cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/715r-9b69
title: An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation (dataset)

description: The dataset used in this study includes raw data from 5 subjects, among whom 4 individuals suffer from Parkinson's Disease (PD), and 1 person has Essential Tremor (ET). The data contains Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals. The main purpose of this dataset is to support the conclusions drawn in the article titled "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation."Regarding the naming convention, each file follows the format "patientnumber_age_sex_disease_arm_timesincediagnose.mat." Additionally, the time vectors for sEMG and IMU are represented as Xs, Xs1, ... Xs17.In the study's sEMG recordings, two electrodes were placed on the ECRL and FCU muscles of the most affected arm. For IMU data, an Inertial Measurement Unit (IMU) was positioned at the back of the hand to capture angular velocity signals, which corresponded to flexion-extension (pitch) and pronation-supination (roll) movements. During data acquisition, volunteers were instructed to extend both arms forward perpendicular to the torso with palms down and maintain steadiness. The sEMG and IMU signals were recorded for one minute each, with respective sampling frequencies of 1925.93 Hz and 148.5 Hz.
DOI: 10.5281/zenodo.8083355
title: Data for "More intensive use and lifetime extension can enable net-zero emissions in China's cement cycle"

description: Part of the data used in the paper "More intensive use and lifetime extension can enable net-zero emissions in China’s cement cycle".
DOI: 10.6084/m9.figshare.23632395.v8
title: Data from Xiao et al.xlsx

description: Data from Xiao et al. submitted to JGR-Biogeosciences
DOI: 10.6084/m9.figshare.23632395.v9
title: Data from Xiao et al.xlsx

description: Data from Xiao et al. submitted to JGR-Biogeosciences
DOI: 10.6084/m9.figshare.22579762
title: Shared data for JGR

description: Data for the article: Alleviation of wintertime wet biases in western Tibetan Plateau using a revised parameterization of orographic gravity wave drag in the Model for Prediction Across Scales (MPAS)
DOI: 10.6084/m9.figshare.22579762.v1
title: Shared data for JGR

description: Data for the article: Alleviation of wintertime wet biases in western Tibetan Plateau using a revised parameterization of orographic gravity wave drag in the Model for Prediction Across Scales (MPAS)
DOI: 10.5281/zenodo.8206975
title: Dual-comb optomechanical spectroscopy

description: The data source file contains all the data supporting the plots and findings in this study.
DOI: 10.5281/zenodo.8206974
title: Dual-comb optomechanical spectroscopy

description: The data source file contains all the data supporting the plots and findings in this study.
DOI: 10.6084/m9.figshare.23599878
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <br><b>Supplemental Material</b><b>Title:</b> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism<b>Authors: </b>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi, Jiqiu Wang, Min Xu<b>Supporting information</b><b>Table S1. </b>Clinical characteristics of study participants.<b>Table S2. </b>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.<b>Table S3.</b> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.<b>Table S4. </b>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.<b>Table S5. </b>Top30 Reactome pathways associated with genes in wb-TRS.<b>Fig S1. </b>Flowchart of study participants selection.<b>Fig S2. </b>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.<b>Fig S3.</b> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.<b>Fig S4. </b>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.6084/m9.figshare.23599878.v2
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <strong>Supplemental Material</strong> <strong>Title:</strong> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism <strong>Authors: </strong>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi, Jiqiu Wang, Min Xu <strong>Supporting information</strong> <strong>Table S1. </strong>Clinical characteristics of study participants. <strong>Table S2. </strong>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes. <strong>Table S3.</strong> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort. <strong>Table S4. </strong>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes. <strong>Table S5. </strong>Top30 Reactome pathways associated with genes in wb-TRS. <strong>Table S6.</strong> Used IVs for wb-TRS in initial cohort. <strong>Table S7. </strong>Transcriptional MR for causal relationships between gene transcripts included in wb-TRS and T2D in initial cohort. <strong>Table S8. </strong>Transcriptional MR for causal relationships between gene transcripts included in wb-TRS and T2D in AGEN. <strong>Fig S1. </strong>Flowchart of study participants selection. <strong>Fig S2. </strong>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset. <strong>Fig S3.</strong> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes. <strong>Fig S4. </strong>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.6084/m9.figshare.23599878.v3
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <br><b>Supplemental Material</b><b>Title:</b> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism<b>Authors: </b>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi, Jiqiu Wang, Min Xu<b>Supporting information</b><b>Table S1. </b>Clinical characteristics of study participants.<b>Table S2. </b>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.<b>Table S3.</b> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.<b>Table S4. </b>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.<b>Table S5. </b>Top30 Reactome pathways associated with genes in wb-TRS.<b>Fig S1. </b>Flowchart of study participants selection.<b>Fig S2. </b>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.<b>Fig S3.</b> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.<b>Fig S4. </b>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.5439/1992989
title: rllog (a0)

description: The Raman lidar (RL) is an active, ground-based, laser remote-sensing instrument that provides height- and time-resolved measurements of water-vapor mixing ratio, temperature, aerosol, and cloud optical properties. The RL operates in the ultraviolet (UV) and is sensitive to both molecular and aerosol backscatter. The RL works by transmitting short pulses of UV laser light into the atmosphere. As the light propagates, a small fraction of the light energy is scattered back to the lidar transceiver where it is collected and recorded as a time-resolved signal. From the delay between the outgoing pulse and the backscattered signal, the instrument infers the distance to the scattering volume. As the name implies, the RL makes use of the Raman effect in which light is inelastically scattered by atmospheric N2, O2, and H2O molecules. The ARM RL uses a number of narrow-band detection channels specifically tuned to sense the Raman backscatter from these molecules. Several other detection channels are configured to measure elastically backscattered light from atmospheric aerosol. The raw signals from these detection channels are combined and processed to yield measurements of water vapor mixing ratio, temperature, aerosol backscatter coefficient, extinction, and depolarization ratio.
DOI: 10.5439/1992990
title: mmcrmode03v0091cloth (c1)

description: mmcrmode03v0091cloth
DOI: 10.5439/1992991
title: mmcrmode02v0071cloth (c1)

description: mmcrmode02v0071cloth
DOI: 10.5439/1992992
title: mmcrmode01v0101cloth (c1)

description: mmcrmode01v0101cloth
DOI: 10.5439/1992993
title: rhbarscl1cloth (c1)

description: rhbarscl1cloth
DOI: 10.6084/m9.figshare.23805006
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <b>Title:</b> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism<br><b>Authors: </b>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi,<b> </b>Jiqiu Wang,<b> </b>Min Xu<br><b>Supporting information</b><b>Table S1. </b>Clinical characteristics of study participants.<b>Table S2. </b>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.<b>T</b><b>able S3.</b> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.<b>Table S4. </b>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.<b>Table S5. </b>Top30 Reactome pathways associated with genes in wb-TRS.<b>Fig S1. </b>Flowchart of study participants selection.<b>Fig S2. </b>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.<b>Fig S3.</b> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.<b>Fig S4. </b>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.6084/m9.figshare.23805006.v2
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <b>Title:</b> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism<br><b>Authors: </b>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi,<b> </b>Jiqiu Wang,<b> </b>Min Xu<br><b>Supporting information</b><b>Table S1. </b>Clinical characteristics of study participants.<b>Table S2. </b>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.<b>T</b><b>able S3.</b> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.<b>Table S4. </b>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.<b>Table S5. </b>Top30 Reactome pathways associated with genes in wb-TRS.<b>Fig S1. </b>Flowchart of study participants selection.<b>Fig S2. </b>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.<b>Fig S3.</b> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.<b>Fig S4. </b>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.6084/m9.figshare.23805006.v1
title: Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"

description: <b>Title:</b> Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism<br><b>Authors: </b>Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi,<b> </b>Jiqiu Wang,<b> </b>Min Xu<br><b>Supporting information</b><b>Table S1. </b>Clinical characteristics of study participants.<b>Table S2. </b>Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.<b>T</b><b>able S3.</b> Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.<b>Table S4. </b>Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.<b>Table S5. </b>Top30 Reactome pathways associated with genes in wb-TRS.<b>Fig S1. </b>Flowchart of study participants selection.<b>Fig S2. </b>Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.<b>Fig S3.</b> Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.<b>Fig S4. </b>Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.
DOI: 10.6084/m9.figshare.23819568
title: Hydrolysis products of Fe-Si systems with different Si/(Si+Fe) molar ratios: Implications to detection of ferrihydrite on Mars

description: These are the raw data for the manuscript "Hydrolysis products of Fe-Si systems with different Si/(Si+Fe) molar ratios: Implications to detection of ferrihydrite on Mars".
DOI: 10.6084/m9.figshare.22335331
title: <strong>Increased interregional virus exchange and nucleotide diversity outline the expansion of chikungunya virus in Brazil</strong>

description: This repository contais the input data, epidemiological reports and BEATS XML and Log files used for the time-scaled pylogenetic analysis performed for the publication of the study "Increased interregional virus exchange and nucleotide diversity outline the expansion of the chikungunya virus ECSA lineage in Brazil" (Xavier et al., 2023).
DOI: 10.5281/zenodo.8035290
title: Fox8-23 AI-powered social bot dataset

description: We release the fox8-23 dataset, which contains the recent tweets from 1140 AI-powered social bots and 1140 human accounts collected from Twitter. See the GitHub repo https://github.com/osome-iu/AIBot_fox8 for details.
DOI: 10.5281/zenodo.8035289
title: Fox8-23 AI-powered social bot dataset

description: We release the fox8-23 dataset, which contains the recent tweets from 1140 AI-powered social bots and 1140 human accounts collected from Twitter. See the GitHub repo https://github.com/osome-iu/AIBot_fox8 for details.
DOI: 10.6084/m9.figshare.23819559
title: Raw microtomography data_July 10-11, 2015 eruption of Volcán de Colima

description: Raw microtomography data of pyroclasts of the July 10-11, 2015 eruption of Volcán de Colima, Mexico. Files can be opened in Avizo, ImageJ, Dragonfly, or any other tomography visualization software
DOI: 10.6084/m9.figshare.23819559.v1
title: Raw microtomography data_July 10-11, 2015 eruption of Volcán de Colima

description: Raw microtomography data of pyroclasts of the July 10-11, 2015 eruption of Volcán de Colima, Mexico. Files can be opened in Avizo, ImageJ, Dragonfly, or any other tomography visualization software
DOI: 10.6084/m9.figshare.23819799.v1
title: Additional file 2 of Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

description: Additional file 2: Table S1. The composition of each of the 6 cell mixtures, based on cell counts, compared to estimates based on flow cytometry and scRNA-seq analysis.
DOI: 10.6084/m9.figshare.23819799
title: Additional file 2 of Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

description: Additional file 2: Table S1. The composition of each of the 6 cell mixtures, based on cell counts, compared to estimates based on flow cytometry and scRNA-seq analysis.
DOI: 10.15468/dl.e7rte6
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jacobaea gallerandiana (Coss. & Durieu) Pelser"
 ]
}

 The dataset includes 45 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040373-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sa38t7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasianthus nigrescens H.Zhu"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040375-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9brm9k
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leichhardtia variabilis (P.I.Forst.) P.I.Forst."
 ]
}

 The dataset includes 16 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040374-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26179/m7ce-bb02
title: Aurora Australis Voyage 1.1 (Macquarie Island, Dumont D'Urville) 2019/20 Track and Underway Data
DOI: 10.15468/dl.zuhf4x
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jurinea abramovii Regel & Herder ex Regel"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040376-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxbgku
title: Occurrence Download

description: A dataset containing 1122 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Afzelia bella Harms"
 ]
}

 The dataset includes 1122 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040377-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gnafdk
title: Occurrence Download

description: A dataset containing 314 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Jacobaea gigantea (Desf.) Pelser"
 ]
}

 The dataset includes 314 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040379-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g2cua2
title: Occurrence Download

description: A dataset containing 166015 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glossiphoniidae"
 ]
}

 The dataset includes 166015 records from 433 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040331-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pd3jtg
title: Occurrence Download

description: A dataset containing 351 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lachnaea diosmoides Meisn."
 ]
}

 The dataset includes 351 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040378-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tvh6y4
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leichhardtia variifolia (Guillaumin) Liede, Gâteblé & Meve"
 ]
}

 The dataset includes 37 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040381-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.