimizing breeding strategies to ensure the sustainability and success of public breeding programs in meeting future food security challenges. In this context, we estimated the genetic gains achieved over 110 years in the rice breeding program of Louisiana State University (LSU) and evaluated through stochastic simulations the impacts of modern selection tools such as genomic selection (GS) and high-throughput phenotyping (HTP).
DOI: 10.17632/c6z323wky2.5
title: Data on herbal consumption in Indonesia during the COVID-19 pandemic

description: This dataset captures the patterns and trends of herbal consumption in Indonesia during the COVID-19 pandemic. It includes detailed information on the types of herbs consumed, the frequency and duration of their usage, the demographics of the consumers (such as age, gender, and region), and the motivations behind their consumption (e.g., immunity boosting, respiratory health). Additionally, the dataset records the sources of information that influenced consumers' decisions, such as social media, traditional knowledge, or medical advice. This comprehensive dataset aims to provide insights into how the pandemic influenced health behaviors and the role of traditional medicine in public health responses.

There are three types of herbal classification in Indonesia:
1. Herbs for jamu (herbal medicine)
2. FF for Ftitofarmaka (Phytopharmaceuticals)
3. OHT for Obat Herbal Terstandar (Standardized Herbal Medicine)
DOI: 10.17632/55bkprcr4t.2
title: Alternate-Day Modified Fasting Shifts Metabolically Unhealthy Obesity to Metabolically Healthy Obesity in Obese Rats

description: Alternate-day fasting is an emerging strategy for caloric restriction and treatment of obesity. We aimed to assess the effects of alternate-day modified fasting (ADMF) on adipose tissue dysfunction and hepatic insulin signaling pathway, inflammation, and oxidative stress in diet-induced obesity rats. Wistar male rats (n=18) were divided equally into three groups: control (regular chow), high-fat diet (HFD), and HFD+ADMF groups. Animals underwent assessments of weekly body weight, glucose tolerance tests, serum biochemical markers, and adiponectin, insulin, and leptin were assessed. Hepatic tissue was assessed for 1) insulin receptors and insulin signaling pathway, 2) inflammatory and oxidative profiles, and 3) histopathological changes, and pancreatic tissue was assessed for histopathological changes. In HFD-fed animals, ADMF decreased body weight and improved glucose tolerance. ADMF restored hepatic and adipose tissue insulin sensitivity and ameliorated inflammatory and oxidative stress profiles and histopathological changes in hepatic tissue. ADMF also restored the normal function of adipose tissue. In conclusion, ADMF changes metabolically unhealthy obesity to metabolically healthy obesity despite maintaining a high-fat diet feeding in rats through improving adipose tissue dysfunction and modulation of the hepatic insulin signaling pathway, inflammation, and oxidative stress.
DOI: 10.17632/6xrtcrnbn5.1
title: Monopoly Capital

description: Data for article.
DOI: 10.17632/cf8wsrzd37.2
title: Data for: Children’s self-reported discomfort of different restorative treatments for deep caries lesions in primary teeth: Results from a randomized clinical trial (CEPECO 1)

description: Data of discomfort from a clinical trial - CEPECO 1.
DOI: 10.17632/r4x3n68cw8.1
title: Arquivo de dados

description: Base de dados; apresentação da proposta do programa para avaliação de aceitabilidade e viabilidade; questionário de avaliação.
DOI: 10.17632/z8r3pvv863.1
title: Samosa

description: **Data Description: Samosa Classification Dataset**

**Overview:**
The dataset comprises images of samosas categorized into two classes: 'Good' and 'Bad.' A total of over 500 images have been collected, capturing the variability in appearance within each class. These images were taken using a Samsung J7 mobile camera under consistent daylight conditions against a white background, ensuring uniform lighting and minimal background interference.

**Data Composition:**
- **Total Images:** Over 500
 - **Good Samosa:** Approximately 250 images
 - **Bad Samosa:** Approximately 250 images

**Image Details:**
- **Camera Model:** Samsung J7
- **Background:** White
- **Lighting:** Daylight, providing natural and consistent illumination
- **Resolution:** Varies depending on the Samsung J7’s camera settings but typically around 1280x720 pixels

**Labeling:**
Each image is meticulously labeled as either 'Good' or 'Bad' based on predefined criteria. 'Good' samosas meet quality standards in terms of shape, color, and texture, while 'Bad' samosas exhibit imperfections such as poor shape, uneven coloring, or visible defects.

**Class Definitions:**
- **Good Samosa:** Characterized by an appealing appearance, consistent color, and proper shape. The samosas in this class exhibit desirable quality attributes expected in a standard product.
- **Bad Samosa:** Exhibits defects such as irregular shape, inconsistent color, or visible damage. These samosas do not meet the quality standards set for 'Good' samosas.

**Usage Context:**
This dataset is intended for developing and testing machine learning models for image classification. It is designed to help improve automated quality control processes in food production or similar applications where visual inspection is critical. The consistency in background and lighting aims to reduce variability in image quality, focusing the classification task on the inherent characteristics of the samosas.

**Preprocessing:**
Images are captured under controlled conditions to minimize external variables. However, preprocessing such as resizing, normalization, or augmentation may be applied depending on the requirements of specific machine learning models.

**Challenges:**
- Variability in samosa appearance due to factors like size, shape, and filling consistency
- Ensuring accurate labeling and quality control for 'Good' versus 'Bad' classification

**Applications:**
- Development of image recognition systems for quality assessment
- Training of machine learning models for automated defect detection
- Research in computer vision techniques applied to food quality evaluation

This dataset provides a foundational resource for advancing image classification and quality assessment technologies in the food industry.
DOI: 10.17632/6mc54phdy2.1
title: Data- Macroalgal diversity drives abundance and richness of amphipod community

description: Data set used in the analysis of the article "Macroalgal diversity drives abundance and richness of amphipod community"
DOI: 10.17632/78mztpjnf6.1
title: Python Code Performance of Machine Learning Models in Season

description: This code implements four machine learning models to predict volume of water consumed by urban poor households in dry and wet seasons