cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fnw59h7y89.2
title: PromitoLipi: A versatile offline dataset of handwritten Bangla words and paragraphs

description: The PromitoLipi Dataset contains two different datasets, PromitoLipi1.1 and PromitoLipi2.1. PromitoLipi1.1 contains 80 single-page handwritten paragraphs of individuals of different personalities and ages, with 7231 words. Among these datasets, most of the paragraphs are comprehensive. They can be used for handwritten line/word segmentation, paragraph recognition, and multimodal natural language processing tasks like document summarization, sentiment analysis, and content extraction. Also, some paragraphs are incomprehensive/incoherent( random unassociated words are written one after another) and are primarily helpful for segmentation-based tasks.
On the other hand, PromitoLipi2.1 contains 9830 open vocabulary word images consisting of 24050 consonant/vowel/diacritic/number/conjunct/punctuation instances and their corresponding annotation files. This dataset can be used for handwritten character segmentation and word recognition. For 70% of the words in this dataset, Writers of different ages from different areas were asked to write random words on paper. For the rest of the 30%, words were collected from different CMATERdb datasets to create versatility in the dataset. Then, the word images were segmented and binarized(background pixels in white and foreground/text pixels in black) so that each image contained no irrelevant information besides the word text consisting of single/multiple classes.
DOI: 10.17632/hxsnvwty3r.1
title: MangoLeafBD Dataset

description: Type of data:	                        240x320 mango leaf images.
Data format:	                        JPG.
Number of images:	        4000 images. Of these, around 1800 are of distinct leaves, and the rest are prepared by zooming and rotating where 
                                                deemed necessary.
Diseases considered:	        Seven diseases, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty 
                                                Mould.
Number of classes:	                Eight (including the healthy category).
Distribution of instances:	Each of the eight categories contains 500 images.
How data are acquired:	        Captured from mango trees through the mobile phone camera.
Data source locations:	        Four mango orchards of Bangladesh, namely Sher-e-Bangla Agricultural University orchard, Jahangir Nagar University 
                                                orchard, Udaypur village mango orchard, and Itakhola village mango orchard.
Where applicable:	                Suitable for distinguishing healthy and diseases leaves (two-class prediction) as well as for differentiating among various 
                                                diseases (multi-class prediction).
DOI: 10.17632/ymr2yhrvyn.2
title: A novel electromagnetic apparatus for in-situ synchrotron X-ray imaging study of the separation of phases in metal solidification

description: As a part of a research into new techniques for purifying recycled aluminium alloys, a novel electromagnetic apparatus had been developed for investigating in real-time the separation mechanisms of detrimental inclusions in aluminium alloy melts under alternating magnetic fields. The magnetic coil was designed based on the Helmholtz coil design. A viewing gap was designed for in-situ imaging studies using synchrotron X-rays. The gap was designed to maintain a uniform magnetic field in the central region where a sample is positioned. The current setup for the magnetic coil pair is able to produce a peak magnetic flux density of ~10 mT at a frequency of 25 kHz. A separate electrical resistance furnace, designed to concentrically fit within the magnetic coils, was used to control the heating (up to ~850°C) and cooling of the samples. After a series of systematic tests and commissioning, the apparatus was used in a number of in-situ and ex-situ experiments.
DOI: 10.17632/29c2r6dm85.1
title: Data from : Plastic debris dataset on the Seine river banks: plastic pellets, unidentified plastic fragments and plastic sticks are the Top 3 items in a historical accumulation of plastics.

description: Inventory of plastic items collected in a quadrat of 1m2 in a historical polluted shore in the Seine river (downstream of the estuary; Lat. 49.4339; Long. 0.6160). Data are representative of the historical plastic pollution occurring in this river with few items dated from 1965, 1974, 1983, 1992 or 2010. Plastic items were classified according to OSPAR and MSFD classifications, which give insights about the origin of the items and their chemical composition. A total of 20 259 plastic debris were individually counted, classified and weighted by category. Those plastic debris are more than 150% heavier in mass (&gt; 4kg) than organic debris, i.e. dead vegetation and gastropod shells, found in this kind of dry march surrounded by reedbeds. The Top 3 categories of items collected are plastic preproduction pellets, unidentified plastic fragments and plastic sticks (cotton bud and lollipop sticks). Plastic preproduction pellets are 15 times more numerous than gastropod shells. High concentration of pellets could be linked to the vicinity of plastic manufacturers near the sampled site. They represent 50% of the items collected during this campaign but only 5.6% of the mass. In contrast, around 30% of the mass is carried by the unidentified fragments of macroplastics &gt;2.5cm, which only represent 7% of the total items. Hundreds of caps, lids, and rings were also found without their associated bottles, which are often prompt to sink. Furthermore, the dataset refers to specific activities in the estuary with for example 100 g/m2 of polyethylene from shotgun plastic wads related to intense hunting activities. Those items have to be mentioned because they are very common in the estuary and their origin is clearly identified, while alternatives such as biodegradable wads do exist. Reporting number of items, associated mass and volumes will improve conversions of unit for other studies related to river pollution when only one of the units are available. To facilitate conversions, mass per item were also reported for the Top 10 items. In addition, specific items such as plastic tag ties (e.g. textile), or plastic fibers from toilet brushes were unusually reported and should be considered as additional categories in OSPAR/MSFD classifications for rivers.
DOI: 10.17632/vxj5n49s95.1
title: Survey Dataset of Factors Influencing The Consumers In Kuching, Sarawak Towards Green Product Purchase Intention

description: Businesses nowadays are trying to apply green marketing which intended to arouse consumer’s awareness in protecting the environment. The purpose of conducting this study is to determine the factors influencing the consumers in Kuching, Sarawak towards green purchase intention. The independent variables for this research are attitude, subjective norm, perceived behavioral control, environmental concern, and environmental knowledge. The conceptual framework is based on Theory of Planned Behavior (TPB) and with add-in variables. This study is a quantitative and descriptive research where hypotheses testing was conducted. 200 respondents were selected to answer the distributed questionnaire. Purposive sampling method was used for this research. SPSS Statistical Software version 25 has been applied in this research to test on the collected data. The research findings showed that there are three independent variables have positive significant relationship with green purchase intention. The findings of this study can contribute to different sectors to improve and enhance green marketing as well as increase the purchase intention of the consumers towards green products.
DOI: 10.17632/289m8hcgt2.3
title: Mass Transfer Solver Tool: A software to calculate mass transfer coefficients for Packed-bed Columns 

description: The Mass Transfer Solver Tool is a calculation software for gas-liquid mass transfer coefficients for random and structured packed-bed columns. This software refers to the work of Flagiello et al. (2021) entitled "A tool for mass transfer evaluation in packed-bed columns for chemical engineering students" and published in the scientific journal of Education for Chemical Engineers (https://doi.org/10.1016/j.ece.2021.08.001).

The MT Solver Tool is based on the use of the most acknowledged models available in the pertinent literature (Onda et al., 1968; Bravo et al., 1985, 1992; Billet and Schultes, 1993; Brunazzi and Paglianti, 1997; Olujić et al. , 2004; Hanley and Chen, 2012) to calculate the gas-side (ky), liquid-side (kx) and interfacial area (ae) coefficients separately.
The software includes a "Library File" where geometric data and model fitting parameters for 144 different packings in size and materials are available. The spreadsheet file "MT Solver" operating in default mode uses the data of the 144 packings available in the "Library File" to calculate the mass transfer coefficients. Futhermore, the software can also be operated in user-defined mode "User-defined MT Solver" in order to customize and expand its use when the considered packing is not included in the Library File.

The MT Solver Tool was also used in teaching activities in the academic year 2020-2021 for the Unit Operation and Sustainable Process Design courses of the Master's degree in Chemical Engineering, at the University of Naples Federico II.

All the instructions for use are given in the work: A tool for mass transfer evaluation in packed-bed columns for chemical engineering students (https://doi.org/10.1016/j.ece.2021.08.001).

Any system bugs, calculation errors deriving from the model equations or the parameters set in the library can be reported to the email address: domenico.flagiello@unina.it (Dr. Domenico Flagiello).
Suggestions for model equations updates or data and packing parameter values ​​included in the library (or new data packing) are also welcome.
DOI: 10.17632/76w6bj5md5.1
title: Data for: Economically robust protection against 21st century sea-level rise.

description: cls_protection_publication: for each coastline-segment used in the study and each scenario combination used the protection decision (binary: 0/1) country_output_publication: time-resolved country level data on protected coast, seaflood cost and protection cost for each scenario combination global_output_publication: : time-resolved global data on protected coast, seaflood cost and protection cost for each scenario combination
DOI: 10.17632/dnx95zwc2x.1
title: Thermal and Electrical Measures of Cold-Welding Epoxy-Polypyrrole

description: Measures presented in the IMRC 2023's conference titled: "Study of Physical Properties of Cold Welding Epoxy Matrix with Polypyrrole Inclusions." The data reservoir contains information from the samples' Electrical Impedance Spectroscopy, Thermal Relaxation, and Laser Flash Instruments. The samples were fabricated from Cold-Welding Epoxies and Polypyrrole inclusions with varying volume fraction ratios. JB Weld SteelStick 82675 (Steel reinforced epoxy putty), Devcon 3-40 (High-grade steel-filled epoxy putty), Devcon R2-42 (Steel filled epoxy adhesive), and Permatex Steel Weld 84109 (Multimetal epoxy adhesive) were cold welding epoxies used.
DOI: 10.17632/mhrt4svjxc.3
title: Breast Thermography

description: It is image termography dataset of female torax zone as scientific contribution in breast cancer thermography research area. Dataset was acquired inside a medical office with dimensions W:3.20m x L:4.14m x H:2.40m, without artificial illumination at a temperature 22 - 24°C and relative humidity 45 - 50%. A FLIR A300 camera was used to capture the images at a working distance of 1m from the patient. The American Academy of Thermology (AAT) protocol was used to prepare the patient and capture. Three images were taken of each patient in the female thorax area: anterior, left oblique and right oblique positions. The patients ranged in age from 18-81 years with different pathologies related to the female breast. The images were captured between 2021 and 2022.
DOI: 10.17632/5mxd93pjt5.1
title: The countervailing power Of AI DAOs influences value transformation; Bitcoin (POW) vs.Ethereum (POS)

description: In this paper, we investigate the process of value transformation influenced by the countervailing power of decentralised autonomous organisations (DAO) controlled by artificial intelligence (AI). There are various values in society. The Internet was believed to lead humanity better by further decentralizing various values. However, the uneven distribution of information causes many problems, such as "cyber cascades", "filter bubbles", and "echo chambers" etc. To solve these problems, DAO using blockchain is expected to become a method of solving those problems in the Internet space. 
This paper intends to capture the value change in three steps. In the first step, we take two philosophical approaches. First, using Hegel's dialectic, we attempt to compare A. current social values (thesis), B. the value of, for example, AI DAOs (antithesis), and C. new values (synthesis) in an Internet world where these contradictions exist (A + B = C). The second philosophical approach is to consider the mutuality of Bitcoin (POW) and Ethereum (POS) by adapting Popper's World 1, 2, and 3 models.　
In the second step, we examine the three core capabilities that amplify "credit" in decentralised finance (DeFi): exchange swap rates, staking, and indices (portfolio). Finally, we consider the value dispersion within the Bitcoin and Ethereum networks as the main two poles. The countervailing power between these two poles and the upcoming expanding AI DAOs, especially Bitcoin-halving, coordinate the interests of stakeholders due to the uneven distribution of values and encourage the interaction between diverse values in cyberspace.
DOI: 10.17632/tg7c985c2n.1
title: Catalog for: Machine and Deep Learning Applied to Galaxy Morphology - A Comparative Study

description: Machine and Deep Learning morphological classification for 670,560 galaxies from Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). Classifications are provided for 2 classes problem (0: elliptical; or, 1: spiral galaxy) and 3 classes problem (0: elliptical, 1: non-barred spiral, or 2: barred spiral galaxy). ML2classes classification is obtained by Traditional Machine Learning Approach, using Morphological non-parametric parameters and Decision Tree. Classifications using Deep Learning are obtained using a Convolutional Neural Network (CNN). Morphological non-parametric parameters are provided as well: Concentration (C), Asymmetry (A), Smoothness (S), Gradient Pattern Analysis (G2) parameter and Entropy (H). We also provide the Error from CyMorph processing. All error flags are mapped as follows: Error = 0: success (no errors); Error = 1: many objects of significant brightness inside 2 Rp of the galaxy; Error = 2: not possible to calculate the galaxy's Rp; Error = 3: problem calculating GPA; Error = 4: problem calculating H; Error = 5: problem calculating C; Error = 6: problem calculating A; Error = 7: problem calculating S.
DOI: 10.17632/5xfzcb4kn8.1
title: Whole-cortex in situ sequencing reveals peripheral input-dependent cellular and area identity - perturbation

description: BARseq in situ sequencing data for 8 brains, with/without enucleation. Also provided scripts to reproduce figures in https://doi.org/10.1101/2022.11.06.515380. Accompanying manuscript: https://doi.org/10.1101/2022.11.06.515380
DOI: 10.17632/nph2cnh6r2.1
title: Loyalty Status of Hotel Guests: Simulated Dataset for Predicting Customer Loyalty

description: This dataset consists of 2,000 simulated records of hotel guests, designed for customer loyalty prediction based on several key features. Each guest's loyalty status ("Yes" for loyal, "No" for non-loyal) indicates their eligibility for loyalty discounts, with "loyal" signifying that the individual can enjoy these discounts. The dataset includes factors such as frequency of bookings, days since the last booking, total revenue generated, average stay duration, and total meal charges.

The dataset was generated using Python with the Faker library to create realistic guest names and emails, featuring names in different languages to reflect a diverse international perspective. The countries represented include India, the United States, the United Kingdom, France, Germany, Spain, Italy, Japan, and China.

A Python code file is provided alongside this dataset for those interested in the methodology used to create it. This resource serves as a practical tool for exploring machine learning techniques and data analysis in the hospitality sector.

We are building this dataset as part of a dynamic pricing project for hotels, aiming to enhance decision-making through customer segmentation.
DOI: 10.17632/2tdr9sjd83.2
title: Reddit Ideology Database

description: Dataset with articles posted in the r/Liberal and r/Conservative subreddits. In total, we collected a corpus of 226,010 articles. We have collected news articles to understand political expression through the shared news articles.
DOI: 10.17632/cvtwgscw86.1
title: The Impact of Green Bond Issuance on Firm’s Green Resource Allocation Efficiency: Insight from Green Total Factor Productivity

description: This research examines the effect of green bond issuance (GBI) on firm’s green resource allocation efficiency (FGRAE) and explores the underlying mechanisms. The results show that GBI significantly improve FGRAE, mainly by contributing to technical progress and best green technology gap. The results hold under various robustness checks, primarily including a placebo test, propensity score matching (PSM), greenwashing test, and heterogeneous treatment effects. Further mechanism analysis reveals the mediating role of promoting green innovation, alleviating financing constraints, and increasing investor attention in positive effect of GBI on FGRAE. We observe stronger effects in firms with larger size, lower debt liability and higher R&D investment, industries with higher pollution intensity, and regions with stricter climate regulation through heterogeneity analysis. Further analysis finds that firms with multiple GBI (MGBI) experience a stronger positive impact on FGRAE, and green bonds issued for refinancing show no substantial effect. Our empirical findings emphasize the role of GBI in driving synergies between the environmental and economic performance, rather than merely serving as a greenwashing tool.
DOI: 10.17632/yxbftwjx57.1
title: Corporate Governance in Russia

description: The dataset contains information about the board of directors of Russian companies, as well as compliance with the requirements of the corporate governance code from the Bank of Russia. The sample contains 33 companies.
DOI: 10.17632/t9r56fh5hm.1
title: ADAM-HA: Arabic DAtaset for Matn-based Hadith Authentication

description: ADAM-HA (Arabic DAtaset for Matn-based Hadith Authentication) is a new dataset for the field of hadith authentication in the Classical Arabic language. The ADAM-HA dataset is a Matn-based database that consists of a large collection of hadiths, labelled with their authenticity status. This dataset can be used for hadiths classification (accepted / rejected) using NLP and AI techniques. It contains more than 109,900 records collected from hadith books and scraped from web pages.
DOI: 10.15468/dl.vzes8w
title: Occurrence Download

description: A dataset containing 542 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platanus orientalis L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 542 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009275-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/64cc5w5dxy.1
title: DrugRev: A comprehensive customers reviews on drugs purchasing and satisfaction

description: DrugRev is a comprehensive dataset created to provide updated information regarding a user's views of a particular drug. The data has been collected from drugs.com and stored in a single CSV file. The raw data has been cleaned thoroughly to provide a ready-to-use dataset that can be used for multiple purposes such as Natural Language Processing, Sentiment Analysis, Data Science, Data Analytics, Numerical Analysis, Health Informatics, etc. The dataset can be found as ‘DrugReviews.csv’ in the repository, and it contains 392510 unique reviews along with the name of the drug, user rating, the credibility of the review in the form of likes, length of the reviews, the condition for which the drug was taken and how long the drug was taken for. Null values which may have been the result of some issues with the old structure of the website have also been handled carefully. Also, the sheer number of data columns gives the dataset more versatility to be used in a variety of research fields.
DOI: 10.17632/3csr652p9y.1
title: Data on Fundus Images for Vessels Segmentation, Detection of Hypertensive Retinopathy, Diabetic Retinopathy and Papilledema

description: We presents a dataset of 100 fundus digital images of retina. The retinal images are taken from Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi, Pakistan and annotated with the help of four expert ophthalmologists for the purpose of computer aided diagnosis of hypertensive retinopaty, diabetic retinopathy and papilledema. This dataset contains retinal blood vessels network, segmented artery/ vein network to calculate Arteriovenous Ratio (AVR), annotation of Optic Nerve Head (ONH) and various retinal abnormalities such as hard exudates (HE) and cotton wool spots. The dataset is valuable for those researchers who are developing automated systems for vessels segmentation; artery/ vein classification; diagnosis of hypertensive retinopathy, diabetic retinopathy and papilledema
DOI: 10.17632/pd64hfttwy.1
title: Diabetes

description: Diabetes Prediction Dataset

This dataset contains medical diagnostic measurements for 768 female patients, used to predict the onset of diabetes. The data includes various physiological factors and a class variable that indicates whether or not a patient has diabetes. The dataset is structured as follows:

Pregnancies: Number of times the patient has been pregnant.
Glucose: Plasma glucose concentration (mg/dL) after 2 hours in an oral glucose tolerance test.
BloodPressure: Diastolic blood pressure (mm Hg).
SkinThickness: Triceps skinfold thickness (mm).
Insulin: 2-Hour serum insulin (mu U/ml).
BMI: Body mass index, calculated as weight in kg/(height in m)^2.
DiabetesPedigreeFunction: A function which scores the likelihood of diabetes based on family history.
Age: Age of the patient in years.
Outcome: Binary variable (0 or 1) indicating whether the patient has diabetes (1) or not (0).
Usage:
This dataset is widely used for training machine learning models to predict the likelihood of diabetes. It's suitable for binary classification tasks, and can be utilized for projects in data science, healthcare analytics, and machine learning education