cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23884524.v1
title: Supplementary Material for: Validation of administrative health data algorithms for identifying persons with Parkinson’s disease and the ten-year prevalence trend in Bologna, Italy

description: Introduction Health administrative databases are widely used for the estimation of the prevalence of Parkinson’s Disease (PD). Few in general, and none used in Italy, have been validated by testing their diagnostic accuracy. The primary objective was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. The second objective was to estimate 10-year trends in PD prevalence in the Bologna Local Health Trust from 2010 to 2019. Methods Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. Sensitivity and specificity with relative 95% confidence intervals (CI) were calculated. The prevalence of PD in a specific year was estimated as the ratio between the number of subjects fulfilling any criteria of the algorithm with better diagnostic accuracy and the total population in the same year (x 1,000), stratified by age, sex and district of residence. Results The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity 94.2% (CI 88.4 – 97.6) and the other with greater specificity 98.1% (CI 97.7 – 98.5). For the estimation of prevalence, we chose the most specific algorithm with the fewest total number of misclassified cases. We identified 3,798 people with PD as of December 31, 2019 corresponding to a prevalence of 4.3 per 1,000 inhabitants (CI 4.2 – 4.4). Prevalence was higher in males (4.7, CI 4.5 – 5.0) than females (3.8, CI 3.7 – 4.0) and increased with age. The crude prevalence over time was slightly elevated as it followed a progressive ageing of the population. When stratifying the prevalence for age groups we did not observe a trend except in the 45-64 year category where we observed an increasing trend over time. Conclusion Algorithms based on administrative data are accurate when detecting people with PD in the Italian public health system. In a large northern Italian population, increased prevalence of about 10% was observed in the decade 2010-2019 and is explained by increased life expectancy. These data may be useful in planning the allocation of health care resources for people with PD.
DOI: 10.34740/kaggle/dsv/6251380
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251402
title:  OpenAssistant-oasst1-fa
DOI: 10.25345/c5j67968g
title: MassIVE MSV000092603 - Artemisiae Argyi-GC-Q-TOF data2022
title: Identification of siginifcant biomarkers in Artemisiae Argyi for origin traceability and quality evaluation
DOI: 10.34740/kaggle/dsv/6251420
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251439
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251469
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251487
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251506
title:  OpenAssistant-oasst1-fa
DOI: 10.21227/04zq-yz45
title: MUSED-I: "Multi-Gesture Surface Electromyography (sEMG) Dataset for Stroke Rehabilitation”

description: The published Surface Electromyography (sEMG) dataset was captured under the supervision of trained physiotherapists and doctors at Mayo Hospital Lahore and National University of Sciences & Technology by following two months long experimental protocol.Data recording was carried out following the World Medical Association's (WMA) Standard operating procedure and Declaration of Helsinki, with approval from the Research Ethical Committee of the National University of Science and Technology (approval no: M-20221206). Before data acquisition, participants received a comprehensive oral explanation of the experiment, safety measures, and associated risks. Informed written consent was obtained from stroke patients before data recording. Participants sat comfortably in an armchair with their arms vertically positioned relative to the floor. The Myo armband was carefully placed on their forearm, ensuring the electrodes remained at a consistent distance of 5 centimetres from the elbow. Participants then followed the on-screen prompts to perform the specified gestures.Ten healthy subjects (with no prior history of upper limb pathology) and two upper limb stroke patients participated in the study.  The dataset comprises the raw surface electromyography (sEMG) signals collected for the six gestures (wrist flexion, wrist extension, hand close, wrist radial deviation, wrist ulnar deviation and rest) via Myo armband. All eight channels of the Myo armband were utilized for data acquisition.
DOI: 10.21227/dx54-h457
title: Composite_Material_Dataset_Data_Analytics

description:  The uploaded dataset appears to be related to various composite materials, and it includes the following columns:Material Composition: Describes the composition of the material, such as "Polymer Matrix" or "Carbon Nanotubes." Fabrication Technique: Specifies the method used to fabricate the composite, like "Langmuir-Blodgett" or "Electrospinning." Tensile Strength (MPa): The tensile strength of the material in Mega Pascals (MPa). Thermal Conductivity (W/m·K): The thermal conductivity of the material in Watts per meter-Kelvin. Elastic Modulus (GPa): The elastic modulus of the material in Giga Pascals (GPa). Density (g/cm³): The density of the material in grams per cubic centimeter. Fiber Volume Fraction (%): The volume fraction of fibers in the composite, represented as a percentage.Here are the summary statistics for the numerical features in the dataset:Tensile Strength (MPa):Count: 10,000 Mean: 999.12 MPa Standard Deviation: 288.29 MPa Min: 500.01 MPa Max: 1499.89 MPa Thermal Conductivity (W/m·K):Count: 10,000 Mean: 30.18 W/m·K Standard Deviation: 11.52 W/m·K Min: 10.00 W/m·K Max: 50.00 W/m·K Elastic Modulus (GPa):Count: 10,000 Mean: 149.46 GPa Standard Deviation: 57.80 GPa Min: 50.02 GPa Max: 249.98 GPa Density (g/cm³):Count: 10,000 Mean: 2.00 g/cm³ Standard Deviation: 0.29 g/cm³ Min: 1.50 g/cm³ Max: 2.50 g/cm³ Fiber Volume Fraction (%):Count: 10,000 Mean: 50.08% Standard Deviation: 5.78% Min: 40.00% Max: 60.00%The frequency distribution of the categorical features is as follows:Material Composition: Carbon Nanotubes: 3,354 occurrences Graphene: 3,347 occurrences Polymer Matrix: 3,299 occurrences Fabrication Technique: Electrospinning: 5,018 occurrences Langmuir-Blodgett: 4,982 occurrences The dataset seems to contain a fairly balanced distribution of different material compositions and fabrication techniques
DOI: 10.34740/kaggle/dsv/6251543
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251564
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8163855
title: Hypertuned Beta parameter SDOT Framework for Robust Forensic File Classification

description: This dataset contains code for implementing our proposed Forensic File classification system with the help of Guided LDA algorithm and SDOT forensic Framework. Along with the code, we have provided auxiillary files that are used in this research.
DOI: 10.5281/zenodo.8163854
title: Hypertuned Beta parameter SDOT Framework for Robust Forensic File Classification

description: This dataset contains code for implementing our proposed Forensic File classification system with the help of Guided LDA algorithm and SDOT forensic Framework. Along with the code, we have provided auxiillary files that are used in this research.
DOI: 10.34740/kaggle/dsv/6251582
title:  OpenAssistant-oasst1-fa
DOI: 10.21227/j0te-1s44
title: YR2S: Efficient Deep Learning Technique for Detecting and Classifying Plant Leaf Diseases

description: Most plant diseases have observable symptoms, and the widely used approach to detect plant leaf disease is by visually examining the affected plant leaves. A model which might carry out the feature extraction without any errors will process the classification task successfully.  The technology currently faces certain limitations such as a large parameter count, slow detection speed, and inadequate performance in detecting small dense spots. These factors restrict the practical applications of the technology in the field of agriculture. Hence, this work is focused on devising an optimized framework that works based on YOLOv7 that encompasses the preprocessing and hybrid optimization techniques. This proposed YR2S(YOLO-Enhanced Rat Swarm Optimizer - Red Fox Optimization(RFO-ShuffleNetv2) has been devised. After the preprocessing feature maps are generated using PCFAN. Later, these feature maps are used for the detection of leaves. ShuffleNet with ERSO is used to optimize the classification process. Segmentation of the area prone to disease could be identified through the FCN-RFO. This framework is deployed on the customized dataset which comprehends images of various plant leaves. The leaf disease dataset is used for simulating and assessing the model. The experimental analysis reveals that the proposed method can effectively classify and detect leaf disease with high accuracy i.e., 99.69%, which outperforms the state-of-the-art approaches in the literature. Practical implication shows that the proposed deep learning classifiers are efficient and highly accurate.
DOI: 10.34740/kaggle/dsv/6251599
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251619
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251637
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251664
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8210398
title: Predicting brain activity from word embeddings during natural language comprehension: Data

description: Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
DOI: 10.5281/zenodo.8210397
title: Predicting brain activity from word embeddings during natural language comprehension: Example Data

description: Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
DOI: 10.5281/zenodo.8216229
title: Predicting brain activity from word embeddings during natural language comprehension: Example Data

description: Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
DOI: 10.34740/kaggle/dsv/6251676
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251689
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251715
title:  OpenAssistant-oasst1-fa
DOI: 10.6084/m9.figshare.23300441
title: “Group of men” as a health promotion device: singular experiences in care practices

description: Abstract The “Group of Men” is a health promotion space developed in a Brazilian Basic Health Unity. The study sought to identify and understand the context of the production of care practices and the participation of men-users in the Group, through ethnography. After systematizing and analyzing the empirical data, two thematic categories emerged: communication space (an opening to the ‘exchange of experiences’ that was not reduced to pathology-focused conversation) and; development of the bond (relationship between users and the health professionals: proximity and freedom to express themselves, pointing out the advantages of the link for men’s care). The results indicated that the power of the “Group of Men” resided in the investment in ‘health work processes’ involved with collective care strategies, ‘meetings’ that favored reception, qualified listening and bonding.
DOI: 10.6084/m9.figshare.23300441.v1
title: “Group of men” as a health promotion device: singular experiences in care practices

description: Abstract The “Group of Men” is a health promotion space developed in a Brazilian Basic Health Unity. The study sought to identify and understand the context of the production of care practices and the participation of men-users in the Group, through ethnography. After systematizing and analyzing the empirical data, two thematic categories emerged: communication space (an opening to the ‘exchange of experiences’ that was not reduced to pathology-focused conversation) and; development of the bond (relationship between users and the health professionals: proximity and freedom to express themselves, pointing out the advantages of the link for men’s care). The results indicated that the power of the “Group of Men” resided in the investment in ‘health work processes’ involved with collective care strategies, ‘meetings’ that favored reception, qualified listening and bonding.
DOI: 10.34740/kaggle/dsv/6251743
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251773
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251795
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251817
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251832
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251845
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251860
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8212693
title: Supplemental Material: Computational Experiments in Computer Science Research: A literature survey

description: This laboratory package contains supplemental material from the study: "Supplemental Material: Computational Experiments in Computer Science Research: A literature survey". The supplementary material contains:<br> 1. The list of the 26 primary studies.<br> 2. The .xlsx file of the dataset used to analyze the RQ.<br> 3. The list of figures published in the scientific article.
DOI: 10.5281/zenodo.8212692
title: Supplemental Material: Computational Experiments in Computer Science Research: A literature survey

description: This laboratory package contains supplemental material from the study: "Supplemental Material: Computational Experiments in Computer Science Research: A literature survey". The supplementary material contains:<br> 1. The list of the 26 primary studies.<br> 2. The .xlsx file of the dataset used to analyze the RQ.<br> 3. The list of figures published in the scientific article.
DOI: 10.34740/kaggle/dsv/6251873
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8183338
title: University Helpdesk Support Data Set

description: This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse. [1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. <em>SEKE 2008</em>, 929.
description: This data set was disclosed with the explicit permission of the first author of the work, Taiseera Al Balushi.
description: {"references": ["Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. SEKE 2008, 929."]}
DOI: 10.5281/zenodo.8183337
title: University Helpdesk Support Data Set

description: This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse. [1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. <em>SEKE 2008</em>, 929.
description: This data set was disclosed with the explicit permission of the first author of the work, Taiseera Al Balushi.
description: {"references": ["Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. SEKE 2008, 929."]}
DOI: 10.5281/zenodo.8207462
title: AndroLibZoo

description: # AndroLibZoo This repository hosts the AndroLibZoo dataset and the artifacts used in our study on Android libraries. ## Artifacts The artifacts folder contains all the artifacts, i.e., datasets, scripts, results, source code, etc., to reproduce our study and produce AndroLibZoo. The file AndroLibZoo.lst is our dataset. The subfolders in « AndroLibZoo » are as follows: The motivation folder contains all artifacts related to our motivation study (i.e., Section 3). The methodology folder contains all artifacts related to our methodology (i.e., Section 4). In particular, we present: How we gather libraries from Maven How we gather libraries from Google How we gather transitive dependencies How we gather libraries from open source apps How we gather libraries from Gradle plugin libraries How we refined our list of libraries. The description folder contains the artifacts useful to describe our dataset (i.e., Section 5). The comparison folder holds the artifacts useful to compare our dataset with the state of the art (i.e., Section 6.a). The importance folder holds the artifacts useful to highlight the importance of our AndroLibZoo to filter libraries for static analysis of Android apps (i.e., Section 6.b). The evaluation folder holds the artifacts useful to show how AndroLibZoo improves existing static analyzers (i.e., Section 6.c). The subfolders in « methodology_for_comparison_with_sota_list » contains the same folder as listed above but to produce the 2016 version of our dataset to compare against the state-of-the-art list. The subfolders in « comparison_libradar » contain all the necessary files to compare our approach with libradar. All folders contain scripts that start with "XX_", with XX being a number that represents the order in which scripts need to be executed. Some of the scripts need to be parametrized with names of servers, AndroZoo API keys, prefixes for Redis server, etc.
DOI: 10.5281/zenodo.8207463
title: AndroLibZoo

description: # AndroLibZoo This repository hosts the AndroLibZoo dataset and the artifacts used in our study on Android libraries. ## Artifacts The artifacts folder contains all the artifacts, i.e., datasets, scripts, results, source code, etc., to reproduce our study and produce AndroLibZoo. The file AndroLibZoo.lst is our dataset. The subfolders in « AndroLibZoo » are as follows: The motivation folder contains all artifacts related to our motivation study (i.e., Section 3). The methodology folder contains all artifacts related to our methodology (i.e., Section 4). In particular, we present: How we gather libraries from Maven How we gather libraries from Google How we gather transitive dependencies How we gather libraries from open source apps How we gather libraries from Gradle plugin libraries How we refined our list of libraries. The description folder contains the artifacts useful to describe our dataset (i.e., Section 5). The comparison folder holds the artifacts useful to compare our dataset with the state of the art (i.e., Section 6.a). The importance folder holds the artifacts useful to highlight the importance of our AndroLibZoo to filter libraries for static analysis of Android apps (i.e., Section 6.b). The evaluation folder holds the artifacts useful to show how AndroLibZoo improves existing static analyzers (i.e., Section 6.c). The subfolders in « methodology_for_comparison_with_sota_list » contains the same folder as listed above but to produce the 2016 version of our dataset to compare against the state-of-the-art list. The subfolders in « comparison_libradar » contain all the necessary files to compare our approach with libradar. All folders contain scripts that start with "XX_", with XX being a number that represents the order in which scripts need to be executed. Some of the scripts need to be parametrized with names of servers, AndroZoo API keys, prefixes for Redis server, etc.
DOI: 10.34740/kaggle/dsv/6251891
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251905
title:  OpenAssistant-oasst1-fa
DOI: 10.5281/zenodo.8216758
title: data

description: this is the data for submission #2323
DOI: 10.5281/zenodo.8216757
title: data

description: this is the data for submission #2323
DOI: 10.34740/kaggle/dsv/6251921
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251935
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251949
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251975
title: AutoXGB
DOI: 10.34740/kaggle/dsv/6251980
title:  OpenAssistant-oasst1-fa
DOI: 10.6084/m9.figshare.23889039
title: INTRUSION DATASET

description: A dataset which we used in our research
DOI: 10.6084/m9.figshare.23888988
title: data for Phys. Rev. Research 4, 043026.zip

description: Data for Phys. Rev. Research 4, 043026 (2022). Each .txt file correspond to a dataset in a Figure in paper. The .txt file format is eitherx, yorx, y, delta ydepending on whether error bars are provided.
DOI: 10.6084/m9.figshare.23888988.v1
title: data for Phys. Rev. Research 4, 043026.zip

description: Data for Phys. Rev. Research 4, 043026 (2022). Each .txt file correspond to a dataset in a Figure in paper. The .txt file format is eitherx, yorx, y, delta ydepending on whether error bars are provided.
DOI: 10.34740/kaggle/dsv/6252002
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6251965
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.34740/kaggle/dsv/6252019
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6252044
title:  OpenAssistant-oasst1-fa
DOI: 10.34740/kaggle/dsv/6252073
title:  OpenAssistant-oasst1-fa
DOI: 10.6084/m9.figshare.23889420
title: materials & data for JEPHPP 0805

description: Reproducibility package containing materials and data of the manuscript for Journal of Experimental Psychology: Human Perception and PerformanceThe role of inhibition in task switching: Empirical evidence from an n-back taskMeiling Fu, Chunnan Wang, Fuhong Liunder view
DOI: 10.6084/m9.figshare.23889420.v1
title: materials & data for JEPHPP 0805

description: Reproducibility package containing materials and data of the manuscript for Journal of Experimental Psychology: Human Perception and PerformanceThe role of inhibition in task switching: Empirical evidence from an n-back taskMeiling Fu, Chunnan Wang, Fuhong Liunder view
DOI: 10.34740/kaggle/dsv/6252092
title:  OpenAssistant-oasst1-fa
DOI: 10.17026/dans-z9y-c5g6
title: Cinema Context. Film in Nederland vanaf 1896: een encyclopedie van de filmcultuur

description: Cinema Context is a historical data collection for the history of film culture, in particular the screening and distribution of films in the Netherlands. Cinema Context was conceived in a project financed by NWO, realized in 2005, under the direction of Dr. Karel Dibbets (1947-2017).Cinema Context is a live instrument, corrections and new data are added regularly by the editors at the University of Amsterdam that manage and maintain the data collection. Technical support and hosting are provided by the Library of the University of Amsterdam (Special Collections).The data collected here are snapshots. Updates will be added to the dataset, the date of a snapshot will be given in its filename. The most up-to-date version of the data can be found online at www.cinemacontext.nlThe RDF documentation pages can be found at: filehttps://uvacreate.gitlab.io/cinema-context/cinema-context-rdf/
DOI: 10.17636/10173212
title: Processing code for optical characterization of flat capillary waves
DOI: 10.3886/e191621v1
title: Data and Code for "Are developed countries outsourcing pollution?"

description: Describes research posing three questions. (1) Have rich countries improved their own environments by importing goods produced in polluting factories? (2) Has pollution worsened in countries manufacturing those goods for export to rich countries? And (3) have the environmental regulations enacted by rich developed countries caused either of the first two changes?