cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19670281.v1
title: Additional file 1 of Genome-wide association mapping and genomic prediction for kernel color traits in intermediate wheatgrass (Thinopyrum intermedium)

description: Additional file 1. Scripts and additional tables generated during the course of the study. There are four sheets in this file. Name and description of each sheet is provided below: ImageJ macro: Macro script used in the ImageJ program to obtain L*, a*, and b* values from seed images. Phenotypic data: L*, a*, b* values obtained from ImageJ and visual rating scores pre-data adjustment. Trait variance components: Genetic and non-genetic variance components generated using a mixed model equation. Narrow sense heritabilities: Narrow sense trait heritability estimates for the kernel color traits. PC model selection BIC values: BIC values calculated by GAPIT when using up to 10 PC axes as cofactors in the GWAS model to evaluate model fit. NCBI BLAST results: Protein BLAST results when intermediate wheatgrass candidate gene sequences were searched against the NCBI protein database. BLAST with wheat R genes: BLAST results when intermediate wheatgrass candidate gene sequences were searched against the three genes in wheat known to be involved in controlling kernel color in wheat.
DOI: 10.6084/m9.figshare.19670282.v1
title: Additional file 1 of Genome-wide identification and expression analysis of magnesium transporter gene family in grape (Vitis vinifera)

description: Additional file 1.
DOI: 10.6084/m9.figshare.19670282
title: Additional file 1 of Genome-wide identification and expression analysis of magnesium transporter gene family in grape (Vitis vinifera)

description: Additional file 1.
DOI: 10.6084/m9.figshare.19670288
title: Additional file 1 of Establishing minimal clinically important difference for effectiveness of corrective exercises on craniovertebral and shoulder angles among students with forward head posture: a clinical trial study

description: Additional file 1.
DOI: 10.6084/m9.figshare.19670288.v1
title: Additional file 1 of Establishing minimal clinically important difference for effectiveness of corrective exercises on craniovertebral and shoulder angles among students with forward head posture: a clinical trial study

description: Additional file 1.
DOI: 10.6084/m9.figshare.19670290
title: Additional file 10 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 10: Table S9. Listing the primers used for pyrosequencing validation.
DOI: 10.6084/m9.figshare.19670290.v1
title: Additional file 10 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 10: Table S9. Listing the primers used for pyrosequencing validation.
DOI: 10.6084/m9.figshare.19670296
title: Additional file 2 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 2: Table S1. Listing the semen samples, information on their origin and processing, and the field fertility of the corresponding bulls.
DOI: 10.6084/m9.figshare.19670296.v1
title: Additional file 2 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 2: Table S1. Listing the semen samples, information on their origin and processing, and the field fertility of the corresponding bulls.
DOI: 10.6084/m9.figshare.19670299
title: Additional file 3 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 3: Table S2. Listing the functional parameters measured on each semen sample.
DOI: 10.6084/m9.figshare.19670305
title: Additional file 4 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 4: Table S3. Listing the fertility-related DMCs, their DNA methylation status in each sample and their annotation regarding genome features.
DOI: 10.6084/m9.figshare.19670299.v1
title: Additional file 3 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 3: Table S2. Listing the functional parameters measured on each semen sample.
DOI: 10.6084/m9.figshare.19670305.v1
title: Additional file 4 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 4: Table S3. Listing the fertility-related DMCs, their DNA methylation status in each sample and their annotation regarding genome features.
DOI: 10.6084/m9.figshare.19670308
title: Additional file 5 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 5: Table S4. Listing the fertility-related DMRs, their methylation status in each fertility group and their annotation regarding genome features.
DOI: 10.6084/m9.figshare.19670311
title: Additional file 6 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 6: Table S5. Listing the CpGs10 obtained after alignment of the RRBS sequences on a Repbase artificial genome, their coverage and DNA methylation status in each sample.
DOI: 10.6084/m9.figshare.19670308.v1
title: Additional file 5 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 5: Table S4. Listing the fertility-related DMRs, their methylation status in each fertility group and their annotation regarding genome features.
DOI: 10.6084/m9.figshare.19670311.v1
title: Additional file 6 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 6: Table S5. Listing the CpGs10 obtained after alignment of the RRBS sequences on a Repbase artificial genome, their coverage and DNA methylation status in each sample.
DOI: 10.6084/m9.figshare.19670314
title: Additional file 7 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 7: Table S6. Listing the differentially methylated genes relevant to fertility together with supporting references.
DOI: 10.6084/m9.figshare.19670314.v1
title: Additional file 7 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 7: Table S6. Listing the differentially methylated genes relevant to fertility together with supporting references.
DOI: 10.6084/m9.figshare.19670317
title: Additional file 8 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 8: Table S7. Listing the differentially methylated genes found in common with four human studies.
DOI: 10.6084/m9.figshare.19670320.v1
title: Additional file 9 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 9: Table S8. Listing the semen samples used for pyrosequencing validation.
DOI: 10.6084/m9.figshare.19670317.v1
title: Additional file 8 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 8: Table S7. Listing the differentially methylated genes found in common with four human studies.
DOI: 10.6084/m9.figshare.19670320
title: Additional file 9 of Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records

description: Additional file 9: Table S8. Listing the semen samples used for pyrosequencing validation.
DOI: 10.6084/m9.figshare.16619773.v7
title: Data from "Notch-dependent and -independent transcription are modulated by tissue movements at gastrulation"

description: Data from "Levels of Notch-regulated transcription are modulated by tissue movements at gastrulation":<br>- raw MS2 Live imaging movies: 124 '.lif' files captured in a Leica SP8 confocal. Each is a 1-2h movie of a developing fly embryo in the genetic condition described in the file name. The metadata file contains the information linking each movie with parameters used for image analysis and identifier of the genetic condition ("nickname" column).<br>- data obtained from image analysis: '.mat' files obtained from MATLAB after image analysis for each movie. README contains information of the data structure and all variables stored in each.<br>- numerical data used for figures: text files containing data points used in all figures in the paper. "NumericalData.txt" contains the information about each plot, axes, figure where it's used and name of the file containing the numerical data.
DOI: 10.6084/m9.figshare.16619773.v8
title: Data from "Notch-dependent and -independent transcription are modulated by tissue movements at gastrulation"

description: Data from "Levels of Notch-regulated transcription are modulated by tissue movements at gastrulation":<br>- raw MS2 Live imaging movies: 124 '.lif' files captured in a Leica SP8 confocal. Each is a 1-2h movie of a developing fly embryo in the genetic condition described in the file name. The metadata file contains the information linking each movie with parameters used for image analysis and identifier of the genetic condition ("nickname" column).<br>- data obtained from image analysis: '.mat' files obtained from MATLAB after image analysis for each movie. README contains information of the data structure and all variables stored in each.<br>- numerical data used for figures: text files containing data points used in all figures in the paper. "NumericalData.txt" contains the information about each plot, axes, figure where it's used and name of the file containing the numerical data.
DOI: 10.5063/f19p3025
title: Nitrogen Deposition to Urban Latin America, 2006-2010

description: This dataset contains observed and Geos-Chem simulated wet N deposition as well as simulated dry N deposition estimates for 16 cities in Latin America for the period 2006-2010.
DOI: 10.6084/m9.figshare.16681771.v4
title: Reliability and validity of the Brief Emotional Experience Scale (BEES) for mood monitoring

description: Raw data for paper titled: Reliability and validity of the Brief Emotional Experience Scale (BEES) for mood monitoring
DOI: 10.25919/fdpb-mf14
title: Genomic Information Desulfuromonas sp. 'CSMB_57'

description: Supplementary genomic data
DOI: 10.6084/m9.figshare.19658538.v1
title: High-temperature multi-element 2021 (HME21) dataset

description: HME21 is the atomic structure dataset aimed for the neural network potential development. It was created in the development of PFP, a universal neural network potential for material discovery [1]. It contains multiple elements in a single structure and was sampled through a high-temperature molecular dynamics simulation. There are a total of 37 elements in the HME21 dataset, i.e., H, Li, C, N, O, F, Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Mo, Ru, Rh, Pd, Ag, In, Sn, Ba, Ir, Pt, Au, and Pb. They are calculated by Spin-polarized DFT calculations using PBE exchange-correlation functional implemented in VASP [2] version 5.4.4. All structures are under periodic boundary conditions. For the details of DFT calculation conditions and structure sampling method, please see the reference [1]. Please cite the reference [1] if you use this dataset. Files HME21 consists of three files with extxyz format: train.xyz: 19956 structures valid.xyz: 2498 structures test.xyz 2495 structures The structures were randomly split into training, validation, and test sub-datasets at a ratio of 8:1:1. They are used as training, validation, and test dataset for the benchmark of neural network potentials [1]. The target values are energy and atomic forces. The energy is shifted such that the energy of a single atom located in a vacuum becomes zero. The length is in angstroms (10^−10 m), and the energy is in electronvolts (eV). For supplementary, <em>vasp_shift_energies.json</em> which corresponds to the reference energy of single atom for each element is also included.
DOI: 10.6084/m9.figshare.19485263
title: Supplementary Material for: Prevalence of childhood obesity in the United States 1999-2018: a 20-year analysis

description: Background Obesity is a public health crisis in the US. This study aims to estimate the prevalence of obesity and severe obesity in US children and adolescents and identify novel targetable risk factors associated with childhood obesity. Methods From the US National Health and Nutrition Examination Survey from 1999-2018, 35,907 children aged 2-19 with body mass index (BMI) data were included. Obesity and severe obesity were defined as BMI ≥95th percentile and ≥120% of 95th percentile of US Centers for Disease Control and Prevention growth charts, respectively. Trends in prevalence of obesity and subgroup analyses according to socio-economic factors and language used in interview, were analyzed. Results The prevalence of obesity and severe obesity increased from 14.7 [95%CI: 12.9-17.0]% to 19.2 [17.2-21.0]% and 3.9 [2.9-5.0]% to 6.1 [4.8-8.0]% in 1999-2018, respectively (p=0.001 and p=0.014, respectively). In 2017-8, the prevalence of obesity among children from Spanish-speaking households was 24.4 [22.4-27.0]%, higher than children from English-speaking households (p=0.027). Conclusions The prevalence of childhood obesity kept increasing in 1999-2018. The problem is worse in children from Spanish-speaking households. Novel and targeted public health intervention strategies are urgently warranted to effectively halt the rising epidemic of childhood obesity.
DOI: 10.6084/m9.figshare.19485263.v1
title: Supplementary Material for: Prevalence of childhood obesity in the United States 1999-2018: a 20-year analysis

description: Background Obesity is a public health crisis in the US. This study aims to estimate the prevalence of obesity and severe obesity in US children and adolescents and identify novel targetable risk factors associated with childhood obesity. Methods From the US National Health and Nutrition Examination Survey from 1999-2018, 35,907 children aged 2-19 with body mass index (BMI) data were included. Obesity and severe obesity were defined as BMI ≥95th percentile and ≥120% of 95th percentile of US Centers for Disease Control and Prevention growth charts, respectively. Trends in prevalence of obesity and subgroup analyses according to socio-economic factors and language used in interview, were analyzed. Results The prevalence of obesity and severe obesity increased from 14.7 [95%CI: 12.9-17.0]% to 19.2 [17.2-21.0]% and 3.9 [2.9-5.0]% to 6.1 [4.8-8.0]% in 1999-2018, respectively (p=0.001 and p=0.014, respectively). In 2017-8, the prevalence of obesity among children from Spanish-speaking households was 24.4 [22.4-27.0]%, higher than children from English-speaking households (p=0.027). Conclusions The prevalence of childhood obesity kept increasing in 1999-2018. The problem is worse in children from Spanish-speaking households. Novel and targeted public health intervention strategies are urgently warranted to effectively halt the rising epidemic of childhood obesity.
DOI: 10.34740/kaggle/dsv/3541441
title: BBC News
DOI: 10.6084/m9.figshare.16619773.v9
title: Data from "Notch-dependent and -independent transcription are modulated by tissue movements at gastrulation"

description: Data from "Levels of Notch-regulated transcription are modulated by tissue movements at gastrulation":<br>- raw MS2 Live imaging movies: 124 '.lif' files captured in a Leica SP8 confocal. Each is a 1-2h movie of a developing fly embryo in the genetic condition described in the file name. The metadata file contains the information linking each movie with parameters used for image analysis and identifier of the genetic condition ("nickname" column).<br>- data obtained from image analysis: '.mat' files obtained from MATLAB after image analysis for each movie. README contains information of the data structure and all variables stored in each.<br>- numerical data used for figures: text files containing data points used in all figures in the paper. "NumericalData.txt" contains the information about each plot, axes, figure where it's used and name of the file containing the numerical data.
DOI: 10.25934/pr00006806.0
title: Available IPD datapackage for study 'A Phase 4, Randomized, Double-blind, Active And Placebo-controlled, Multicenter Study Evaluating The Neuropsychiatric Safety And Efficacy Of 12 Weeks Varenicline Tartrate 1mg Bid And Bupropion Hydrochloride 150mg Bid For Smoking Cessation In Subjects With And Without A History Of Psychiatric Disorders'

description: This study is being conducted to assess varenicline and bupropion as aids to smoking cessation treatment in subjects with and without an established diagnosis of major psychiatric disorder and to characterize the neuropsychiatric safety profile (pre-specified adverse events (AEs) in both of these populations).
DOI: 10.6084/m9.figshare.19524058
title: Consent form.docx

description: Blank patient consent form
DOI: 10.25934/pr00007498
title: A Machine Learning approach to improve smoking cessation trials

description: 8144 current smokers at baseline from EAGLES dataset will be included in our study.
Identifying individual characteristics that can predict relapse and quit success in smoking cessation trials could help shape a strategy help to optimize health care resources and refine state funded tobacco policies.
Delivering on these aspirations requires an improved understanding of success factors in well characterized cohorts of smokers. Currently, no accurate determinants of smoking cessation have been found. Socio-demographic features, psychological factors, smoking history, severity of nicotine dependence, past quit attempts, and measures of motivation to stop have been extensively investigated both in aided and unaided smoking cessation, but results are not clear-cut (1,2). Most of the studies have focused on the prediction model of smoking cessation using standard statistical methods such as bivariate analysis and multivariate logistic regression applied to traditional widely studied risk factors (3-10). Their findings have been generally unhelpful to optimize smoking cessation studies and to improve success rate outcomes.
Predictors of smoking cessation are derived from very different populations, factors usually explored may not be the significant ones, and study outcomes are not always clearly defined.
Without this knowledge, improved behavioural classification of smokers with a strong desire to quit and adopting a successful personalized management plan for their tobacco dependence will remain just a dream. A novel innovative research approach identifying stronger predictors of smoking relapse and quit success is the only recipe for success.
Acquisition and analysis of extensive datasets is, today, a central tool in most research fields.  Machine learning provides powerful methods to obtain descriptive and predictive models for the data in many applications. The acquisition of quality information is fundamental for the reliability and accuracy of predictive and classification models increasingly used in various applications.  A correct and adequate use of Artificial Intelligence (A.I.) models allows to extend and overcome the classical statistical methods, thus helping experts and professionals of different fields in decisions and policy-making.
DOI: 10.21227/epgv-bx80
title: Dataset collected for rough and plane roads

description: This dataset curbs real time sensory data collected through different vehicles such as Cycle, Car, Bike and Bus on the humpty-dumpty road. This dataset is collected by using Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the seats of vehicle. Through some vehicles (Cycle and Bike) are not having place to keep sensor, but it was designed to handle all the hurdles of road having potholes. The dataset aims to tell the exact accuracy of pothole and plane road. This dataset can be used in future for government to allocate budget to repair the rough road.
DOI: 10.48370/ofd/0uzz9b/qv0nz9
title: HMW_Pyrola_chlorantha_Sw._H_1022.jpg

description: :unav
DOI: 10.48370/ofd/0uzz9b
title: Gruszyczka zielonawa (Pyrola chlorantha Sw.), 1022

description: Herbarium
DOI: 10.48370/ofd/i8ujzo/zvisxz
title: HMW_Pyrola_chlorantha_Sw._H_6962.jpg

description: :unav
DOI: 10.48370/ofd/i8ujzo
title: Gruszyczka zielonawa (Pyrola chlorantha Sw.), 6962

description: Herbarium
DOI: 10.48370/ofd/60mfsf/43zuow
title: HMW_Pyrola_chlorantha_Sw._H_1552.jpg

description: :unav
DOI: 10.48370/ofd/60mfsf
title: Gruszyczka zielonawa (Pyrola chlorantha Sw.), 1552

description: Herbarium
DOI: 10.48370/ofd/u2yp41/5twxb4
title: HMW_Pyrola_minor_L._H_8516.jpg

description: :unav
DOI: 10.48370/ofd/u2yp41
title: Gruszyczka mniejsza (Pyrola minor L.), 8516

description: Herbarium
DOI: 10.48370/ofd/5uf1gt/bmjlfp
title: HMW_Pyrola_minor_L._H_6983.jpg

description: :unav
DOI: 10.48370/ofd/5uf1gt
title: Gruszyczka mniejsza (Pyrola minor L.), 6983

description: Herbarium
DOI: 10.6084/m9.figshare.19671498
title: Supplementary Material for: Huntington’s Disease Progression and Caregiver Burden

description: Huntington’s disease (HD) is a neurodegenerative, progressive disorder conditioned by a mutation in the <i>HTT</i> gene. Its progression is dependent on the causative mutation extension. Caregivers of individuals affected by HD, most often patients’ relatives, are burdened with the care. This study aims to assess the caregivers’ burden cross-sectionally and longitudinally and look for biological and clinical patients-related burdening factors. In total, 144 caregiver-patient pairs observed annually for up to 8 years were included in the study. In all of the patients, demographic data were collected, Unified Huntington's Disease Rating Scale (UHDRS) assessments were conducted, and disease burden (DB) was calculated when caregivers were assessed in Caregiver Burden Inventory (CBI). Caregivers’ burden measured in CBI at the first visit reached 18.7 ± 18.4 scores. Longitudinal observation showed no evidence for any discrepancy between clinical progression measured in UHDRS, nor biological progression measured in DB and the caregivers’ burden progression measured in CBI. Caregivers were burdened mostly by patients’ dependence and a discrepancy between reality and life expectations. This study indicates factors to be addressed to reduce caregivers’ burden. Strict relation between caregivers’ burden and biological and clinical progression denies conception of overloaded with care tasks or adaptation to the burden.