cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3p5v7g3bgw.1
title: Artificial Intelligence in South Africa: Survey Data of Master’s Students

description: The dataset, titled “Artificial Intelligence in South Africa: Survey Data of Master’s Students,” comprises two sheets, namely “Uncoded data” and “Coded data.” Both sheets comprise the raw data provided by the participants. The categorical data gathered via the ranking and statement selection questions were coded to enable variance analysis, regression analysis, and correlation analysis of the data. Therefore, in addition to the raw data, the “Coded data” sheet contains the data code associated with each survey data point. The “Uncoded Data” sheet contains, amongst others, columns for Participant ID, Gender (text), and Academic Year (text). The “Coded Data” sheet mirrors these columns and includes columns with numeric codes. The subsequent section details the coding process. Each row corresponds to a single participant’s responses. As the survey was completed anonymously, unique numeric participant IDs were automatically assigned to the participants’ answers by Microsoft Forms, the survey instrument.
The dataset was organized into thematic sections to improve clarity, each corresponding to related questions. 
The combination of coded categorical variables and demographic information supports a range of statistical techniques, including reflective and formative measurement models, variance analyses, and regression analyses. This versatility allows other scholars to replicate or extend studies on AI, GenAI, and technology acceptance and usage within higher education. By serving as a baseline, this dataset can be integrated into longitudinal research. Future studies can compare these findings with cohorts from different regions or fields of study.
DOI: 10.25582/data.2024-02.3028304/2513367
title: Data for EMSL Project 51619 from February 2024
DOI: 10.17632/8m53z6tk5d.1
title: MSELF

description: Prediction Method for Type II Diabetes Based on Causal Feature Selection and Multilayer Stacking Ensemble Learning Framework
DOI: 10.25582/data.2024-02.3028305/2513368
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028322/2513369
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028329/2513370
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028334/2513371
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028335/2513372
title: Data for EMSL Project 51619 from February 2024
DOI: 10.17632/5cxd5trv9k.1
title: LINF_060006600

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.25582/data.2024-02.3028336/2513373
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028338/2513374
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2024-02.3028376/2513375
title: Data for EMSL Project 51619 from February 2024
DOI: 10.25582/data.2025-02.3375458/2513376
title: Data for EMSL Project 60942 from February 2025
DOI: 10.25582/data.2025-02.3375466/2513377
title: Data for EMSL Project 60946 from February 2025
DOI: 10.25582/data.2025-02.3375721/2513378
title: Data for EMSL Project 60946 from February 2025
DOI: 10.25582/data.2025-02.3375740/2513379
title: Data for EMSL Project 60946 from February 2025
DOI: 10.17632/7jvykzzkg3.1
title: Chemotherapy triggers immune evasion by inducing LEPR+ Kupffer cell differentiation in liver metastases.

description: Western blot, immunofluorescence staining and hematoxylin and eosin staining images for this research.
DOI: 10.17632/7jvykzzkg3.2
title: Chemotherapy triggers immune evasion by inducing LEPR+ Kupffer cell differentiation in liver metastases.

description: Western blot, immunofluorescence staining and hematoxylin and eosin staining images for this research.
DOI: 10.17632/7jvykzzkg3.3
title: Chemotherapy triggers immune evasion by inducing LEPR+ Kupffer cell differentiation in liver metastases.

description: Western blot, immunofluorescence staining and hematoxylin and eosin staining images for this research.
DOI: 10.17632/33857v7tyc.1
title: On-the-Go Wheat Head Counting Using Robotics and AI Datasets

description: Navigation: This dataset includes the original images captured in the field by the camera mounted on the wheel of the Thorvald robot, along with annotated images featuring masks and lines. It also contains the training, validation, and test split, as well as text files with the predicted values for the line after training the model. Additionally, the dataset includes model checkpoints and weights for the trained model.
Wheat Head Counting: This dataset consists of images captured in the field by the camera with a top-down view mounted on the Thorvald robot, which were used for wheat head counting. And the results of the counting model for each plot and resolution.
DOI: 10.17632/6tyv663nrs.1
title: Dataset of Tourist Sentiments and Satisfaction in African Destinations: Insights from User-Generated Content on TripAdvisor

description: This article describes a dataset of user-generated content (UGC) extracted from Tripadvisor, encompassing 5,043 tourist reviews from ten African destinations between August 2018 and August 2023. The data was systematically scraped using Tripadvisor’s scraping operator, ensuring accuracy and relevance. Preprocessing techniques were applied to clean and refine the data while preserving user sentiments and feedback. The data includes structured fields such as destination names, timestamps, and review text. This dataset is valuable for tourism researchers, policymakers, and industry stakeholders seeking insights into destination popularity, visitor experiences, and service quality. It supports trend analysis, predictive modeling, and comparative studies of regional tourism patterns. The structured format allows integration with other datasets for advanced tourism analytics. Through a longitudinal view of tourist sentiments, this dataset offers a valuable resource for understanding evolving travel behaviors and optimizing destination management strategies in Africa and beyond.
DOI: 10.17632/33857v7tyc.2
title: On-the-Go Wheat Head Counting Using Robotics and Deep Learning Datasets

description: Navigation: This dataset includes the original images captured in the field by the camera mounted on the wheel of the Thorvald robot, along with annotated images featuring masks and lines. It also contains the training, validation, and test split, as well as text files with the predicted values for the line after training the model. Additionally, the dataset includes model checkpoints and weights for the trained model.
Wheat Head Counting: This dataset consists of images captured in the field by the camera with a top-down view mounted on the Thorvald robot, which were used for wheat head counting and the results of the counting model for each plot and resolution.
DOI: 10.17632/b5btkfw3tt
title: Drosophila adipose exosome miR-277-3p targets Dbct across tissues to participate in the mechanism of regular exercise improving obese cardiomyopathy

description: Document S1. Figure S1–S7; Table S1 and S2.
Document S2. Original uncropped western blot graphical. 
Table S3. All raw data.
DOI: 10.17632/b5btkfw3tt.1
title: Drosophila adipose exosome miR-277-3p targets Dbct across tissues to participate in the mechanism of regular exercise improving obese cardiomyopathy

description: Document S1. Figure S1–S7; Table S1 and S2.
Document S2. Original uncropped western blot graphical. 
Table S3. All raw data.
DOI: 10.17632/pdf9c6mhjb.1
title: LINF_060006800

description: Trm112p-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/f8vgrb42zf.1
title: Fostering Creativity in Agile Education: A Comparative Study of Students and Software Practitioners

description: The file “dataset-fostering_creativity_in_agile_education_a_comparative_study” provides the dataset of the surveys and table completed by the participants in each experiment of this study.
DOI: 10.17632/5f2xsfyckv.1
title: Bio-Waste Management

description: Effective BWM is essential for preserving urban hygiene and advancing environmental objectives in smart city settings. The fast development of bio-waste frequently outpaces the capabilities of conventional waste treatment techniques, resulting in inefficiencies and problems with the environment. This research suggests a unique strategy that uses sophisticated predictive analytics to improve the handling of waste to solve these issues. Cities may enhance recycling procedures, schedule waste pickup more efficiently, and anticipate waste creation more precisely with this strategy. The proposed solution combines reliable predictive analytics with real-time trash detection to overcome the drawbacks of conventional waste management techniques. This combination results in lower operating costs, improved distribution of resources, and increased forecast accuracy. Use of YOLOv8-SPP in predictive analytics is a noteworthy development in BWM, resulting in smart city operations that are more sustainable and efficient.
DOI: 10.17632/p4n9p9zn9y.1
title: LINF_060007400

description: FHA domain containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/kj5txfgkm8.1
title: LINF_060008300

description: MYND finger domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/drpzpwfyk6.1
title: LINF_060007100

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/cdfbn3cryv.1
title: LINF_060007200

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/fz7d6x4bwx.1
title: Artificial Intelligence Reveals the Predictions of Hematological Indexes in Children with Acute Leukemia

description: Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by examining nutritional markers, key leukemia indicators, and granulocytes in patients' blood. Using a machine learning algorithm and ten indices, 826 pediatric patients with ALL and 255 children with AML were analyzed, comparing them with a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an Area Under the Curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.
DOI: 10.17632/fz7d6x4bwx.2
title: Artificial Intelligence Reveals the Predictions of Hematological Indexes in Children with Acute Leukemia

description: Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by examining nutritional markers, key leukemia indicators, and granulocytes in patients' blood. Using a machine learning algorithm and ten indices, 826 pediatric patients with ALL and 255 children with AML were analyzed, comparing them with a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an Area Under the Curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.
DOI: 10.17632/c247h7d977.1
title: Non-fundamental Information Disclosure and Discretionary Liquidity Trading

description: This replication folder provides the codes and data to replicate all the results of the paper entitled ‘Non-fundamental Information Disclosure and Discretionary Liquidity Trading’. The zip file includes the files necessary to replicate the empirical results illustrated in the paper. The readme.pdf file contains useful information on the replication folder. Please read it before using the replication folder. If you have any problems or questions, please do not hesitate to contact Hong Liu. liuh653@nenu.edu.cn.
DOI: 10.17632/3mhn86m3mz.1
title: Research data for “Novel insight into chromium availability and transformation in basalt-derived paddy soil-rice plant system”

description: Supplementary data for the manuscript entitled “Novel insight into chromium availability and transformation in basalt-derived paddy soil-rice plant system”
DOI: 10.17632/2nhm7vpx7y.1
title: Supplemental Material - Efficacy and Tolerability of Low-Dose Versus High-Dose Doxycycline in the Management of Lymphocytic Scarring Alopecias

description: Supplemental tables for the publication entitled, "Efficacy and Tolerability of Low-Dose Versus High-Dose Doxycycline in the Management of Lymphocytic Scarring Alopecias." These supplemental tables include a breakdown of adjunctive treatments, associations between doxycycline dose and clinical outcomes for the FFA/LPP cohort alone, and associations between doxycycline dose and patient/provider evaluations and change in trichoscopic measures.
DOI: 10.17632/3mbzb96fyh.1
title: Evaluating the Effectiveness of Global and Local Transport Policies in Reducing Carbon Emissions

description: Transportation is a major contributor to global carbon emissions, necessitating policy interventions at both international and local levels. This study evaluates the effectiveness of various transport policies in mitigating carbon emissions, comparing global agreements such as the Paris Agreement with local initiatives like congestion pricing, low-emission zones, and public transit expansion. Using a mixed-methods approach, the research analyzes policy frameworks, emission reduction data, and case studies from key regions. The findings highlight the relative success of integrated policy approaches, the role of technological advancements, and the challenges of policy implementation. The study concludes that while global policies set crucial targets, local policies play a pivotal role in execution and enforcement. Policy recommendations emphasize the need for stronger coordination between global directives and localized strategies to achieve long-term emission reduction goals.
DOI: 10.17632/p8c6s3w5yr.1
title: PLoS One

description: Data of clinical research
DOI: 10.17632/bj5vtsj7bj.1
title: Data for learning and motivation in H E

description: This study explores the relationship between motivational and learning strategies and academic performance among university students in Romania, an area that remains underexplored, particularly in relation to academic performance across different study programs. Data were collected from students enrolled in full-time and hybrid study programs in the field of educational science (N = 225). The results reveal statistically significant findings, with a model precision of 58.7%. While the holistic Motivation Scale and Learning Strategy Scale did not significantly impact academic performance, specific subscales emerged as influential predictors. Key motivational strategies, such as task value, control of learning beliefs, self-efficacy, and learning strategies like effort regulation, metacognitive self-regulation, and help-seeking, were found to impact academic performance significantly. Additionally, the frequency of study programs (full-time vs. hybrid) emerged as a significant moderator, particularly for lower-performing students. These findings highlight the importance of fostering students' beliefs in their learning abilities, their perceived control over learning outcomes, and their valuation of academic tasks. The study underscores the need for targeted interventions to enhance students' motivation and self-regulation, ultimately improving academic performance. These insights offer valuable guidance for educators and policymakers aiming to design effective support systems for students in higher education.
DOI: 10.17632/wrh82jcypw.1
title: Data on disintegration- suffosion characteristics and microscopic mechanism of WGS

description: The data are about disintegration- suffosion characteristics and microscopic mechanism of weathered granite soil in Conghua District, Guangzhou (latitude 23.59°N, longitude 113.41°E).
DOI: 10.17632/d9cmf8zf6k.1
title: Geospatial phenomenology in literary creativity: a comparative study of human and AI textual constructs

description: This study aims to understand how the human epistemological sphere differ from artificial intelligence (AI) in producing non-specific texts. The research hypothesis is set to comprehend human written production and how it is deeply rooted in space perception while its related production is oriented and constructed via geographical markers that are totally absent in AI productions. Using a simple prompt as a starter for a broad investigation of the aesthetical output, this paper will examine statistically the main features regarding both sides. By analyzing a dataset of 64 human-authored and 180 AI-generated texts produced in response to identical literary prompts, the research employs a computational framework adapted from media narrative analysis to identify distinct patterns in textual construction and interpretation. 
The methodology integrates natural language processing techniques such as document embedding with BERT, topic modeling using Latent Dirichlet Allocation (LDA), and sentiment analysis. To assess phenomenological markers, it has been employed keyword extraction (TF-IDF and RAKE) and semantic graph analysis, identifying human-specific geographical and experiential references within the texts. Statistical methods, including PCA for dimensionality reduction and logistic regression, are applied to compare patterns across the corpora. Visualization tools such as heatmaps and annotated semantic graphs elucidate thematic and structural divergences.
Preliminary findings indicate significant disparities between the two groups. Human-authored texts exhibit rich, contextually grounded depictions of spaces and experiences, often interwoven with cultural and historical nuances. By contrast, AI-generated texts demonstrate repetitive syn-tactic structures and a lack of experiential depth, reflecting their reliance on probabilistic language models rather than embodied cognition. Notably, the absence of geographically specific references and phenomenological engagement in AI texts supports the hypothesis.
This research contributes to broader debates on AI's limitations in creative domains, underscoring the role of human phenomenology in literary production. By interrogating the boundaries of machine creativity, the study highlights the irreplaceable value of human cognitive and cultural frameworks in interpreting and recreating reality through literature.
DOI: 10.17632/k52tmp95p9.1
title: data to support Days with a maximum temperature of 40 C in 2100 with SSP5-8.5 pathway

description: The is the data used to produce the analysis of Days with a maximum temperature of 40 C in 2100 with SSP5-8.5 pathway.
DOI: 10.17632/d9cmf8zf6k.2
title: Geospatial phenomenology in literary creativity: a comparative study of human and AI textual constructs

description: This study aims to understand how the human epistemological sphere differ from artificial intelligence (AI) in producing non-specific texts. The research hypothesis is set to comprehend human written production and how it is deeply rooted in space perception while its related production is oriented and constructed via geographical markers that are totally absent in AI productions. Using a simple prompt as a starter for a broad investigation of the aesthetical output, this paper will examine statistically the main features regarding both sides. By analyzing a dataset of 64 human-authored and 180 AI-generated texts produced in response to identical literary prompts, the research employs a computational framework adapted from media narrative analysis to identify distinct patterns in textual construction and interpretation. 
The methodology integrates natural language processing techniques such as document embedding with BERT, topic modeling using Latent Dirichlet Allocation (LDA), and sentiment analysis. To assess phenomenological markers, it has been employed keyword extraction (TF-IDF and RAKE) and semantic graph analysis, identifying human-specific geographical and experiential references within the texts. Statistical methods, including PCA for dimensionality reduction and logistic regression, are applied to compare patterns across the corpora. Visualization tools such as heatmaps and annotated semantic graphs elucidate thematic and structural divergences.
Preliminary findings indicate significant disparities between the two groups. Human-authored texts exhibit rich, contextually grounded depictions of spaces and experiences, often interwoven with cultural and historical nuances. By contrast, AI-generated texts demonstrate repetitive syn-tactic structures and a lack of experiential depth, reflecting their reliance on probabilistic language models rather than embodied cognition. Notably, the absence of geographically specific references and phenomenological engagement in AI texts supports the hypothesis.
This research contributes to broader debates on AI's limitations in creative domains, underscoring the role of human phenomenology in literary production. By interrogating the boundaries of machine creativity, the study highlights the irreplaceable value of human cognitive and cultural frameworks in interpreting and recreating reality through literature.
DOI: 10.17632/hbr34wzz65.1
title: Liberalisation, concentration and diversification: Business Groups in India, 2000-2020- Data Resources

description: We analyse the evolution of market structure in India between 2000 and 2020 using a rich dataset at high levels of disaggregation. We examine the extent to which business groups – notably family-owned groups – have sustained their dominant market positions in the Indian economy. We focus on two key dimensions. The first is the extent of concentration in markets and market shares by industry. The second concerns the dynamics and the extent to which business groups have focussed on consolidating their position in specific, narrow sectors or diversified by entering new sectors. We find that while market concentration has been falling, a bloc of high concentration sectors remains. Further, diversification has been actively pursued across sectors by most business groups. While this points to greater competition among business groups, the ratio of revenues to variable costs – a measure of the markup – has shifted upwards, particularly after 2013. The weight and persistence of these large business groups in the economy, as measured by the ratio of their revenues to GDP, has also increased. Finally, we discuss possible policy options. 

This dataset provides data for Figures and the STATA .do file for the replication of the replication of figures and tables. Due to restrictions on sharing the data by the data service provider "CMIE Prowessdx", the authors are not at liberty to share the dataset for the analysis.
DOI: 10.17632/nfb6yk2m2f
title: Research data of soil characteristics and amino sugars

description: Research data of soil characteristics and amino sugars in three typical management measures across the soil profile (topsoil: 0–20 cm; subsoil: 140–200 cm) from a long-term experiment on Loess Plateau