cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/sp3hcpmczn.1
title: Machine-Learning based root stress calculation model for gears with a progressive curved path of contact  

description: The study aims to investigate the possibility of employing Machine-Learning models in the design of non-involute gears. Such a model would be useful for design calculations of non-standard gears, where there are no available guidelines. Multiple models for numerical prediction were tested, i.e. Linear Regression, Support Vector Machine, K-nearest neighbour, Neural Network, AdaBoost, and Random Forest. The aim is to create a surrogate model to the final element analysis simulations, from which the data for training was collected. The models were firstly validated with the N-fold cross-validation. Further validation was done with new simulations. The results from the simulations and the models were in good agreement. The best-performing ones were Random Forest and AdaBoost. Based on the validation results a Machine-Learning constructed model for calculation of root stress in gears with a progressive curved path of contact is proposed. The model can be used as an alternative to simulations for determining the root stress in real-time, and is able to calculate the stress for gears with different number of teeth, widths, modules, paths of contact, materials, and loads. Therefore, many combinations of gear geometries can be analysed and the most suitable can be chosen.
DOI: 10.17632/57726vkm48.1
title: Institutional Thyroid Cancer Dataset ( 01/02/2013–12/31/2015)

description: This dataset was obtained from the Penn Medicine cancer registries. 

*  Dataset contains data from 5351 tumors from 5127 patients with thyroid cancer
*  Data was extracted for patients/tumors for two years, between 01/02/2013 and 12/31/2015
*  Other selection criteria: 
    +  PrimarySiteCode == C73.9
    +  PrimarySiteCategory == Endocrine
    +  PrimarySiteDesc == thyroid gland. 
*  The data has been stripped of protected health information/PHI. 
* The original data contained separate `patient` and `tumor` datasets, which have been combined into a single spreadsheet. 

Blinding: 
* The original `TumorId` and `PatientSystemId` from the institutional registry have been replaced with unrelated sequential identifiers for blinding. 
* All dates have been converted from MM-DD-YYYY format to YYYY format
* Reporting hospital names have been replaced with "Hospital.1," "Hospital.2," etc. 
* Ages >99 have been replaced with ">99."
DOI: 10.17632/rgt5c4xvc2.1
title: Data for: How many words do we read per minute? A review and meta-analysis of reading rate

description: Supplementary data meta-analysis reading rate
DOI: 10.15468/dl.kxveej
title: Occurrence Download

description: A dataset containing 407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prosopis chilensis (Molina) Stuntz",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 407 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009297-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2wkdkmndcy.1
title: Gratitude and the self

description: The amplification theory of gratitude posits that gratitude amplifies the good in people's lives and orients them toward seeking out those good things. This study tested the hypothesis that gratitude would amplify the good that people see in themselves as evidenced by higher levels of self-compassion and self-acceptance. A second hypothesis was that grateful people would tend to derive their self-worth from intrinsic domains. Participants (N = 290) completed an online survey. Structural equation modeling with latent variables showed strong positive relationships between gratitude, self-compassion, and self-acceptance. Multiple regression analyses showed that grateful people tended to stake their self-worth in intrinsic domains. Results suggest that gratitude helps to reshape the relationship with the self in ways that are conducive to positive psychological functioning.
DOI: 10.17632/ddw8rjmm6k
title: The role of e-commerce, exchange rate volatility and tourism in export demand: Evidence from the UK

description: Data obtained from the following organisations:
 Organisation for Economic Co operation and Development (OECD) 
Federal Reserve Bank of St Louis
UK Office for National Statistics
International Financial Statistics
In some cases author calculations have been preformed to the data obtained from the sources above to derived the published series.
DOI: 10.17632/ps6dntdftk.1
title: Replication code and data for Huo, Levchenko and Pandalai-Nayar (2023)

description: Replication package for Huo, Levchenko and Pandalai-Nayar (2023)
DOI: 10.17632/32d6r6r6zk.1
title: Dataset acquired by Internet of Things-enabled Industrial Multichannel Wireless Sensors Networks for Active Monitoring and Control in the Smart Grid Industry 4.0.

description: • The data provided in this paper provides can be used for efficient monitoring and control of the power generation and distribution processes in the smart grid. • The data provided in this paper can be used for the integration of distributed power generation sources into the power transmission and distribution systems within realistic network scenarios. • It can also support reliable and dynamic data capacity requirements of different types of advanced cyber-physical systems equipped with sensors and devices to operate them optimally, either manual or automatic controls, and provide information about their operations to the utilities. • In case of faults, the designed scheme intelligently monitoring and identifies the faulty systems located in a remote position and notifies the user in real-time, so that appropriate actions can be taken to supply steady electricity to the customers.
DOI: 10.17632/46v7bhb7sr.1
title: Assessing knowledge of and attitudes towards plagiarism and ability to recognize plagiaristic writing among university students in Rwanda

description: This study was conducted to assess knowledge of and attitudes towards plagiarism, as well as ability to recognize plagiaristic writing, among university students in Rwanda. An anonymous online questionnaire containing 10 knowledge questions, 10 attitude statements, and 5 writing cases with excerpts to test identification of plagiarism was administered between February and April 2021. The four key measures of this study were: - Knowledge level on plagiarism. The knowledge score was calculated as the percentage of correct answers. The knowledge level was defined as high if the score was 80% or above, and as low if the score was below 80% (Koo, Poh, Ruzita, 2015). - Attitude towards plagiarism. Percentages of respondents strongly agreed/agreed and strongly disagreed/disagreed on each of the 10 attitude statements. - Level of ability to recognize plagiaristic writing. The score was calculated as the percentage of correctly identified plagiaristic writings in the case studies. The ability level was defined as high if the score was 80% or above, and as low if the score was below 80% (Koo, Poh, Ruzita, 2015). - Themes emerging from the open-ended question. Out of the 330 university students from 40 universities completed the survey, 75.8% had a high knowledge level (score ≥80%) and 11.6% had a high score in recognizing plagiaristic writing (score ≥80%). There was no statistical significant association between knowledge level and ability to recognize plagiaristic writing (P=0.109). Lower odds were found in both diploma/certificate and bachelor students of having high knowledge as well as of having high ability to recognize plagiaristic writing. Although the general disapproves of plagiarism, approximately half of the respondents indicated that sometimes plagiarism is unavoidable, and self-plagiarism should not be punished in the same way as plagiarism of others.
DOI: 10.17632/4h9tmy3jh5.2
title: CODI Test

description: - Examination of students' error patterns in the field of functions in a quantitative way - research questions: How often do typical error patterns occur among students? Which of them can be ascribed to underlying obstacles? Which of them turns out to be robust over time? - Test instrument and background available online at www.codi-test.de - Data set of 569 students in the main study (28 items); Data set of 168 students in the additional test (six months later) - Eight error patterns which occur among more than 10% of the students -Identification of four error patterns which seem to be particularly robust over a longer period of time
DOI: 10.17632/7m7555vgrn.1
title: DiaHealth: A Bangladeshi Dataset for Type 2 Diabetes Prediction

description: his dataset provides comprehensive information on 5,437 patients, including 14 independent attributes such as demographics, clinical parameters, and medical history. Key features include age, gender, pulse rate, blood pressure (systolic and diastolic), glucose level, BMI, and family history of diabetes and related conditions like hypertension and cardiovascular disease. The dataset is labeled with a binary outcome indicating whether each patient has diabetes. This rich dataset is designed to support the development and evaluation of machine learning models for diabetes detection, management, and treatment.
DOI: 10.17632/h8mcfz95bf.2
title: A dataset of circulating estradiol and progesterone, and fecal estradiol and progesterone metabolite concentrations of female felids

description: The data presented here are associated with the following articles: Andrews C.J., Thomas D.G., Welch M.V., Yapura J., Potter M.A. (2020). Monitoring ovarian function and detecting pregnancy in felids: a review. Theriogenology, In Press. DATA DESCRIPTION: Supplement 1 provides basal (anestrus or interestrus) and peak (estrus) estradiol and fecal estradiol metabolite concentrations reported for felid species in the existing peer-reviewed literature. The basal (non-luteal) and peak (diestrus) progesterone and fecal progesterone metabolite concentrations for each species are also summarized in Supplement 1. Supplement 2 contains the raw primary data used to calculate the values (secondary data) presented in Supplement 1. DATA COLLECTION: Peer-reviewed literature (published between January 1975 and February 2020) discussing the estradiol and progesterone, and fecal estradiol and progesterone metabolites concentrations of felids were reviewed. Searches were conducted for each felid species in both Web of Science and Google Scholar using the following search terms: ‘common name’ OR ‘other name’ OR ‘Genus species’ AND ‘serum OR plasma OR fecal OR faecal’ AND ‘estr* OR ovar* OR pregnan* OR gestat* OR luteal OR progest*’. For each publication and species, the mean and range of the basal and peak estradiol (pg/ml), fecal estradiol metabolite (ng/g), progesterone (ng/ml), and/or fecal progesterone metabolite (µg/g) concentrations were recorded. The overall (i.e., across all publications) weighted mean, sample size (both the number of animals and number of samples), and range of the basal and peak estradiol, fecal estradiol metabolite, progesterone, and/or fecal progesterone metabolite concentrations were calculated for each species.
DOI: 10.17632/mn9fb7xdx6.3
title: DIB_Data

description: Contains the full set of test data for the battery degradation and state of health estimation
DOI: 10.17632/wgd66f8n6h.1
title: Wheat Leaf Dataset

description: Ethiopia has a huge resource for planting several crops, thus wheat is one of the major crops which feed the population, but the crop has been infected by viruses, Bacteria, and Fungi. In this study, the two major diseases namely Stripe Rust and Septoria wheat leaf image were used for experimentation. Currently, the wheat disease mentioned above is a major headache not only for farmers but also for plant pathologists. Furthermore, the pathologist uses the naked eye observation for the detection of wheat disease, sometimes it is challenging to detect without using the laboratory material. Several types of research conducted disease identification and detection using the traditional machine learning algorithms. Thus algorithms have drawbacks since the feature extraction is expert-based and the amount of data processing required is high relative to the machine learning. Because of this, the deep learning methodology was used to detect wheat disease. The approach has three main phases. The first phase is to collect the dataset from the wheat farm, and the image has three categories i.e. ‘Healthy Wheat Leaf’, ‘Strip Rust’, and ‘Septoria Disease’. Then the dataset is partitioned using the 80%-10%-10% approach which is used for training, validation, and testing respectively. The second phase is to design a neural network by experimenting with the best hyperparameter. Finally, the best model was selected and tested with unseen image data. The dataset contains 208 stripe rust affected leaf, 102 healthy leaf and 97 septoria affected leaf disease-infected image pictures. From this, 80% of the images were used for training, l0% were used for validation, and the remaining 10% were used for testing. During training, the data augmentation technique is used to generate more images to fit the proposed model. The experimental result demonstrates that the proposed model is effective for the detection of wheat leaf disease (Strip Rust and Septoria). The pretrained model used for experimentation are VGG19, InceptionV3, MobileNet, and EfficientNet. Among mentioned pretrained models MobileNet has achieved the best result and the model can successfully classify the given image with a testing accuracy of 90% with images captured in the real wheat farm with a heterogeneous environment.
DOI: 10.17632/zmrt6k83wk.1
title: Data for: Free, open, quantitative and adaptable digital soil map and database for Nigeria

description: Nigeria’s DSM data and soil database of Nigeria is a predictive or computer-assisted digital depiction of Nigeria’s soil types, properties and spatial distribution. In this novel DSM data and soil database, secondary data in the form of compiled hard copies, derived from the 1985 reconnaissance soil survey are transformed into vectors and symbols encoded and held as data structures within the map layout. The DSM data and its database hold information relating to the Nigerian soil in geographically referenced numerical format. Thus, it delineates quantitatively and interactively the 58 soil components (soil mapping units), based on three ecological zones, twenty-four broad geomorphic units and parent materials in Nigeria. These soil mapping units are numbered from ‘1a’ to ‘24b’ and provides much quantitative information for environmental modelling and analyses using the Nigeria’s DSM data and database (Refer to [1] for a full list of the mapping units that describes the Nigeria’s soil distribution and their spatial distinctions). This DSM and its database have a highly unique legend with flexible scales, are easy to update with location-specific measure of accuracy, and guarantees data security. Users can quickly generate spatial soil information that provides ample solutions to the growing demand for high-resolution soil data. This solves the cartographic and qualitative limitations of the 1990 FDALR hard copy soil maps. The DSM incorporates queries and spatial analyses tools to generate thematic maps of basic soil properties within the context of Nigeria such as soil mapping unit, geology (basement complex, basalt, sandstone, Aeolian sandstone, shale, etcetera), soil classification (USDA and FAO), pH scales (acidic, basic, neutral, etcetera) and ecological zones (wetland, rainforest, savanna).
DOI: 10.17632/vn6skw58bv.1
title: Spot and futures prices for corn, soybeans, WTI crude oil and Henry Hub natural gas, Chicago Mercantile Exchange Group

description: Research Data contains the following files: Sheet 'Energy': oilFutures: Weekly futures price of NYMEX crude oil oilSpot: Weekly spot price of NYMEX crude oil oil_long: Weekly long positions held by money managers in crude oil futures market gasFutures: Weekly futures price of Henry Hub natural gas gasSpot: Weekly spot price of Henry Hub natural gas gas_long: Weekly long positions held by money managers in Henry Hub natural gas futures market oil_short: Weekly short positions held by money managers in crude oil futures market gas_short: Weekly short positions held by money in managers Henry Hub natural gas futures market netoil: net log of weekly positions held by money managers in crude oil futures market netgas: net log of weekly positions held by money managers in Henry Hub natural gas futures market Sheet 'Agric' cornFutures: Weekly futures price of CBOT corn cornSpot: Weekly spot price of CBOT corn corn_long: Weekly long positions held by commodity index traders in corn futures market soyFutures: Weekly futures price of CBOT soybeans soySpot: Weekly spot price of CBOT soybeans soy_long: Weekly long positions held by commodity index traders in corn futures market corn_short: Weekly short positions held by commodity index traders in corn futures market soy_short: Weekly short positions held by commodity index traders in soybeans futures market netcorn: net log of weekly positions held by commodity index traders in corn futures market netsoy: net log of weekly positions held by commodity index traders in soybeans futures market The folders CointegrationTests, Graphics, UnitRootTests contain MATLAB files
DOI: 10.17632/36m6fjsm4s.1
title: Tafsir Tematik Dakwah

description: Referensi Tafsir Tematik Dakwah
DOI: 10.17632/y63frd6pmf.2
title: Multimodal Hinglish Tweet  Dataset for  Deep Pragmatic Analysis

description: This dataset explores the highlish tweet datasets and reveals a significant gap in understanding the emotions and sentiments surrounding world wars amidst the multiple ongoing armed crises in the world. To address this gap, a tweet dataset can aid researchers and leaders of nations in conflict in making informed decisions regarding public opinions on war. The corpus is composed of essential keywords that are valuable for mining tweets in code-mixed language (Hinglish).
DOI: 10.17632/nh5xdwkg27.1
title: Dataset. Techno-economic analysis of the regulatory framework for collective self-consumption in Spain

description: Data considered in the work entitled 'Techno-economic analysis of the regulatory framework for collective self-consumption in Spain'. The first 20 columns correspond to the hourly profiles of energy demanded (kWh) for each of the 20 customers considered in the self-consumption community. Each row represents one hour of the year under study. Columns 22-24 provide the average hourly day-ahead market price, the energy surplus selling price and the regulated tariff price (PVPC), respectively. Columns 26 and 27 correspond, respectively, to the irradiance and ambient temperature considered.
DOI: 10.17632/5vrhysd55b.2
title: Data for: Mid-Pliocene global land monsoon from PlioMIP1 simulations

description: Those data are for mid-Pliocene global land monsoon from PlioMIP1 simulations. Note: The data used here are primarially from the Pliocene Model Intercomparison Project Phase 1 (Pliomip1). Please cite original reference when using this data!!!!!
DOI: 10.17632/4k964xyz65.2
title: Bangla Transformation of Sentence Dataset (BTSD)

description: 
The presented dataset comprises a collection of Bangla sentences that have been meticulously labeled with their respective sentence types. The dataset comprises a total of 3,793 annotated sentences with three distinct sentence types, including simple, compound, and complex sentences. This dataset can be leveraged for a broad range of natural language processing (NLP) tasks, such as sentence classification, sentiment analysis, and machine translation.
DOI: 10.17632/rryrbf7b54.5
title: Vaginal discharge score_Figueiredo et al.

description: Vaginal discharge score for dairy cows
DOI: 10.17632/663j22s43c.3
title: Fabric Defects Dataset

description: The data collected for this project were from :
● Fabric Defect Dataset from Kaggle (Ranathunga, 2020)
● Fabric Stain Dataset from Kaggle (Pathirana, 2020). 
● Aitex fabric image database (Silvestre-Blanes et al., 2019)
● Dataset from the author of the literature review paper (Peng, 2020): only the non-defect images from the dataset.
Aitex fabric image database 
This textile fabric dataset consisted of 245 images of 7 different fabrics with image sizes of 4096×256 pixels. There were 140 defect-free images, 20 for each type of fabric. In each of the defected category, there were 105 images (Silvestre-Blanes et al., 2019).
Fabric Stain Dataset
This was taken from Kaggle (Pathirana, 2020). The dataset was built as a part of the fabric defect detection project of the Intelligence Lab of University of Moratuwa, Sri Lanka. The dataset consisted of images with resolution of 1920x1080 or 1080x1920. It consisted of 398 defected images with different types of stains and 68 defect free images.
Fabric Defect Dataset 
The dataset was also taken from Kaggle and it was supplied by the Intelligence Lab, Department of computer science and Engineering, University of Moratuwa (Ranathunga, 2020). This dataset consisted of 3 classes of defective images namely horizontal, vertical and holes along with 3 mask images for each defective image sample. The folder named 'captured' consisted of raw images. Images have a size of 640x360.
Dataset from the author of the literature review paper 
Authors of the research papers in the literature review were contacted through emails for the dataset. With the consent of the author of the paper “Automatic fabric defect detection method using pran-net” (Peng, 2020) named Troy Peng, the annotated data was used (Peng et. al, 2020).
After collecting the images from all these sources, the distribution of the images is as given below:
● Defect free 1,666
● Defected 1,073
Defected hole 281 
Defected horizontal 136 
Defected lines images 157 
Defected stain images 398 
Defected verticle images 101
Total Images = 2,739
DOI: 10.17632/rrnz8p6rvw.3
title: Digital Repression Index 2003-2022

description: The data describes global patterns of digital repression from 2003-2022 and incorporates two composite indexes: a latent construct of digital repression and a latent construct of digital repression capacity. Key issues addressed by this data: Which countries are deploying digital repression techniques and how prevalent is their use? What is the relationship between regime type and digital repression? How might we understand the relationship between specific types of repression (violating political liberties, physical violence committed by the state, or curtailing private civil liberties) and digital repression? How does the deployment of individual components of digital repression differ by political system and regime type?