cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/95f3wmnm46
title: Data in Brief

description: OCD patients information with ROCFT, Digits, Raven matrices and clinical measures
DOI: 10.17632/htsngg9tpc
title: Vehicle Type Image Dataset (Version 2): VTID2

description: After creating VTID, the researchers decided to extend the collection process to create another larger dataset to add further diversity to the dataset in order to avoid data overfitting. Finally, the new dataset, called "Vehicle Type Image Dataset 2 (VTID2)", consisted of 4,356 image samples that could be separated into five vehicle type classes as follows: 1,230 sedans, 1,240 pick-ups, 680 SUVs, 606 hatchbacks, and 600 other vehicle images.
DOI: 10.17632/jf3pv98m9g
title: A dataset of DDH x-ray images

description: The hip X-ray images (in anteroposterior view) were collected locally at King Abdullah University Hospital, Jordan University of Science and Technology, Irbid, Jordan and at Alsafa specialized hospital, Jarash, Jordan . The dataset used in the relevant research article included 354 subjects (120 DDH, 234 normal) .  The images are available in  sizes suitable for various deep learning models (e.g., [227 227 3])
DOI: 10.17632/k72nh7xj5c
title: COVID-19 Situation Assessment: Data from Belarus and Russia

description: Materials contain the data on the assessment of COVID-19 situation by adult respondents (age from 18 to 76) coming from countries with the common past, language and similar mentality but differing in governmental security measures during pandemic spring outbreak (no precautionary measures in Belarus (N=267); lockdown and financial support in Russia (N=397)). The data is given in .xlsx and .sav formats. The data was obtained using online survey platform (Google forms) from 2020.04.11 to 2020.06.04 (during the period of lock-down in Russia). The data contains socio-demographical information (sex, age, country of citizenship), survey answers and the results of two standardized psychological tests (to measure dangerous and threatening social world view and hardiness). The survey consists of four blocks: specific impact of the COVID-19 situation on various aspects of respondents’ life; estimation of different fears; estimation of various aspects of COVID-19 situation, and estimation of personal resources. Russian and English versions of the survey are given.
DOI: 10.17632/tmvw6pmmrt
title: PLCP Audit Database

description: The detailed dataset consists of the title of the Bills, the Ministry or Department that came up with the mentioned Bills, and the details of the consultation, whether the Bill had undergone the thirty-day stipulated public consultation as directed through the Pre-Legislative Consultation Policy of 2014. The Dataset also consist of the Date of the introduction of the Bill in the Parliament and whether it was introduced in the Upper House or the Lower House. The Dataset contains a separate section of Remarks, which mentions specific details of the Bills that are essential to interpret the public consultation of the Bills.
DOI: 10.17632/yttdbxcsm2
title: Navigating Body Image Anxiety: Unraveling the Interplay of Appearance Evaluation Fear, Eating Attitudes, and Physical Activity Objectives amidst COVID-19

description: Age, Gender, BMI values ,FNAES,EAt-26 and IPAO score
DOI: 10.17632/cn4nddskzx
title: The MCEF code for nuclear evaporation and fission calculations

description: Abstract We present an object oriented algorithm, written in the Java programming language, which performs a Monte Carlo calculation of the evaporation-fission process taking place inside an excited nucleus. We show that this nuclear physics problem is very suited for the object oriented programming by constructing two simple objects: one that handles all nuclear properties and another that takes care of the nuclear reaction. The MCEF code was used to calculate important results for nuclear reactions,... Title of program: MCEF Catalogue Id: ADQC_v1_0 Nature of problem The competition between evaporation and fission is an important problem in intermediate and high energy nuclear reactions. Here, neutrons, protons, alphas and possibly other particles escape from the excited nucleus in competition with the fission process. Considering the fact that an exact calculation including all the channels in this nuclear reaction could be a complex problem, a statistical description of all these possible decaying modes could be a major simplification. In fact, rough analy ... Versions of this program held in the CPC repository in Mendeley Data ADQC_v1_0; MCEF; 10.1016/S0010-4655(02)00278-3 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/hhksb972pp
title: CUTEly MAD Dataset

description: CUTEly MAD is a curated dataset for document-level sentiment analysis in the Malayalam language. CUTEly MAD is short for Curated Twitter Malayalam Dataset. The dataset is created by extracting Malayalam tweets from Twitter. A set of both positive and negative sentiment-oriented Malayalam words are identified, which are used as hashtags to extract tweets using Twitter API. Further, these tweets were manually labeled by a proficient annotator, based on their sentiment polarity into two classes, viz. negative and positive. If the sentiment is positive, then 1 is annotated. Otherwise, 0 is labeled for negative sentiment. A total of 2,000 tweets are labeled, where 50% are positive tweets and the other 50% are negative sentiment oriented.
DOI: 10.17632/rcb6mbr893
title: A nationwide study of Stevens-Johnson syndrome and toxic epidermal necrolysis in hospitalized pregnant women, 2009-2020

description: Supplementary Table I: Demographic, geographic, and social characteristics of pregnancies with and without Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). National Inpatient Sample, Healthcare Cost and Utilization Project (HCUP), 2009-2020. Excludes hospitalizations with length of stay less than 3 days.

Supplementary Table II: Clinical characteristics of pregnancies with and without Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). National Inpatient Sample, Healthcare Cost and Utilization Project (HCUP), 2009-2020. Excludes hospitalizations with length of stay less than 3 days.

Supplementary Table III: Risk factors for the development of Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in pregnant women. National Inpatient Sample, Healthcare Cost and Utilization Project (HCUP), (2009-2020). Excludes hospitalizations with length of stay less than 3 days.
DOI: 10.17632/zy4v2k9vxz
title: Dataset for HSO4- dissociation by Cui et al.

description: This dataset is about Raman data of the experimental and standard samples to investigate the HSO4- dissociation.
DOI: 10.6084/m9.figshare.773119.v1
title: Unique ID datasets

description: Datasets for paper: On the reproducibility of science: Unique Identification of Research Resources in the Biomedical Literature.
DOI: 10.17632/dkx9xmp6jm
title: Dataset containing individual characters of the Devanagari Script including Vowels and Consonants.

description: This dataset contains 8271 images of Hindi handwritten characters that are resized to a size of 32x32 pixels and gray scaled. It contains 2,314 samples of vowels and 5,957 samples of consonants respectively. The given dataset is divided into two parts of training and testing dataset.
DOI: 10.17632/2n2ns36msd
title: Data for: IMMIGRANT ENTREPRENEURSHIP: A REVIEW AND RESEARCH AGENDA

description: List of cluster keywords and full reference data
DOI: 10.17632/5bg6tjc3t9
title: Data for: Selective Sequestration of p-Arsanilic Acid from Water by Using Nano-hydrated Zirconium Oxide Encapsulated inside Hyper-cross-linked Anion Exchanger

description: Characterization and performance.
DOI: 10.6084/m9.figshare.22632808.v1
title: GO and KEGG significant enrichment terms for down-regulated genes

description: This dataset presents the significant GO and KEGG terms (p-value&lt;0.05) for the common down-regulated genes of our dataset <br>
DOI: 10.17632/9f4898jfr9
title: XRD, NMR, and mercury injection/withdrawal capillary pressure (MICP) measurements on 4 sandstones and 3 limestones

description: These data were acquired for Tanino & Blunt (2012) doi:10.1029/2011WR011712 . Porosity and brine permeability measured on cores of the same set of rocks can be found in Tanino & Blunt 2012.
DOI: 10.17632/mjzksgsykm
title: Immersion bath CAD

description: CAD filed of an immersion cooling bath
DOI: 10.17632/7k9wcm4kwb
title: Green Bonds Bibliometrix-Export-File-2023-12-18

description: This is downloaded from Scopus search on 'Green Bonds' research data converted by Rstudio Biblioshiny software into Bibliometrix-Export-File-2023-12-18.
DOI: 10.6084/m9.figshare.1351920.v1
title: Polarization modulation gifs

description: Animations of a precessing torus ray traced in the Kerr metric. The animations show flux, polarization and red shift. The calculations are described in detail in Ingram, Maccarone, Poutanen & Krawczynski (2015).
DOI: 10.6084/m9.figshare.13906643.v3
title: Atmosphere Interaction Research

description: Data collected for our atmosphere-coastal ocean experiment at the Centre for Marine and Coastal Studies (CEMACS) USM, Pulau Pinang, Malaysia<br>Coordinates: 5.49 N, 100.2025 EDuration: 2015-11-12 until 2021-02-10https://atmosfera.usm.my/api.html<br>
DOI: 10.17632/7gt4vtfwpn
title: Decision matrix including performance values of localization for offshore wind farm in the Polish Baltic Sea zone

description: This dataset contains values collected for seven offshore wind farm projects that are planned to be developed in the Polish Exclusive Economic Zone of the Baltic Sea in the area designated in the offshore development plan in the area of Słupsk Shoal, Central Shoal, and Odrzana Shoal. The data was collected during the course of the company's own work on the basis of the Offshore Wind Farm Development Program, for seven selected alternative sites included therein https://www.gov.pl/web/morska-energetyka-wiatrowa/program-rozwoju-morskich-farm-wiatrowych .

The criteria of the multi-criteria evaluation model for which data was collected are listed below. Criteria are grouped into 4 main groups: Localization criteria, Market criteria, Economic benefits, and Costs.

The collected dataset, including 32 criteria for assessing wind farms' localizations, is provided in the XLSX file. The data is arranged in a two-dimensional decision matrix, with seven alternatives placed in rows and 32 criteria placed in columns.

Detailed dataset description including particular criteria names is provided in PDF file.
DOI: 10.17632/hd964vvjgp
title: Technical and allocative efficiencies of rice farmers in Bangladesh: Data and STATA codes

description: This dataset contains household-level data collected from the two greater Rangpur and Mymensingh regions (Lalmonirhat, Kurigram, Rangpur, Gaibandha, Jamalpur, and Sherpur districts) of northwest Bangladesh in 2016. To collect data, a multi-stage stratified random sampling technique was employed: (i) each district mentioned above was divided into two strata, flood-prone and not flood-prone, based on historical flood information from the Bangladesh Bureau of Statistics (BBS), making 12 sub-districts; (ii) five local administrative units from each sub-district were selected, which are called Unions; (iii) from each Union, several villages were randomly selected; (iv) an equal number of rice farmers (twenty-five) were randomly selected and interviewed from each village with a structured questionnaire; (v) finally, a total of 1,500 farmers were face-to-face interviewed. In this study, a total of 998 samples were used, excluding observations that feature missing information and outliers.
DOI: 10.17632/yw75dzs677
title: Data for: Capital Accumulation and Dynamic Gains from Trade

description: All the data we use in the paper are publicly available. The description of the datasets is in the paper.
DOI: 10.17632/jkcf6hvwvb
title: Stickleback shoaling dynamics

description: The processed dataset investigating the shoaling dynamics of three-spined sticklebacks swimming in an open channel flume at four velocities (denominated A, B, C, and D in the dataset). Various behavioral and kinematic parameters were quantified and are present in this streamlined CSV document. Shoals of 5 fish were compared to control conditions with a single fish.
DOI: 10.6084/m9.figshare.25998949.v1
title: Genome assembly and annotation files of Rosa laevigata

description: 基因组组装（R.laevigata.genome.fasta.gz）和注释文件（R.laevigata.cds.fasta.gz、R.laevigata.pep.fasta.gz、R.laevigata.gff3.gz和R.laevigata.repeat.gff.gz)<i>.</i>
DOI: 10.17632/4hzbhn26d3
title: IPDD Open Data Eco Friendly Tote Bag

description: Open data for Eco frinedly Tote Bag research
DOI: 10.17632/nm5b82wbmt
title: Physico- chemical properties of soil from six firkas in Dharmapuri district,Tamil nadu, India.

description: The study area consisted of six firkas in Dharmapuri district, Tamil Nadu, India: Marandahalli, Vellichandai, Palacode, Karimangalam, Pulikarai, and Periyanahalli. A GPS device was used to record the locations of soil samples. pH, EC, sand, silt, clay, BD, WHC, OC, Calcium carbonate, and available NPK were determined in the samples. After converting the data to vector layers, kriging was used to build a spatial variability map of each soil parameter.
DOI: 10.17632/z95cb7f9mr
title: Data and videos for lithium-ion batteries' thermal propagation and cycle performance in air and insulating oil

description: Data(excel form) and videos, Images
DOI: 10.17632/3mxh852x4p
title: Supplementary Methods

description: Semaglutide Manifestations Supplementary Methods
DOI: 10.17632/x3fz2nfm4w
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 (raw) consists of 24 fresh fish and 91 infected fish. [Due to server cleaning in the past, some raw datasets have been deleted]

The SalmonScan dataset (augmented) 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.
- 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: 10.6084/m9.figshare.28919069.v1
title: Supplementary Table2 Solanum pennellii・Solanum arcanum and Solanum lycopersicum sample metadata.csv

description: Currently, domesticated species available on the market have been developed through the breeding of wild relatives. We collected gene expression data of domesticated species of rice, tomato, and soybean, along with their presumed ancestral species, and conducted a meta-analysis to investigate differentially expressed genes during domestication. In wild relatives, the expression of gene groups involved in osmotic, drought, and wound stress tolerance was upregulated. In contrast, in domesticated species, upregulated ex-pression was observed in gene groups related to auxin and those involved in the efflux of heavy metals and harmful substances. By identifying these genes with differential ex-pression levels, we can gain insights into how domestication has influenced changes in crop traits. In particular, breeding strategies that make use of wild relatives with high genetic diversity are attracting attention as an important approach for addressing climate change and ensuring a sustainable food supply. The findings obtained in this study are expected to contribute to the realization of rapid breeding and the development of new varieties capable of growing in harsh natural environments. To address this issue, a new cultivation method called "De novo domestication" has been proposed, which combines the genetic diversity of currently unused wild relatives and wild relatives with genome editing technologies that enable rapid breeding.
DOI: 10.17632/hk7mydst66
title: Good and Bad Classification of fenugreek leaves

description: Title: Good and Bad Classification of Fenugreek Leaves: Techniques, Parameters, and Perspectives

1. Introduction

Fenugreek (Trigonella foenum-graecum) is a widely cultivated herb in the Fabaceae family, used both as a spice and a medicinal plant. Its fresh leaves are commonly consumed as a leafy vegetable in many parts of Asia and the Middle East. Given its high nutritional and therapeutic value, the quality of fenugreek leaves is of paramount importance to ensure consumer satisfaction and safety.

One crucial step in the post-harvest handling of fenugreek leaves is the classification or sorting of good and bad leaves. Manual sorting is time-consuming and inconsistent, leading to the growing interest in automated classification using image processing and machine learning. This essay provides an in-depth analysis of the classification of fenugreek leaves into "good" and "bad" categories, covering the criteria, techniques, technologies, challenges, and future outlook.


---

2. Importance of Classification

Classification of fenugreek leaves is essential for several reasons:

Quality Control: Ensures only fresh, uncontaminated leaves reach consumers.

Market Value: Improves pricing and demand for higher-quality produce.

Shelf Life: Discarding spoiled or infected leaves extends the storage life of the batch.

Processing Efficiency: Reduces contamination during processing for frozen or dried leaf products.



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3. Classification Criteria

To effectively classify fenugreek leaves, various quality parameters are considered:

a. Visual Appearance

Color: Good leaves exhibit a vibrant green color, while bad leaves may show yellowing, browning, or black spots.

Texture: Healthy leaves are turgid and crisp; bad ones are wilted, dry, or slimy.

Size and Shape: Good leaves maintain uniform shape; damaged, malformed, or shriveled leaves are considered bad.


b. Physiological Condition

Decay or Rot: Fungal or bacterial infection can cause soft rot, making the leaf unfit.

Pest Damage: Holes, discoloration, or patterns indicating pest attacks disqualify leaves from the good category.


c. Foreign Matter

Leaves contaminated with soil, dust, or foreign plant matter are categorized as bad unless cleaned and inspected.


d. Odor

A fresh herbal aroma indicates good leaves; any sour, foul, or fermented smell implies spoilage.



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4. Manual Classification Techniques

Traditionally, manual sorting is conducted at collection centers or processing units. Workers rely on:

Visual inspection under natural or artificial light.

Touch and feel to identify wilting or softness.

Occasionally smell for spoilage detection.


Limitations:

Inconsistent results due to fatigue or subjective judgment.

Time- and labor-intensive.

Higher chances of contamination.
5. Automated Classification Using Image Processing

Advancements in machine vision and artificial intelligence have enabled the development of automated classification systems.

a. Image Acquisition
DOI: 10.17632/kg7nmzmnwm
title: Refractory Eosinophilic Annular Erythema Responsive to Dupilumab: A Case Report and Review of the Literature

description: Supplemental Table
DOI: 10.17632/nymb8dnw3s
title: Raw Data for Forecast of the Trend in Sales Data of a Confectionery Baking Industry Using Exponential Smoothing and Moving Average Models

description: Raw Data for Forecast of the Trend in Sales Data of a Confectionery Baking Industry Using Exponential Smoothing and Moving Average Models
DOI: 10.17632/shtsm3gg29
title: Education Linguistic Research Dataset (1961-2020)

description: The educational linguistics reseach & publication dataset, which was indexed by Scopus from 1961-2020.
The dataset contains data authors, authors ID Scopus, tittle, year, source tittle, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, correspondence address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, and EID.
DOI: 10.17632/v4dvk86gbz
title: PepperDisease

description: This dataset is a curated collection of images and metadata used for the study titled "A Multi-Modal Framework for Pepper Disease and Pest Detection". The dataset includes high-quality images of pepper plants exhibiting various symptoms of diseases and pest infestations, annotated with relevant metadata for use in machine learning and deep learning applications. It is designed to support researchers and practitioners in developing and evaluating models for accurate disease diagnosis and pest identification in agricultural settings.
This dataset is particularly suited for:

Developing supervised learning models for disease and pest detection.
Training image classification, object detection, and segmentation algorithms.
Evaluating the robustness of algorithms under varying environmental conditions.

Due to confidentiality agreements with agricultural partners, this dataset represents only a subset of the complete data used in the study.
The complete dataset and associated code are available upon request. Please contact the corresponding author for details.
After project completion, the full dataset will be made publicly available on GitHub.