cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19590658
title: Supplementary Material for: Induction of Radiodermatitis in Nude Mouse Model using Gamma Irradiator IBL 637

description: Introduction: Acute radiodermatitis is a common, though severe, side effect of radiotherapy against cancer that may lead to an interruption or even abortion of the radiotherapy. Mouse models provide an excellent tool to study pathomechanisms of a radiation-induced dermatitis as well as to test and develop novel innovative treatment strategies. Objective: The aim of this study is to provide an overview of different mouse models and irradiation devices that have been used so far and to describe the process of the induction of a radiation dermatitis in an immune proficient nude mouse model (SKH1-Hrhr) using a IBL 637 Cesium-137γ-ray machine. Methods: This process includes the construction of a radiation shielding chamber, restricting the radiation to the right hind leg of the mouse, a dosimetry and a dose finding study to identify the appropriate irradiation dose to induce a moderate radiation dermatitis. Results: A radiation shielding chamber was successfully constructed allowing selective irradiation of the right hind leg. A moderate radiodermatitis is induced with irradiation doses in the range of 60 – 70 Gy under the here described conditions. Symptoms peak about eight days after irradiation and decrease relatively quickly thereafter. Histological analyses confirmed typical signs of inflammation. Conclusion: This study describes for the first time a protocol to induce a moderate radiodermatitis in the nude mouse model SKH1-Hrhr using a IBL 637 gamma irradiator. This protocol will allow researchers to study novel treatment strategies to alleviate the burden of a radiodermatitis as a side effect of cancer treatment.
DOI: 10.6084/m9.figshare.19590658.v1
title: Supplementary Material for: Induction of Radiodermatitis in Nude Mouse Model using Gamma Irradiator IBL 637

description: Introduction: Acute radiodermatitis is a common, though severe, side effect of radiotherapy against cancer that may lead to an interruption or even abortion of the radiotherapy. Mouse models provide an excellent tool to study pathomechanisms of a radiation-induced dermatitis as well as to test and develop novel innovative treatment strategies. Objective: The aim of this study is to provide an overview of different mouse models and irradiation devices that have been used so far and to describe the process of the induction of a radiation dermatitis in an immune proficient nude mouse model (SKH1-Hrhr) using a IBL 637 Cesium-137γ-ray machine. Methods: This process includes the construction of a radiation shielding chamber, restricting the radiation to the right hind leg of the mouse, a dosimetry and a dose finding study to identify the appropriate irradiation dose to induce a moderate radiation dermatitis. Results: A radiation shielding chamber was successfully constructed allowing selective irradiation of the right hind leg. A moderate radiodermatitis is induced with irradiation doses in the range of 60 – 70 Gy under the here described conditions. Symptoms peak about eight days after irradiation and decrease relatively quickly thereafter. Histological analyses confirmed typical signs of inflammation. Conclusion: This study describes for the first time a protocol to induce a moderate radiodermatitis in the nude mouse model SKH1-Hrhr using a IBL 637 gamma irradiator. This protocol will allow researchers to study novel treatment strategies to alleviate the burden of a radiodermatitis as a side effect of cancer treatment.
DOI: 10.5281/zenodo.6557622
title: Live imaging data of XBP1 mRNA under ER stress conditions.

description: Live microscopy images of single XBP1 mRNA transcripts under ER stress conditions (TM).
DOI: 10.5281/zenodo.6559057
title: Live imaging data of XBP1 mRNA under ER stress and IRE1a inhibition

description: Live microscopy data of XBP1 mRNA transcript in TM and 4u8C.
DOI: 10.5281/zenodo.6559124
title: Live cell imaging data of IRE1a-GFP foci formation

description: Live cell imaging of XBP1 mRNA and IRE1a-GFP expressing cells.
DOI: 10.5281/zenodo.6559108
title: Fixed cell imaging data of XBP1 mRNA

description: Fixed cell imaging data consisting of smFISH agains XBP1 mRNA and IF against scAB-GFP.
DOI: 10.26188/6284ad82dd824
title: Timeline of events created on Padlet: The Hungarian government response to COVID-19 in 2020

description: Image Quilt and PNG image of timeline of Hungarian government response to COVID-19 in 2020 based on the official website of the Hungarian government (koronavirus.gov.hu). Created from within Padlet
DOI: 10.6084/m9.figshare.16815569
title: Additional file 1 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 1. Additional information on literature searches. ROSES form systematic map.
DOI: 10.6084/m9.figshare.16815569.v2
title: Additional file 1 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 1. Additional information on literature searches. ROSES form systematic map.
DOI: 10.6084/m9.figshare.16815569.v1
title: Additional file 1 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 1. Additional information on literature searches. ROSES form systematic map.
DOI: 10.6084/m9.figshare.16815572
title: Additional file 2 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 2. Additional information on literature searches. Contains description of search sources including web addressed used, date and language of the search, and search string used in each source. Searches in different languages for the same source are indicated as separate rows. The number of documents retrieved from each source is reported.
DOI: 10.6084/m9.figshare.16815572.v2
title: Additional file 2 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 2. Additional information on literature searches. Contains description of search sources including web addressed used, date and language of the search, and search string used in each source. Searches in different languages for the same source are indicated as separate rows. The number of documents retrieved from each source is reported.
DOI: 10.6084/m9.figshare.16815575
title: Additional file 3 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 3. List of articles. Contains all search results retrieved from the literature searches with information on exclusion stage and (for full text stage) reasons for exclusion, and definitions of variables.
DOI: 10.6084/m9.figshare.16815572.v1
title: Additional file 2 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 2. Additional information on literature searches. Contains description of search sources including web addressed used, date and language of the search, and search string used in each source. Searches in different languages for the same source are indicated as separate rows. The number of documents retrieved from each source is reported.
DOI: 10.6084/m9.figshare.16815575.v2
title: Additional file 3 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 3. List of articles. Contains all search results retrieved from the literature searches with information on exclusion stage and (for full text stage) reasons for exclusion, and definitions of variables.
DOI: 10.6084/m9.figshare.16815578
title: Additional file 4 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 4. Coding template and description of coded variables. Coding template used for data entry, including descriptions of coded variables, definitions of dropdown menus and references to the spatial data sources for variables extracted from other data sources.
DOI: 10.6084/m9.figshare.16815578.v2
title: Additional file 4 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 4. Coding template and description of coded variables. Coding template used for data entry, including descriptions of coded variables, definitions of dropdown menus and references to the spatial data sources for variables extracted from other data sources.
DOI: 10.6084/m9.figshare.16815581
title: Additional file 5 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 5. Coded raw data. Raw data coded from full text studies. This file is compiled with the geospatial data through scripts available https://github.com/JamesDMSpeed/ArcticHerbivorySystematicMap , and the dataset with geospatial data can be downloaded from https://shiny.vm.ntnu.no/users/speed/ArcticHerbivorySystematicMap/ .
DOI: 10.6084/m9.figshare.16815581.v2
title: Additional file 5 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 5. Coded raw data. Raw data coded from full text studies. This file is compiled with the geospatial data through scripts available https://github.com/JamesDMSpeed/ArcticHerbivorySystematicMap , and the dataset with geospatial data can be downloaded from https://shiny.vm.ntnu.no/users/speed/ArcticHerbivorySystematicMap/ .
DOI: 10.6084/m9.figshare.16815578.v1
title: Additional file 4 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 4. Coding template and description of coded variables. Coding template used for data entry, including descriptions of coded variables, definitions of dropdown menus and references to the spatial data sources for variables extracted from other data sources.
DOI: 10.6084/m9.figshare.16815587
title: Additional file 6 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 6. Plant functional group data aggregated from raw data. Plant functional group data, aggregated from raw data.
DOI: 10.6084/m9.figshare.16815575.v1
title: Additional file 3 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 3. List of articles. Contains all search results retrieved from the literature searches with information on exclusion stage and (for full text stage) reasons for exclusion, and definitions of variables.
DOI: 10.6084/m9.figshare.16815587.v2
title: Additional file 6 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 6. Plant functional group data aggregated from raw data. Plant functional group data, aggregated from raw data.
DOI: 10.6084/m9.figshare.16815581.v1
title: Additional file 5 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 5. Coded raw data. Raw data coded from full text studies. This file is compiled with the geospatial data through scripts available https://github.com/JamesDMSpeed/ArcticHerbivorySystematicMap , and the dataset with geospatial data can be downloaded from https://shiny.vm.ntnu.no/users/speed/ArcticHerbivorySystematicMap/ .
DOI: 10.6084/m9.figshare.16815587.v1
title: Additional file 6 of Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map

description: Additional file 6. Plant functional group data aggregated from raw data. Plant functional group data, aggregated from raw data.
DOI: 10.5281/zenodo.5887389
title: Real-time PAAO

description: Real-time thickness measurements <strong>Originals</strong> - Directory with all saved spectra with preserved timestamps <strong>Organized files</strong> - Directory with processed data <strong>1. Pre anodizing spectrum</strong> - Directory with spectra before anodization process <strong>2. Anodizing spectrum</strong> - Directory with spectra during anodization process <strong>3. Post anodizing spectrum</strong> - Directory with spectra after anodization process <strong>4. Anodizing Plots</strong> - Directory with images of spectra and fitted spectra during an anodization process <strong>5. Anodizing Data</strong> - Directory with tab delimited data of spectra and fitted spectra during an anodization process. <em>(1st column: Wavelength (nm); 2nd column: Experimental reflection(a.u.); 3rd column: Fitted reflection (a.u.))</em> <strong>Current_per_time.dat</strong> - tab delimited data for current dependence on anodization time <em>(1st column: Anodization time (s); 2nd column: Current (mA))</em> <strong>Thickness_per_time.dat</strong> - tab delimited data for PAAO thickness dependence on anodization time <em> (1st column: Anodization time (s); 2nd column: Thickness (nm))</em> <strong>Thickness_per_time.png</strong> - plot of PAAO thickness dependence on anodization time <strong>Anodization_parameters.txt</strong> - data containing information about anodization parameters ( Voltage, Temperature, etc.) Zip file name consists of: 1.Date 2.Time 3.Desired_Thickness 4.Fitted_Thickness 5.Anod_time 6.Temperature 7.Voltage 8.Solution 9.Material.zip Project: Nanostructured multilayer hybrid coatings for interferometric and optoelectronic sensors (LZP-2020/1-0200)
DOI: 10.5281/zenodo.5886728
title: Real-time PAAO

description: Real-time thickness measurements <strong>Originals</strong> - Directory with all saved spectra with preserved timestamps <strong>Organized files</strong> - Directory with processed data <strong>1. Pre anodizing spectrum</strong> - Directory with spectra before anodization process <strong>2. Anodizing spectrum</strong> - Directory with spectra during anodization process <strong>3. Post anodizing spectrum</strong> - Directory with spectra after anodization process <strong>4. Anodizing Plots</strong> - Directory with images of spectra and fitted spectra during an anodization process <strong>5. Anodizing Data</strong> - Directory with tab delimited data of spectra and fitted spectra during an anodization process. <em>(1st column: Wavelength (nm); 2nd column: Experimental reflection(a.u.); 3rd column: Fitted reflection (a.u.))</em> <strong>Current_per_time.dat</strong> - tab delimited data for current dependence on anodization time <em>(1st column: Anodization time (s); 2nd column: Current (mA))</em> <strong>Thickness_per_time.dat</strong> - tab delimited data for PAAO thickness dependence on anodization time <em> (1st column: Anodization time (s); 2nd column: Thickness (nm))</em> <strong>Thickness_per_time.png</strong> - plot of PAAO thickness dependence on anodization time <strong>Anodization_parameters.txt</strong> - data containing information about anodization parameters ( Voltage, Temperature, etc.) Zip file name consists of: 1.Date 2.Time 3.Desired_Thickness 4.Fitted_Thickness 5.Anod_time 6.Temperature 7.Voltage 8.Solution 9.Material.zip Project: Nanostructured multilayer hybrid coatings for interferometric and optoelectronic sensors (LZP-2020/1-0200)
DOI: 10.6084/m9.figshare.19785518
title: Additional file 10 of Machine learning approach informs biology of cancer drug response

description: Additional file 10. Supplementary Table S8. Top 20 GO Biological Processes enrichment of elastic-net derived genes for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785524
title: Additional file 12 of Machine learning approach informs biology of cancer drug response

description: Additional file 12. Supplementary Table S10. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785518.v1
title: Additional file 10 of Machine learning approach informs biology of cancer drug response

description: Additional file 10. Supplementary Table S8. Top 20 GO Biological Processes enrichment of elastic-net derived genes for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785521
title: Additional file 11 of Machine learning approach informs biology of cancer drug response

description: Additional file 11. Supplementary Table S9. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785521.v1
title: Additional file 11 of Machine learning approach informs biology of cancer drug response

description: Additional file 11. Supplementary Table S9. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785524.v1
title: Additional file 12 of Machine learning approach informs biology of cancer drug response

description: Additional file 12. Supplementary Table S10. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785527
title: Additional file 13 of Machine learning approach informs biology of cancer drug response

description: Additional file 13. Supplementary Table S11. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785527.v1
title: Additional file 13 of Machine learning approach informs biology of cancer drug response

description: Additional file 13. Supplementary Table S11. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785530.v1
title: Additional file 14 of Machine learning approach informs biology of cancer drug response

description: Additional file 14. Supplementary Table S12. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785530
title: Additional file 14 of Machine learning approach informs biology of cancer drug response

description: Additional file 14. Supplementary Table S12. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785539
title: Additional file 3 of Machine learning approach informs biology of cancer drug response

description: Additional file 3. Supplementary Table S1. KEGG pathways that passed pathway activity selection for ML210 analysis.
DOI: 10.6084/m9.figshare.19785542
title: Additional file 4 of Machine learning approach informs biology of cancer drug response

description: Additional file 4. Supplementary Table S2. Top 20 enriched GO Biological Processes of genes returned by Boruta. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785539.v1
title: Additional file 3 of Machine learning approach informs biology of cancer drug response

description: Additional file 3. Supplementary Table S1. KEGG pathways that passed pathway activity selection for ML210 analysis.
DOI: 10.6084/m9.figshare.19785542.v1
title: Additional file 4 of Machine learning approach informs biology of cancer drug response

description: Additional file 4. Supplementary Table S2. Top 20 enriched GO Biological Processes of genes returned by Boruta. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785545
title: Additional file 5 of Machine learning approach informs biology of cancer drug response

description: Additional file 5. Supplementary Table S3. Top 20 Enriched GO Biological Processes of t-test derived genes for ML210 analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785545.v1
title: Additional file 5 of Machine learning approach informs biology of cancer drug response

description: Additional file 5. Supplementary Table S3. Top 20 Enriched GO Biological Processes of t-test derived genes for ML210 analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785548
title: Additional file 6 of Machine learning approach informs biology of cancer drug response

description: Additional file 6. Supplementary Table S4. Top 20 Enriched GO Biological Processes enrichment of elastic-net derived genes for ML210 analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785548.v1
title: Additional file 6 of Machine learning approach informs biology of cancer drug response

description: Additional file 6. Supplementary Table S4. Top 20 Enriched GO Biological Processes enrichment of elastic-net derived genes for ML210 analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785551
title: Additional file 7 of Machine learning approach informs biology of cancer drug response

description: Additional file 7. Supplementary Table S5. KEGG pathways that passed pathway activity selection for BRAFi analysis.
DOI: 10.6084/m9.figshare.19785551.v1
title: Additional file 7 of Machine learning approach informs biology of cancer drug response

description: Additional file 7. Supplementary Table S5. KEGG pathways that passed pathway activity selection for BRAFi analysis.
DOI: 10.6084/m9.figshare.19785554
title: Additional file 8 of Machine learning approach informs biology of cancer drug response

description: Additional file 8. Supplementary Table S6. Top 20 GO Biological Processes enriched in important modules for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785554.v1
title: Additional file 8 of Machine learning approach informs biology of cancer drug response

description: Additional file 8. Supplementary Table S6. Top 20 GO Biological Processes enriched in important modules for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.
DOI: 10.6084/m9.figshare.19785557
title: Additional file 9 of Machine learning approach informs biology of cancer drug response

description: Additional file 9. Supplementary Table S7. Top 20 GO Biological Processes enrichment of t-test derived genes for BRAFi analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785557.v1
title: Additional file 9 of Machine learning approach informs biology of cancer drug response

description: Additional file 9. Supplementary Table S7. Top 20 GO Biological Processes enrichment of t-test derived genes for BRAFi analysis. P-values shown are uncorrected for multiple hypothesis testing.
DOI: 10.6084/m9.figshare.19785578
title: Additional file 2 of Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival

description: Additional file 2: Table S1. Subcell-type marker genes.
DOI: 10.6084/m9.figshare.19785578.v1
title: Additional file 2 of Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival

description: Additional file 2: Table S1. Subcell-type marker genes.