cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3mn9843tz2
title: Aruzz22.5K: An Image Dataset of Rice Varieties

description: This extensive dataset presents a meticulously curated collection of low-resolution images showcasing 20 well-established rice varieties native to diverse regions of Bangladesh. The rice samples were carefully gathered from both rural areas and local marketplaces, ensuring a comprehensive and varied representation. Serving as a visual compendium, the dataset provides a thorough exploration of the distinct characteristics of these rice varieties, facilitating precise classification.
#Dataset Composition#
The dataset encompasses 20 distinct classes, encompassing Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. In total, the dataset comprises 4,730 original JPG images and 23,650 augmented images.
#Image Capture and Dataset Organization#
These images were captured using an iPhone 11 camera with a 5x zoom feature. Each image capturing these rice varieties was diligently taken between October 18 and November 29, 2023. To facilitate efficient data management and organization, the dataset is structured into two variants: Original images and Augmented images. Each variant is systematically categorized into 20 distinct sub-directories, each corresponding to a specific rice variety.
#Original Image Dataset#
The primary image set comprises 4,730 JPG images, uniformly sized at 853 × 853 pixels. Due to the initial low resolution, the file size was notably 268 MB. Employing compression through a zip program significantly optimized the dataset, resulting in a final size of 254 MB.
#Augmented Image Dataset#
To address the substantial image volume requirements of deep learning models for machine vision, data augmentation techniques were implemented. Total 23,650 images was obtained from augmentation. These augmented images, also in JPG format and uniformly sized at 512 × 512 pixels, initially amounted to 781 MB. However, post-compression, the dataset was further streamlined to 699 MB.
#Dataset Storage and Access#
The raw and augmented datasets are stored in two distinct zip files, namely 'Original.zip' and 'Augmented.zip'. Both zip files contain 20 sub-folders representing a unique rice variety, namely 1_Subol_Lota, 2_Bashmoti, 3_Ganjiya, 4_Shampakatari, 5_Katarivog, 6_BR28, 7_BR29, 8_Paijam, 9_Bashful, 10_Lal_Aush, 11_Jirashail, 12_Gutisharna, 13_Red_Cargo,14_Najirshail, 15_Katari_Polao, 16_Lal_Biroi, 17_Chinigura_Polao, 18_Amon, 19_Shorna5, 20_Lal_Binni.
# Train and Test Data Organization
To ease the experimenting process for the researchers we have balanced the data and split it in an 80:20 train-test ratio. The ‘Train_n_Test.zip’ folder contains two sub-directories: ‘1_TEST’ which contains 1125 images per class and ‘2_VALID’ which contains 225 images per class.
DOI: 10.6084/m9.figshare.28334369.v1
title: Raw data-NGS-2024-07-01565.xlsx (Nature Geoscience)

description: The dataset is involved in the effects of novel ditched-buried straw return on soil organic carbon and crop yield using a long-term field experiment, as well as a meta-analysis around China.
DOI: 10.17632/xdfw54ybrb
title: Knowledge Assessment on High-Risk Pregnancies Among ASHA Workers in Magadi Taluk: An Interventional Study-Manuscript data

description: Government of India under an umbrella program "National Rural Health Mission (Now known as National Health Mission) introduced group of workers called as ASHA’s in view of Constructing bridge between the Community and Public health system. Objective: To assess the existing knowledge and effectiveness of the awareness program on improving the knowledge regarding high-risk pregnancies among ASHA workers. Method: An interventional study using educational videos and facilitated discussions was conducted in selected PHC and Subcentre of Magadi Taluk, Karnataka, India using complete enumeration method. Out of 159 ASHA workers 110 were engaged in the Quantitative study. Result: The study revealed that the knowledge of the ASHA workers on high-risk pregnancy after intervention was considerably high when compared to pre-intervention phase. The intervention led to significant improvements in ASHAs' knowledge on multiple aspects of maternal health. Awareness of the correct full-term pregnancy duration (37–42 weeks), the minimum number of ANC visits, and the hemoglobin cutoff increased. Misconceptions about miscarriage, HIV transmission risks, and the need for referral services for high-risk pregnancies (HRP) were addressed. Participants showed better understanding of complications like antepartum hemorrhage, severe eclampsia, intrauterine growth restriction, and gestational diabetes. Additionally, knowledge about the recommended conception gap after two C-sections and the risks of high blood pressure during pregnancy improved. Conclusions: The overall knowledge of ASHA workers regarding high-risk pregnancies boosted after intervention. However further research is needed to delve deeper into interventions of holistic effects on maternal and foetal health outcomes to ensure sustained improvements in Maternal and Child Health.
DOI: 10.17632/2mpd7d3vry
title: Automatic Water Stress detection in wheat crop canopy using Chlorophyll fluorescence image dataset

description: Pre-processed Chlorophyll fluorescence dataset of wheat canopy images for automatic water stress detection using machine learning models. The dataset is comprised of file system-based information about the wheat variety Raj 3765. The analysis is done for the period of sixty days, twenty-four chlorophyll fluorescence images every day for each (Control and Drought) have been recorded. A total of (1440 x 2 (for both control and drought) =2880) images has been utilized. The size of the dataset was increased to (2880*20) for this study's endeavor using data argumentation techniques in order to develop the more generalized model.

The created dataset is subjected to different pre-processing pipelines comprising noise and contrast enhancement procedures. Pre-processed output images are subjected to novel segmentation algorithm called "Cfit k-means " to extract appropriate ROI which maximizes photosynthetic activity. The dataset and publications links are already shared in related links.

PSII (photosystem -II) colour features and GLCM correlation-based features are used for further analysis in the automation process.

The automation procedure involves the selection of an appropriate algorithm from the top 9 algorithms listed below for the said purpose.

1. Logistic Regression
2. Linear Discriminant Analysis (LDA)
3. K-nearest neighbours (KNN)
4. Decision Tree (CART)
5. Gaussian Naïve Bayes (GNB)
6. Support Vector Machine (SVM)
7. Extra Trees (ETC)
8. Gradient Boosting (GB)
9. Random forest (RF)
DOI: 10.6084/m9.figshare.3795825.v2
title: Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, southwest China.csv

description: This dataset contains human elephant conflict data collected by authors of a paper published in PLosOne, titled "Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, southwest China".These data were collected in cooperation with the following four institutions: the Local Forest Administration, the Nature Reserves, Community Committees and the insurance company “Yunnan branch, China Pacific property insurance company”. The data includes information on the characteristic of the households affected by conflicts with Asian elephants, and number of affected events per household during year 2008 and 2012. Please refer to this published paper for more infomration on data collection and analysis.
DOI: 10.6084/m9.figshare.7926998.v1
title: gVCF_HG02089

description: 1000 Genomes gVCF mapped to hs37d5 for HG02089. Complete collection: https://doi.org/10.6084/m9.figshare.c.4414307
DOI: 10.15468/dl.qnud5z
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Noticastrum sericeum (Less.) Less. ex Phil.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012927-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5c3dtp6zr9
title: PISA 2018_Canadian samples

description: Here are ten global competency assessment datasets from the Canadian sample of PISA 2018. These data sets are organized by groups of Booklets.
DOI: 10.17632/khvpypw8bs
title: Downy Mildew Detection in Luffa Leaves Dataset

description: In this dataset, a total of 3483 Luffa leaf samples were collected across various field plots in Ashulia, Dhaka, Bangladesh, during the 2024 growing season. The leaves were carefully documented and categorized into healthy and diseased groups to create a comprehensive dataset for accurate classification of Downy Mildew Disease caused by Phytophthora spp.. The distribution of leaf samples is as follows:
• Healthy Leaves: 414 samples
• Diseased Leaves: 3069 samples
• Augmented Healthy Leaves: 2483 samples
• Augmented Diseased Leaves: 17593 samples

Purpose:
The Luffa Downy Mildew Disease Detection Dataset serves a critical role in machine learning applications, particularly in image classification for automatic disease detection in Luffa plants. It can be used to train models like convolutional neural networks (CNNs) to identify and classify healthy and diseased leaves based on visual features such as leaf shape, color, and texture. The dataset includes both original and augmented images, enhancing model robustness by providing diverse samples that improve generalization in various real-world scenarios. This dataset is useful in areas like agricultural automation, early disease detection, and disease management in the Luffa farming industry. Additionally, it can be integrated with environmental data (e.g., temperature, humidity, rainfall) to improve predictive models that forecast disease risks under different climatic conditions. Ultimately, this dataset helps advance machine learning models for automated disease monitoring and management in the agricultural sector.
DOI: 10.6084/m9.figshare.7583369.v1
title: Whitehead Data

description: Reformatted data using Whitehead library as used in JACKS 2019 paper. Please see README.txt for more information about each file and where it comes from.
DOI: 10.17632/jp6frx2gwf
title: Disease rating_Population123

description: Race 2 anthracnose disease rating data for genetic study in three watermelon populations: population 1 (PI 189225 x New Hampshire Midget), population 2 (Perola x PI 189225), and population 3 (Verona x PI 189225).
DOI: 10.25394/pgs.28934759.v1
title: <b>Intimate Partnership and the Gender Spectrum: An Analysis of Gender Exploration within Queer Relationships</b>

description: For trans and gender diverse populations, whose psychological wellbeing is particularly impacted by romantic partnerships, the ability to navigate transition within relationships is essential. The concept of co-transition is a helpful tool to contextualize this process, yet clinical literature on how to best support diverse queer relationships experiencing co-transition is limited. This study utilized Interpretative Phenomenological Analysis (IPA) through a combined lens of Relational Intersectionality and Queer Theory to better understand the experiences of partners who began their relationship as queer-identified and same-gender and later experienced gender transition within the relationship. Interviews were conducted with 5 participants regarding their individual and relational experiences of either their own or their partner’s transition, focusing on topics of identity affirmation, communication, relationship satisfaction and sexual satisfaction. Analysis revealed that elements of these themes occurred within the contexts of self, other, partnership and process, highlighting the transformational journeys of individual partners and the relational system. This research has implications for clinicians supporting couples or individuals through co-transition and encourages focusing on opportunities for mutual growth and exploration.<i>Keywords: transgender, nonbinary, queer, relationships, transition</i>
DOI: 10.17632/99ps9dz428
title: SIMPLE code: Optical properties with optimal basis functions

description: We present SIMPLE, a code developed to calculate optical properties of metallic and insulating extended systems using the optimal basis method originally proposed by E. L. Shirley in 1996. Two different approaches for the evaluation of the complex dielectric function are implemented: (i) the independent-particle approximation considering both interband and intraband contributions for metals and (ii) the Bethe–Salpeter equation for insulators. Since, notably, the optimal basis set is systematically improvable, accurate results can be obtained at a strongly reduced computational cost. Moreover, the simplicity of the method allows for a straightforward use of the code; improvement of the optimal basis and thus the overall precision of the simulations is simply controlled by one (for metals) or two (for insulators) input thresholds. The code is extensively tested, in terms of verification and performance, on bulk silver for metals and on bulk silicon for insulators.
DOI: 10.17632/hh7kjk8twz
title: 3D image data of digitally-generated porous material samples

description: This dataset contains 3D image data of digitally-generated 528 granular porous material samples, which can be used to train a machine learning model for predicting material performance properties from geometric features of porous material samples. The dataset has been prepared in https://doi.org/10.1016/j.cej.2021.130069.
DOI: 10.17632/b33xrj9ryf
title: ESCAPE approach for the sustainability evaluation of spent lithium-ion batteries recovery: dataset of 33 available technologies 

description: Several studies have proposed the possibility to recover a variety of materials from LIBs, even if the processes are developed only at the lab-scale.
The ESCAPE approach is presented and applied to evaluate the available strategies to recover materials from LIBs.
Following this approach, the data about energy consumption and emissions are calculated for all the steps of the 33 selected literature processes, proposed for LIBs recovery.
The calculated data are used to evaluate the sustainability of selected technologies, allowing to provide an instrument to support the most suitable activities able to extract materials from waste acting in substitution of natural resources use.
The parameters evaluated in the ESCAPE approach (embodied energy and carbon footprint) are evaluated for the technologies presented in https://doi.org/10.1016/j.jclepro.2022.130493.

Table 5 reports the detailed values of the embodied energy and the carbon footprint evaluated for all the steps of each considered process (see https://doi.org/10.1016/j.jclepro.2022.130493), divided into four categories: thermal treatments, mechanical treatments, chemicals, and water use. The data were calculated considering the processes as exactly described by the authors.

Table 6 reports the detailed values of the embodied energy and the carbon footprint evaluated for all the steps of each considered process (see https://doi.org/10.1016/j.jclepro.2022.130493), divided into four categories: thermal treatments, mechanical treatments, chemicals, and water use. The data were calculated considering the use of distilled water instead of ultrapure water or deionized water for chemicals dilution. In addition, for the products washing, only tap water was considered.
DOI: 10.17632/cshfcfv334
title: Efficient Degradation of Nicosulfuron using a Biomass@LDH/MOF Nanohybrid as Catalyst: Elucidation of the One-step Synergistic Adsorption-Photocatalysis Process (Dataset).

description: This file contains the triplicate experimental data for the photocatalytic design, pH study, photocatalyst dosage, adsorption kinetics, hydrogen peroxide dosage study, degradation kinetics, scavenger tests, material reuse, and phytotoxicity study. The data pertain to two processes: Adsorption and Photocatalytic Degradation (System A) and the One-Step Synergistic Adsorption-Photocatalysis Process (System B).
DOI: 10.17632/xcz8vb448y
title: COVID19 Rumor Detection

description: The data set contains information about the COVID-19 pandemic. Twitter data has been collected based on the hashtags #CoronaOutbreak, #CoronaVirus, #CoronaVirusOutbreak, #COVID19, #COVID-19, #COVID2019, and #SARSCoV2, between February 12, 2020 and June 15, 2020. 

The goal of this data set is to detect whether a tweet is identified as a rumor or not (given by the 'label' column). A tweet that is identified as a rumor is labeled as 1, and 0 otherwise. The tweets were labeled by two independent annotators using the following guidelines. Whether a tweet is a rumor or not depends on 3 important aspects: 
(1) A rumor is a piece of information that is unverified or not confirmed by official instances. In other words, it does not matter whether the information turns out to be true or false in the future. 
(2) More specifically, a tweet is a rumor if the information is unverified at the time of posting.
(3) For a tweet to be a rumor, it should contain an assertion, meaning the author of tweet commits to the truth of the message. 

In sum, the annotators indicated that a tweet is a rumor if it consisted of an assertion giving information that is unverifiable at the time of posting. Practically, to check whether the information in a tweet was verified or confirmed by official instances at the moment of tweeting, the annotators used BBC News and Reuters. After all the tweets were labeled, the annotators re-iterated over the tweets they disagreed on to produce the final tweet label.

Besides the label indicating whether a tweet is a rumor or not (i.e., ‘label’), the data set contains the tweet itself (i.e., ‘full_text’), and additional metadata (e.g., ‘created_at’, ‘favorite_count’) . In total, the data set contains 4,612 observations of which 485 (11%) are identified as rumors. 

This data set can be used by researchers to make rumor detection models (i.e., statistical, machine learning and deep learning models) using both unstructured (i.e., textual) and structured data.
DOI: 10.6084/m9.figshare.29135492.v2
title: Ground_Truth_Opti_ModulH_FE

description: Optitrack ground truth of the facilities at the University of Ljubljana, Faculty of Electrical Engineering
DOI: 10.15468/dl.y8uc87
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Platytroctes Günther, 1878",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((8.9648 19.8496,-0.0352 19.8496,-0.0352 10.8496,8.9648 10.8496,8.9648 19.8496))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012928-250525065834625/datasets/export for details.
DOI: 10.17632/dkn3m8bf2r
title: Data for: A causal perspective on reliability assessment

description: The zip file contains R scripts needed to reproduce the results in Sections 4.1 and 4.2 of the manuscript.
DOI: 10.17632/kys6x6wykj
title: Femur Dual-Energy X-ray Absorptiometry (DEXA) Scan Dataset of the Indian Population

description: The dataset contains femoral neck bone mineral density (BMD) measurements from 700 (350 males and 350 females) Indian subjects within the age range of 25 to 85 years. The DEXA scan values were further processed to compute each individual's Body Mass Index (BMI), obesity group, body fat percentage (BFP) and soft tissue density (STD). This dataset is the outcome of preliminary research work focused on developing a low-cost bone density measurement technique using quantitative ultrasound. 
Following parameters of each subject are present in the dataset.
1] Gender (Male or Female)
2] Age (in years)
3] Height (in cm)
4] Weight (in Kg)
5] BMD (g/sq cm)
6] T-Score
7] Z-Score
8] Area in (sq cm)
9] Bone Mineral Content (BMC) (gm)
10] Body Mass Index (BMI)
11] Obesity Group 
12] Body Fat Percentage (BFP)
13] Soft Tissue Density (STD)
DOI: 10.17632/xngss375gk
title: CRM and customer evaluation using MCDM

description: This dataset contains supplementary data related to the manuscript entitled "A combined multicriteria and fuzzy sets approach to customer relationship management". It contains two files. The first presents data and evaluation of 21 potential customers (of a digital marketing company) using the multicriteria decision making (MCDM) method PROMETHEE II; it contains the implementation of VBA code of this method. The second presents data and evaluation of 8 customers (of the same digital marketing company) using the multicriteria decision making (MCDM) method fuzzy TOPSIS; it also contains the implementation of VBA code of the method.
To better explain used data and evaluations: the potential customers are evaluated considering criteria "audience" (which means number of followers of the potential customers in social media), "used platform" (expresses which platform the customer uses to disseminate content) and "niche relevance" (importance of the niche in which customers produce content). Finally, applying the PROMETHEE II method results in a final score for each potential customer, allowing to rank them according to these scores. 
Current customers are evaluated using sets of criteria grouped in three performance dimension: growth potential, relationship and cost-effectiveness. Applying fuzzy TOPSIS method results in a score for each dimension. Thus, customers are classified using two portfolio matrices. 
Steps to reproduce the applications can be found in the respective worksheets.
DOI: 10.17632/5pvj2262bt
title: LINF_190016700

description: Ankyrin repeat-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.6084/m9.figshare.19214148.v1
title: Fresh perspectives on an established technique: Pulsed Amplitude Modulation (PAM) chlorophyll a fluorescence

description: Data are experimental data we collected.
DOI: 10.17632/pgnbjspy4f
title: MagneticKP: A package for quickly constructing k⋅p models of magnetic and non-magnetic crystals

description: We propose an efficient algorithm for constructing k⋅p effective Hamiltonians, which is much faster than previously proposed algorithms. This algorithm is implemented in MagneticKP package. The package applies to both single-valued (spinless) and double-valued (spinful) cases, and to both magnetic and nonmagnetic systems. By interfacing with SpaceGroupIrep or MSGCorep packages, it can directly output the k⋅p Hamiltonian around arbitrary momentum point and expanded to arbitrary order in k.