cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xm2n894f64.1
title: E-entrepreneurship Publication Dataset from 2000 to 2021

description: The E-entrepreneurship dataset, which was indexed Scopus from 2000 to 2021. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, index keywords, reference, correspondence address, editors, publisher, conference name, conference data, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/x3fz2nfm4w.2
title: SalmonScan: A Novel Image Dataset for Machine Learning and Deep Learning Analysis in Fish Disease Detection in Aquaculture

description: The SalmonScan dataset is a collection of images of salmon fish, including healthy fish and infected fish. The dataset consists of two classes of images:

Fresh salmon 🐟
Infected Salmon 🐠

This dataset is ideal for various computer vision tasks in machine learning and deep learning applications. Whether you are a researcher, developer, or student, the SalmonScan dataset offers a rich and diverse data source to support your projects and experiments.

So, dive in and explore the fascinating world of salmon health and disease!

The SalmonScan dataset consists of approximately 1,208 images of salmon fish, classified into two classes:

- Fresh salmon (healthy fish with no visible signs of disease), 456 images
- Infected Salmon containing disease, 752 images

Each class contains a representative and diverse collection of images, capturing a range of different perspectives, scales, and lighting conditions. The images have been carefully curated to ensure that they are of high quality and suitable for use in a variety of computer vision tasks.

Data Preprocessing

The input images were preprocessed to enhance their quality and suitability for further analysis. The following steps were taken:

Resizing 📏: All the images were resized to a uniform size of 600 pixels in width and 250 pixels in height to ensure compatibility with the learning algorithm.

Image Augmentation 📸: To overcome the small amount of images, various image augmentation techniques were applied to the input images. These included:

Horizontal Flip ↩️: The images were horizontally flipped to create additional samples.
Vertical Flip ⬆️: The images were vertically flipped to create additional samples.
Rotation 🔄: The images were rotated to create additional samples.
Cropping 🪓: A portion of the image was randomly cropped to create additional samples.
Gaussian Noise 🌌: Gaussian noise was added to the images to create additional samples.
Shearing 🌆: The images were sheared to create additional samples.
Contrast Adjustment (Gamma) ⚖️: The gamma correction was applied to the images to adjust their contrast.
Contrast Adjustment (Sigmoid) ⚖️: The sigmoid function was applied to the images to adjust their contrast.

Usage

To use the salmon scan dataset in your ML and DL projects, follow these steps:

- Clone or download the salmon scan dataset repository from GitHub.
- Unzip the file to access the two folders (FreshFish and InfectedFish).
- Load the images into your preferred programming environment, such as Python.
- Use standard libraries such as numpy or pandas to convert the images into arrays, which can be input into a machine learning or deep learning model.
- Split the dataset into training, validation, and test sets as per your requirement.
- Preprocess the data as needed, such as resizing and normalizing the images.
- Train your ML/DL model using the preprocessed training data.
- Evaluate the model on the test set and make predictions on new, unseen data.
DOI: 10.15468/dl.yab5sm
title: Occurrence Download

description: A dataset containing 6449 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pseudosasa Makino ex Nakai"
 ]
}

 The dataset includes 6449 records from 231 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009389-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.svh2j3
title: Occurrence Download

description: A dataset containing 2529 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus acutissima Carruth.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 2529 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009390-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/39kzs5pxmk.2
title: TLFS23 - Tamil Language Finger Spelling Image Dataset

description: Tamil is an Indian Language of an remarkable antiquity that stands proudly as one of the oldest languages in the world. We have compiled a dataset for Tamil Language Finger Spelling which has finger representations for all 247 Tamil letters. TLFS23 is an elaborate dataset that comprises 248 classes of at least 1000 images in each class contributing to a massive 2,55,155 images in total. Each class represents Tamil characters finger spelled by participants of a diverse age group. Tamil has 12 Uyir ezuthukal (vowels), 18 Mei ezuthukal (consonants), 216 Uyir Mei ezuthukal (combination of consonant and vowels) and 1 Ayudha ezuthu (special character). This dataset has unique binary equivalent representations for all 247 characters of the Tamil language. The dataset could be used by scholars and researchers to develop vision based systems to assist the speech disabled.
DOI: 10.17632/b8rgs5hbw3.1
title: Data for: The Impact of Employment Quotas on the Economic Lives of Disadvantaged Minorities in India
DOI: 10.17632/vdtcp2yt9n.3
title: Hinglish Language Corpus: A Blend of Synthetically Generated and Manually Written Sentences for NLP Research

description: This dataset is a unique collection of Hinglish (a mix of Hindi and English) sentences, consisting of both synthetically generated text using various Large Language Models (LLMs) such as ChatGPT, Gemini AI, Claude, Groq, and Deep Seek, as well as manually written sentences. The dataset encompasses a diverse range of text sources, including meeting minutes, debates, articles, short essays, emails, letters, tweets, communications, and quotes, all composed in Hinglish.
The primary objective of this dataset is to support and facilitate research in the field of Natural Language Processing (NLP), particularly in the context of code-mixed languages like Hinglish. By providing a substantial corpus of Hinglish text from various domains and sources, this dataset aims to enable researchers to develop and test novel NLP techniques, models, and applications tailored to handle the unique challenges posed by code-mixed languages.
The synthetic portion of the dataset, generated using state-of-the-art LLMs, offers a large volume of diverse Hinglish text that can be used for training and fine-tuning NLP models. The manually written sentences, on the other hand, provide a valuable benchmark for evaluating the performance of these models on human-generated Hinglish text.
DOI: 10.15468/dl.ebz7yh
title: Occurrence Download

description: A dataset containing 950 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Lupinus mutabilis Sweet"
 ]
}

 The dataset includes 950 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009391-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/v89356ncmz.1
title: "Skeletal Fluorosis in Patients with Dental Fluorosis due to Groundwater Consumption in the Inhabitants of the Azua Compostela Province, Dominican Republic Period February-March of 2022”

description: Since 1888, a condition that manifests itself when fluoride is ingested in high amounts and for long periods of time, called fluorosis, has been analyzed, recorded and studied. In that order, when said effects of fluoride affect the teeth, it is usually called dental fluorosis or when it occurs particularly in the bones and joints, it is called skeletal fluorosis. Both effects of fluoride have been the subject of research to date, countries such as Mexico, Pakistan, India, the United States, and Argentina have significant data that serve as a reference for the identification of risk factors and future prevention.
DOI: 10.15468/dl.h8ddx7
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((85.40812 11.62352,123.59188 11.62352,123.59188 38.37648,85.40812 38.37648,85.40812 11.62352))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pseudostachyum Munro"
 ]
}

 The dataset includes 42 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009392-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8bn4tprmkw.1
title: Constraint Programming-Based Solution Approaches for Three-Dimensional Loading Capacitated Vehicle Routing Problems

description: The 3L-CVRP which integrates the three-dimensional loading problem with the capacitated vehicle routing problem. To solve this np-hard problem, we formulated all constraints and objective functions in the context of constraint programming and developed five models: two integrated, two decomposed CP-based, and one decomposed hybrid model. The hybrid model uses a CP model for the routing part and a genetic algorithm for the loading part. First, we compared these models and a MIP model adapted from the literature by solving 24 small-size problems. The hybrid model (CP&EA) gave the highest performance based on the comparison results. Then, a computational study was conducted with Moura and Oliveira's (2009), Bortfeldt and Homberger's (2013)  and Gendreau et al. (2006)’s benchmark instances to test the hybrid model's performance in large-size problems. We improved the solution values for 36 of 93 problems and found better results on average than the existing approaches, considering the computational results on bigger problem instances. According to the computational results, Thus, it has been shown that the proposed CP&EA model can help solve the problems in distribution logistics.  The related benchmark instances and computational results has been attached the website.
DOI: 10.17632/g5yn28myjb.2
title: Ketogenic diet (but not sugar restriction) alters glucose tolerance, lipid metabolism, peripheral tissue phenotype and the gut microbiome: an RCT.

description: This aim of this study was to investigate how dietary sugar and carbohydrates influence metabolism and health across a 12-week period, with a focus on physical activity. One third of participants will eat a diet with typical amounts of sugar and carbohydrate, one third of participants will eat a diet with sugar intake restricted, and the final third of participants will eat a diet where both sugar and total carbohydrates are restricted and replaced with fat.

To achieve this, healthy non-obese adults, aged 18-65 years were recruited to take part in an intervention study with measures of energy intake, energy expenditure, metabolic health, gut microbiota, and appetite. All laboratory trials took place at the University of Bath. Participants were be randomised to consume one of three diets for a period of 12 weeks, with laboratory visits at baseline, at week 4, and at week 12:

CONTROL (moderate sugar) - reflecting the composition of a typical European diet
Low sugar - the same composition of a typical European diet but with &lt;5% energy intake from sugar
Low carbohydrate - low carbohydrate diet with &lt;5% energy intake from sugar, replacing carbohydrate energy with fat

The dataset includes demographic data of the participants (n=60) randomised and data from the trials they participated in. The types of data that were collected during the trials are as follows:
Age
Height
Body mass
Body composition (by Dual-energy x-ray absorptiometry)
Resting metabolic rate
Postprandial substrate oxidation
Exercise metabolism
Physical activity energy expenditure
Fasting and postprandial concentrations of circulating metabolites (e.g., glucose, lactate triacylglycerol) and hormones (e.g. insulin, C-peptide)
Nuclear magnetic resonance spectroscopy measures of plasma (e.g., lipoprotein size and particle number)
Skeletal muscle protein levels
Adipose tissue mRNA levels
Fasting haemotology (e.g., white blood cell count, haematocrit, haemoglobin concentration)
Urinary acetoacetate concentrations
Self-reported macronutrient and energy intake
Calculated change in energy intake
Visual analogue scales for appetite and mood
Continuous glucose monitoring
DOI: 10.17632/y7ccj9nx3w.1
title: Data for: Adjustable Photoacoustic Tomography Probe Improves Light Delivery and Image Quality

description: This excel spreadsheet contains the revised raw measurement points for our quantitative Monte Carlo multilayer simulation, as well as ex vivo and in vivo validation experiments.
DOI: 10.17632/k2bj6fmwhp.1
title: Supplement for Tapinarof cream 1% once daily for the treatment of plaque psoriasis: Patient-reported outcomes from the PSOARING 3 trial

description: Supplemental materials for the article entitled, "Tapinarof cream 1% once daily for the treatment of plaque psoriasis: Patient-reported outcomes from the PSOARING 3 trial". Includes Supplemental Tables 1-4 and Supplemental Figures 1-3.
DOI: 10.17632/h7f8rct3fr.1
title: MULTI2D – a computer code for two-dimensional radiation hydrodynamics

description: Abstract Simulation of radiation hydrodynamics in two spatial dimensions is developed, having in mind, in particular, target design for indirectly driven inertial confinement energy (IFE) and the interpretation of related experiments. Intense radiation pulses by laser or particle beams heat high-Z target configurations of different geometries and lead to a regime which is optically thick in some regions and optically thin in others. A diffusion description is inadequate in this situation. A new numeri... Title of program: MULTI2D Catalogue Id: AECV_v1_0 Nature of problem In inertial confinement fusion and related experiments with lasers and particle beams, energy transport by thermal radiation becomes important. Under these conditions, the radiation field strongly interacts with the hydrodynamic motion through emission and absorption processes. Versions of this program held in the CPC repository in Mendeley Data AECV_v1_0; MULTI2D; 10.1016/j.cpc.2008.12.033 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/ms2f63ng3y.1
title: BatteryPredict_Code

description: The BatteryPredict_Code is developed by Yixin Yang to predict the lithium-ion battery life based on machine learning for different applications (battery life early prediction and RUL prediction). This document will briefly introduce how to use this code. Specific algorithm principles, please refer to the paper [1]. [1] A machine-learning prediction method of lithium-ion battery life based on charge process for different applications
DOI: 10.17632/6s3f7y96s8.1
title: NAKED KING

description: NAKED KING
(Poem) 


Written by 
 Nirendranath Chakraborty 
&
 Translated by 
Dr. Md Siddique Hossain 
M.A.(Bengali & in Education), B.Ed., M.Ed., M.Phil., Ph.D.(Bengali & in Education), D.Litt.
Assistant Professor, Department of Bengali, Bangabasi Morning College
19, Rajkumar Chakraborty Sarani, Kolkata- 700009, West Bengal, India
ORCID ID: https://orcid.org/0009-0008-3912-7101-ID : 541708
Viwan-ID : 541708
Email: mdsh803@gmail.com
………………………..



Even though everyone is aware that the king
is naked, they are still
clapping enthusiastically. 
Everyone is shouting. Well done, 
well done! Some people harbor reforms in their minds, 
while others harbor fear; One has mortgaged one's intellect to another 
man. Some are gluttons, some 
are philanthropists, postulant, 
and swindlers. Some think that the garment is really 
exquisite and not visible, but still, 
at least it is not impossible. 
Everyone knows the 
story. But the story wasn't 
just some catchphrase-spewing 
idiom, contrivance, or silly stanza. There 
was also a child.
 A truthful, simple,
 and brave child. 
The king of stories has descended on the public streets of reality. 
The crowd of staff members 
is clapping 
again. However, I don't 
see that child anywhere in the 
crowd today.
 Where did the child go? Has someone hidden him 
in a secret cave in the mountains? 
Or did he play with stones, grass, and soil and fall asleep on the banks of a 
distant, lonely river or in 
the shade of a desert tree? Regardless, go find him. 
Let him come and stand fearlessly in front of 
the naked king. He 
arrived and raised his voice above the applause. 
Let him ask: 
King, where are your clothes?


*The End*
DOI: 10.17632/m3kg8p8cxf.2
title: Deep Learning-Based Style Transfer from Ultra-Widefield to Traditional Fundus Photography

description: This study included 451 anonymized UWF and 745 FP images. The ultra-widefield (UWF) images, which include both normal and pathologic retinal images, were based on Tsukazaki Optos Public Project. The traditional fundus photograph (FP) images were extracted from the publicly accessible database by using the Google image and Google dataset search that included English keywords related to retina. The search strategy was based on the following key terms: “fundus photography”, “retinal image”, and “fundus dataset”. The images were manually reviewed by two board-certified ophthalmologists, and blurred and low-quality images were removed to clarify the image domains. Duplicated images were also removed. Consequently, 451 images with artifacts and 745 images without artifacts were collected. The UWF images were cropped and masked after registration for CycleGAN.
DOI: 10.17632/wbyswm62vv.3
title: Standardized tensile testing of soft tissue using a 3D printed clamping system

description: Biomechanical testing of soft tissues forms the backbone in the experimental validation of tissue engineering and for modelling purposes. The standardized testing of soft tissues requires different experimental protocols and fixtures compared to hard tissues or non-biological materials due to their characteristics. Some of the most commonly-used clamping methods for soft tissue testing affect the tissues’ mechanical properties as chemicals are involved to decelerate degradation and autolysis. Moreover, they are unsuitable for standardized and high-throughput testing. Material slippage is also a recurrent unwanted influence on the testing routine with impact on measurement validity. Addressing these issues, this protocol presents a clamping system for simplified testing of biological soft tissues with all necessary components manufactured utilizing 3D printing technology. Templates allow trimming the samples into standardized shapes and sizes while preparation tables facilitate clamping in a fixed distance. The key parts of the system are clamps with a pyramid design, which allow to mount biological soft tissues before transferring it into the testing device and minimize material slippage during tensile testing. Flexible holder arms are used to transfer samples from preparation tables into the testing device and simplify positioning. Mechanical testing itself is performed with digital image correlation (DIC) for precise strain measurements. Files included: STL files for 3D printing and CAD files (PTC Creo): Clamp flat pyramids, Clamp flat pyramids, Cutting plate bottom, Cutting plate top, Holder arm, Molding device bottom, Molding device top, Mounting adapters, Preparation table Video: Standardized soft tissue tensile testing Also see publication: Utilization of 3D printing technology to facilitate and standardize soft tissue testing Nature Scientific Reports https://doi.org/10.1038/s41598-018-29583-4
DOI: 10.17632/jwvhbb7hsj.1
title: Veterinary Medicine Corpus (VMC)

description: The Veterinary Medicine Corpus (VMC, Özer and Akbaş, 2023), is a machine-generated collection of high-quality, open-access Research Articles (RAs) published between 2010 and 2022. Compiled in March 2022 using AntCorGen, a software utilizing the PLOS webpage's Application Programming Interface (API), the VMC captures and organizes complete articles. 

The VMC comprises articles that maintain structural consistency, featuring five primary sections: abstract, introduction, materials and methodology, results and discussion, and conclusion. Notably, metadata is absent from the corpus.

Initially, the system produced a corpus of 1,488 veterinary medicine articles, totaling 8,000,000 words. Subsequent to the procedures outlined in Özer & Akbaş (2024) titled ''Assembling a justified list of academic words in veterinary medicine: The veterinary medicine academic word list (VMAWL)'', the corpus was refined to 1,449 articles. These articles were automatically labeled and sorted using Digital Object Identifiers (DOIs). The PLOS API employed a machine-aided term-mapping algorithm to suggest categories for the published papers, utilizing the PLOS thesaurus (GitHub, 2022). However, the categorization produced by the PLOS website was not optimal. 

The default categorization provided by the PLOS thesaurus resulted in an uneven distribution across categories rather than a flat one. Additionally, the suggested categories in Table 2 were generated by machines and based on lexis rather than scientific criteria. To assess the statistical outcomes, we conducted a mock analysis using LancsBox, which revealed high dispersion values (as indicated by the coefficient of variation, CV) for frequently occurring tokens. This raised concerns about the reliability of developing a discipline-specific word list.

As a solution, we opted to refine the categorization process by seeking expert advice. Consequently, we established 4 parent categories and 17 child categories to streamline the classification, as illustrated below:

1. Pre-clinic (n=352)
Disease control and prevention/epidemiology
Veterinary microbiology
Veterinary parasitology
Veterinary pathology
Veterinary pharmaceutics and pharmacology
Veterinary virology

2. Internal medicine (n=426)
Veterinarians
Veterinary biometry
Veterinary diagnostics
Veterinary hospitals
Veterinary medicine.
Veterinary surgery

3. Zootechnics (n=121)
Livestock care and wildlife sciences

4. Veterinary diseases (n=550)
Bacterial diseases
Parasitic diseases
Viral diseases
Other (inherited/genetic diseases/disorders, genetical studies of species regarding diseases)

The filnal VMC is 1,449 artcles with 7,962,021 tokens.