cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rw7wzch267.1
title: Systematic literature review of relationships between non-financial reporting quality and company-level factors

description: This systematic review deals with empirical research on European Union companies that apply NFRD, starting with 2017 onwards. According to the Directive 2014/95/EU, Member States shall bring into force the laws, regulations and administrative provisions necessary to comply with this Directive, which is to be applied starting on 1 January 2017 or during the calendar year 2017. All identified records, for 2016-2023, were downloaded in Excel files, the unique field being the digital object identifier (DOI). The date of database access and download was 12 April 2023. The results comprise 618 articles which are subject to further screening. After the screening process, 602 articles were excluded from the analysis. The end result of the systematic procedure contains 16 articles, analyzed in the deposited files.
DOI: 10.17632/mp76mwrc2s.1
title: Journal of Islamic Education; No. 22

description: The journal is set to publish original articles on a wide range of issues including;

 Philosophy of Islamic education;
 Schools and approaches to Islamic education;
 Educational jurisprudence (Fiqh);
 Foundations, principles and methodology of Islamic education;
 History of Islamic education;
 Educational thoughts, views and ideas of Muslim scholars;
 Educational sciences such as educational management, curriculum planning and educational psychology with an Islamic approach;
 Comparative studies;
 The areas and dimensions of education in Islam include religious education, devotional education, moral education, sexual education, political education, and so on;
 Methodology of research in Islamic education;
 Examining different aspects of formal education with Islamic perspective;
 Religious education in the thought of international scholars, a comparative approach to Islamic education.

JIE is an international, professional refereed journal in the interdisciplinary fields that relevant to Islamic education. JIE serves as a platform for presenting and discussing the emerging issues on Islamic education. The journal is committed in publishing high quality articles in persian and welcomes articles in English, ranging from original research papers in the area of Islamic education. This journal welcomes original and qualified researches on all aspects of Islamic education and religious education. The topics may include, but are not limited to: 
Philosophy of Islamic education, schools and approaches in Islamic education, educational Islamic jurisprudence, foundations, principles and methods of Islamic education, history of Islamic education, educational approaches and ideas of Muslim scholars, educational management, curriculum and educational planning, educational psychology with an Islamic perspective, comparative issues, areas and dimensions of education in Islam, religious education, devotional education, moral education, sexual education, political education, research methodology in Islamic education and examination of various aspects of formal education with an Islamic outlook.
DOI: 10.17632/889jhhhpky.1
title: Data for: LSTM Based Encoder-Decoder for Short-Term Predictions of Gas Concentration using Multi-Sensor Fusion
DOI: 10.17632/mzxmt8tfjs.1
title: Uddessho: An Extensive Benchmark Dataset for Multimodal Author Intent Classification in Low-Resource Bangla Language

description: The "Uddessho" dataset, meaning "Intent" in English, is designed for multimodal author intent classification. It contains 3048 post instances categorized into six intent types: Informative, Advocative, Promotive, Exhibitionist, Expressive, and Controversial. The dataset is divided into a training set with 2423 posts, a testing set with 313 posts, and a validation set with 312 posts, totaling 3048 posts. 

Distribution of Dataset Splits Across Different Intent Categories
=========================================
Intent Taxonomy Training Testing Validation
=========================================
Informative 514 67 67
Advocative 386 49 49
Promotive 315 43 42
Exhibitionist 371 47 48
Expressive 518 66 66
Controversial 319 41 40 
========================================= 
Total 2423 313 312
=========================================
DOI: 10.17632/zysdrt29ft.1
title: Data for: Capillary pressure characteristics of CO2-brine-sandstone systems

description: This is the coreflood data we measured during the different experiments
DOI: 10.17632/dkg3b6vz65.1
title: Anomaly Detection

description: Time Series for Anomaly Detection The file is a Matlab data file. It contains 3 time series, representing the packet rate of 3 different traffic traces, related to inbound traffic of the UNINA Network. The traces were collected in year 2004. The packet rate was sampled with a period of 2 seconds and each trace lasts 2 hours. These data have been used for studies on volume-based anomaly detection and are related to time intervals during which no anomalies were observed on the UNINA network by the NOC operators. In other words, they can be considered anomaly-free. When refering to our Anomaly Detection Dataset, please cite the following reference: A. Dainotti, A. Pescapè, G. Ventre, "A cascade architecture for DoS attacks detection based on the wavelet transform", Journal of Computer Security, Volume 17, Number 6/2009, Pages 945-968.
DOI: 10.17632/tyy7jrth6y.1
title: Datacost Fusion: Big Data-Driven Efficiency for Production and Advertising

description: This dataset used in this study encompasses a comprehensive collection of records detailing production and advertising.

Key Attributes:
Clustering data
Date: Timestamp of each record, allowing for temporal analysis.
Product_ID: Unique identifier for each product.
Production_Quantity: Number of units produced on each day.
Advertising_Channel: The medium used for advertising (e.g., social media, television, print).
Ad_Spend: Amount of money spent on advertising for each channel.
Sales: Number of units sold per day.
Customer_Demographics: Information on customer segments, including age, gender, and location.
Page_Views: Number of page views generated by the advertisement.
Clicks: Number of clicks on the advertisements.
Data Preprocessing:
To prepare the dataset for analysis, several preprocessing steps were undertaken:

Data Cleaning: Removal of duplicate entries and handling of missing values through imputation.
Normalization: Scaling of numerical features to ensure uniformity.
Categorical Encoding: Conversion of categorical variables (e.g., Advertising_Channel) into numerical format using one-hot encoding.
Usage in Study:
This dataset facilitated the development and validation of our budget allocation algorithms and predictive models. By analyzing historical performance data, we were able to derive insights into optimal budget distribution and forecast future advertising impacts on sales and production efficiency.
DOI: 10.17632/24xs577dyp.1
title: Scatter Plot

description: Data set contains two variables and 10 observations use this data set to plot scatter diagram and run correlation Age: in Years Monthly Income: in Rs
DOI: 10.17632/2kx995rscj.2
title: Vibration Signals Feature for Fault Diagnosis of wind turbine blade

description: The datasets involve vibration measurements for vibration measurements of wind turbine blades in healthy and faulty conditions. The faulty condition consists of cracking the blade, blade surface erosion, and blade imbalance. the vibration signals are recorded by a uniaxial piezoelectric sensor fixed on the hub of the turbine.
DOI: 10.17632/b6s2rkpmvh.1
title: Pomegranate Fruit Diseases Dataset for Deep Learning Models

description: Pomegranate Fruit Diseases datasets should be efficient and clean for creating accurate and consistent machine-learning models and minimizing misclassification in real-time scenarios. The publicly available, standardized datasets for the categorization of pomegranate fruit illnesses currently used in agriculture are insufficiently precise and comprehensive to effectively train the models. Our main objective for the current project is to produce a publicly accessible dataset of pomegranate fruits and an image dataset of pomegranate fruits with various diseases that is ready for use to address this issue. Five different pomegranate fruit diseases—from areas like Ballari, Bangalore, Bagalkote, etc.—have been compiled by us. The photos were shot in July and October of 2023. The collection includes 5099 labeled and categorized images of healthy and diseased pomegranate fruit, divided into five categories: Healthy, Bacterial blight, Anthracnose, Cercospora fruit spot, and Alternaria fruit spot. The dataset consists of five folders named by related medical conditions. This dataset may help identify pomegranate diseases in other countries and boosting pomegranate yield output.
DOI: 10.17632/r3n8ctfwh7.2
title: Multi-center Study on the Application Potential of Siaα-2,6Gal in Early and Differential Diagnosis of Lung Cancer

description: Objective: This study aimed to investigate the application potential of the abnormal glycan structure Siaα-2,6Gal in the early and differential diagnosis of lung cancer. Methods: Clinical data and serum samples from 730 patients and 120 healthy individuals participating in clinical trials on Siaα-2,6Gal were collected at three medical centers between January 2022 and June 2023. Serum levels of Siaα-2,6Gal, carcinoembryonic antigen (CEA), cytokeratin 19 fragment antigen (CYFRA21-1), squamous cell carcinoma antigen (SCC), neuron-specific enolase (NSE), and pro-gastrin-releasing peptide (ProGRP) were measured. The application potentials of these markers in the early and differential diagnosis, classification, and staging of lung cancer were explored. Results: (1) Serum Siaα-2,6Gal levels in the lung cancer group were 2,606 (1,970–3,458) U/mL, significantly higher than those in the benign lung disease, miscellaneous malignant tumor, miscellaneous benign disease, and healthy individual groups at 1,359 (950–1,528), 1,252 (903–1,532), 1,196 (850–1,490), and 1,210 (1,100–1,287) U/mL (P &lt; 0.0001). (2) Serum Siaα-2,6Gal levels in the early-stage lung cancer (stages 0–II) group were 2,576 (1,929–3,338) U/mL, significantly higher than those in the benign pulmonary nodule group at 1,419 (1,105–1,820) U/mL (P &lt; 0.0001). (3) Receiver operating characteristic curves showed that Siaα-2,6Gal had a high diagnostic efficiency for lung cancer (area under the curve (AUC) = 0.9217), significantly superior to CEA, CYFRA21-1, SCC, NSE, and ProGRP (AUCs of 0.6618, 0.6605, 0.5783, 0.5985, and 0.6381). Conclusion: Siaα-2,6Gal is a promising biomarker for lung cancer diagnosis and may offer superior differential diagnosis of early-stage lung cancer from benign pulmonary nodules compared to traditional tumor markers.
DOI: 10.17632/37jx7gkz3y.1
title: Data for: Electoral Rules, Strategic Entry and Polarization

description: Data and do file regarding our laboratory experiment.
DOI: 10.17632/x8t4gn6mt6.1
title: Indonesian Tweets Dataset for Identifying Emotion Changes Among Twitter Users Following the Onset of the COVID-19

description: Tweet data was collected using the Twitter API services based on a point location with a radius of 10 km to obtain high tweet intensity in strategic locations. The location for data collection was the Setiabudi area, Jakarta, Indonesia, obtained by rounding the latitude value to -6.22 and longitude to 106.83; this location point was chosen via probability sampling to enhance the analysis process, as this area was most affected by COVID-19 cases in Indonesia during the pandemic.

Data collection was divided into two periods: before the COVID-19 outbreak (i.e., December 2019 to March 2020) and the beginning of the COVID-19 outbreak (i.e., March 2020 to June 2020). This study considered the first day of the COVID-19 pandemic in Indonesia to be March 14, 2020, according to the rules released by the Indonesian government. This mechanism resulted large and varied data, as a data filtering process based on a specific context was not conducted. Then, our work performed three steps of data reduction to obtain the appropriate data dimensions; a) select active users based on tweet intensity, b) remove tweet data with a word count below five, and c) eliminate data based on the suitability of discussion topics. The data used in the modeling process has passed three stages of data reduction. 

Linear with our work, there are three labeling processes: discussion topic, emotion and sentiment. For discussion topic labeling, this data performed a topic modeling mechanism using the LDA algorithm.  On the other hand, for emotion and sentiment labeling, three annotators manually labeled the data and used the majority vote strategy for the final class label on sample data. In our annotation strategy, for emotion labeling, each annotator was asked to annotate the individual tweets as "Happiness", "Love", "Fear", "Sadness", and "Anger". While for sentiment labeling, each tweet has been annotated into three predetermined category, namely "Positive", "Negative", and "Neutral".
DOI: 10.17632/3v376xwmwh.1
title: Running spatio-temporal parameter variability - "Stride" package

description: R scripts (package "Stride") used to process data on spatio-temporal running parameters (step time TS, flight time TV, contact time TC, duty factor DF, cadence C and estimated vertical leg stiffness Kleg) obtained using an Optogait system (Optogait, Microgate, Bolzano, Italy). The scripts synthesise and clean the data and then calculate various variability indices as a function of the length of the time series. Linear (coefficient of variation, standard deviation) and non-linear (coefficient alpha 1 of detrend fluctuation analysis, Higuchi fractal index, box counting algorithm) cycle-by-cycle analyses are performed according to the calculation recommendations (Phinyomark et al. DOI :10.3389/fphys.2020.00333).
The processing is done in several steps: first, the time series are extracted and outliers are managed, then analyses by time series and series size are performed , and finally the data are concatenated . An example of a dataset obtained using this procedure is attached (concatenated_files_12.xls). It contains a data set from 25 subjects who ran on a treadmill (Medical Treadmill, MONARK, Sverige) for 3 sessions of 6 minutes each, 24 hours apart. All subjects were trained to run on a treadmill. All participants ran more than twice a week for at least two years. Their age ranged between (20-36) years, body mass index (18.3-24.5) kg/m², running experience (3-15) years, kilometres run per year (1000-2500) km. This data set was collected as part of an ethics committee approved study (RCB number ID-RCB: 2019-A03012-55). 
Outlier detection was performed according to previously published values (García-Pinillos et al. DOI: 10.2478/hukin-2019-0098), obtained with the same instrument. Outlier management was performed using the imputeTS package, which has been validated for outlier management in biological time series. (http://cran.r-project.org/package=imputeTS ; http://cran.r-project.org/web/packages/imputeTS/imputeTS.pdf)
DOI: 10.17632/ds45yy9jrc.3
title: Mulberry Leaf Dataset

description: Data collection: 
We collected the mulberry leaf cultivars from three regions of Thailand (northern, central, and northeast) that included five provinces in total (Chiang Mai, Phitsanulok, Nakhon Ratchasima, Burriram, and Mahasarakham). DSLR and phone cameras were used to take images with different perspectives from ten mulberry leaf cultivars recorded in the natural environments with different perspectives. The mulberry leaf dataset includes 5,262 images of 10 mulberry leaf cultivars: King Red, King White, Taiwan Maechor, Taiwan Strawberry, Black Austurkey, Black Australia, Chiang Mai 60, Buriram 60, Kamphaeng Saen 42, and Mixed Chiang Mai 60+Buriram 60.

Data description:
The mulberry leaf dataset was curated in 2020 using digital single-lens reflex (DSLR) and smartphone cameras to collect 5,262 images categorized into ten classes existing in diverse environmental conditions. No seasonal considerations were factored in during the data collection; however, all data was collected on the sunny days. There is no existence of external plants in the samples of the mulberry leaves, but there are some natural backgrounds of the brown soil and the mulberry tree, which exist in small proportion relative to the mulberry leaves. The image format of the mulberry leaf dataset exists in JPEG format and has varying resolution sizes. The researcher captured mulberry leaf images from various regions and provinces in Thailand for eight months. The image captures of mulberry leaves were taken from five Thai areas, as shown in Fig. 1. Further, the dataset was annotated by a domain expert responsible for classifying each of the mulberry leaves into their respective classes or categories. The leaves with similar features or properties were stored in a specific folder (class), resulting in ten possible classes. 

Data source location:
The mulberry leaf dataset was collected from three regions of Thailand: northern (Chiang Mai), central (Phitsanulok), and northest (Nakhon Ratchasima, Burriram, and Mahasarakham).

Related research article:
Chompookham, T. & Surinta, O. (2021). Ensemble methods with deep convolutional neural networks for plant leaf recognition. ICIC Express Letters, 15(6), 553-565. DOI: 10.24507/icicel.15.06.553
DOI: 10.17632/p997fvf526.2
title: BDPapayaLeaf: A annotation based image dataset of papaya leaf disease.

description: The dataset contains images of papaya leaf diseases. Images were collected from Changao , Ashulia , Dhaka, Bangladesh area with coordinates at 23° 53' 2" N and 90° 19' 28" E. The images were taken from July 12-August 2, 2023 , approximately 22 days. The .rar and .zip directory has two different parent folder. Inside the parent folder there are 3 sub directory. Original Images, Annotations, Labels. ‘Original Images’ folder has all the original jpg images classified into five classes. ‘Annotations’ folder has annotation images in xml format . ‘Labels’ folder has annotation images in txt format .
Number of images: 2159
Number of classes: 5
Name if the classes: Anthracnose, Bacterial spot, Curl, Ring spot, and Healthy .
Number of annotation images : 1050
Annotation format: XML,TXT
DOI: 10.17632/hpc938kng2.1
title: Meteorological and air quality observations at four automatic station in Rio de Janeiro, BRA, 2014-2018.

description: The dataset contains records from a four automatic air Quality and meteorological station (AQMS) on four differents sites (ADN- Adalgisa Nery, VSL – Vila São Luiz, PDC – Porto das Caixas, INE – Inea) in Metropolitan area of Rio de Janeiro, Brazil, and represent four features distinct (mobile fleet amount, industrial facilities, traffic urban, relief, microclimates and pollutant sources). The database was built from the daily hourly data (to 6:30 up to 18:30) provided by INEA (http://200.20.53.7/qualiar/home/index) between January 1, 2014 to December 31, 2018. These stations provide hourly data for several variables, such as: nitrogen dioxide (NO2), nitrogen monoxide (NO), carbon monoxide (CO), ozone (O3), solar radiation (SR), temperature (TEMP), relative humidity (RH), wind speed (WS), wind direction (WD), rainfall (RF), among others, which will be the variables studied in this project. The data provided here represents a cleaned and processed data set extracted from the original data. There are uncertainties associated with the data related to the circumstances of the station's operation (AQMS) and the extreme conditions at the site. Please see the related journal article for details on these issues.
DOI: 10.3886/e212261
title: Chronic morbidity levels and associated factors among older adults in western Nepal

description: The study aims to assess the burden of chronic conditions and
explore the association between contributing factors using ordinal logistic regression. The participants were recruited using multistage sampling from three districts, one each from one ecological region, and were 60 years or above at the interview. This study is based on a secondary data analysis. Link: (https://www.openicpsr.org/openicpsr/project/209442/version/V1/view
description: Multistage, stratified, clustered sampling design
description: Older adults who were 60 years or above, proficient in Nepali speaking, had no hearing or speech impairment, and permanent residents of Gandaki province. Smallest Geographic Unit: wards
description: face-to-face interview;
DOI: 10.3886/e212261v1
title: Chronic morbidity levels and associated factors among older adults in western Nepal

description: The study aims to assess the burden of chronic conditions and
explore the association between contributing factors using ordinal logistic regression. The participants were recruited using multistage sampling from three districts, one each from one ecological region, and were 60 years or above at the interview. This study is based on a secondary data analysis. Link: (https://www.openicpsr.org/openicpsr/project/209442/version/V1/view
description: Multistage, stratified, clustered sampling design
description: Older adults who were 60 years or above, proficient in Nepali speaking, had no hearing or speech impairment, and permanent residents of Gandaki province. Smallest Geographic Unit: wards
description: face-to-face interview;
DOI: 10.17632/pmzdpf33sh.1
title: Caderno didático - Estatística Descritiva

description: Este caderno didático com abordagem de estatística, voltado especificamente ao tema estatística descritiva, abordado em diferentes cursos de graduação, foi elaborado com o objetivo auxiliar e contribuir com professores que trabalham com este tema em suas respectivas disciplinas. Os principais conteúdos abordados neste caderno didático são: - Estatística Descritiva - Definições e Conceitos - Análise Exploratória dos dados - Distribuições de Frequência - Exercícios e Aplicações
DOI: 10.17632/x4x86r6tzd.1
title: Evaluation Dataset: RAG System with LLM for Migrant Integration in an HCAI

description: The data reflect the results of the experimentation with an HCAI system implemented using a RAG framework and the Llama 3.0 model. During the experimentation, 91 questions were utilized in the domain of legal advice and migrant rights. Metrics assessed included contextual enrichment, textual quality, discourse analysis, and sentiment evaluation. This allows for the analysis of sentiments and emotions, bias detection, content and toxicity classification, as well as an analysis of inclusion and diversity.
DOI: 10.17632/2ph2cm7zvs.1
title: P2P Lending Bibliometric Literature Review_General

description: This dataset design for P2P Lending bibliometric literature review.
DOI: 10.17632/2cbxgsn7vx.1
title: Olink proteomics in COVID-19

description: Olink proteomics in COVID-19
DOI: 10.15468/dl.95qsnk
title: Occurrence Download

description: A dataset containing 1200 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus halepensis Mill.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 1200 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009218-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wcyb9c7kx2
title: pilot_recycling test

description: It is a database with information for validation of a CAP instrument in recycling