cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14516338.v1
title: Additional file 11 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 11: Table S10. Subtype classification profiles of PDXs.
DOI: 10.6084/m9.figshare.14516341
title: Additional file 12 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 12: Table S11. General classification profiles of GEMMs.
DOI: 10.6084/m9.figshare.14516344
title: Additional file 13 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 13: Table S12. Subtype classification profiles of GEMMs.
DOI: 10.6084/m9.figshare.14516341.v1
title: Additional file 12 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 12: Table S11. General classification profiles of GEMMs.
DOI: 10.6084/m9.figshare.14516344.v1
title: Additional file 13 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 13: Table S12. Subtype classification profiles of GEMMs.
DOI: 10.6084/m9.figshare.14516347
title: Additional file 14 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 14: Table S13. General classification profiles of tumoroids.
DOI: 10.6084/m9.figshare.14516347.v1
title: Additional file 14 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 14: Table S13. General classification profiles of tumoroids.
DOI: 10.6084/m9.figshare.14516350
title: Additional file 15 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 15: Table S14. Subtype classification profiles of tumoroids.
DOI: 10.6084/m9.figshare.14516350.v1
title: Additional file 15 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 15: Table S14. Subtype classification profiles of tumoroids.
DOI: 10.6084/m9.figshare.14516353.v1
title: Additional file 1 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 1: Table S1. Specific parameters used for the training of all CCN classifiers.
DOI: 10.6084/m9.figshare.14516353
title: Additional file 1 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 1: Table S1. Specific parameters used for the training of all CCN classifiers.
DOI: 10.6084/m9.figshare.14516356.v1
title: Additional file 2 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 2: Table S2. Gene-pairs selected for final training of CCN general, subtype classifiers and single-cell classifier.
DOI: 10.6084/m9.figshare.14516359
title: Additional file 3 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 3: Table S3. Decision thresholds and the corresponding average precision, recall and F1 for the general classifier and subtype classifiers.
DOI: 10.6084/m9.figshare.14516359.v1
title: Additional file 3 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 3: Table S3. Decision thresholds and the corresponding average precision, recall and F1 for the general classifier and subtype classifiers.
DOI: 10.6084/m9.figshare.14516362
title: Additional file 4 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 4: Table S4. Training parameters used to train 16 cross-species classifiers.
DOI: 10.6084/m9.figshare.14516338
title: Additional file 11 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 11: Table S10. Subtype classification profiles of PDXs.
DOI: 10.6084/m9.figshare.14516362.v1
title: Additional file 4 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 4: Table S4. Training parameters used to train 16 cross-species classifiers.
DOI: 10.6084/m9.figshare.14516365
title: Additional file 5 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 5: Table S5. Accessions of tumor microarray data used in validation.
DOI: 10.6084/m9.figshare.14516371.v1
title: Additional file 7 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 7: Table S6. General classification profiles of CCLs.
DOI: 10.6084/m9.figshare.14516371
title: Additional file 7 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 7: Table S6. General classification profiles of CCLs.
DOI: 10.6084/m9.figshare.14516377
title: Additional file 9 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 9: Table S8. LUSC CCLs subtype comparison between CCN, Yu et al, Salvadores et al.
DOI: 10.6084/m9.figshare.14516356
title: Additional file 2 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 2: Table S2. Gene-pairs selected for final training of CCN general, subtype classifiers and single-cell classifier.
DOI: 10.6084/m9.figshare.14516374
title: Additional file 8 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 8: Table S7. Subtype classification profiles of CCLs.
DOI: 10.6084/m9.figshare.14516374.v1
title: Additional file 8 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 8: Table S7. Subtype classification profiles of CCLs.
DOI: 10.6084/m9.figshare.14516365.v1
title: Additional file 5 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 5: Table S5. Accessions of tumor microarray data used in validation.
DOI: 10.6084/m9.figshare.14516513.v1
title: Additional file 2 of LCM and RNA-seq analyses revealed roles of cell cycle and translational regulation and homoeolog expression bias in cotton fiber cell initiation

description: Additional file 2: Supplemental Table 1. RNA-seq statistics of LCM samples. Supplemental Table 2. Upregulated and down-regulated genes in fiber cell initials at 0 DPA. Supplemental Table 3. Upregulated and down-regulated genes in early fiber cells at 2 DPA. Supplemental Table 3a. Up-regulated and down-regulated genes in epidermal cells in 0 and 2 DPA. Supplemental Table 4. Upregulated genes in early fiber cells (0 and 2 DPA) specific to species. Supplemental Table 4a. Upregulated and down-regulated genes in fiber cells between GhTM-1 and Gb3–79 at 2 DPA. Supplemental Table 4b. Significant GO terms in upregulated and down-regulated genes between GhTM-1 and Gb3–79 in fiber cells at 2 DPA. Supplemental Table 5. List of A and D homoeologs with biased expression values. Supplemental Table 5a. Orthologs of G. hirstum and G. barbadense. Supplemental Table 6. List of putative ribosomal protein subunit and cell cycle genes with expression values in LCM samples. Supplemental Table 7. Expression values of putative TOR, RAPTOR, LST8, and EPB1 genes in LCM samples. Supplemental Table 8. Expression values of phytohormone and MYB transcription factor related genes in LCM samples.
DOI: 10.6084/m9.figshare.14516513
title: Additional file 2 of LCM and RNA-seq analyses revealed roles of cell cycle and translational regulation and homoeolog expression bias in cotton fiber cell initiation

description: Additional file 2: Supplemental Table 1. RNA-seq statistics of LCM samples. Supplemental Table 2. Upregulated and down-regulated genes in fiber cell initials at 0 DPA. Supplemental Table 3. Upregulated and down-regulated genes in early fiber cells at 2 DPA. Supplemental Table 3a. Up-regulated and down-regulated genes in epidermal cells in 0 and 2 DPA. Supplemental Table 4. Upregulated genes in early fiber cells (0 and 2 DPA) specific to species. Supplemental Table 4a. Upregulated and down-regulated genes in fiber cells between GhTM-1 and Gb3–79 at 2 DPA. Supplemental Table 4b. Significant GO terms in upregulated and down-regulated genes between GhTM-1 and Gb3–79 in fiber cells at 2 DPA. Supplemental Table 5. List of A and D homoeologs with biased expression values. Supplemental Table 5a. Orthologs of G. hirstum and G. barbadense. Supplemental Table 6. List of putative ribosomal protein subunit and cell cycle genes with expression values in LCM samples. Supplemental Table 7. Expression values of putative TOR, RAPTOR, LST8, and EPB1 genes in LCM samples. Supplemental Table 8. Expression values of phytohormone and MYB transcription factor related genes in LCM samples.
DOI: 10.6084/m9.figshare.14516524
title: Additional file 1 of nPhase: an accurate and contiguous phasing method for polyploids

description: Additional file 1. This file is an excel spreadsheet containing all of the supplementary tables. Table S1 - Saccharomyces cerevisiae strains used in our validation experiment. Table S2 - Results of running nPhase on our validation dataset using the optimal parameters identified. Table S3 - Performance metrics of running nPhase on that validation dataset.
DOI: 10.6084/m9.figshare.14516524.v1
title: Additional file 1 of nPhase: an accurate and contiguous phasing method for polyploids

description: Additional file 1. This file is an excel spreadsheet containing all of the supplementary tables. Table S1 - Saccharomyces cerevisiae strains used in our validation experiment. Table S2 - Results of running nPhase on our validation dataset using the optimal parameters identified. Table S3 - Performance metrics of running nPhase on that validation dataset.
DOI: 10.6084/m9.figshare.14516534
title: Additional file 1 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 1: The immune related gene list obtained from the ImmPort database.
DOI: 10.6084/m9.figshare.14516534.v1
title: Additional file 1 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 1: The immune related gene list obtained from the ImmPort database.
DOI: 10.6084/m9.figshare.14516537
title: Additional file 2 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 2: The differential expressed genes in different risk groups.
DOI: 10.6084/m9.figshare.14516537.v1
title: Additional file 2 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 2: The differential expressed genes in different risk groups.
DOI: 10.6084/m9.figshare.14516543
title: Additional file 4 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 4: Source code ( https://github.com/huojunyu/JTRM-D-21-00055 ).
DOI: 10.6084/m9.figshare.14516543.v1
title: Additional file 4 of Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

description: Additional file 4: Source code ( https://github.com/huojunyu/JTRM-D-21-00055 ).
DOI: 10.6084/m9.figshare.14516663.v1
title: Additional file 1 of Five-year microevolution of a multidrug-resistant Mycobacterium tuberculosis strain within a patient with inadequate compliance to treatment

description: Additional file 1: Supplementary Table 1. Within-host microevolution of eight time-serial isolates from a patient with multidrug-resistant tuberculosis and inadequate compliance to treatment
DOI: 10.6084/m9.figshare.14516663
title: Additional file 1 of Five-year microevolution of a multidrug-resistant Mycobacterium tuberculosis strain within a patient with inadequate compliance to treatment

description: Additional file 1: Supplementary Table 1. Within-host microevolution of eight time-serial isolates from a patient with multidrug-resistant tuberculosis and inadequate compliance to treatment
DOI: 10.6084/m9.figshare.14516666.v1
title: Additional file 2 of Five-year microevolution of a multidrug-resistant Mycobacterium tuberculosis strain within a patient with inadequate compliance to treatment

description: Additional file 2.
DOI: 10.6084/m9.figshare.14516666
title: Additional file 2 of Five-year microevolution of a multidrug-resistant Mycobacterium tuberculosis strain within a patient with inadequate compliance to treatment

description: Additional file 2.
DOI: 10.6084/m9.figshare.14516674.v1
title: Additional file 3 of Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

description: Additional file 3: Table S1. Differential_LncRNAs.
DOI: 10.6084/m9.figshare.14516677
title: Additional file 4 of Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

description: Additional file 4: Table S2. Differential_mRNAs.
DOI: 10.6084/m9.figshare.14516677.v1
title: Additional file 4 of Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

description: Additional file 4: Table S2. Differential_mRNAs.
DOI: 10.6084/m9.figshare.14516674
title: Additional file 3 of Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

description: Additional file 3: Table S1. Differential_LncRNAs.
DOI: 10.6084/m9.figshare.14516686.v1
title: Additional file 1 of Protective genes and pathways in Alzheimer’s disease: moving towards precision interventions

description: Additional file 1.
DOI: 10.6084/m9.figshare.14516686
title: Additional file 1 of Protective genes and pathways in Alzheimer’s disease: moving towards precision interventions

description: Additional file 1.
DOI: 10.6084/m9.figshare.14516692.v1
title: Additional file 2 of Adverse childhood experiences and psychological distress among higher education students in Southeast Nigeria: an institutional-based cross-sectional study

description: Additional file 2.
DOI: 10.6084/m9.figshare.14516692
title: Additional file 2 of Adverse childhood experiences and psychological distress among higher education students in Southeast Nigeria: an institutional-based cross-sectional study

description: Additional file 2.
DOI: 10.6084/m9.figshare.14516695
title: Additional file 3 of Adverse childhood experiences and psychological distress among higher education students in Southeast Nigeria: an institutional-based cross-sectional study

description: Additional file 3.
DOI: 10.6084/m9.figshare.14516695.v1
title: Additional file 3 of Adverse childhood experiences and psychological distress among higher education students in Southeast Nigeria: an institutional-based cross-sectional study

description: Additional file 3.
DOI: 10.6084/m9.figshare.14516190.v1
title: Supporting informaton.zip

description: 边疆支持文件 supporting information and 9 figures
DOI: 10.5281/zenodo.4729059
title: Interpretation of temporal and spatial trends of SARS-CoV-2 RNA in San Francisco Bay Area wastewater

description: *Authors contributed equally to the work ** Corresponding authors
DOI: 10.5281/zenodo.4729066
title: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores

description: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores
DOI: 10.5281/zenodo.4729062
title: Liquidus Phase Relations and Solid-Liquid Partitioning in the Fe-Si-C System Under Core Pressures (Table S1 and Figure S1)

description: Supplementary Information Table S1 and Fig. S1. Table S1 contains EPMA data for coexisting liquid and solid obtained in each experiment. Fig. S1 shows a typical XRD pattern for phase identification.
description: Funding provided by JSPS grant.
DOI: 10.5281/zenodo.4729069
title: PathDSP: Explainable drug sensitivity prediction through cancer pathway enrichment

description: A deep learning model for predicting drug response of cancer cell lines with cancer signaling pathway enrichment
DOI: 10.5281/zenodo.4729061
title: Liquidus Phase Relations and Solid-Liquid Partitioning in the Fe-Si-C System Under Core Pressures (Table S1 and Figure S1)

description: Supplementary Information Table S1 and Fig. S1. Table S1 contains EPMA data for coexisting liquid and solid obtained in each experiment. Fig. S1 shows a typical XRD pattern for phase identification.
description: Funding provided by JSPS grant.
DOI: 10.5281/zenodo.4716703
title: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning

description: example input dataset for one cross-validation run
DOI: 10.5281/zenodo.4729067
title: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores

description: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores
DOI: 10.5281/zenodo.4729065
title: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores

description: Explainable Drug Sensitivity Prediction through Cancer Pathway Enrichment Scores
DOI: 10.5281/zenodo.4728943
title: pbmc single cell RNA-seq matrix processed

description: Single cell RNA-sequencing dataset of peripheral blood mononuclear cells (pbmc: T, B, NK and monocytes) extracted from two healthy donors. Cells labeled as C26 come from a 30 years old female and cells labeled as C27 come from a 53 years old male. Cells have been isolated from blood using ficoll. Samples were sequenced using standard 3' v3 chemistry protocols by 10x genomics. Cellranger v4.0.0 was used for the processing, and reads were aligned to the ensembl GRCg38 human genome (GRCg38_r98-ensembl_Sept2019). QC metrics were calculated on the count matrix generated by cellranger (filtered_feature_bc_matrix). Cells with less than 3 genes per cells, less than 500 reads per cell and more than 20% of mithocondrial genes were discarded. The processing steps was performed with the R package Seurat (https://satijalab.org/seurat/), including sample integration, data normalisation and scaling, dimensional reduction, and clustering. SCTransform method was adopted for the normalisation and scaling steps. The clustered cells were manually annotated using known cell type markers. Files content: - normalized_dataset.csv: normalized gene counts (single cell matrix) - cell_types.csv: cell types identified from annotated cell clusters - cell_types_macro.csv: cell macro types - UMAP_coordinates.csv: 2d cell coordinates computed with UMAP algorithm in Seurat
DOI: 10.6084/m9.figshare.14380883.v1
title: General public’s knowledge and practices on face mask use during the COVID-19 pandemic: a cross-sectional exploratory survey from Dharan, Nepal

description: The dataset describes the knowledge and practice of the public in Dharan, Nepal regarding facemask use during the COVID-19 pandemic in 2020
DOI: 10.6084/m9.figshare.13621169.v13
title: Speedtest Open Data - Four International cities - MEL, BKK, SHG, LAX

description: This dataset compares four cities broadband internet speeds:- Melbourne, AU- Bangkok, TH- Shanghai, CN- Los Angeles, US<br>ERRATA:1.Data is for Q3 2020, but some files are labelled incorrectly as 02-20 of June 20. They all should read Sept 20, or 09-20 as Q3 20, rather than Q2. Will rename and reload.Amended in v7.<br>2. LAX file named 0320, when should be Q320.Amended in v8.<br><br>*lines of data for each geojson file; a line equates to a 600m^2 location, inc total tests, devices used, and average upload and download speed- MEL 16181 locations/lines =&gt; 0.85M speedtests (16.7 tests per 100people)- SHG 31745 lines =&gt; 0.65M speedtests (2.5/100pp)- BKK 29296 lines =&gt; 1.5M speedtests (14.3/100pp)- LAX 15899 lines =&gt; 1.3M speedtests (10.4/100pp)<br>Geojsons of these 2* by 2* extracts for MEL, BKK, SHG now added, and LAX added v6.<br>This dataset unpacks, geospatially, data summaries provided in Speedtest Global Index (linked below). See Jupyter Notebook (*.ipynb) to interrogate geo data. See link to install Jupyter.<br>** To DoWill add Google Map versions so everyone can see without installing Jupyter.- Link to Google Map (BKK) added below. Key:Green &gt; 100Mbps(Superfast). Black &gt; 500Mbps (Ultrafast). CSV provided. Code in Speedtestv1.1.ipynb Jupyter Notebook.<br>** Pythonmelb = au_tiles.cx[144:146 , -39:-37] #Lat/Lon extract<br>shg = tiles.cx[120:122 , 30:32] #Lat/Lon extract<br>bkk = tiles.cx[100:102 , 13:15] #Lat/Lon extract<br>lax = tiles.cx[-118:-120, 33:35] #lat/Lon extract<br>Histograms (v9), and data visualisations (v3,5,9,11) will be provided.Data Sourced from - This is an extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).<br>**VERSIONSV14. Added Melb Q1 2021 data - geojson.v13. Added Twitter link to pics.v12 Add Line-Compare pic (fastest 1000 locations) inc Jupyter (nbn-intl-v1.2.ipynb).v11 Add Line-Compare pic, plotting Four Cities on a graph.v10 Add Four Histograms in one pic.v9 Add Histogram for Four Cities. Add NBN-Intl.v1.1.ipynb (Jupyter Notebook).v8 Renamed LAX file to Q3, rather than 03.v7 Amended file names of BKK files to correctly label as Q3, not Q2 or 06.v6 Added LAX file.v5 Add screenshot of BKK Google Map.v4 Add BKK Google map(link below), and BKK csv mapping files. v3 replaced MEL map with big key version. Prev key was very tiny in top right corner.v2 Uploaded MEL, SHG, BKK data and Jupyter Notebookv1 Metadata record<br>** LICENCEAWS data licence on Speedtest data is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.<br>** Other uses of Speedtest Open Data;- see link at Speedtest below.<br>
DOI: 10.5517/ccdc.csd.cc255h40
title: CCDC 2001271: Experimental Crystal Structure Determination

description: Related Article: Arunika I. Ekanayake, Lena Sobze, Payam Kelich, Jihea Youk, Nicholas J. Bennett, Raja Mukherjee, Atul Bhardwaj, Frank Wuest, Lela Vukovic, Ratmir Derda|2021|J.Am.Chem.Soc.|143|5497|doi:10.1021/jacs.1c01186
DOI: 10.5517/ccdc.csd.cc25fxj2
title: CCDC 2009374: Experimental Crystal Structure Determination

description: Related Article: Xiu-yue Yu, Hui Su, Xin Zheng, Wen-bo Liu, Yao He, Na-na Fei, Ru Qiao, Yun-lai Ren, Cao-yuan Niu|2021|J.Mol.Struct.|1237|130358|doi:10.1016/j.molstruc.2021.130358
DOI: 10.5517/ccdc.csd.cc25ttzt
title: CCDC 2020827: Experimental Crystal Structure Determination

description: Related Article: Hong Hou, Bing Zhou, Jiawei Wang, Dengyang Zhao, Duhao Sun, Xiaoyun Chen, Ying Han, Chaoguo Yan, Yaocheng Shi, Shaoqun Zhu|2021|Org.Lett.|23|2981|doi:10.1021/acs.orglett.1c00626
DOI: 10.5517/ccdc.csd.cc2665xj
title: CCDC 2031737: Experimental Crystal Structure Determination

description: Related Article: Xiu-yue Yu, Hui Su, Xin Zheng, Wen-bo Liu, Yao He, Na-na Fei, Ru Qiao, Yun-lai Ren, Cao-yuan Niu|2021|J.Mol.Struct.|1237|130358|doi:10.1016/j.molstruc.2021.130358
DOI: 10.5517/ccdc.csd.cc26njs6
title: CCDC 2045528: Experimental Crystal Structure Determination

description: Related Article: Guangxing Pan, Shaoheng Qin, Dawen Xu, Fritz E. Kühn, Hao Guo|2021|Org.Lett.|23|2959|doi:10.1021/acs.orglett.1c00642