cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12751427.v1
title: Gene matrix for cervical adenocarcinoma

description: Gene matrix for cervical adenocarcinoma
DOI: 10.6084/m9.figshare.12751430.v1
title: Clinical information of cervical cancer patients from TCGA

description: Clinical information of cervical cancer patients from TCGA
DOI: 10.6084/m9.figshare.12751439.v1
title: Raw data of survival analysis

description: Raw data of survival analysis
DOI: 10.6084/m9.figshare.12751415
title: Gene matrix for all types of cervical cancer.txt

description: Gene matrix for all types of cervical cancer.txt
DOI: 10.6084/m9.figshare.12751391
title: The Demo of Feature Matrix used in FeAture Explorer (FAE)

description: We used PyRadiomics to extract the shape-based, intensity-based and texture-based features from PROSTATEx data set. This data set included feature value and the corresponding labels. We used one to denote the clinical significant prostate cancer (CsPCa) and zero to denote the non-CsPCa. This data set could be used directly in the open-source software, FeAture Explorer (FAE).
DOI: 10.6084/m9.figshare.12751433.v1
title: Raw data of in-house tissue microarray

description: Raw data of in-house tissue microarray
DOI: 10.6084/m9.figshare.12751326
title: Additional file 2 of Deciphering the immunogenic potential of wheat flour: a reference map of the salt-soluble proteome from the U.S. wheat Butte 86

description: Additional file 2. MS/MS data for individual protein spots shown in Figure 2. Scaffold data for all proteins for which the number of unique peptides was equal to or greater than half the number assigned to the predominant protein are shown.
DOI: 10.6084/m9.figshare.12751323
title: Additional file 1 of Deciphering the immunogenic potential of wheat flour: a reference map of the salt-soluble proteome from the U.S. wheat Butte 86

description: Additional file 1. Proteins identified by MS/MS in 2-DE protein spots from the KCl-soluble fraction of Butte 86 flour. The positions of the spots are shown in Figure 2. For each spot, the predominant protein and all proteins that contained at least half the number of unique peptides as the predominant protein are highlighted in yellow. Proteins that contained less than half the number of unique peptides found in the predominant protein are highlighted in blue. Gluten proteins that contained less than half the number of unique peptides found in the predominant protein are highlighted in green.
DOI: 10.6084/m9.figshare.12751326.v1
title: Additional file 2 of Deciphering the immunogenic potential of wheat flour: a reference map of the salt-soluble proteome from the U.S. wheat Butte 86

description: Additional file 2. MS/MS data for individual protein spots shown in Figure 2. Scaffold data for all proteins for which the number of unique peptides was equal to or greater than half the number assigned to the predominant protein are shown.
DOI: 10.6084/m9.figshare.12751323.v1
title: Additional file 1 of Deciphering the immunogenic potential of wheat flour: a reference map of the salt-soluble proteome from the U.S. wheat Butte 86

description: Additional file 1. Proteins identified by MS/MS in 2-DE protein spots from the KCl-soluble fraction of Butte 86 flour. The positions of the spots are shown in Figure 2. For each spot, the predominant protein and all proteins that contained at least half the number of unique peptides as the predominant protein are highlighted in yellow. Proteins that contained less than half the number of unique peptides found in the predominant protein are highlighted in blue. Gluten proteins that contained less than half the number of unique peptides found in the predominant protein are highlighted in green.
DOI: 10.6084/m9.figshare.12751275
title: Additional file 4 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 4. Query of relationships between Probα and Txα codes for Tajikistan’s PCSDR.
DOI: 10.6084/m9.figshare.12751269
title: Additional file 2 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 2. Tajikistan PCSDR table.
DOI: 10.6084/m9.figshare.12751266
title: Additional file 1 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 1. PCSDR Database for Tajikistan. The database is comprised of the PCSDR table, the PCSDR data entry form, the WHO PHC-IMPACT classification system, citations and contextual tables.
DOI: 10.6084/m9.figshare.12751272
title: Additional file 3 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 3. Query of relationships between Txα and Probα codes for Tajikistan’s PCSDR.
DOI: 10.6084/m9.figshare.12751272.v1
title: Additional file 3 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 3. Query of relationships between Txα and Probα codes for Tajikistan’s PCSDR.
DOI: 10.6084/m9.figshare.12751266.v1
title: Additional file 1 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 1. PCSDR Database for Tajikistan. The database is comprised of the PCSDR table, the PCSDR data entry form, the WHO PHC-IMPACT classification system, citations and contextual tables.
DOI: 10.6084/m9.figshare.12751269.v1
title: Additional file 2 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 2. Tajikistan PCSDR table.
DOI: 10.6084/m9.figshare.12751275.v1
title: Additional file 4 of A problem-oriented systems approach to primary care system development: development and initial testing of the problem-oriented primary care system development record

description: Additional file 4. Query of relationships between Probα and Txα codes for Tajikistan’s PCSDR.
DOI: 10.6084/m9.figshare.12751205
title: SupplementaryTables_transcriptional_response_to_cold_in_Paniceae.xlsx

description: Supplementary Table S1-S4
DOI: 10.6084/m9.figshare.12751205.v1
title: SupplementaryTables_transcriptional_response_to_cold_in_Paniceae.xlsx

description: Supplementary Table S1-S4
DOI: 10.6084/m9.figshare.12751163
title: Source Data.xlsx

description: Raw data and uncropped western blots from manuscript " Caspase-8 mediates inflammation and disease in rodent malaria"Larissa M. N. Pereira<sup>*</sup>, Patrícia A. Assis<sup>*</sup>,<b> </b>Natalia M. de Araújo, Danielle F. Durso, Caroline Junqueira, Marco Antônio Ataíde, Dhelio B. Pereira, Egil Lien, Katherine A. Fitzgerald, Dario S. Zamboni, Douglas T. Golenbock and Ricardo T. Gazzinelli<br>
DOI: 10.6084/m9.figshare.12751169
title: mindmapsSR_16_ANINN222_0.h5

description: MRI super-resolution weights<br>
DOI: 10.6084/m9.figshare.12751169.v1
title: mindmapsSR_16_ANINN222_0.h5

description: MRI super-resolution weights<br>
DOI: 10.6084/m9.figshare.12751106
title: Data

description: This file contains the simulation result for the time and the number of clusterHead ratios
DOI: 10.6084/m9.figshare.12751097
title: Anoka County COVID by race

description: An exploratory analysis of COVID-19 cases in Anoka County through 7-30-2020
DOI: 10.6084/m9.figshare.12751106.v1
title: Data

description: This file contains the simulation result for the time and the number of clusterHead ratios
DOI: 10.6084/m9.figshare.12751079
title: forClassification.zip

description: All datasets classified with the ccAF classifier.
DOI: 10.6084/m9.figshare.12751094
title: Data base MezaZertucheMorehart.xlsx

description: We study the variation between different past groups across time and space in central Mexico using the analysis of non-metric cranial. We focus on a total of 280 adult skulls from archaeological sites that range from the Preclassic period to the early Post- classic period, including the Epiclassic.Teotihuacan is represented by the neighbors of La Ventilla and San Sebasti an Xolalpan. From the Epiclassic we study: Xaltocan, Toluca, Los Mogotes and Xico. Finally early Postclassic Xico.
DOI: 10.6084/m9.figshare.12751097.v1
title: Anoka County COVID by race

description: An exploratory analysis of COVID-19 cases in Anoka County through 7-30-2020
DOI: 10.6084/m9.figshare.12751082
title: U5_hNSC.zip

description: Single cell RNA-seq dataset used to construct the ccAF classifier.
DOI: 10.6084/m9.figshare.12751076
title: ssGSEA.GBM.classification.zip

description: Data for each glioma dataset for subtype classification using Wang et al., 2017 method.
DOI: 10.6084/m9.figshare.12751079.v1
title: forClassification.zip

description: All datasets classified with the ccAF classifier.
DOI: 10.6084/m9.figshare.12751070
title: Whitfield.zip

description: Used to validate the S & M phases of the ccAF classifier.
DOI: 10.6084/m9.figshare.12751073
title: geneConversions.zip

description: Data used to convert data from one gene ID to another.
DOI: 10.6084/m9.figshare.12751082.v1
title: U5_hNSC.zip

description: Single cell RNA-seq dataset used to construct the ccAF classifier.
DOI: 10.6084/m9.figshare.12751055
title: cellcycle_int_integrated.loom

description: Output of U5-hNSC cell cycle data from Seurat for import into Python/.
DOI: 10.6084/m9.figshare.12750968
title: DATASET from “Analyzing the effect of process parameters on the shape of 3D profiles” by B.M.Colosimo, M.Pacella, JQT, 43(3), 2011

description: The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures. The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.<br> All the details about the dataset can be found in: <pre> </pre><pre>Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.</pre><pre>DOI: 10.1080/00224065.2011.11917856</pre><pre> </pre><pre>Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.</pre><pre>DOI: 10.1080/16843703.2016.1226710</pre>
DOI: 10.6084/m9.figshare.12750968.v1
title: DATASET from “Analyzing the effect of process parameters on the shape of 3D profiles” by B.M.Colosimo, M.Pacella, JQT, 2011

description: The dataset refers to the measurement of axes of Ti-6Al-4V cylindrical surfaces obtained by lathe turning. The machined surfaces were measured using a Coordinate Measuring Machine (CMM) and the axis of each cylinder was derived from the CMM measures. The dataset consists of a MAT-file including the CMM measurements and a Matlab function “LoadData.m” to extract and convert the data into Cartesian coordinates.<br> All the details about the dataset can be found in: <pre> </pre><pre>Colosimo, B.M., Pacella, M. Analyzing the effect of process parameters on the shape of 3D profiles (2011) Journal of Quality Technology, 43 (3), pp. 169-195.</pre><pre>DOI: 10.1080/00224065.2011.11917856</pre><pre> </pre><pre>Pacella, M., Colosimo, B.M. Multilinear principal component analysis for statistical modeling of cylindrical surfaces: a case study (2018) Quality Technology and Quantitative Management, 15 (4), pp. 507-525.</pre><pre>DOI: 10.1080/16843703.2016.1226710</pre>
DOI: 10.6084/m9.figshare.12750827
title: Comtrade data on PPE

description: Comtrade data on top importing and exporting countries for face masks, protective eyewear, and gloves
DOI: 10.6084/m9.figshare.12750827.v1
title: Comtrade data on PPE

description: Comtrade data on top importing and exporting countries for face masks, protective eyewear, and gloves
DOI: 10.6084/m9.figshare.12750632
title: Probiotics effect on length of hospitalization in mild acute pancreatitis: a cohort study from China

description: acute pancreatitis
DOI: 10.6084/m9.figshare.12750632.v1
title: Probiotics effect on length of hospitalization in mild acute pancreatitis: a cohort study from China

description: acute pancreatitis
DOI: 10.6084/m9.figshare.12750598
title: HeLanetss_results.xls from A unifying modelling formalism for the integration of stoichiometric and kinetic models

description: Current research on systems and synthetic biology relies heavily on mathematical models of the systems under study. The usefulness of such models depends on the quantity and quality of biological data, and on the availability of appropriate modelling formalisms that can gather and accommodate such data so that they can be exploited properly. Given our incomplete knowledge of biological systems and the fact that they consist of many subsystems, biological data are usually uncertain and heterogeneous. These facts hinder the use of mathematical models and computational methods. In the scope of dynamic biological systems, e.g. metabolic networks, this difficulty can be overcome by the novel modelling formalism of flexible nets (FNs). We show that an FN can combine, in a natural way, a stoichiometric model and a kinetic model. Moreover, the resulting net admits nonlinear dynamics and can be analysed in both transient and steady states.
DOI: 10.6084/m9.figshare.12750598.v1
title: HeLanetss_results.xls from A unifying modelling formalism for the integration of stoichiometric and kinetic models

description: Current research on systems and synthetic biology relies heavily on mathematical models of the systems under study. The usefulness of such models depends on the quantity and quality of biological data, and on the availability of appropriate modelling formalisms that can gather and accommodate such data so that they can be exploited properly. Given our incomplete knowledge of biological systems and the fact that they consist of many subsystems, biological data are usually uncertain and heterogeneous. These facts hinder the use of mathematical models and computational methods. In the scope of dynamic biological systems, e.g. metabolic networks, this difficulty can be overcome by the novel modelling formalism of flexible nets (FNs). We show that an FN can combine, in a natural way, a stoichiometric model and a kinetic model. Moreover, the resulting net admits nonlinear dynamics and can be analysed in both transient and steady states.
DOI: 10.6084/m9.figshare.12750548
title: Additional file 3 of Molecular characterization of B. anthracis isolates from the anthrax outbreak among cattle in Karnataka, India

description: Additional file 3. PCR for the screening of Bacillus anthracis specific prophages and loci in the 12 B. anthracis DFR.BHE strains 1–12. A. PCR with B. anthracis specific prophage genes, (a).lambda01, (b). lambda02, (c). lambda03 and (d). lambda04 and B. PCR with B. anthracis specific loci (a) dhp 61.183 (loci A), (b). dhp 77.002 (loci C), (c). dhp 73.019 (loci D), and (d). dhp 73.017 (loci E). Lane 1–12, B. anthracis DFRL.BHE strains 1–12 along with positive (Lane P: B. anthracis BA10) and negative (Lane N: B. cereus ATCC 14579) controls. Lane M: 100 bp molecular marker.
DOI: 10.6084/m9.figshare.12750548.v1
title: Additional file 3 of Molecular characterization of B. anthracis isolates from the anthrax outbreak among cattle in Karnataka, India

description: Additional file 3. PCR for the screening of Bacillus anthracis specific prophages and loci in the 12 B. anthracis DFR.BHE strains 1–12. A. PCR with B. anthracis specific prophage genes, (a).lambda01, (b). lambda02, (c). lambda03 and (d). lambda04 and B. PCR with B. anthracis specific loci (a) dhp 61.183 (loci A), (b). dhp 77.002 (loci C), (c). dhp 73.019 (loci D), and (d). dhp 73.017 (loci E). Lane 1–12, B. anthracis DFRL.BHE strains 1–12 along with positive (Lane P: B. anthracis BA10) and negative (Lane N: B. cereus ATCC 14579) controls. Lane M: 100 bp molecular marker.
DOI: 10.6084/m9.figshare.12750459
title: Additional file 5 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 5: Supplement Table 4. Master spreadsheet listing showing sample and donor covariates for each of the 151 KTB barcodes including batch assignment (F or P), Active/Inactive phenotype assignment and score, donor features (including age, BMI, 5 year Gail risk scores), percentages of adipocyte/stromal/epithelial cell nuclei, TDLU counts, mean adipocyte areas, immune modules, and all gene signatures and single genes values used to calculate the Pearson correlations shown in Fig. 5.
DOI: 10.6084/m9.figshare.12750456
title: Additional file 4 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 4: Supplement Table 3. Rank ordered GSEA ( www.gsea-msigdb.org/gsea ) analysis showing 186 gene sets upregulated (from total set of 18,408 gene sets) in the batch-integrated Active vs. Inactive transcriptome samples, at FDR ≤ 10%. Also shown are their individual nominal p-values, FDR q-values, their gene set size, and the 21 (11.2%) that are specifically involved in adipose-associated pathways.
DOI: 10.6084/m9.figshare.12750459.v1
title: Additional file 5 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 5: Supplement Table 4. Master spreadsheet listing showing sample and donor covariates for each of the 151 KTB barcodes including batch assignment (F or P), Active/Inactive phenotype assignment and score, donor features (including age, BMI, 5 year Gail risk scores), percentages of adipocyte/stromal/epithelial cell nuclei, TDLU counts, mean adipocyte areas, immune modules, and all gene signatures and single genes values used to calculate the Pearson correlations shown in Fig. 5.
DOI: 10.6084/m9.figshare.12750450
title: Additional file 2 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 2: Supplement Table 1. Lists of genes included within each gene expression signature analyzed to determine Active/Inactive, cAMP lipolysis, adipocyte browning, SASP, AST, IGF1, IGF1R, IFN, TGFβ, and CSR activity scores. Also indicated is their reference sources and method of score calculation.
DOI: 10.6084/m9.figshare.12750453
title: Additional file 3 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 3: Supplement Table 2. RNAseq cancer gene hotspot mutations detected in 151 KTB samples. Sheet 1 identifies the &gt; 5100 unthresholded cancer mutation hotspot calls from the KTB RNAseq analysis occurring in the combined set of MSK-IMPACT curated cancer hotspot clinical targets [10] and the experimentally determined RNAseq identified set of expressed cancer gene hotspot mutations within &gt; 6700 normal GTEx human tissue samples as recently described [11]. Sheet 2 (“normal_breast_rna.getz_list.092”) lists only those &gt; 1760 cancer gene hotspot mutations from sheet 1 meeting the threshold mutation likelihood score &gt; 5, while sheets 3 and 4 list the thresholded hotspot mutations according to F and P sample batches with each sample phenotyped as either Active or Inactive. Sheet 5 is a sample key mapping all KTB identification numbers, barcodes, and UUID numbers pertaining to the RNAseq results and cancer gene hotspot mutation calls. Of note, Fig. 2 panels (“Breast sample scatterplots and TumorMaps of RNA expressed cancer hotspot mutations”) include plots derived from the curated and thresholded hotspot mutations as listed in sheet 2.
DOI: 10.6084/m9.figshare.12750450.v1
title: Additional file 2 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 2: Supplement Table 1. Lists of genes included within each gene expression signature analyzed to determine Active/Inactive, cAMP lipolysis, adipocyte browning, SASP, AST, IGF1, IGF1R, IFN, TGFβ, and CSR activity scores. Also indicated is their reference sources and method of score calculation.
DOI: 10.6084/m9.figshare.12750456.v1
title: Additional file 4 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 4: Supplement Table 3. Rank ordered GSEA ( www.gsea-msigdb.org/gsea ) analysis showing 186 gene sets upregulated (from total set of 18,408 gene sets) in the batch-integrated Active vs. Inactive transcriptome samples, at FDR ≤ 10%. Also shown are their individual nominal p-values, FDR q-values, their gene set size, and the 21 (11.2%) that are specifically involved in adipose-associated pathways.
DOI: 10.6084/m9.figshare.12750453.v1
title: Additional file 3 of A risk-associated Active transcriptome phenotype expressed by histologically normal human breast tissue and linked to a pro-tumorigenic adipocyte population

description: Additional file 3: Supplement Table 2. RNAseq cancer gene hotspot mutations detected in 151 KTB samples. Sheet 1 identifies the &gt; 5100 unthresholded cancer mutation hotspot calls from the KTB RNAseq analysis occurring in the combined set of MSK-IMPACT curated cancer hotspot clinical targets [10] and the experimentally determined RNAseq identified set of expressed cancer gene hotspot mutations within &gt; 6700 normal GTEx human tissue samples as recently described [11]. Sheet 2 (“normal_breast_rna.getz_list.092”) lists only those &gt; 1760 cancer gene hotspot mutations from sheet 1 meeting the threshold mutation likelihood score &gt; 5, while sheets 3 and 4 list the thresholded hotspot mutations according to F and P sample batches with each sample phenotyped as either Active or Inactive. Sheet 5 is a sample key mapping all KTB identification numbers, barcodes, and UUID numbers pertaining to the RNAseq results and cancer gene hotspot mutation calls. Of note, Fig. 2 panels (“Breast sample scatterplots and TumorMaps of RNA expressed cancer hotspot mutations”) include plots derived from the curated and thresholded hotspot mutations as listed in sheet 2.
DOI: 10.6084/m9.figshare.12750413
title: Additional file 1 of Circulating unmethylated CHTOP and INS DNA fragments provide evidence of possible islet cell death in youth with obesity and diabetes

description: Additional file 1. Supplemental Table S1: Human Islet Donor Characteristics. Supplemental Table S2: Human tissues/cell lines used as controls for Illumina 450-Array. Supplemental Table S3: Primers for sequencing. Supplemental Table S4: Table of all differentially methylated CpGs. Supplemental Figure S1: Experimental Workflow. Supplemental Figure S2: Immunofluorescence staining of flow-sorted human β cells.