cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22155239.v1
title: Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference

description: The Multivariate Contaminated Normal (MCN) distribution which contains two extra parameters with respect to parameters of the multivariate normal distribution, one for controlling the proportion of mild outliers and the other for specifying the degree of contamination, has been widely applied in robust statistics in the case of elliptically heavy-tailed empirical distributions. This article extends the MCN model to data with possibly censored values due to limits of quantification, referred to as the MCN with censoring (MCN-C) model, and further establishes the censored multivariate linear regression model where the random errors have the MCN distribution, named as the MCN censored regression (MCN-CR) model. Two computationally feasible Expectation Conditional Maximization (ECM) algorithms are developed for maximum likelihood estimation of MCN-C and MCN-CR models. An information-based method is used to approximate the standard errors of location parameters and regression coefficients. The capability and effectiveness of the MCN-C and MCN-CR models are illustrated via two real-data examples. A simulation study is conducted to investigate the superiority of the proposed models in terms of fit, accuracy of parameter estimation and censored data recovery as compared with classical approaches. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.22155239
title: Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference

description: The Multivariate Contaminated Normal (MCN) distribution which contains two extra parameters with respect to parameters of the multivariate normal distribution, one for controlling the proportion of mild outliers and the other for specifying the degree of contamination, has been widely applied in robust statistics in the case of elliptically heavy-tailed empirical distributions. This article extends the MCN model to data with possibly censored values due to limits of quantification, referred to as the MCN with censoring (MCN-C) model, and further establishes the censored multivariate linear regression model where the random errors have the MCN distribution, named as the MCN censored regression (MCN-CR) model. Two computationally feasible Expectation Conditional Maximization (ECM) algorithms are developed for maximum likelihood estimation of MCN-C and MCN-CR models. An information-based method is used to approximate the standard errors of location parameters and regression coefficients. The capability and effectiveness of the MCN-C and MCN-CR models are illustrated via two real-data examples. A simulation study is conducted to investigate the superiority of the proposed models in terms of fit, accuracy of parameter estimation and censored data recovery as compared with classical approaches. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.7853104
title: WikiPathways April 2023 Release - RDF data

description: Archive of the WikiPathways April 2022 RDF data for all species as GPMLRDF and WPRDF (.ttl format). The data is licensed under the CCZero waiver.
DOI: 10.5281/zenodo.7853103
title: WikiPathways April 2023 Release - RDF data

description: Archive of the WikiPathways April 2022 RDF data for all species as GPMLRDF and WPRDF (.ttl format). The data is licensed under the CCZero waiver.
DOI: 10.25380/iastate.19719176
title: Global 1 km resolution daily near-surface air temperature for 2006

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2006 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185 <br> Change log: All 2006 files updated 5/9/23
DOI: 10.25380/iastate.19719176.v3
title: Global 1 km resolution daily near-surface air temperature for 2006

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2006 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185 <br> Change log: All 2006 files updated 5/9/23
DOI: 10.25380/iastate.19719176.v2
title: Global 1 km resolution daily near-surface air temperature for 2006

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2006 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185
DOI: 10.34740/kaggle/dsv/5645754
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.5066/p91ohxde
title: 12-Digit Hydrologic Unit Outlet Locations for Selected Watersheds Draining to the Gulf of Mexico in the South-Central and Southeastern United States, Derived from the USGS Watershed Boundary Dataset (dated August 13, 2020) and NHDPlus version 2.1

description: This USGS data release includes a shapefile of outlet point locations for 12-digit hydrologic units (HU12s) from the Watershed Boundary Dataset (WBD), dated August 13, 2020 (U.S. Geological Survey and others, 2020), draining to the Gulf of Mexico in the south-central and southeastern United States. The outlet locations were derived by combining the WBD boundaries with flow direction grids from the National Hydrography Dataset Plus (NHDPlus) v 2.1 (U.S. Environmental Protection Agency and U.S. Geological Survey, 2012). Most of the outlet locations were determined by finding the cell with highest flow accumulation from the NHDPlus flow-direction grids within each WBD HU12 boundary. Some locations were found to be inaccurate upon inspection, and other sources were used for the locations as described in the attribute description and process description sections of the metadata document associated with this data release. A shapefile of the HU12 boundaries from the WBD (U.S. Geological Survey and others, 2020) for the study area also is included in this data release for comparison to outlet locations. References listed as Type: Citation under Related External Resources.
DOI: 10.6084/m9.figshare.22310383
title: Representative sequences

description: Representative sequences for the ASVs from soil and freshwater habitats with bacterial pH preference information.
DOI: 10.6084/m9.figshare.22310383.v1
title: Representative sequences

description: Representative sequences for the ASVs from soil and freshwater habitats with bacterial pH preference information.
DOI: 10.6084/m9.figshare.21968660
title: Data for `Forecast U.S. Covid-19 Numbers by Open SIR Model with Testing'

description: All mfiles starting with 'Run_*' can be run on Matlab without any pre-loaded variables or data. <br> The three files in the root directory consist of the only input data to all program mfiles. Of which, SICM.m is the mathematical model, Input_Matched_InitialsAndParameters.mat contains initial conditions and parameter values for each day from day 50 to day 590, 30 best-fitted values for each day, ranked from the best to worst fit. The third csv file is the US cases and deaths data from CDC. <br> The order in which to generate the output data files is to run all Run_* mfiles in folders (1) Build_Real_Data (2) Build_Solution_Book (3) Build_Forecast_Book (4) Plot_Output <br> When you reach the Plot_Output folder, you have build all data files for plotting and annimation. <br> Run "Run_Plot_Output" will generate the first batch of output plots. <br> Note: It requires zero knowledge to verify the result. All one needs is the model SICM.m and the initial values and parameters provided to explore the dynamics of the model, and to analyze the fit of the model to the data, with the parameter and initial values provided from the data file `Input_Matched_InitialsAndParameters.mat'. All the remaining folders contain Matlab programs to present the fit and the forecast presented in the manuscript `Forecast U.S. Covid-19 Numbers by Open SIR Model with Testing'. <br>
DOI: 10.6084/m9.figshare.21968660.v2
title: Data for `Forecast U.S. Covid-19 Numbers by Open SIR Model with Testing'

description: All mfiles starting with 'Run_*' can be run on Matlab without any pre-loaded variables or data. <br> The three files in the root directory consist of the only input data to all program mfiles. Of which, SICM.m is the mathematical model, Input_Matched_InitialsAndParameters.mat contains initial conditions and parameter values for each day from day 50 to day 590, 30 best-fitted values for each day, ranked from the best to worst fit. The third csv file is the US cases and deaths data from CDC. <br> The order in which to generate the output data files is to run all Run_* mfiles in folders (1) Build_Real_Data (2) Build_Solution_Book (3) Build_Forecast_Book (4) Plot_Output <br> When you reach the Plot_Output folder, you have build all data files for plotting and annimation. <br> Run "Run_Plot_Output" will generate the first batch of output plots. <br> Note: It requires zero knowledge to verify the result. All one needs is the model SICM.m and the initial values and parameters provided to explore the dynamics of the model, and to analyze the fit of the model to the data, with the parameter and initial values provided from the data file `Input_Matched_InitialsAndParameters.mat'. All the remaining folders contain Matlab programs to present the fit and the forecast presented in the manuscript `Forecast U.S. Covid-19 Numbers by Open SIR Model with Testing'. <br>
DOI: 10.6084/m9.figshare.21890712.v2
title: Data and R Code from the Tassie forest degradation study

description: Data and R code from the forest degradation study across Tasmania, Australia. Data includes soil properties as well as microbial community data.
DOI: 10.6084/m9.figshare.22588963.v2
title: Prediction of bacterial pH preferences using machine learning

description: This code includes hyperparameter optimization and evaluation of a boosted regression model to predict bacterial pH preferences using information on the presence/absence of functional genes in bacterial genomes. For a more detailed description of the approach see https://github.com/fiererlab/ph_preference/blob/main/Whole_FeatureSelection_Final.ipynb.
DOI: 10.7303/syn50697536
title: indicators.csv
DOI: 10.5281/zenodo.7909331
title: Anxa1-iCre and Aldh1a1-iCre Raw Image Source Data for Azcorra, Gaertner et al

description: Raw, unprocessed microscopy image data in multichannel tif format for IF images from Anxa1-iCre and Aldh1a1-iCre validation, localization, and projection mapping experiments. Image files are names according to the figure and panel which they apply to.
DOI: 10.5281/zenodo.7909330
title: Anxa1-iCre and Aldh1a1-iCre Raw Image Source Data for Azcorra, Gaertner et al

description: Raw, unprocessed microscopy image data in multichannel tif format for IF images from Anxa1-iCre and Aldh1a1-iCre validation, localization, and projection mapping experiments. Image files are names according to the figure and panel which they apply to.
DOI: 10.5281/zenodo.4247872
title: Protocol and data analysis scripts for high-throughput phenotyping of specific root respiration

description: This repository contains a PDF written protocol with figures describing a method for measuring root respiration using an infrared gas analyzer (LI-850) and the R script necessary for processing the text files to derive CO<sub>2</sub> flux. Just download the ZIP file, unzip locally, open either the PDF or the .R file. <strong>root_respiration_protocol.pdf </strong>- The illustrated protocol includes instruments and parts needed. <strong>root_respiration_script.R - </strong>A script for processing the included 15 <em>.txt </em>files as examples from a LI-850 Gas Analyzer and extract absolute CO<sub>2</sub> flux with example code for how to incorporate root mass or length to derive specific root respiration. It is intended to run directly in RStudio and will automatically set the working directory in that case. <strong>Please cite this repository if the protocol or R script is used in your work. </strong> Guo, H., Ayalew, H., Seethepalli, A., Dhakal, K., Griffiths, M., Ma, X., York, L. M. (2020). Protocol and data analysis scripts for high-throughput phenotyping of specific root respiration [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4247873 <strong>The first manuscript using and describing this method is available:</strong> Guo, H., Ayalew, H., Seethepalli, A., Dhakal, K., Griffiths, M., Ma, X., York, L. M. (2021). Functional phenomics and genetics of the root economics space in winter wheat using high-throughput phenotyping of respiration and architecture. <em>New Phytologist. DOI: 10.1111/nph.17329</em>
DOI: 10.6084/m9.figshare.22310341.v1
title: Bacterial ASV-pH preference-functional gene associations

description: Presence/absence of 332 gene types significantly associated to bacterial pH preference in representative genomes of bacterial taxa from soil and freshwater habitats.
DOI: 10.6084/m9.figshare.22773845
title: Table2_KDs_FAM122A_Compiled.xlsx

description: Data for activity and binding
DOI: 10.6084/m9.figshare.22773845.v1
title: Table2_KDs_FAM122A_Compiled.xlsx

description: Data for activity and binding
DOI: 10.5517/ccdc.csd.cc2dfmwc
title: CCDC 2217643: Experimental Crystal Structure Determination

description: Related Article: Min Zhang, Min-Yu Wang, Yue-Ming Yu, Su-Su Meng, Cui-Wei Yan, Zhi-Yong Wu, Yan-Tuan Li|2023|CrystEngComm|25|2789|doi:10.1039/D3CE00082F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfr2p
title: CCDC 2217742: Experimental Crystal Structure Determination

description: Related Article: Hyoju Choi, Ruibin Wang, Suyeon Kim, Dongwook Kim, Mu-Hyun Baik, Sehoon Park|2023|Cat.Sci.Tech.|13|2735|doi:10.1039/D3CY00347G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dfr4r
title: CCDC 2217744: Experimental Crystal Structure Determination

description: Related Article: Hyoju Choi, Ruibin Wang, Suyeon Kim, Dongwook Kim, Mu-Hyun Baik, Sehoon Park|2023|Cat.Sci.Tech.|13|2735|doi:10.1039/D3CY00347G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dg5ln
title: CCDC 2218161: Experimental Crystal Structure Determination

description: Related Article: Josue Valdes-García, Julio Zamora-Moreno, Cristian Pinzón-Vanegas, Alejandro O. Viviano-Posadas, Diego Martínez-Otero, Joaquín Barroso-Flores, Benjamin Ortiz-Lopez, Vianney Francisco Ortiz-Navarrete, Alejandro Dorazco-González|2023|Inorg.Chem.|62|6629|doi:10.1021/acs.inorgchem.2c04558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dg5mp
title: CCDC 2218162: Experimental Crystal Structure Determination

description: Related Article: Josue Valdes-García, Julio Zamora-Moreno, Cristian Pinzón-Vanegas, Alejandro O. Viviano-Posadas, Diego Martínez-Otero, Joaquín Barroso-Flores, Benjamin Ortiz-Lopez, Vianney Francisco Ortiz-Navarrete, Alejandro Dorazco-González|2023|Inorg.Chem.|62|6629|doi:10.1021/acs.inorgchem.2c04558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dg5nq
title: CCDC 2218163: Experimental Crystal Structure Determination

description: Related Article: Josue Valdes-García, Julio Zamora-Moreno, Cristian Pinzón-Vanegas, Alejandro O. Viviano-Posadas, Diego Martínez-Otero, Joaquín Barroso-Flores, Benjamin Ortiz-Lopez, Vianney Francisco Ortiz-Navarrete, Alejandro Dorazco-González|2023|Inorg.Chem.|62|6629|doi:10.1021/acs.inorgchem.2c04558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dghlz
title: CCDC 2218471: Experimental Crystal Structure Determination

description: Related Article: Dinesh Mullangi, Hayden A. Evans, Taner Yildirim, Yuxiang Wang, Zeyu Deng, Zhaoqiang Zhang, Thuc T. Mai, Fengxia Wei, John Wang, Angela R. Hight Walker, Craig M. Brown, Dan Zhao, Pieremanuele Canepa, Anthony K. Cheetham|2023|J.Am.Chem.Soc.|145|9850|doi:10.1021/jacs.3c02100
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dghm0
title: CCDC 2218472: Experimental Crystal Structure Determination

description: Related Article: Dinesh Mullangi, Hayden A. Evans, Taner Yildirim, Yuxiang Wang, Zeyu Deng, Zhaoqiang Zhang, Thuc T. Mai, Fengxia Wei, John Wang, Angela R. Hight Walker, Craig M. Brown, Dan Zhao, Pieremanuele Canepa, Anthony K. Cheetham|2023|J.Am.Chem.Soc.|145|9850|doi:10.1021/jacs.3c02100
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dghn1
title: CCDC 2218473: Experimental Crystal Structure Determination

description: Related Article: Dinesh Mullangi, Hayden A. Evans, Taner Yildirim, Yuxiang Wang, Zeyu Deng, Zhaoqiang Zhang, Thuc T. Mai, Fengxia Wei, John Wang, Angela R. Hight Walker, Craig M. Brown, Dan Zhao, Pieremanuele Canepa, Anthony K. Cheetham|2023|J.Am.Chem.Soc.|145|9850|doi:10.1021/jacs.3c02100
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.22789826
title: Tbx20 raw data

description: These raw data belong to an original article
DOI: 10.6084/m9.figshare.22789826.v1
title: Tbx20 raw data

description: These raw data belong to an original article
DOI: 10.5281/zenodo.7915634
title: FUN-LDA scores for human genome assembly GRCh37

description: FUN-LDA is based on a Latent Dirichlet Allocation (LDA) model for predicting functional effects of non-coding genetic variants in a cell type and tissue-specific way by integrating diverse epigenetic annotations for specific cell types and tissues from large-scale genomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome for 127 tissues and cell types in ENCODE and Roadmap Epigenomics. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Format</strong> The FUN-LDA scores are stored in the UCSC Genome Browser bigWig Track Format. To extract FUN-LDA scores, the bigWigAverageOverBed utility is required. It can be downloaded from the Genome Browser website at https://hgdownload.cse.ucsc.edu/admin/exe. User should prepare a UCSC Genome Browser bed file, a tab-separated four-column file. The first column is the chromosome; the second is the zero-based coordinate of the position of interest; the third is that zero-based coordinate plus one; and the fourth is a unique identifier for the position. The command below is an example of extracting FUN-LDA scores in tissue E007 for the positions defined in a bed file, "input.bed". <pre><code class="language-bash">bigWigAverageOverBed E007.valley9.c89.bigwig input.bed output.tab</code></pre> It produces a six-column file, output.tab. The first column includes the unique position id defined in the input bed file. The last column, average over the covered bases, is the score for this position. <strong>Reference</strong> Daniel Backenroth, Zihuai He, Krzysztof Kiryluk, Valentina Boeva, Lynn Pethukova, Ekta Khurana, Angela Christiano, Joseph Buxbaum, Iuliana Ionita-Laza. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: Methods and applications. American Journal of Human Genetics, 2018.<br>