cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10658240
title: TRAIL dampig tests, lms raw data

description: Raw damping characterization test data.

The project leading to this application has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 864726.The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Clean Sky 2 JU members otherthan the Union.
DOI: 10.6084/m9.figshare.20175680
title: Additional file 3 of Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

description: Additional file 3: Supplementary file 3. The reconstructed model icrab4665.
DOI: 10.6084/m9.figshare.20175677
title: Additional file 2 of Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

description: Additional file 2: Supplementary file 2. The results of nutritional composition detection of E. sinensis.
DOI: 10.6084/m9.figshare.20175692
title: Additional file 6 of Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

description: Additional file 6: Supplementary file 6. The list of reactions with flux changed when the biomass synthesis rate changes.
DOI: 10.6084/m9.figshare.20175802
title: Additional file 2 of PITX2C increases the stemness features of hepatocellular carcinoma cells by up-regulating key developmental factors in liver progenitor

description: Additional file 2: Table S2. The gene ontology (GO) analysis of PITX2C ChIP-seq.
DOI: 10.6084/m9.figshare.20175856.v1
title: Additional file 12 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 12: Supplementary Table 2. MS analysis of IP AEP.
DOI: 10.6084/m9.figshare.20175862
title: Additional file 14 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 14: Supplementary Table 4. Clinical characteristics of HGG patients.
DOI: 10.6084/m9.figshare.20175865.v1
title: Additional file 15 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 15: Supplementary Table 5. MS analysis of IP tTmod3-C.
DOI: 10.6084/m9.figshare.19651419
title: Sample-wise Combined Missing Effect Model with Penalization

description: Modern high-dimensional statistical inference often faces the problem of missing data. In recent decades, many studies have focused on this topic and provided strategies including complete-sample analysis and imputation procedures. However, complete-sample analysis discards information of incomplete samples, while imputation procedures have accumulative errors from each single imputation. In this paper, we propose a new method, Sample-wise COmbined missing effect Model with penalization (SCOM), to deal with missing data occurring in predictors. Instead of imputing the predictors, SCOM estimates the combined effect caused by all missing data for each incomplete sample. SCOM makes full use of all available data. It is robust with respect to various missing mechanisms. Theoretical studies show the oracle inequality for the proposed estimator, and the consistency of variable selection and combined missing effect selection. Simulation studies and an application to the Residential Building Data also illustrate the effectiveness of the proposed SCOM.
DOI: 10.6084/m9.figshare.19651419.v1
title: Sample-wise Combined Missing Effect Model with Penalization

description: Modern high-dimensional statistical inference often faces the problem of missing data. In recent decades, many studies have focused on this topic and provided strategies including complete-sample analysis and imputation procedures. However, complete-sample analysis discards information of incomplete samples, while imputation procedures have accumulative errors from each single imputation. In this paper, we propose a new method, Sample-wise COmbined missing effect Model with penalization (SCOM), to deal with missing data occurring in predictors. Instead of imputing the predictors, SCOM estimates the combined effect caused by all missing data for each incomplete sample. SCOM makes full use of all available data. It is robust with respect to various missing mechanisms. Theoretical studies show the oracle inequality for the proposed estimator, and the consistency of variable selection and combined missing effect selection. Simulation studies and an application to the Residential Building Data also illustrate the effectiveness of the proposed SCOM.
DOI: 10.6084/m9.figshare.7593017.v1
title: An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy

description: MicroRNAs (miRNAs) play an important role in prevention, diagnosis and treatment of human complex diseases. Predicting potential miRNA-disease associations could provide important prior information for medical researchers. Therefore, reliable computational models are expected to be an effective supplement for inferring associations between miRNAs and diseases. In this study, we developed a novel calculative model named Negative Samples Extraction based MiRNA-Disease Association prediction (NSEMDA). NSEMDA filtered reliable negative samples by two positive-unlabeled learning models, namely, the Spy and Rocchio techniques and calculated similarity weights for ambiguous samples. The positive samples, reliable negative samples and ambiguous samples with similarity weights were used to construct a Support Vector Machine-Similarity Weight model to predict miRNA-disease associations. NSEMDA improved the credibility of negative samples and reduced the impact of noise samples by introducing ambiguous samples with similarity weights to train prediction model. As a result, NSEMDA achieved the AUC of 0.8899 in global leave-one-out cross validation (LOOCV) and AUC of 0.8353 under local LOOCV. In 100 times 5-fold cross validation, NSEMDA obtained an average AUC of 0.8878 and standard deviation of 0.0014. These AUCs are higher than many classical models. Besides, we also carried out three kinds of case studies to evaluate the performance of NSEMDA. Among the top 50 potential related miRNAs of esophageal neoplasms, lung neoplasms and carcinoma hepatocellular predicted by NSEMDA, 46, 50 and 45 miRNAs were verified to be associated with the investigated disease by experimental evidences, respectively. Therefore, NSEMDA would be a reliable calculative model for inferring miRNA-disease associations.
DOI: 10.6084/m9.figshare.20175853
title: Additional file 11 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 11: Supplementary Table 1. Primers and siRNAs.
DOI: 10.6084/m9.figshare.14932954.v1
title: The prognosis of glioblastoma: a large, multifactorial study

description: Glioblastoma is the most common and fatal primary brain tumor in adults. Even with maximal resection and a series of postoperative adjuvant treatments, the median overall survival (OS) of glioblastoma patients remains approximately 15 months. The Huashan Hospital glioma bank contains more than 2000 glioma tissue samples with long-term follow-up data; almost half of these samples are from glioblastoma patients. Several large glioma databases with long-term follow-up data have reported outcomes of glioblastoma patients from countries other than China. We investigated the prognosis of glioblastoma patients in China and compared the survival outcomes among patients from different databases. The data for 967 glioblastoma patients who underwent surgery at Huashan Hospital and had long-term follow-up records were obtained from our glioma registry (diagnosed from 29 March 2010, through 7 June 2017). Patients were eligible for inclusion if they underwent surgical resection for newly diagnosed glioblastomas and had available data of survival and personal information. Data of 778 glioblastoma patients were collected from three separate online databases (448 patients from The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov), 191 from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) database (GSE108476) and 132 from data set GSE16011(Hereafter called as the French database). We compared the prognosis of glioblastoma patients from records among the different databases and the changes in survival outcomes of glioblastoma patients from Huashan Hospital over an 8-year period. The median OS of glioblastoma patients was 16.3 (95% CI: 15.4–17.2) months for Huashan Hospital, 13.8 (95% CI: 12.9–14.9) months for TCGA, 19.3 (95% CI: 17.0–20.0) months for the REMBRANDT database, and 9.1 months for the French database. The median OS of glioblastoma patients from Huashan Hospital improved from 15.6 (2010–2013, 95% CI: 14.4–16.6) months to 18.2 (2014–2017, 95% CI: 15.8–20.6) months over the study period (2010–2017). In addition, the prognosis of glioblastoma patients with total resection was significantly better than that of glioblastoma patients with sub-total resection or biopsy. Our study confirms that treatment centered around maximal surgical resection brought survival benefits to glioblastoma patients after adjusting to validated prognostic factors. In addition, an improvement in prognosis was observed among glioblastoma patients from Huashan Hospital over the course of our study. We attributed it to the adoption of a new standard of neurosurgical treatment on the basis of neurosurgical multimodal technologies. Even though the prognosis of glioblastoma patients remains poor, gradual progress is being made.
DOI: 10.3886/icpsr33822.v1
title: Integrated Public Health Surveys, 2010-2011
title: Version 1

description: This collection comprises a single data file which was produced as part of the data harmonization efforts of the Robert Wood Johnson Foundation and the United States Centers for Disease Control and Prevention. The file contains merged data from five sources: 2010 National Profile of Local Health Departments, a survey of local health departments conducted by the National Association of County and City Health Officials (NACCHO).; 2011 National Profile Survey of Local Boards of Health, a survey of local boards of health conducted by the National Association of Local Boards of Health (NALBOH).; 2010 State and Territorial Public Health Survey, a survey of state and United States territory health departments conducted by the Association of State and Territorial Health Officials (ASTHO).; 2011 County Health Rankings, a compilation of county-level health measures and within-state county health rankings produced by the University of Wisconsin Population Health Institute.; 2010 Census Demographic Profile Summary File, a series of tables with housing and population data from the 2010 Census.; Produced by matching data from the last four sources to the NACCHO data, the data file contains one case for each of the 2,107 local health departments (LHD) that responded to the NACCHO survey. Each LHD's record in the file includes the ASTHO data for its state health department and the NALBOH data for its local board of health (LBH), if it had a LBH and the LBH responded to the NALBOH survey. (If a LHD had multiple LBHs, then the first one in the NALBOH data was matched to the LHD). In addition, county (or county equivalent)-level data from the County Health Rankings and Census Demographic Profile Summary File were matched to the records of the 1,535 LHDs represented in the data file with a jurisdiction covering a single county or county equivalent.
description: ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes..
description: Response Rates: ASTHO survey: 100 percent. NACCHO survey: 83 percent. NALBOH survey: 43 percent.
description: Datasets: 
 
 DS1: Dataset
description: mail questionnaire, web-based survey 
The data file includes health measures from the County Health Rankings but not the ranking scores in this source.
description: ASTHO survey: state health departments. NACCHO survey: LHDs. NALBOH survey: LBHs.
description: The NACCHO, NALBOH, and ASTHO surveys attempted to interview all LHDs, LBHs, and state health departments in the United States.
DOI: 10.3886/icpsr33822
title: Integrated Public Health Surveys, 2010-2011
title: Archival Version

description: This collection comprises a single data file which was produced as part of the data harmonization efforts of the Robert Wood Johnson Foundation and the United States Centers for Disease Control and Prevention. The file contains merged data from five sources:


2010 National Profile of Local Health Departments, a survey of local health departments conducted by the National Association of County and City Health Officials (NACCHO).

2011 National Profile Survey of Local Boards of Health, a survey of local boards of health conducted by the National Association of Local Boards of Health (NALBOH).

2010 State and Territorial Public Health Survey, a survey of state and United States territory health departments conducted by the Association of State and Territorial Health Officials (ASTHO).

2011 County Health Rankings, a compilation of county-level health measures and within-state county health rankings produced by the University of Wisconsin Population Health Institute.

2010 Census Demographic Profile Summary File, a series of tables with housing and population data from the 2010 Census.

Produced by matching data from the last four sources to the NACCHO data, the data file contains one case for each of the 2,107 local health departments (LHD) that responded to the NACCHO survey. Each LHD's record in the file includes the ASTHO data for its state health department and the NALBOH data for its local board of health (LBH), if it had a LBH and the LBH responded to the NALBOH survey. (If a LHD had multiple LBHs, then the first one in the NALBOH data was matched to the LHD). In addition, county (or county equivalent)-level data from the County Health Rankings and Census Demographic Profile Summary File were matched to the records of the 1,535 LHDs represented in the data file with a jurisdiction covering a single county or county equivalent.
description: Response Rates: ASTHO survey: 100 percent. NACCHO survey: 83 percent. NALBOH survey: 43 percent.
description: The NACCHO, NALBOH, and ASTHO surveys attempted to interview all LHDs, LBHs, and state health departments in the United States.
description: Datasets: 
DS1: Integrated Public Health Surveys, 2010-2011
description: ASTHO survey: state health departments. NACCHO survey: LHDs. NALBOH survey: LBHs.
description: mail questionnaire; web-based surveyThe data file includes health measures from the County Health Rankings but not the ranking scores in this source.
DOI: 10.3886/icpsr33822.v2
title: Integrated Public Health Surveys, 2010-2011
title: Version 2

description: This collection comprises a single data file which was produced as part of the data harmonization efforts of the Robert Wood Johnson Foundation and the United States Centers for Disease Control and Prevention. The file contains merged data from five sources:


2010 National Profile of Local Health Departments, a survey of local health departments conducted by the National Association of County and City Health Officials (NACCHO).

2011 National Profile Survey of Local Boards of Health, a survey of local boards of health conducted by the National Association of Local Boards of Health (NALBOH).

2010 State and Territorial Public Health Survey, a survey of state and United States territory health departments conducted by the Association of State and Territorial Health Officials (ASTHO).

2011 County Health Rankings, a compilation of county-level health measures and within-state county health rankings produced by the University of Wisconsin Population Health Institute.

2010 Census Demographic Profile Summary File, a series of tables with housing and population data from the 2010 Census.

Produced by matching data from the last four sources to the NACCHO data, the data file contains one case for each of the 2,107 local health departments (LHD) that responded to the NACCHO survey. Each LHD's record in the file includes the ASTHO data for its state health department and the NALBOH data for its local board of health (LBH), if it had a LBH and the LBH responded to the NALBOH survey. (If a LHD had multiple LBHs, then the first one in the NALBOH data was matched to the LHD). In addition, county (or county equivalent)-level data from the County Health Rankings and Census Demographic Profile Summary File were matched to the records of the 1,535 LHDs represented in the data file with a jurisdiction covering a single county or county equivalent.
description: Response Rates: ASTHO survey: 100 percent. NACCHO survey: 83 percent. NALBOH survey: 43 percent.
description: The NACCHO, NALBOH, and ASTHO surveys attempted to interview all LHDs, LBHs, and state health departments in the United States.
description: Datasets: 
DS1: Integrated Public Health Surveys, 2010-2011
description: ASTHO survey: state health departments. NACCHO survey: LHDs. NALBOH survey: LBHs.
description: mail questionnaire; web-based surveyThe data file includes health measures from the County Health Rankings but not the ranking scores in this source.
DOI: 10.6084/m9.figshare.7593017
title: An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy

description: MicroRNAs (miRNAs) play an important role in prevention, diagnosis and treatment of human complex diseases. Predicting potential miRNA-disease associations could provide important prior information for medical researchers. Therefore, reliable computational models are expected to be an effective supplement for inferring associations between miRNAs and diseases. In this study, we developed a novel calculative model named Negative Samples Extraction based MiRNA-Disease Association prediction (NSEMDA). NSEMDA filtered reliable negative samples by two positive-unlabeled learning models, namely, the Spy and Rocchio techniques and calculated similarity weights for ambiguous samples. The positive samples, reliable negative samples and ambiguous samples with similarity weights were used to construct a Support Vector Machine-Similarity Weight model to predict miRNA-disease associations. NSEMDA improved the credibility of negative samples and reduced the impact of noise samples by introducing ambiguous samples with similarity weights to train prediction model. As a result, NSEMDA achieved the AUC of 0.8899 in global leave-one-out cross validation (LOOCV) and AUC of 0.8353 under local LOOCV. In 100 times 5-fold cross validation, NSEMDA obtained an average AUC of 0.8878 and standard deviation of 0.0014. These AUCs are higher than many classical models. Besides, we also carried out three kinds of case studies to evaluate the performance of NSEMDA. Among the top 50 potential related miRNAs of esophageal neoplasms, lung neoplasms and carcinoma hepatocellular predicted by NSEMDA, 46, 50 and 45 miRNAs were verified to be associated with the investigated disease by experimental evidences, respectively. Therefore, NSEMDA would be a reliable calculative model for inferring miRNA-disease associations.
DOI: 10.6084/m9.figshare.20175853.v1
title: Additional file 11 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 11: Supplementary Table 1. Primers and siRNAs.
DOI: 10.6084/m9.figshare.20175907
title: Additional file 1 of Transthoracic, thoracoabdominal, and transabdominal surgical approaches for gastric cardia adenocarcinomas: a survival evaluation based on a cohort of 7103 patients

description: Additional file 1: Supplemental Table S1. Comparison of lymph node variables among the three surgical approaches for 4644 GCA patients. Supplemental Figure S1. Kaplan–Meier curves comparing different surgical approaches of GCA patients in different tumor stages: (A) Stage 0 - I, (B) Stage II, (C) Stage III, and (D) Stage IV. Supplemental Figure S2. Kaplan–Meier curves comparing different surgical approaches of GCA patients in N0 stage (A) and N1 stage (B). Supplemental Figure S3. Kaplan–Meier curves comparing surgical approach of GCA patients in different periods: 1974–1999 (A); 2000–2011 (B); and 2012–2020 (C).
DOI: 10.6084/m9.figshare.20175856
title: Additional file 12 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 12: Supplementary Table 2. MS analysis of IP AEP.
DOI: 10.6084/m9.figshare.20175862.v1
title: Additional file 14 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 14: Supplementary Table 4. Clinical characteristics of HGG patients.
DOI: 10.6084/m9.figshare.20175865
title: Additional file 15 of Cleavage of tropomodulin-3 by asparagine endopeptidase promotes cancer malignancy by actin remodeling and SND1/RhoA signaling

description: Additional file 15: Supplementary Table 5. MS analysis of IP tTmod3-C.
DOI: 10.34740/kaggle/dsv/7626614
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.6084/m9.figshare.19112665
title: Additional file 2 of Mesenchymal stem cells combined with autocrosslinked hyaluronic acid improve mouse ovarian function by activating the PI3K-AKT pathway in a paracrine manner

description: Additional file 2: Table S1. Differential expression genes between control and HA-pretreated UC-MSCs. Table S2. Fertility test results of VCD induced-POI mice after MSC combined with HA transplantation. Table S3. Differential expression cytokines between MSC-CM and Fib-CM. Table S4. KEGG pathways analysis of differential cytokines between MSC-CM and Fib-CM. Table S5. Twenty two up-regulated cytokines in MSC-CM related with the PI3K-AKT pathway. Table S6. Differential expression cytokines between MSC-CM and MSC-CM after HA pretreated. Table S7. KEGG pathways analysis of differential cytokines between MSC-CM and MSC-CM after HA pretreated. Table S8. Fertility test results of aged mice after MSC combined with HA transplantation.
DOI: 10.6084/m9.figshare.19112628
title: Additional file 2 of YBX1 mediates alternative splicing and maternal mRNA decay during pre-implantation development

description: Additional files 2: Table S1. Differentially expressed genes at the 8-cell stage YBX1 knockdown embryos.
DOI: 10.6084/m9.figshare.25222115
title: LLO-CV-QKD Key Dataset

description: Dataset with the raw keys before post-processing, using a CV-QKD system with true local oscillator. The sender's keys consist of values that follow a normal distribution, according to the Gaussian modulation scheme. The receiver's keys have been obtained by applying a software-defined pilot-tone assisted demodulation algorithm. Since this is CV-QKD, the keys are expressed as float numbers, and are separated into the actual key (x_key, y_key) and the fraction of the key used for parameter estimation (x_pe, y_pe).
DOI: 10.6084/m9.figshare.20175905
title: Additional file 4 of A novel hypoxic lncRNA, HRL-SC, promotes the proliferation and migration of human dental pulp stem cells through the PI3K/AKT signaling pathway

description: Additional file 4. Details of GO analysis for HRL-SC.
DOI: 10.34740/kaggle/dsv/7626619
title: Public Opinion on Climate Change (Updated Daily)