cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5dpc5gbgpz.1
title: Tobacco Aerial Dataset

description: A new aerial tobacco weed dataset with various field conditions.
We have acquired a new tobacco-weed dataset using a Mavic Mini drone. Eight fields of tobacco crops are captured in Mardan, Khyber Pakhtunkhwa, Pakistan. At different growth stages, these eight fields are captured at a crop age of 15 to 40 days approximately. Images are captured at 1920 × 1080-pixel resolution; due to system memory limitations, we have cropped non-overlapping images of resolution 480 × 352 for processing. This image patch cropping is implemented using a code that reads images and creates non-overlapping tile images using two nested loops; the respective annotation images are also cropped simultaneously. 
Dataset is captured at an average altitude of 4 m with a ground sampling distance of 0.1 cm/ pixel.  Images are labeled manually; background, crop, and weed have label values of 0, 1, and 2, respectively.

Citation Request: if you use these datasets in your research or projects by any means, please cite following publications 

1) Patch-wise weeds coarse segmentation mask from aerial imagery of sesame crop
(Published in Computers and Electronics in Agriculture 2022, HEC Recognized W category, Impact factor 6.757, Q1)
2) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery 
(Published in Smart Agricultural Technology (A companion journal of Computers and Electronics in Agriculture))
3) A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop
(Published in IEEE Access, Impact factor 3.1, Q1)

Acknowledgement Request
This work is funded by the Higher Education Commission of Pakistan and the National center for Robotics and Automation (DF-1009–31). We thank Pakistan Tobacco Company for helping us find farms for data collection. 

Steps to Access Mendeley datasets
1.	Click on the link
2.	The link with ask you to sign in or register with institutional email.
3.	Use your institutional/organization email to register and then sign in.
4.	Once sign in, dataset will be visible in compressed folders
5.	Download and unzip/umcompress folder
6.	Use dataset in your research as you see fit (folders contains original images, and their labeled groundtruths, along with binary vegetation masks. In groundtruths background have label value of 0, crop have label 1 and weeds have label of 2. maskref subfolders shows labelled data for visualization)
DOI: 10.17632/z72jt5fb27.1
title: Artificial intelligence capital stock in Europe, UK, USA and Japan 1995 - 2020

description: Following (Rimmington et al., 2018), we target the EUKLEMS & INTANProd database data on capital stocks, categorized by SIC code and country, including software, databases, computer hardware, and machinery. From their definition of AI, we measure four distinctive AI capital stock categories:
1.	Data and equipment: computing equipment, communication equipment and compute software and databases, chained linked volumes (2015), millions of national currencies.
2.	Research and development: chained linked volumes (2015), millions of national currency.
3.	Intangible assets: organizational capital, brand, industrial design, chained linked volumes (2015), millions of national currency.
4.	Skills: training, labor compensation for NACE_R2 Computer programming, consultancy, and information service activities and NACE_R2 Education, chained linked volumes (2015), millions of national currency.
Building on our methodology, we get a closer proxy for AI stock using the data from (Bontandini et al., 2023) and (Rimmington et al., 2018) approach. We improve them to get more reliable and exact AI stock measurements using four AI dimensional coefficients. The AI dimensional coefficients approach we develop here builds on AI intensify coefficient applied to calculate AI investments (Evas et al., 2022).
We derive four AI dimensional coefficients following (Evas et al., 2022) analysis of AI investments in the EU. These coefficients will enable us to calculate the AI net stock per country accurately. 
1.	AI dimensional coefficient 1 (AI1): number of AI economic players / GDP in billion € (ratio).  For details, see G2: AI player intensity (Righi et al., 2021),
2.	AI dimensional coefficient 2 (AI2): number of AI players in AI R&D / GDP in billion € (ratio). For details, see R1: AI players in AI R&D (Righi et al., 2021),
3.	AI dimensional coefficient 3(AI): University places with AI content in the EU(Bachelor and master level) / GDP in millions € (ratio). For details, see S6: University places with AI content in the EU (Righi et al., 2021). Data for the USA, UK, and Japan for AI dimensional coefficients AI3 were derived using (OECD.AI, 2023),
4.	AI dimensional coefficient 4 (AI4): AI investments in the EU in million € / GDP in billion €, (ratio). For details, see G6: AI investments in the EU (Righi et al., 2022). Data for the USA, UK, and Japan for AI dimensional coefficients AI4 were derived using  - venture capital investments in AI start-ups (OECD.AI, 2023).
We have developed and implemented AI dimensional coefficients (AI1 - AI4) to improve the accuracy of data analysis. Our revised data is presented as follows: Total revised AI net capital stock = AI data and equipment net capital stock x (AI1) + AI research and development net capital stock x (AI2) + AI skills stock net x (AI3) + AI net intangible assets stock x (AI4).
DOI: 10.17632/6nnfmth838.1
title: The 2 Goldbach's Conjectures with Proof

description: The 1741 Goldbach [1] made his most famous contribution to mathematics with the conjecture that all even numbers can be expressed as the sum of the two primes (currently Conjecture referred to as "all even numbers greater than 2 can be expressed as the sum-two primes) .Yet, no proof of Goldbach's Conjecture has been found. This assumption seems to be right for a large amount of numbers using numerical calculations. Some examples are 10 = 3 + 7, 18 = 7 + 11, 100 = 97 + 3, and so on. But there are a multitude sums primes meeting an even number which I would say is irregular, but increases with the size of the even number.To same happens and with an odd number as example 25, ie. 25 = 3 + 3 + 19 = 3 + 5 + 17 = 3 + 11 + 11 = 5 + 7 + 13 = 7 + 7 + 11. We say only 5 cases and only those that meet the constant sum of 25 .First turns in Theorem 3 that there may be at least a pair of primes, such that their sum is equal to every even number. But at the same time reveals the method of finding all pairs that satisfy this condition. According to Theorem 4, the second guess switches to form first guess and this is primarily elementary, to prove the truth of 1.The alternative and the side stream assistant, to prove Goldbach's Conjecture in this research was the Mathematica program which is the main tool for data collection, but also for finding all the steps in order to demonstrate each part of the proof. We must mention that Vinogradov proved to1937, that for every sufficiently large number can be expressed, that is the sum of the three primes. And finally the Chinese mathematician Chen Jing Run proved big first for a constant number that is the sum of the first three in 1966 [2]. Finally, the investigation of Goldbach's Conjecture has acted as a catalyst for the creation and development of many methods that are useful, with many theorems that help and other areas of mathematics.
DOI: 10.17632/8dv486w9w7.1
title: Africa capital structure - firms

description: This is a panel data for listed firms in Africa to analyze their capital structure
DOI: 10.17632/8z2ps64c6d.2
title: Analysis of Factors Affecting Individual Decision-Making Styles

description: The data is based on an online survey conducted at a worldwide scale. There are a total of 356 participants. There is a relatively balanced gender ratio where 204 (57.3%) of the respondents are females, and 152 (42.70%) are male. 139 (39.04%) of the survey participants work in the education sector as educators, academicians, lab teachers, course coordinators. The rest of the participants belong to different occupations. The point of the survey is to identify the factors that affect the decision-makers' basic leadership styles. These factors are characterized as Rationality, Intuitive Awareness, Decision Freedom, Cautiousness, Pro-Activity, Emotion-Neutrality, Decision Difficulty, Comfortability, Goal-Orientation, Plan-Orientation. The information uncovers how these factors change with respect to socio-demograprahic differences such as gender, age, education, location, and occupational sector.
DOI: 10.17632/vh7hx5m8th.1
title: Adaptation of Healthy Volunteers to the Impact of Microclimatic Weather Factors: Cross-sectional Study

description: Data from a study of heart rate variability, microcirculation, and body composition in healthy volunteers, as well as heliomagnetic factors in 6 areas of the terrainkur are presented.
DOI: 10.17632/xf7ryhs3cd.1
title: Comprehensive lychee leaf data set: Digital images for analysis and classification

description: The lychee leaves in the investigated dataset were carefully selected for a variety of research and analysis uses. The 4,129 high-resolution images in this dataset each show intricate lychee foliage details. The photographs' standardization at a resolution of 1080 x 1920 pixels makes them uniform and easy to use for a range of applications. Each of the three sets of leaves in this large dataset indicates a distinct aspect of lychee health and vitality. 1. Diseased Lychee Leaves (Class I): Total Count: 3,539 images Subcategories: This extensive class further subdivides into six subcategories, each representing a specific lychee leaf disease: -Entomosporium Leaf Spot on Woody Ornamentals - Leaf Blight Lychee Leaf Diseases - Lychee Algal Spot in Non-Direct Sunlight - Lychee Anthracnose on Cloudy Day - Lychee Leaf Mites in Direct Sunlight - Lychee Mayetiola After Raining 2. Dry Lychee Leaves (Class II): Total Count: 476 images Description: This category comprises images of lychee leaves in a desiccated state, reflecting the natural progression of foliage as it transitions from vigor to senescence. The dry lychee leaves may showcase characteristic features such as wilting, discoloration, and a withered appearance. 3. Healthy Lychee Leaves (Class III): Total Count: 114 images Description: These pristine specimens exemplify the ideal state of lychee leaves, exhibiting vibrant green hues, uniform textures, and the absence of any visible blemishes. They serve as a benchmark for assessing the well-being of lychee foliage.
DOI: 10.17632/9jnf2jvghy.2
title: Caries-Spectra: A dataset of Enamel Caries

description: This dataset comprises a diverse collection of low-resolution images featuring two types of dental caries along with a category that represents no caries. Carefully selected from different patients, these caries offer a comprehensive and varied representation. The images were sourced from several medical clinics from Rajshahi and Dhaka, Bangladesh, spanning the time period from 2021 to 2022. Functioning as a valuable visual resource, the dataset provides a detailed portrayal of the distinctive characteristics of these caries, facilitating accurate classification. It is divided into two specific categories: Advanced Enamel Caries (800 images) and Early Stage Enamel Caries (800 images), and No Enamel Caries (400 images) totaling 2000 original JPG images. The dataset is organized into three sub-folders: "AdvanceEnamel_Caries" and "EarlyStageEnamel_Caries, and "NoEnamel_Caries".
DOI: 10.17632/d9yy8w52ns.2
title: ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in Arabic Online Content during COVID-19 Pandemic.

description: The dataset consists of 11,128 online posts manually labelled for ten emotion categories or neutral, with Fleiss’ kappa of 0.71. We have considered ten emotions, including six of the basic human emotions that were originally proposed by Ekman (1999): anger, disgust, fear, happiness, sadness, and surprise. Additionally, we have incorporated four other emotions that we deemed pertinent to the pandemic period, namely, anticipation, confusion, optimism, and pessimism. The number of instances are relatively balanced across emotion categories in ArPanEmo dataset.

ArPanEmo is unique in that it focuses on a specific dialect, namely Saudi, and covers topics related to healthcare and the ways in which the COVID-19 pandemic has affected various aspects of life.

The online posts were collected from three distinct online sources:

    YouTube comments,
    Online newspaper comments
    Twitter.

Regarding the part of ArPanEmo corpus collected from Twitter, we only release Tweet ID along with annotations due to Twitter’s Service Terms restricting the distribution of tweet contents. Researchers can use Twitter ID to collect the tweets contents when conducting their studies on the dataset. 


To ensure reproducibility, specific training and test portions are well defined for the ArPanEmo dataset. The dataset is formatted in CSV, where each row represents an online post collected from one of the three aforementioned resources. The file includes three columns: number, post, and label. 

     The number represents the Twitter ID, and in the case of a Youtube or online newspaper comment, the number is set to 0. 
     The post column contains the collected online post, and in the case of Twitter, the post is replaced with three dots (...). 
     The label column indicates tthe corresponding emotion label for each post.

*** NOTE *** 
Version 2 of the ArPanEmo dataset differs from version 1 only in that it includes the correct training set file (ArPanEmo_train.csv), whereas in version 1, the training set file (ArPanEmo_train.csv) was mistakenly a duplicate of the test set file (ArPanEmo_test.csv).
DOI: 10.17632/gxdd74czwh.1
title: Current Signature Dataset of Three-Phase Induction Motor under Varying Load Conditions

description: The time domain dataset includes the instantaneous values of three-phase current signature data of loaded induction motor with healthy and unhealthy conditions. The motor with various faults were operated under different load conditions to observe their impact on current signatures. The motor faults include inner-race and outer-race bearing faults of (i) 0.7mm, (i) 0.9mm, (i) 1.1mm, (i) 1.3mm, (i) 1.5m, and (i) 1.7mm. The bearings with different severity levels of faults were operated under the load conditions of 100W, 200W, and 300W. In addition, the motor health condition was also experimented under broken rotor bar (BRB) fault with 100W and 300W loads. There are a total 39 datasets including the three-phase current data acquired under different motor health conditions. The data was acquired at the sampling rate of 10 kHz at the rate of 1000 samples per channel using non-invasive current sensors.
DOI: 10.17632/yxzxvxtvpr.1
title: Data for: The Economic Cost of Air Pollution Due to Stubble Burning: Evidence from Delhi

description: The datasets comprise a compilation of air pollution and economic data for the city of New Delhi from publicly available data by Indian government, as well as FIRECOUNT data made available by NASA for academic purposes.

Pollution data is 24h data, processed from data taken from Central Control Room for Air Quality Management: https://app.cpcbccr.com/ccr/

FIRECOUNT Data is processed from the VIIRS 375 m (S-NPP) 24h data made available by NASA: https://firms.modaps.eosdis.nasa.gov/active_fire/#firms-txt
DOI: 10.17632/22p2vcbxfk.3
title: Dataset of groundnut plant leaf images for classification and detection

description: Data for publication in Data in Brief.
DOI: 10.17632/ngpd634smf.1
title: mSANN model benchmarks

description: This data set contains scripts and dataset needed to reproduce the results in the following paper: Mohamed Amine Bouhlel, Sicheng He, and Joaquim R. R. A. Martins. Scalable gradient-enhanced artificial neural networks forairfoil shape design in the subsonic and transonic regimes. In this paper, we mainly produced three studies: * An analytical test case stored in the repository "Rosenbrock": This repository contains the dataset for running the training and validation of the models. It also contains three repositories ANN, SANN, and mSANN that contains the scripts needed to rerun the models, respectively. * The airfoil shape design analysis test case in both subsonic and transonic regimes in the repository "Analysis/mSANN": This repository contains three repositories "cd", "cl", and "cd" for training the mSANN model on the aerodynamic coefficients. Each sub-repository contains the dataset (for training, validation, and testing the model) and two script files run.py and prediction.py for training the neural network and make predictions. * The airfoil shape design optimization test case in both subsonic and transonic regimes in the repossitory "Optimization": This repository contains two repositories "subsonic" and "transonic" for running an optimization either using the mSANN model or CFD.
DOI: 10.17632/zsj5pgrsg9.1
title: Malware webpages data

description: Malware dataset features contains the web pages categories as malwares.
DOI: 10.17632/hd96prn3nc.2
title: DTU - Drone inspection images of wind turbine

description: This dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark.
DOI: 10.17632/fnvwk9637p.1
title: Dataset for Smart Irrigation Systems in Agriculture: A systematic review

description: This is the dataset used for the systematic review article Smart Irrigation Systems in Agriculture.
DOI: 10.17632/h6nbws5h6s.1
title: radial: A Fortran subroutine package for the solution of the radial Schrödinger and Dirac wave equations

description: The Fortran subroutine package radial for the numerical solution of the Schrödinger and Dirac wave equations of electrons in central potentials is described. The considered potentials V(r) are such that the function ${\cal V}(r) \equiv rV(r)$ is finite for all r and tends to constant values when r -&gt; 0 and r -&gt; infinity. This includes finite-range potentials as well as combinations of Coulomb and finite-range potentials. The function ${\cal V}(r)$ used in the numerical calculation is the natural cubic spline that interpolates a table of values provided by the user. The radial wave equations are solved by using piecewise exact power series expansions of the radial functions, which are summed up to the prescribed accuracy so that truncation errors can be completely avoided. The radial subroutines compute radial wave functions, eigenvalues for bound states and phase shifts for free states. Specific subroutines are also provided for computing the radial functions and phase shifts for free states of complex optical potentials having a finite-range absorptive imaginary part. The solution subroutines are accompanied by example main programs, as well as with specific programs that perform calculations relevant in atomic, nuclear, and radiation physics (the self-consistent solution of the Dirac–Hartee–Fock–Slater equations for neutral atoms and positive ions, and the calculation of cross sections for elastic scattering of high-energy electrons and positrons by atoms and of nucleons by nuclei). The distribution package includes a detailed manual with a description of the basic physics and the mathematical formulas implemented in the subroutines.
DOI: 10.17632/h4nrs68zvy.1
title: A Cenozoic record of deep oceanic Zn isotopic composition in ferromanganese crusts

description: The zinc (Zn) isotopic composition (d66Zn) of the deep ocean (&gt;1000 m) can provide insights into the carbon cycle, the biological pump, and hydrothermal activity. However, we have an incomplete view of the temporal and spatial evolution of deep-ocean Zn isotopes. Here, we present new d66Zn values of Fe-Mn crusts from the Pacific Ocean, which we used to reconstruct the evolution of deep-ocean d66Zn for the Cenozoic. Our results suggest that the d66Zn values remain stable in the deep Pacific Ocean at around ~ 0.5‰ through the Cenozoic. Our results limit the extent of change in organic zinc burial through the Cenozoic. However, given uncertainties in the global mass balance and analytical error, variations of roughly 20% in organic zinc burial are still possible.
DOI: 10.17632/srn7dsmc37.1
title: Antimony isotopic composition of the upper continental crust

description: This study aims to estimate the Sb isotopic composition of the upper continental crust，which is key to studying Sb transport and transformation in the supergene environment . We collected a set of well-characterized granites, granodiorites, sedimentary rocks, loess and stream sediments from the upper continental crust for Sb isotope analysis.
DOI: 10.17632/m28n6h2wjm.1
title: Matlab code for Default

description: Replication file for The paper "Sovereign Default and Liquidity: The Case for a World Safe Asset"
DOI: 10.17632/yv4fzv6cnm.1
title: A single-cell atlas of chromatin accessibility in the human genome. Zhang et al.

description: This dataset contains the numerical data used to generate the Figures in the paper titled "A single-cell atlas of chromatin accessibility in the human genome".
DOI: 10.17632/cdcztymf4k.1
title: English/Turkish Wikipedia Named-Entity Recognition and Text Categorization Dataset

description: TWNERTC and EWNERTC are collections of automatically categorized and annotated sentences obtained from Turkish and English Wikipedia for named-entity recognition and text categorization. Firstly, we construct large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The final gazetteers has 77 domains (categories) and more than 1000 fine-grained entity types for both languages. Turkish gazetteers contains approximately 300K named-entities and English gazetteers has approximately 23M named-entities. By leveraging large-scale gazetteers and linked Wikipedia articles, we construct TWNERTC and EWNERTC. Since the categorization and annotation processes are automated, the raw collections are prone to ambiguity. Hence, we introduce two noise reduction methodologies: (a) domain-dependent (b) domain-independent. We produce two different versions by post-processing raw collections. As a result of this process, we introduced 3 versions of TWNERTC and EWNERTC: (a) raw (b) domain-dependent post-processed (c) domain-independent post-processed. Turkish collections have approximately 700K sentences for each version (varies between versions), while English collections contain more than 7M sentences. We also introduce "Coarse-Grained NER" versions of the same datasets. We reduce fine-grained types into "organization", "person", "location" and "misc" by mapping each fine-grained type to the most similar coarse-grained version. Note that this process also eliminated many domains and fine-grained annotations due to lack of information for coarse-grained NER. Hence, "Coarse-Grained NER" labelled datasets contain only 25 domains and number of sentences are decreased compared to "Fine-Grained NER" versions. All processes are explained in our published white paper for Turkish; however, major methods (gazetteers creation, automatic categorization/annotation, noise reduction) do not change for English.