cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23962569.v1
title: Supplementary Material for: AKAP12 and IGFBP4 are prognostic factors for chronic lymphocytic leukemia

description: Introduction: To develop a prognostic model for chronic lymphocytic leukemia (CLL). Methods: GEO2R was used to retrieve the gene expression data of CLL and normal B cells from the Gene Expression Omnibus (GEO, GSE22529 and GSE50006 datasets) database. Perl (Practical Extraction and Report Language) was used to extract the gene expression and overall survival (OS) data of CLL patients from the CLLE-ES project in the ICGC (International Cancer Genome Consortium) database. Cox regression with Lasso was used to create and validate a prognostic model for CLL. Results: A total of 267 genes exhibited differential expression between CLL and normal B cells. Uni-Cox analysis identified 14 DEGs that correlated with overall survival (OS). Lasso multivariate evaluation demonstrated that AKAP12 and IGFBP4 are independent prognostic factors for CLL. Kaplan-Meier survival analysis revealed a significant association between the estimated risk score and survival. The area under the receiver operating characteristics (ROC) curve (AUC) was calculated to be 0.97, indicating high predictive accuracy. In addition, high AKAP12 and IGFBP4 risk score was associated with the high incidence of trisomy 12q. Conclusion: Taken together, AKAP12 and IGFBP4 are independent prognostic factors for CLL.
DOI: 10.6084/m9.figshare.23962590
title: Supplementary Material for: AKAP12 and IGFBP4 are prognostic factors for chronic lymphocytic leukemia

description: Introduction: To develop a prognostic model for chronic lymphocytic leukemia (CLL). Methods: GEO2R was used to retrieve the gene expression data of CLL and normal B cells from the Gene Expression Omnibus (GEO, GSE22529 and GSE50006 datasets) database. Perl (Practical Extraction and Report Language) was used to extract the gene expression and overall survival (OS) data of CLL patients from the CLLE-ES project in the ICGC (International Cancer Genome Consortium) database. Cox regression with Lasso was used to create and validate a prognostic model for CLL. Results: A total of 267 genes exhibited differential expression between CLL and normal B cells. Uni-Cox analysis identified 14 DEGs that correlated with overall survival (OS). Lasso multivariate evaluation demonstrated that AKAP12 and IGFBP4 are independent prognostic factors for CLL. Kaplan-Meier survival analysis revealed a significant association between the estimated risk score and survival. The area under the receiver operating characteristics (ROC) curve (AUC) was calculated to be 0.97, indicating high predictive accuracy. In addition, high AKAP12 and IGFBP4 risk score was associated with the high incidence of trisomy 12q. Conclusion: Taken together, AKAP12 and IGFBP4 are independent prognostic factors for CLL.
DOI: 10.6084/m9.figshare.23962590.v1
title: Supplementary Material for: AKAP12 and IGFBP4 are prognostic factors for chronic lymphocytic leukemia

description: Introduction: To develop a prognostic model for chronic lymphocytic leukemia (CLL). Methods: GEO2R was used to retrieve the gene expression data of CLL and normal B cells from the Gene Expression Omnibus (GEO, GSE22529 and GSE50006 datasets) database. Perl (Practical Extraction and Report Language) was used to extract the gene expression and overall survival (OS) data of CLL patients from the CLLE-ES project in the ICGC (International Cancer Genome Consortium) database. Cox regression with Lasso was used to create and validate a prognostic model for CLL. Results: A total of 267 genes exhibited differential expression between CLL and normal B cells. Uni-Cox analysis identified 14 DEGs that correlated with overall survival (OS). Lasso multivariate evaluation demonstrated that AKAP12 and IGFBP4 are independent prognostic factors for CLL. Kaplan-Meier survival analysis revealed a significant association between the estimated risk score and survival. The area under the receiver operating characteristics (ROC) curve (AUC) was calculated to be 0.97, indicating high predictive accuracy. In addition, high AKAP12 and IGFBP4 risk score was associated with the high incidence of trisomy 12q. Conclusion: Taken together, AKAP12 and IGFBP4 are independent prognostic factors for CLL.
DOI: 10.5281/zenodo.8200320
title: Geostationary Satellite Surface Gaseous Pollutants Dataset - CO

description: High spatiotemporal resolution CO dataset of China's near-surface based on the Geostationary satellite Himawari-8 (2015.09 - 2021.08)
DOI: 10.5281/zenodo.7029643
title: Geostationary Satellite Surface Gaseous Pollutants Dataset - CO

description: High spatiotemporal resolution CO dataset of China's near-surface based on the Geostationary satellite Himawari-8 (2015.09 - 2021.08)
DOI: 10.5281/zenodo.8251338
title: Synergistic Observation of Geostationary Satellites FY-4A and FY-4B to Estimate Near-surface Pollutant Concentration dataset - CO

description: High spatiotemporal resolution CO dataset of China's near-surface for summer and autumn 2022 based on the Geostationary satellite FY-4A and FY-4B
DOI: 10.5281/zenodo.8251337
title: Synergistic Observation of Geostationary Satellites FY-4A and FY-4B to Estimate Near-surface Pollutant Concentration dataset - CO

description: High spatiotemporal resolution CO dataset of China's near-surface for summer and autumn 2022 based on the Geostationary satellite FY-4A and FY-4B
DOI: 10.23719/1500922
title: Soil organic matter and amphibian exposure dataset

description: Dataset containing experimental data and R scripts used for data analyses.
DOI: 10.23719/1500920
title: Varroapop sensitivity analysis scripts and output

description: Code repository for scripts and model output associated with sensitivity analysis of the VarroaPop honeybee hive simulation model.
DOI: 10.23719/1412697
title: The First Acid Ionization Constant of Cyanuric Acid from 5 to 35 °C

description: No data set is provided
DOI: 10.23719/1434903
title: Data used and links to data and metadata

description: The provided link will take users to all the data and metadata used in this project
DOI: 10.23719/1502631
title: Supplementary material for Lee et al. in review: Harmonization and Revision of a National Diatom Dataset for Use in the Development of Water Quality Indicators

description: ABSTRACT
Diatom data have been collected in large-scale biological assessments in the United States, such as the U.S. Environmental Protection Agency’s National Rivers and Streams Assessment (NRSA). However, the effectiveness of diatoms as indicators may suffer if inconsistent taxon identifications across different analysts obscure the relationships between assemblage composition and environmental variables. To reduce these inconsistencies, we harmonized the 2008-2009 NRSA data from nine analysts by updating names to current synonyms and by statistically identifying taxa with high analyst signal (taxa with more variation in relative abundance explained by the analyst factor, relative to environmental variables). We then screened a subset of samples with QA/QC data and combined taxa with mismatching identifications by the primary and secondary analysts. When these combined “slash groups” did not reduce analyst signal, we elevated taxa to the genus level or omitted taxa in difficult species complexes. We examined the variability explained by analyst in the original and revised datasets. Further, we examined how revising the datasets to reduce analyst signal can reduce inconsistency, thereby uncovering the variation in assemblage composition explained by total phosphorus (TP), an environmental variable of high priority for water managers. To produce a revised dataset with the greatest taxonomic consistency, we ultimately made 124 slash groups, omitted 7 taxa in the small naviculoid (e.g., Sellaphora atomoides) species complex, and elevated Nitzschia, Diploneis, and Tryblionella taxa to the genus level. Relative to the original dataset, the revised dataset had more overlap among samples grouped by analyst in ordination space, less variation explained by the analyst factor, and more than double the variation in assemblage composition explained by TP. Elevating all taxa to the genus level did not eliminate analyst signal completely, and analyst remained the most important predictor for the genera Sellaphora, Mayamaea, and Psammodictyon, indicating that these taxa present the greatest obstacle to consistent identification in this dataset. Although our process did not completely remove the analyst signal, this work clarifies the extent of the problem and provides a method to minimize analyst signal. Resolution of these taxonomic issues makes large datasets such as the NRSA more suitable for the development of diatom-based water quality indicators.
DOI: 10.23719/1503031
title: NEEAR Water Study-human markers

description: Data set consists of survey data containing PII, water quality sample test results for fecal indicator bacteria and additional supporting information
DOI: 10.23719/1408779
title: NEEAR Water Study-Chemicals

description: data includes human health survey data, linked records of chemical measures and measures of water quality
DOI: 10.23719/1503101
title: Coliphages and gastrointestinal illness in recreational waters: pooled analysis of six coastal beach cohorts

description: Data consists of health and survey data from epidemiological studies at beach sites and water quality measurements
DOI: 10.23719/1503105
title: The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993-2005.

description: records of mortality, dust storms and demographic/census data for the United States
DOI: 10.5281/zenodo.8164398
title: brca_tcga_pyg_dataset

description: This is a dataset that was gotten by integrating the breast cancer(tcga) dataset from the cBioportal and data that shows node connections from Pathway commons. They were preprocessed together to form one huge dataset that could be converted to Pytorch geometric data objects. This data is in the CSV format and can be downloaded, the processed to form a graph dataset comprising different graphs if it is to be used to train Graph Neural Networks(GNN). It could also be used as is for training Deep Neural Networks(DNN) for situations where you want to compare the performance of these two types of networks. The dataset contains the gene features of each patient in graph_idx and the overall survival time(in months) of each patient, which are the labels.
DOI: 10.5281/zenodo.8179187
title: brca_tcga_pyg_dataset

description: This is a dataset that was gotten by integrating the breast cancer(tcga) dataset from the cBioportal and data that shows node connections from Pathway Commons. They were preprocessed together to form one huge dataset that could be converted to Pytorch geometric data objects. This data is in the CSV format and can be downloaded, and then processed to form a graph dataset comprising different graphs for training Graph Neural Networks(GNN) for Graph regression. It could also be used as is for training Deep Neural Networks(DNN). The dataset contains the gene features of each patient in graph_idx and the overall survival time(in months) of each patient, which are the labels.
DOI: 10.5281/zenodo.8212008
title: brca_tcga_pyg_dataset

description: This is a dataset that was gotten by integrating the breast cancer(tcga) dataset from the cBioportal and data that shows node connections from Pathway Commons. They were preprocessed together to form one huge dataset that could be converted to Pytorch geometric data objects. This data is in the CSV format and can be downloaded, and then processed to form a graph dataset comprising different graphs for training Graph Neural Networks(GNN) for Graph regression. It could also be used as is for training Deep Neural Networks(DNN). The dataset contains the gene features of each patient in graph_idx and the overall survival time(in months) of each patient, which are the labels.
DOI: 10.5281/zenodo.8232723
title: brca_tcga_pyg_dataset

description: This is a dataset that was gotten by integrating the breast cancer(tcga) dataset from the cBioportal and data that shows node connections from Pathway Commons. They were preprocessed together to form one huge dataset that could be converted to Pytorch geometric data objects. This data is in the CSV format and can be downloaded, and then processed to form a graph dataset comprising different graphs for training Graph Neural Networks(GNN) for Graph regression. It could also be used as is for training Deep Neural Networks(DNN). The dataset contains the gene features of each patient in graph_idx and the overall survival time(in months) of each patient, which are the labels.
DOI: 10.23719/1503102
title: Water Recreation and Illness Severity

description: Data included health survey data from beach goers and water quality measurements
DOI: 10.23719/1503068
title: Region 5 RARE air manganese data set

description: A cross-sectional design was used where 86 residents of East Liverpool, Ohio, 100 residents from Marietta, Ohio and 90 residents from Mount Vernon, Ohio were recruited and participated in the study. The Marietta/Mount Vernon data collection took place in August, 2009 as this was the original study location. Marietta was an air manganese (air-Mn) exposed community and Mt. Vernon was a comparison community believed to have little or no air-Mn exposure. After receiving additional funding and approvals, East Liverpool was added and data collection occurred in November, 2011 using identical study protocols to the Marietta/Mount Vernon study with the exception of additional specimen collections of hair and toenails (only collected in East Liverpool). All participants underwent a neuropsychological battery of tests of mood, motor and cognitive function. A comprehensive health questionnaire was administered inquiring about sociodemographics, symptoms, diagnosed illnesses, medication use, health habits, work history, and dietary consumption (used to compute dietary intake of Mn and Fe). Additionally, the study included data acquisition on air monitoring and modeling, biomarkers, and health.
DOI: 10.23719/1503104
title: Medication use associated with exposure to manganese in two Ohio towns

description: A cross-sectional design was used where 86 residents of East Liverpool, Ohio, 100 residents from Marietta, Ohio and 90 residents from Mount Vernon, Ohio were recruited and participated in the study. The Marietta/Mount Vernon data collection took place in August, 2009 as this was the original study location. Marietta was an air manganese (air-Mn) exposed community and Mt. Vernon was a comparison community believed to have little or no air-Mn exposure. After receiving additional funding and approvals, East Liverpool was added and data collection occurred in November, 2011 using identical study protocols to the Marietta/Mount Vernon study with the exception of additional specimen collections of hair and toenails (only collected in East Liverpool). All participants underwent a neuropsychological battery of tests of mood, motor and cognitive function. A comprehensive health questionnaire was administered inquiring about sociodemographics, symptoms, diagnosed illnesses, medication use, health habits, work history, and dietary consumption (used to compute dietary intake of Mn and Fe). Additionally, the study included data acquisition on air monitoring and modeling, biomarkers, and health.
DOI: 10.23719/1503103
title: County-level cumulative environmental quality associated with cancer incidence.

description: Population based cancer incidence rates were abstracted from National Cancer Institute,
State Cancer Profiles for all available counties in the United States for which data were
available. This is a national county-level database of cancer data that are collected by state
public health surveillance systems. All-site cancer is defined as any type of cancer that is
captured in the state registry data, though non-melanoma skin cancer is not included. All-site
age-adjusted cancer incidence rates were abstracted separately for males and females.
County-level annual age-adjusted all-site cancer incidence rates for years 2006–2010 were
available for 2687 of 3142 (85.5%) counties in the U.S. Counties for which there are fewer
than 16 reported cases in a specific area-sex-race category are suppressed to ensure
confidentiality and stability of rate estimates; this accounted for 14 counties in our study.
Two states, Kansas and Virginia, do not provide data because of state legislation and
regulations which prohibit the release of county level data to outside entities. Data from
Michigan does not include cases diagnosed in other states because data exchange
agreements prohibit the release of data to third parties. Finally, state data is not available for
three states, Minnesota, Ohio, and Washington. The age-adjusted average annual
incidence rate for all counties was 453.7 per 100,000 persons.
We selected 2006–2010 as it is subsequent in time to the EQI exposure data which was
constructed to represent the years 2000–2005. We also gathered data for the three leading
causes of cancer for males (lung, prostate, and colorectal) and females (lung, breast, and
colorectal). 

The EQI was used as an exposure metric as an indicator of cumulative environmental
exposures at the county-level representing the period 2000 to 2005. A complete description
of the datasets used in the EQI are provided in Lobdell et al.  and methods used for index
construction are described by Messer et al. The EQI was developed for the period 2000–
2005 because it was the time period for which the most recent data were available when
index construction was initiated. The EQI includes variables representing each of the
environmental domains. The air domain includes 87 variables representing criteria and
hazardous air pollutants. The water domain includes 80 variables representing overall water
quality, general water contamination, recreational water quality, drinking water quality,
atmospheric deposition, drought, and chemical contamination. The land domain includes 26
variables representing agriculture, pesticides, contaminants, facilities, and radon. The built
domain includes 14 variables representing roads, highway/road safety, public transit
behavior, business environment, and subsidized housing environment. The
sociodemographic environment includes 12 variables representing socioeconomics and
crime.
DOI: 10.23719/1503121
title: Additive interaction between heterogeneous environmental quality domains (air, water, land, sociodemographic and built environment) on preterm birth

description: The study population included live births from the National Center for Health Statistics (NCHS) for the entire United States for the years 2000–2005 for all 3141 counties. Domain-specific EQIs were used to represent environmental exposure at the county-level for the entire U.S. over the 2000–2005 time period. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime.
DOI: 10.23719/1503120
title: Associations between environmental quality and mortality in the contiguous United States 2000-2005

description: Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime.
DOI: 10.23719/1503119
title: Data for this project include human subjects PII and cannot be shared. 

description: Data on approximately 2 million births occurring in NJ, OH, and PA from 2000 - 2005. Linked to PM2.5 and ozone concentration estimates from EPA CMAQ fused model.
DOI: 10.23719/1376675
title: Ozone-induced changes in pulmonary metabolites in Humans

description: Dataset contains a list of metabolites, their fold change after ozone exposure and a p value for that change.
DOI: 10.6084/m9.figshare.23962794
title: violence against teachers

description: research of the prevalence and influencing factors of violence against teachers by middle school students
DOI: 10.6084/m9.figshare.23962794.v1
title: violence against teachers

description: research of the prevalence and influencing factors of violence against teachers by middle school students
DOI: 10.23719/1503087
title: Sequencing Data for Hospital Metagenomes

description: FASTA files containing the sequence data and for Assembled contigs (FastA), Predicted genes (FastA), Predicted proteins (FastA), Gene prediction (GFF v2)
DOI: 10.23719/1407606
title: Zipped NetCDF data for Precipitation partitioning in Multi-Scale Atmospheric Simulations: Impacts of Stability Restoration Methods  

description: Data for all figures in NetCDF format zipped files