cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xkbjx8959c
title: FruitVision: A Benchmark Dataset for Fresh, Rotten, and Formalin-mixed Fruit Detection

description: This dataset, initially consisting of 10,154 high-resolution images of five fruit types—apple, banana, mango, orange, and grapes—has been expanded to over 81,000 using advanced augmentation techniques like rotation, scaling, and brightness adjustment. The images are classified into three key categories: fresh, rotten, and formalin-mixed. This dataset offers a unique opportunity for researchers in computer vision, agriculture, and food safety to develop machine learning and deep learning models for tasks such as real-time fruit quality assessment, contamination detection, and automating food inspection processes. Its extensive and diverse collection makes it a valuable resource for innovations in public health, export quality control, and agricultural productivity.

(1) Everyone seeks fresh and high-quality fruits, as fruits naturally degrade over time, leading to spoilage. It is estimated that roughly one-third of harvested fruits rot, causing significant financial loss. Additionally, the sale of fruits is impacted by consumer concerns over the health risks associated with consuming spoiled or chemically treated fruits. The manual classification of fresh and rotten fruits by individuals is inefficient, particularly for farmers, sellers, and the fruit processing industry.
(2) In recent years, computer vision techniques have shown great promise in automating tasks like classification and detection of fresh and rotten fruits.
(3) To aid in the development of computer vision-based algorithms, we present an extensive fruit dataset containing five fruit classes: Fresh Apple, Rotten Apple, Formalin-mixed Apple, Fresh Banana, Rotten Banana, Formalin-mixed Banana, Fresh Grape, Rotten Grape, Formalin-mixed Grape, Fresh Mango, Rotten Mango, Formalin-mixed Mango, Fresh Orange, Rotten Orange, and Formalin-mixed Orange. Classification into fresh, rotten, and formalin-treated categories was carried out with the assistance of agricultural experts.
DOI: 10.17632/yc926dpnxt
title: GPU accelerated Monte Carlo simulation of Brownian motors dynamics with CUDA

description: This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018) Abstract This work presents an updated and extended guide on methods of a proper acceleration of the Monte Carlo integration of stochastic differential equations with the commonly available NVIDIA Graphics Processing Units using the CUDA programming environment. We outline the general aspects of the scientific computing on graphics cards and demonstrate them with two models of a well known phenomenon of the noise induced transport of Brownian motors in periodic structures. As a source of fluctuations ... Title of program: poisson, dich Catalogue Id: AEVP_v1_0 Nature of problem Graphics processing unit accelerated numerical simulation of stochastic differential equation. Versions of this program held in the CPC repository in Mendeley Data AEVP_v1_0; poisson, dich; 10.1016/j.cpc.2015.01.021 AEVP_v1_0; poisson, dich; 10.1016/j.cpc.2015.01.021
DOI: 10.6084/m9.figshare.28829762.v1
title: The threat of COVID-19 will influence consumers' taste compensation

description: This study examined how taste compensation is affected by mortality salience and its mechanisms during the pandemic. It conducted four studies and confirmed that mortality salience can cause consumers to seek stronger taste compensation, and the mediating role of compensation motivation. Additionally, low self-efficacy leads consumers to rely more on external compensation, as the impact of mortality salience upon compensating motivation can be negatively moderated by self-efficacy. This study identified new antecedents of F&B taste that add to the literature on compensatory consumption and pandemic research. It also helps hospitality managers and marketers to better understand consumers' dietary preferences and modify their business strategies accordingly during and after the COVID-19 outbreak.
DOI: 10.17632/3km4b3sfcw
title: Elastic buckling and post-buckling strength of CFS decks in bending: FE simulation results

description: The database contains the results of FE simulations of CFS decks in bending. The FE models are described in detail in the referenced paper.
Additional simulation results were added to Version 3 of the database.
DOI: 10.6084/m9.figshare.14176664.v2
title: The Code and Data

description: This zipped folder contains the code and data used to generate the figures in our manuscript "Quantitative analysis of non-equilibrium systems from short-time experimental data"<br>
DOI: 10.17632/bz4754xbwm
title: A study of IL-1β-Associated NNT Acetylation Orchestrates Iron–sulfur Cluster Maintenance and Cancer Immunotherapy Resistance. Han et al

description: Interleukin-1β (IL-1β) is a key protein in inflammation and contributes to tumor progression. However, the role of IL-1β in cancer is ambiguous or even contradictory due to its pro- and antitumor potential. Here, we found that upon IL-1β stimulation, nicotinamide nucleotide transhydrogenase (NNT) in cancer cells is acetylated at lysine (K) 1042 (NNT K1042ac) and thereby induces the mitochondrial translocation of p300/CBP-associated factor (PCAF). This acetylation enhances NNT activity by increasing the binding affinity of NNT for NADP+ and therefore boosts NADPH production, which subsequently sustains sufficient iron–sulfur cluster maintenance and protects tumor cells from ferroptosis. Abrogating NNT K1042 acetylation dramatically attenuates IL-1β-promoted tumor immune evasion and synergizes with PD-1 blockade. In addition, NNT K1042ac is associated with IL-1β expression and the prognosis of human gastric cancer. Our findings demonstrate a novel mechanism of IL-1β-promoted tumor immune evasion through which tumor cell metabolism is regulated, implicating the therapeutic potential of disrupting the link between IL-1β and tumor cells by inhibiting NNT acetylation.
This is the original data set for the study  that contains all of the unprocessed microscopy images, gels, western blots used in this study.
DOI: 10.17632/dwhfp2ycyw
title: Genetically stable CRISPR-based kill switches for engineered microbes

description: Raw data and plasmid maps for the article "Genetically stable CRISPR-based kill switches for engineered microbes," DOI : 10.1038/s41467-022-28163-5.
DOI: 10.6084/m9.figshare.18517874.v2
title: Nontarget mass spectrometry and in silico molecular characterization of air pollution from the Indian subcontinent

description: <br>Supplementary Information, datasets, and source data (Figures 1-8) for the study<i> </i><b>Papazian <i>et al</i>., 2022</b><i>, Nontarget mass spectrometry and in silico molecular characterization of air pollution from the Indian subcontinent</i><i>,</i><i> </i>published in Communications Earth & Environment.<br>
DOI: 10.17632/pf4wycty2p
title: Data for: The influence of environmental and health indicators on premature mortality: An empirical analysis of the City of Toronto’s 140 neighborhoods

description: Environmental and health indicator data; also includes population and neighbourhood area data used in data standardization.
DOI: 10.17632/sdw4wdn6kk
title: Evolutionary Converging Functions

description: High learning rate have been a big challenge in case of CNN which make CNN computationally
expensive and requires huge amount of training data. In our research we have aimed to increase
the learning rate of the Image segmentation process. Image of any object in the universe taken
from any type of camera can broadly be divided into spectral and geometric properties. Both
these properties are prominently visible in satellite image. Hence if a satellite image can be
classified using a method any image can be classified using the same technique. Hence, we
proposed Evolutionary converging functions which converts the spectral and geometric
properties of features in a satellite image into mathematical equations using decision tree and
then converges with a neural network. Different highresolution data have been chosen for
extracting different features out of it. This transformation process, anchored in decision tree
methodology, converges seamlessly with neural networks to yield unparalleled results, all while
eliminating the need for computationally intensive convolutions. In our pioneering study, we
venture beyond traditional boundaries by employing diverse high-resolution datasets. Each
dataset, carefully selected, promises to unlock a treasure trove of distinctive features. As we
embark on this uncharted journey, we are committed to unraveling the latent potential of
Evolutionary Converging Functions, thereby opening up new horizons for automated image
classification. The synergy between spectral and geometric properties emerges as a powerful
combination, endowing our methodology with the ability to extract nuanced and context-rich
information, redefining the landscape of image analysis. The results show a high accuracy i.e.
above 90% in almost all objects of different shape and spectral signature.
DOI: 10.6084/m9.figshare.7700885.v1
title: Datureae_location_points_full_3cols_Dfer.RData

description: Database with location points for 18 Datureae species.
DOI: 10.17632/gwf2rtc9z2
title: Good and Bad classification of Holy Basil Leaves (Ocimum sanctum)

description: data description within 3000 character for project Good and Bad classification of Holy Basil Leaves (Ocimum sanctum)) which contain 500 good and 500 bad sample of Marsh Barbel Leaves
ChatGPT
The project titled "Good and Bad Classification of Holy Basil Leaves (Ocimum sanctum)" focuses on creating a robust system for differentiating between good and bad samples of Holy Basil leaves using a dataset consisting of 1,000 samples. Holy Basil, known scientifically as Ocimum sanctum, is a plant widely revered for its medicinal properties and is integral to various traditional health practices. The quality of these leaves is crucial for their efficacy in medicinal use, making this classification task both practically significant and scientifically valuable.

Data Description
Dataset Composition:

Total Samples: 1,000
Good Samples: 500
Bad Samples: 500
Features:
The dataset contains various features that are crucial for the classification task. These features are derived from both visual inspection and possibly from chemical analysis. Here’s a breakdown of potential features:

Visual Features:

Color: RGB or HSV values capturing the color distribution of the leaf.
Texture: Measures such as smoothness, roughness, or the presence of visible spots.
Shape: Geometric descriptors including length, width, and the perimeter of the leaves.
Vein Patterns: Analysis of the venation patterns which may indicate health.
Defects: Presence of holes, discolorations, or other deformities.
Chemical Features:

Moisture Content: Levels of water within the leaf, indicating freshness.
Nutrient Levels: Concentrations of essential oils, vitamins, and minerals.
pH Levels: Acidity or alkalinity which might affect the leaf's condition.
Labeling:

Good Samples: These are leaves that are fresh, free from significant defects, and contain optimal levels of beneficial compounds.
Bad Samples: These leaves may show signs of aging, disease, pest infestation, or have suboptimal levels of essential nutrients.
Data Collection:

Visual Data: High-resolution images taken under controlled lighting conditions to ensure consistency in color and texture representation.
Chemical Data: Laboratory tests conducted to measure the chemical properties of the leaves accurately.
Data Preprocessing:
To prepare the data for analysis and model training, several preprocessing steps are employed:

Image Augmentation: Techniques such as rotation, scaling, and flipping to increase the variability of the visual dataset.
Normalization: Standardizing the values of chemical features to ensure they are on a comparable scale.
Feature Extraction: Applying methods such as Principal Component Analysis (PCA) to reduce dimensionality while retaining significant information.
Classification Methodology:
The goal of the project is to develop a machine learning model capable of accurately classifying the leaves. Potential approaches include:
DOI: 10.17632/s6y4yzssy6
title: In situ Soil Fractions (Sand, Silt and Clay Percentage) and pH Data at Sample Sites in Arid Region of India

description: The data presented here consists percentage of sand, silt, clay and pH at surface (~5cm) and subsurface (~5-15cm)  at various sample locations in arid region of Great Indian Thar Desert falling in Rajasthan state of India. Mainly, the samples collected falls in four districts namely, Jaisalmer, Bikaner, Nagaur, and Jodhpur. 

A total of 24 soil samples, each with ~2 kg weight were collected at surface (~ 5 cm depth) and subsurface (between 5-15 cm depth) in the sample locations. The samples were randomly selected but ensured to be well distributed with a rationale that the sample site is having minimum of 5 ha of homogeneous signature with respect to soil texture variation as appeared from satellite imagery. Care was taken while selecting sampling locations such at that it won’t have any litter deposition or weed growth. Locations with excessive moisture content were also avoided. Also, the samples are chosen such that the landscape contains sparse vegetation and avoided sand dunes due to their potential to participate in aeolian process. Predominantly, species like Calligonum polygonoides, Aerva persica, Citrullus colocynthi, Prosopis Juliflora, Ziziphus mauritiana, Prosopis cineraria and Fagonia bruguieri were found in this landscape. Out of 24 samples, in one location we are unable to collect subsurface soil as there is bedrock after at a depth of 6 cm.
DOI: 10.17632/wwnvw28n2m
title: Lower Limb Kinematic, Kinetic and Spatial-temporal Gait Data for Healthy Adults Using a Self-paced Treadmill

description: The data presented in this repository, acquired from 100 “normal” participants of different ages, will provide a valuable gait dataset for future work and studies examining locomotion in humans.
DOI: 10.17632/2fc7dr9vc2
title: The Fertility Impact of Intense Precipitation in Rural Areas: The Role of Income Expectation Shocks

description: This replication package contains all the available data, code, and instructions to reproduce the results presented in the paper:
The Fertility Impact of Intense Precipitation in Rural Areas: The Role of Income Expectation Shocks
DOI: 10.17632/x768pztf5v
title: Using wearable technology to measure sleep behaviour and general physiology of domestic dogs – a prospective tool for animal welfare assessment

description: Data used to compare behavioural data from the petpace and from normal collection (by humans).
DOI: 10.6084/m9.figshare.26404867.v1
title: Data and Codes from 'Microbial photosynthesis mitigates carbon loss from northern peatlands under warming'

description: This file contains the main data used in the manuscript Microbial photosynthesis mitigates carbon loss from northern peatlands under climate warming. 

R codes and GEE codes used to make the main figures are also provided. 

Please read the READ_ME file for details.
DOI: 10.6084/m9.figshare.28945454.v1
title: Leaf color meta analysisNew Pyhtologist, May 2025

description: Files associated to the manuscript "<b>Signaling defences with color: a meta-analysis of leaf color variation, palatability and herbivore damage", New Phytologist, 2025</b>
DOI: 10.6084/m9.figshare.5579662.v3
title: Verilog A MEMRISTOR HP (Linear Ion Drift model)

description: Verilog A code for memristor based in the HP model with flux and charge extra outputs.<br><br>model parameters:<br>R<sub>off</sub>: off resistance (HRS)<br>R<sub>on</sub>: on resistance (LRS)<br>D: thickness of the oxide layer<br>uv : ions movitily<br>x<sub>0</sub>: initial state<br>
DOI: 10.6084/m9.figshare.5769828.v3
title: Repository_data_Soda_Saline_2018

description: The major ion concentration data of sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), sulphate (SO<sub>4</sub>), bicarbonate (HCO<sub>3</sub>), carbonate (CO<sub>3</sub>) and pH (if it was coupled with ion data) were input into the database. The data were drawn from a large geographic scale across Eurasia (Austria, China, Hungary, Kazakhstan, Mongolia, Russia, Serbia, Turkey) and from a large number of saline lakes and pans (N=220) with minimum a 1.0 g L<sup>–1</sup> salinity threshold. The 1.0 g L<sup>–1</sup> salinity threshold was selected based on a former study (Boros et al., 2014), where this threshold was experimentally found to be the characteristic boundary of soda ecosystems. Salinity was estimated by the sum of measured concentrations of eight major ions (Na, K, Ca, Mg, Cl, SO<sub>4</sub>, HCO<sub>3</sub>, CO<sub>3</sub>). As generally known, sodium is by far the most common cation in saline lakes and necessarily the dominant cation in soda type lakes. Therefore, sites were excluded from the dataset if Na was not the most abundant ion. If seasonal or annual water data were available, mean values were put into database. Most of the data came from papers (sources are indicated in the table) published in the period from 1956‒2017. <br>
DOI: 10.6084/m9.figshare.7907249.v1
title: 1856 SCOtag Sequences for Rosa phylogenomics

description: Here we released 29,399 SCO<sub>Tag</sub> sequences mined in <b>17 <i>Rosa</i> species</b> (<i>R. arvensis</i>, <i>R. chinensis</i>, <i>R</i>. x <i>damascena</i>, <i>R</i>., <i>gigantea</i>, <i>R</i>. <i>laevigata</i>, <i>R</i>. <i>majalis</i>, <i>R. minutifolia</i>, <i>R. moschata</i>, <i>R. multiflora</i>, <i>R. odorata</i>, <i>R</i>. 'Old Blush', <i>R</i>. <i>palustris</i>, <i>R. pendulina</i>, <i>R. persica</i>, <i>R. rugosa</i>, <i>R. wichurana</i>, <i>R. xanthina</i>) and <b>7 other Rosaceae species</b> (<i>Fragaria iinumae</i>, <i>Fragaria nipponica</i>, <i>Fragaria nubicola</i>, <i>Fragaria vesca</i>, <i>Geum urbanum</i>, <i>Potentilla micrantha</i>, <i>Rubus occidentalis</i>).These SCO<sub>Tag</sub> sequences can potentially amplify genomic regions that show interest in resolving both recent and ancient divergences in the evolutionary history of the genus <i>Rosa</i>. They are also millestones well-distributed on the seven pseudomolecules and unanchored scaffolds of the genome of roses and can serve for genome comparative studies in this genus.<br>
DOI: 10.17632/59bzskfh8z
title: Data for: Rock Climbing Injuries and Time to Return to Sport in the Recreational Climber
DOI: 10.17632/nym6y9g82y
title: Basal Cell Carcinoma with Bone Invasion: A Systematic Review and Pooled Survival Analysis

description: Supplemental documents for "Basal Cell Carcinoma with Bone Invasion: A Systematic Review and Pooled Survival Analysis"
DOI: 10.6084/m9.figshare.28666520.v5
title: Ethology

description: The data is for the submitted manuscript about coffee concentration-dependent enhancement and sleep deprivation recovery in <i>Drosophila melanogaster</i> (fruit fly).
DOI: 10.17632/4769fjr4yd
title: Integrated analysis of transcriptional and metabolic responses to mitochondrial stress

description: Code for RNA sequencing alignment and analysis, metabolomics analysis, and mixOmics omics integration analysis used for study on mitochondrial stress responses in fibroblasts.
DOI: 10.17632/2wsmtkyczm
title: Data Replication for Sentiment and Emotion Network Analysis [SENA]

description: Textual database to replicate procedures showcase in SENA, an analytic framework and software that conducts Sentiment and Emotion Network Analysis of textual data.  

The compressed folder contains two main forms of textual data. 

The folders called: "Essays_not_labeled" and "Essays_named_labeled" contain the same 11 essays on the reasons to participate in a data science seminar. The main difference of these files is that there in the "Essays_not_labeled" folder, the essays show the original name of the participants. In the "Essays_named_labeled" folder, these names have been replaced by an attribute of the respondent. In our case, this attribute is the gender of each participant. Based on this process, the analyses will conduct analyses based on the categories represented in these file names. When no labels or categories are added to file names, as in the case of the files contained in the folder "Essays_not_labeled" only aggregated SENA worldcloud analyses will be deployed. 

Finally, both folders contain Microsoft Word files saved as "*.doc" files. If your texts are in ".*docx" format, you need to save them as "*.doc" before uploading them to the software SENA.  

  The second type file is a "*.csv" database with the same textual content as the two folders but stored in rows and columns. Each of these documents were decomposed into sentences and then the sentences were classified into topics following LACOID (Gonzalez Canche, 2023, reference below). To demonstrate the use of SENA, users may decide to include the topic identified with LACOID as an attribute of the text. If this is done, the resulting SENA analyses will yield comparative results. If users decide to ignore these attributes, only aggregate analyses will be displayed. 
  
González Canché, M. S. (2023). Latent Code Identification (LACOID): A Machine Learning-Based Integrative Framework [and Open-Source Software] to Classify Big Textual Data, Rebuild Contextualized/Unaltered Meanings, and Avoid Aggregation Bias. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069221144940
DOI: 10.17632/78bk5wctcs
title: Clinimetric properties of Equilibrium Coordination tests among young adults

description: This research is conducted to estimate the clinimetric properties of the equilibrium coordination test among healthy young adults with age group between 18-25 years. This study focus on establishment of inter-rater reliability, intra-rater reliability , ceiling and floor effect as well as the normative reference values of the outcome measures. Three recordings were taken for each outcome measure to calculate mean for normative values and two independent rater recorded the readings for the reliability. Possible highest and lowest score was identify to evaluate the ceiling and floor effect