cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4805497.v1
title: Genomic and Chemical Diversity in <i>Cannabis</i>

description: Plants of the <i>Cannabis</i> genus are the only prolific producers of phytocannabinoids, compounds that strongly interact with the evolutionarily ancient endocannabinoid receptors shared by most bilaterian taxa. For millennia, the plant has been cultivated not only for these compounds, but also for food, rope, paper, and clothing. Today, specialized varieties yielding high-quality textile fibers, nutritional seed oil, or high cannabinoid content are cultivated across the globe. However, the genetic identities and histories of these diverse populations remain largely obscured. We analyzed the nuclear genomic diversity among 340 <i>Cannabis</i> varieties, including fiber and seed oil hemp, high cannabinoid drug-types, and feral populations. These analyses demonstrate the existence of at least three major groups of diversity with European hemp varieties more closely related to narrow leaflet drug-types (NLDTs) than to broad leaflet drug-types (BLDTs). The BLDT group appears to encompass less diversity than the NLDT, which reflects the larger geographic range of NLDTs, and suggests a more recent origin of domestication of the BLDTs. As well as being genetically distinct, hemp, NLDT, and BLDT genetic groups produce unique cannabinoid and terpenoid content profiles. This combined analysis of population genomic and trait variation informs our understanding of the potential uses of different genetic variants for medicine and agriculture, providing valuable insights and tools for a rapidly emerging valuable industry.
DOI: 10.17632/sbbm87s638
title: Blood Supply Logistics Optimization Delhi Data

description: This dataset contains the logistics data used for optimizing blood supply chains in a network of hospitals located in Delhi. The data focuses on drone routing for blood bag deliveries, aimed at improving healthcare logistics efficiency. The dataset consists of 10 Excel files, each containing data from 20 hospitals to 100 hospitals. Additionally, there is a main dataset that includes logistics data for 25 hospitals.

Each Excel file includes the following key variables:

Hospital ID: A unique identifier for each hospital in the network.
Geographical Coordinates (Latitude, Longitude): The specific locations of each hospital.
Demand for Blood Bags: The quantity of blood bags requested by each hospital.

The purpose of this dataset is to support the development and testing of optimization algorithms (e.g., Nearest Neighbor, Genetic Algorithm, Simulated Annealing, and HybridNGS) for the Drone Routing Problem (DRP). These algorithms are designed to minimize the total travel distance, operational costs, and number of drones used, while ensuring timely delivery of blood bags to hospitals.

This dataset is essential for researchers and practitioners in the fields of healthcare logistics, supply chain management, and optimization, providing real-world data for testing routing algorithms under various logistical constraints.
DOI: 10.6084/m9.figshare.20226444.v1
title: Data description.txt

description: S1 File. Mean_HXF_1_4 There are 59 samples and 1814 features in the data. File includes spectrum of the Hongxiangfei without cutting. S1 File. Mean_Hxf_4_5 There are 59 samples and 1814 features in the data. File includes spectrum of the Hongxiangfei after cutting. S1 File. Mean_MNT_1_4 There are 60 samples and 1814 features in the data. File includes spectrum of the Manaiti without cutting. S1 File. Mean_MNT_4_5 There are 60 samples and 1814 features in the data. File includes spectrum of the Manaiti after cutting. S1 File. Mean_MNG_1_4 There are 60 samples and 1814 features in the data. File includes spectrum of the Munage without cutting. S1 File. Mean_MNG_4_5 There are 60 samples and 1814 features in the data. File includes spectrum of the Munage after cutting.
DOI: 10.17632/43d75vptz4
title: Java Plum Leaf Disease Dataset

description: Description

The Java Plum Leaf Disease Detection Dataset is a comprehensive collection of high-quality images of Java Plum leaves, designed to facilitate the development of robust Java Plum leaf disease detection systems using deep learning models. The dataset was meticulously gathered from two prominent areas in Cumilla, Bangladesh: Titas and Barura. It encompasses a diverse range of Java Plum leaf conditions encountered in real-world Java Plum cultivation, ensuring its practical relevance.

Dataset Characteristics

Origin: Titas and Barura, Cumilla, Bangladesh
Image Type: Color photographs
Image Resolution: Varies
Number of Images: Adequate for training and testing deep learning models
Number of Classes: 6
Bacterial_Spot
Brown Blight
Dry
Healthy
Powdery Mildew
Sooty Mold

Usage

The Java Plum Leaf Disease Detection Dataset is primarily intended for developing and evaluating deep learning models for Java Plum leaf disease detection. It can also be used for various research purposes, such as analyzing the prevalence of different Java Plum leaf diseases and investigating the impact of environmental factors on leaf health.

Significance

The Java Plum Leaf Disease Detection Dataset is a valuable resource for advancing the field of precision agriculture in Bangladesh. By enabling the development of accurate Java Plum leaf disease detection systems, it has the potential to significantly enhance Java Plum cultivation practices, leading to increased productivity and reduced economic losses.
DOI: 10.15468/dl.de5xe8
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acheilognathus chankaensis (Dybowski, 1872)"
 ]
}

 The dataset includes 172 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013330-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.16442025.v2
title: SeasonDepth: Cross-Season Monocular Depth Prediction Training Dataset

description: <pre>Changing environments poses great challenge on the outdoor visual perception and scene understanding for robust long-term autonomous driving and mobile robots, where depth-auxiliary geometric information plays an essential role to the robustness under challenging scenes. Although monocular depth prediction has been well studied recently, there are few work focused on the depth prediction across multiple environmental conditions, e.g. changing illumination and seasons, owing to the lack of such real-world dataset and benchmark. In this work, a new cross-season scaleless monocular depth prediction dataset SeasonDepth (Available on https://seasondepth.github.io) is derived from CMU Visual Localization dataset through structure from motion. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset without fine-tuning, the influence of multiple environments on mean and variance of performance is analyzed, showing that the long-term monocular depth prediction is far from solved. We further give promising solutions especially with self-supervised stereo geometry and multi-task training to enhance the robustness to changing environments. </pre>
DOI: 10.6084/m9.figshare.28236299.v4
title: Machine learning to assess troglofauna occurrences in the northern partof Western Australia

description: This release includes the Species Distribution Model (SDM) and associated data used in the study <i>"Machine Learning to Assess Troglofauna Occurrences in the Northern Part of Western Australia."</i> The model was developed to predict the distribution of troglofauna species in this ecologically significant region using advanced machine learning techniques.
DOI: 10.6084/m9.figshare.3522962.v1
title: Appendix D. EQS protocol and output for structural equation modeling of the original explanatory variables.

description: EQS protocol and output for structural equation modeling of the original explanatory variables.
DOI: 10.6084/m9.figshare.6820679.v1
title: IEEE 39-node power system and modified Belgian 20-node gas system

description: Data of an integrated IEEE 39-node power system and modified Belgian 20-node gas system is attached. Please cite my paper "Corrective Security-Constrained Optimal Power and Gas Flow with Binding Contingency Identification"if published online.
DOI: 10.6084/m9.figshare.16783552.v8
title: Mitogenome

description: In this data set, we will share our team's mitochondrial genome assembly and annotation files, as well as the original sequencing data.
DOI: 10.6084/m9.figshare.12199038.v1
title: COVID-19 related Weibo Data from "Did the world overlook the media’s early warning of COVID-19?"

description: Between December 1, 2019 and February 27, 2020, Weiboscope collected 11,362,502 posts, among which 1,230,353 contain at least an outbreak-related keyword (please refer to the paper) and 2,104 (1.7 per 1,000) have been censored.<br>Data fields:Column 1: "created_at": date of publicationColumn 2: "censorship_type": directly censored (return of "permission_denied") or retweet of censored post ("retweet of a “permission denied” post")Column 3: "id_hashed": hashed post ID Column 4: "retweeted_status_hashed": hashed retweet statusColumn 5: "text_cleaned": text body with all @XXX mentions removed<br>Please cite the referenceKing-wa Fu & Yuner Zhu (2020) Did the world overlook the media’s early warning of COVID-19?, Journal of Risk Research, DOI: 10.1080/13669877.2020.1756380<br><br>
DOI: 10.6084/m9.figshare.25745637.v1
title: Raw LOTUS data

description: Database extracted from LOTUS (https://lotus.naturalproducts.net/)
DOI: 10.6084/m9.figshare.28925159.v1
title: coco_insulator

description: Missing Insulators in Transmission Lines
DOI: 10.17632/269vc7b2z5
title: Program packages for point groups and space groups with subgroup chain symmetry adaptation

description: Abstract A new program for point groups is released and the program package for space groups by Ping et al. (Comput. Phys. Commun. 52 (1989) 355) is upgraded. The characters, irreducible matrices and Clebsch-Gordan coefficients of any crystallographic point group G or space group G adapted to any canonical group chain G ⊂ H ⊂ K can be calculated ab initio for both single-valued and double-valued representations and tabulated with exact values in the form of √p/q exp (iπ√m/n) or √p/q exp (i cos^(-1)... Title of program: PGSG Catalogue Id: ABHE_v2_0 [ADKG] Nature of problem The program computes the characters, subgroup chain symmetry adapted irreducible representations (irreps) and Clebsch-Gordan (CG) coefficients of 32 point groups and the little-cogroups, as well as the grounded representations, the wave-vector selection rules in 230 space groups, for both single-valued and double-valued representations. The components of irreps of point groups are labeled by the Koster irrep labels [2] of the subgroups contained in the subgroup chain, and the subgroup chain can ... Versions of this program held in the CPC repository in Mendeley Data ABHE_v1_0; IR, CGC; 10.1016/0010-4655(89)90111-2 ABHE_v2_0; PGSG; 10.1016/S0010-4655(99)00201-5 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/39p5cyh7s8
title: Mapping Internal Lee Wave Generation and Dissipation in the Deep South China Sea

description: Maps for lee-wave energy flux and parameterized mixing in paper "Mapping Internal Lee Wave Generation and Dissipation in the Deep South China Sea"

This 2D self-describing nc file contains the longitude and latitude axes,and the variables Ef_lee (The energy flux into lee waves, mWm^-2), Eps_lee_btm500 (The parameterized lee wave dissipation integrated within the bottom 500 m, mWm^-2 ), Eps_tide_btm500_DL (Internal tide dissipation integrated within the bottom 500 m from de Lavergne et al. (2020), mWm^-2 ).
DOI: 10.17632/rphh82n3ws
title: High school students' views on cinema-based EFL instruction: Quantitative and qualitative data from the questionnaire at the pre- and post- test times, and analyses performed

description: It deals with the data gathered from 48 high school learners in order to explore their attitude towards the implementation of cinema in the English lessons. These were collected through a questionnaire comprising demographic features, a Likert scale, as well as an open-ended part below each item where the participants were asked to justify their scale choices. It was administered twice, before and after the intervention. Several documents are uploaded: two Excel and one SPSS files with the raw data, a Nvivo document with the qualitative information, and a word document with all the statistical analyses employed to study the quantitative results.
DOI: 10.6084/m9.figshare.11786238.v1
title: Tubuloid kidney organoid - single cell RNA-seq

description: It is included data derived from the processing of single-cell and single-nuclei RNA-seq from several samples (see below). This data corresponds to the input and intermediate output files from https://github.com/saezlab/Xu_tubuloid . Data The data include: Binary sparse matrices for the UMI gene expression quantification from cellranger (filtered feature-barcode matrices). These are TAR archive files named with the name of the sample. Seurat Objects with normalized data, embeddings of dimensionality reduction, clustering and cell cluster annotation. These are TAR archive files including final objects, grouped by sample type: SeuratObjects_[SortedCells | Organoids | Human Kidney Tissue]. The HumanKidneyTissue also includes the SeuratObject after Harmony integration. Exported barcode idents from unsupervised clustering and manual annotation ("barcodeIdents*.csv" files). Label transfer via Symphony mapping to tubuloid cells from each organoid to a integrated reference atlas of human kidney tissue (SymphonyMapped*.csv). Samples The data corresponds to the following samples, which were profiled at the single-cell resolution: CK5 early organoid (Healthy). Organoid generated from CD24+ sorted cells from human adult kidney tissue at an early stage. CK119 late organoid (Healthy). Organoid generated from CD24+ sorted cells from human adult kidney tissue at a late stage.<br> JX1 late organoid (Healthy). Organoid generated following Hans Clever's protocol for kidney organoids. JX2 PKD1-KO organoid (PKD). Organoid generated from CD24+ sorted cells from human adult kidney tissue, for which PKD1 was gene-edited to reproduce PKD phenotype, developed at a late stage. JX3 PKD2-KO organoid (PKD). Organoid generated from CD24+ sorted cells from human adult kidney tissue, for which PKD2 was gene-edited to reproduce PKD phenotype, developed at a late stage. CK120 CD13. CD13+ sorted cells from human adult kidney tissue. CK121 CD24. CD24+ sorted cells from human adult kidney tissue.<br> In addition, human adult kidney tissue were profiled in the context of ADPKD:<br> CK224 : human specimen with ADPKD (PKD2- genotype).<br> CK225 : human specimen with ADPKD (PKD1- genotype). ADPKD3: human specimen with ADPKD (ND genotype).<br> Control1 : human specimen with healthy tissue. Control2 : human specimen with healthy tissue.
DOI: 10.6084/m9.figshare.22060340.v2
title: CanaryClim - Climatic maps

description: Monthly precipitation, minimal and maximal temperature and bioclimatic variables at 100m resolution at present time (1979-2013), and in the future (2071-2100) under 5 Global Circulation Models and 3 shared socio-economic pathways These climatic datasets are described in the paper entitled "<strong>Spatial resolution determines projected plant responses to climate change on topographically complex islands</strong>" which has been accepted in Diversity and Distributions. If you are using these datasets, please cite us: Patiño, J. & Collart, F., Vanderpoorten, V., Martin-Esquivel, J.L., Naranjo-Cigala, A., Mirolo, S., Karger, D.N. Spatial resolution determines projected plant responses to climate change on topographically complex islands. <em>Diversitity and Distributions. </em>(doi: 10.1111/ddi.13757).
DOI: 10.6084/m9.figshare.5125294.v1
title: Supplementary Material for: Human Cardiovascular Disease IBC Chip-Wide Association with Weight Loss and Weight Regain in the Look AHEAD Trial

description: <b><i>Background/Aims:</i></b> The present study identified genetic predictors of weight change during behavioral weight loss treatment. <b><i>Methods:</i></b> Participants were 3,899 overweight/obese individuals with type 2 diabetes from Look AHEAD, a randomized controlled trial to determine the effects of intensive lifestyle intervention (ILI), including weight loss and physical activity, relative to diabetes support and education, on cardiovascular outcomes. Analyses focused on associations of single nucleotide polymorphisms (SNPs) on the Illumina CARe iSelect (IBC) chip (minor allele frequency &gt;5%; n = 31,959) with weight change at year 1 and year 4, and weight regain at year 4, among individuals who lost ≥3% at year 1. <b><i>Results:</i></b> Two novel regions of significant chip-wide association with year-1 weight loss in ILI were identified (p &lt; 2.96E-06). <i>ABCB11 </i>rs484066 was associated with 1.16 kg higher weight per minor allele at year 1, whereas <i>TNFRSF11A,</i> or<i> RANK,</i> rs17069904 was associated with 1.70 kg lower weight per allele at year 1. <b><i>Conclusions:</i></b> This study, the largest to date on genetic predictors of weight loss and regain, indicates that SNPs within <i>ABCB11</i>, related to bile salt transfer, and <i>TNFRSF11A</i>, implicated in adipose tissue physiology, predict the magnitude of weight loss during behavioral intervention. These results provide new insights into potential biological mechanisms and may ultimately inform weight loss treatment.
DOI: 10.6084/m9.figshare.14723883.v2
title: Processed_Model_Data.zip

description: Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invasive laboratory and noninvasive clinical and demographic data from patients’ day of admission. Three Support Vector Machine (SVM) models were developed and compared using invasive, non-invasive, and both groups. The results suggested that non-invasive features could provide mortality predictions that are similar to the invasive and roughly on par with the joint model. Feature inspection results from SVM-RFE and sparsity analysis displayed that, compared with the invasive model, the non-invasive model can provide better performances with a fewer number of features, pointing to the presence of high predictive information contents in several non-invasive features, including SPO<sub>2</sub>, age, and cardiovascular disorders. Furthermore, while the invasive model was able to provide better mortality predictions for the imminent future, non-invasive features displayed better performance for more distant expiration intervals. Early mortality prediction using non-invasive models can give us insights as to where and with whom to intervene. Combined with novel technologies, such as wireless wearable devices, these models can create powerful frameworks for various medical assignments and patient triage.
DOI: 10.17632/9d55cttn5h
title: Lung X-Ray Image

description: Lung disease encompasses a wide range of conditions that affect the lungs and their ability to function effectively. These conditions can be caused by various factors, including infections, environmental factors, genetic predispositions, and lifestyle choices. Lung diseases can result in symptoms such as coughing, shortness of breath, chest pain, and reduced lung function. Detecting and diagnosing lung diseases is crucial for patient care, as they can have a significant impact on an individual's health and quality of life. Global Impact: Lung diseases have a substantial global impact. According to the World Health Organization (WHO), respiratory diseases, including lung diseases, are responsible for a significant portion of global mortality. In 2016, respiratory diseases were the fourth leading cause of death worldwide, with an estimated 3.0 million deaths attributed to them. Conditions like pneumonia, chronic obstructive pulmonary disease (COPD), and lung cancer contribute to this high mortality rate. Early detection and accurate diagnosis are essential for reducing the burden of lung diseases on public health. The Need to Detect Lung Diseases: Detecting lung diseases is vital for several reasons: Early Intervention: Early detection allows for timely medical intervention and treatment, increasing the chances of successful management and recovery. Disease Classification: Differentiating between various lung diseases, such as pneumonia, tuberculosis, and lung cancer, is crucial for appropriate treatment planning. Public Health: Effective disease detection and management can have a positive impact on public health by reducing the overall disease burden. Lung X-Ray Image Dataset: The "Lung X-Ray Image Dataset" is a comprehensive collection of X-ray images that plays a pivotal role in the detection and diagnosis of lung diseases. This dataset contains a large number of high-quality X-ray images, meticulously collected from diverse sources, including hospitals, clinics, and healthcare institutions. Dataset Contents: Total Number of Images: The dataset comprises a total of 3,475 X-ray images. Classes within the Dataset: Normal (1250 Images): These images represent healthy lung conditions, serving as a reference for comparison in diagnostic procedures. Lung Opacity (1125 Images): This class includes X-ray images depicting various degrees of lung abnormalities, providing a diverse set of cases for analysis. Viral Pneumonia (1100 Images): Images in this category are associated with viral pneumonia cases, contributing to the understanding and identification of this specific lung infection. In conclusion, the "Lung X-Ray Image Dataset" plays a crucial role in the healthcare sector by providing a diverse and well-documented collection of X-ray images that support the detection, classification, and understanding of lung diseases. This resource is instrumental in advancing the field of respiratory medicine and improving patient outcomes.
DOI: 10.17632/hr9tjzj72v
title: Predicting Tie Strength with Ego Network Structures

description: This repository contains 41 anonymized Facebook ego networks used in Stolz and Schlereth "Predicting Tie Strength with Ego Network Structures" in Journal of Interactive Marketing (forthcoming). The Facebook ego network data was extracted in combination with a survey. The survey asked users about who they perceived as their closest friends, and who they had recently interacted with on the platform. Furthermore, the data contains a binary matching based on Facebook attributes of ego and alter to assess the similarity of users. These dyad-level binary attributes are stored as csv file. All 41 ego networks are stored as graphml files, named by the identifier of the user (i.e., ego) they belong to. Additionally, the first author's "seed" network is saved separately (without attributes), intended for replication of the sampled perspective. For anonymization, all Facebook identifiers were replaced by independent numeric identifiers.
DOI: 10.6084/m9.figshare.20485701.v1
title: SI_7_data_of_Figure_4-5.xlsx

description: Average single-game data (mean, SEM, N) for teams in the Champions League for the 2018_2019 and 2019_2020 seasons.
DOI: 10.17632/5fjyt8z6vc
title: Effect of vital bleaching on surface roughness and microhardness of nanofilled and nanohybrid composite resins

description: Surface roughness and Microhardness data before and after bleaching. 
 Ceram.x® SphereTEC™ one (Dentsply, Sirona) [Group 1] and Filtek Z350 XT (3M ESPE, St. Paul, USA) [Group 2]