cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13370504.v15
title: Speedtest Open Data - Australia 2020 Q2, Q3, Q4 extract

description: This is an Australian extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).<br>AWS data licence 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>A world speedtest open data was dowloaded (&gt;400Mb, 7M lines of data). An extract of Australia's location (lat, long) revealed 88,000 lines of data (attached as csv).<br>A Jupyter notebook of extract process is attached.A link to Twitter thread of outputs provided.A link to Data tutorial provided (GitHub), including Jupyter Notebook to analyse World Speedtest data, selecting one US State.<br>Data Shows: (Q2)- 3.1M speedtests- 762,000 devices- 88,000 grid locations (600m * 600m), summarised as a point- average speed 33.7Mbps (down), 12.4M (up)- Max speed 724Mbps- data is for 600m * 600m grids, showing average speed up/down, number of tests, and number of users (IP). Added centroid, and now lat/long.<br>See tweet of image of centroids also attached.<br>Versions:v15. Add Hist comparing Q1-21 vs Q2-20.v14 Add AUS Speedtest Q1 2021 geojson.(79k lines avg d/l 45.4Mbps)v13 - Added three colour MELB map (less than 20Mbps, over 90Mbps, 20-90Mbps)v12 - Added AUS - Syd - Mel Line Chart Q320.v11 - Add line chart compare Q2, Q3, Q4 plus Melb - result virtually indistinguishable. Add line chart to compare Syd - Melb Q3. Also virtually indistinguishable. Add HIST compare Syd - Melb Q3. Add new Jupyter with graph calcs (nbn-AUS-v1.3). Some ERRATA document in Notebook. Issue with resorting table, and graphing only part of table. Not an issue if all lines of table graphed.v10 - Load AURIN sample pics. Speedtest data loaded to AURIN geo-analytic platform; requires edu.au login.v9 - Add comparative Q2, Q3, Q4 Hist pic.v8 - Added Q4 data geojson. Add Q3, Q4 Hist pic.v7 - Rename to include Q2, Q3 in Title.v6 - Add Q3 20 data. Rename geojson AUS data as Q2. Add comparative Histogram. Calc in International.ipynb.v5 - add Jupyter Notebook inc Histograms. Hist is count of geo-locations avg download speed (unweighted by tests).v4 - added Melb choropleth (png 50Mpix) inc legend. (To do - add Melb.geojson). Posted Link to AURIN description of Speedtest data.v3 - Add super fast data (&gt;100Mbps) less than 1% of data - 697 lines. Includes png of superfast.plot(). Link below to Google Maps version of superfast data points. Also Google map of first 100 data points - sample data. Geojson format for loading into GeoPandas, per Jupyter Notebook. New version of Jupyter Notebook, v.1.1.v2 - add centroids image.v1 - initial data load.<br>** Future Work- combine Speedtest data with NBN Technology by location data (national map.gov.au); https://www.data.gov.au/dataset/national-broadband-network-connections-by-technology-type- combine Speedtest data with SEIFA data - socioeconomic categories - to discuss with AURIN.- Further international comparisons- discussed collaboration with Assoc Prof Tooran Alizadeh, USyd.
DOI: 10.6084/m9.figshare.14096912.v2
title: Supplementary Information for Current research on microbe-plastic interactions in the marine environment

description: Placeholder for the table of studies that are included in the figures of the review chapter
DOI: 10.6084/m9.figshare.14515962.v1
title: data.zip

description: This dataset is the result of manuscript data published in PLoS ONE
DOI: 10.6084/m9.figshare.14515962
title: data.zip

description: This dataset is the result of manuscript data published in PLoS ONE
DOI: 10.6084/m9.figshare.14481258
title: Cost-effective mapping of genetic interactions in mammalian cells

description: LaTeX DOCUMENTSThe LaTeX documents "master.tex", "main.tex" and "supp.tex" are located in the "Words" directory and provide the key to navigate the scripts and data. These ".tex" documents can be found by search. The pdf output file of the latex documents is "master.pdf".<br>SCRIPTS AND DATAThe scripts necessary to analyze the data are in the various directories and can also be found by search. The vast majority of the data files needed to run the scripts are in the directory "data". A few miscellaneous data files are in the local "data" directory for each analysis. The file "RH_Ix_seq.txt" documents the RH-Seq genetic interactions and is also provided as a standalone file.<br>FINDING RELEVANT SCRIPTSFor example, the first paragraph in the "Results" ("master.pdf") includes the phrase "Fifteen independent RH clones...", and provides some basic statistics on sequencing depth. Searching for this phrase in "main.tex" reveals the names of three R scripts "Seq_reads_1.R", "Retent_1.R" and "RH_cnv_1.R" above the relevant paragraph. "Seq_reads_1.R" provides the corresponding results. <br>Inspection of the script "Seq_reads_1.R" shows that it uses the data file "hg38_chr_length_ucsc.txt", while the script "Retent_1.R" uses the data file "RH_1Mb.txt". Both these data files can be found in the directory "data". "Seq_reads_1.R" also uses "GENE_INT_mapped_reads_info.txt", which can be found in the "data" directory local to "Seq_reads_1.R", in the "Seq_reads" directory.<br>FINDING RELEVANT PARTS OF SCRIPTSStatistics in the LaTeX documents have been quoted to unrealistic levels of precision, and are rounded in the pdf output file, "master.pdf". Nevertheless, the redundant digits can be useful. For example, in the second paragraph of the "Results", we are informed that "The human DNA fragment length was 2.3 ± 0.1 Mb..."<br>Looking at "main.tex" reveals that the exact fragment length is "2.265945" Mb and that there are three R scripts corresponding to the paragraph. The relevant script is "RH_cnv_1.R" and searching "RH_cnv_1.R" for "2.265945" takes the reader to the relevant part of the script.<br>
DOI: 10.6084/m9.figshare.14481258.v1
title: Cost-effective mapping of genetic interactions in mammalian cells

description: LaTeX DOCUMENTSThe LaTeX documents "master.tex", "main.tex" and "supp.tex" are located in the "Words" directory and provide the key to navigate the scripts and data. These ".tex" documents can be found by search. The pdf output file of the latex documents is "master.pdf".<br>SCRIPTS AND DATAThe scripts necessary to analyze the data are in the various directories and can also be found by search. The vast majority of the data files needed to run the scripts are in the directory "data". A few miscellaneous data files are in the local "data" directory for each analysis. The file "RH_Ix_seq.txt" documents the RH-Seq genetic interactions and is also provided as a standalone file.<br>FINDING RELEVANT SCRIPTSFor example, the first paragraph in the "Results" ("master.pdf") includes the phrase "Fifteen independent RH clones...", and provides some basic statistics on sequencing depth. Searching for this phrase in "main.tex" reveals the names of three R scripts "Seq_reads_1.R", "Retent_1.R" and "RH_cnv_1.R" above the relevant paragraph. "Seq_reads_1.R" provides the corresponding results. <br>Inspection of the script "Seq_reads_1.R" shows that it uses the data file "hg38_chr_length_ucsc.txt", while the script "Retent_1.R" uses the data file "RH_1Mb.txt". Both these data files can be found in the directory "data". "Seq_reads_1.R" also uses "GENE_INT_mapped_reads_info.txt", which can be found in the "data" directory local to "Seq_reads_1.R", in the "Seq_reads" directory.<br>FINDING RELEVANT PARTS OF SCRIPTSStatistics in the LaTeX documents have been quoted to unrealistic levels of precision, and are rounded in the pdf output file, "master.pdf". Nevertheless, the redundant digits can be useful. For example, in the second paragraph of the "Results", we are informed that "The human DNA fragment length was 2.3 ± 0.1 Mb..."<br>Looking at "main.tex" reveals that the exact fragment length is "2.265945" Mb and that there are three R scripts corresponding to the paragraph. The relevant script is "RH_cnv_1.R" and searching "RH_cnv_1.R" for "2.265945" takes the reader to the relevant part of the script.<br>
DOI: 10.6084/m9.figshare.14507823
title: Microbial Composition and Co-Occurrence Patterns in the Gut Microbial Community of Normal and Obese Mice in Response to Astaxanthin

description: The microbial genomic DNA from the colon contents of NO, NA, MO and MA groups were extracted with a QIAamp DNA stool Mini kit (Qiagen, cat. No 51604). The 16S rRNA sequencing and analysis was used for obtaining the gut microbiota profiling. The V3-V4 regions of the 16S rRNA gene were selected for amplification. The total DNA were amplified with specific primers (forward primer, 341/357F: NNNNCCTACGGGNGGCWGCAG; reverse primer, 805/785R: GACTACHVGGGTATCTAATCC) tagged with barcodes. The PCR reactions were carried out with Phusion high-fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA). The concentration of the library pool was quantified by a BioAnalyzer DNA chip kit (Agilent) and the library was sequenced by an Illumina sequencer.
DOI: 10.6084/m9.figshare.13370504.v16
title: Speedtest Open Data - Australia 2020 Q2, Q3, Q4 extract

description: This is an Australian extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).<br>AWS data licence 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>A world speedtest open data was dowloaded (&gt;400Mb, 7M lines of data). An extract of Australia's location (lat, long) revealed 88,000 lines of data (attached as csv).<br>A Jupyter notebook of extract process is attached.A link to Twitter thread of outputs provided.A link to Data tutorial provided (GitHub), including Jupyter Notebook to analyse World Speedtest data, selecting one US State.<br>Data Shows: (Q2)- 3.1M speedtests- 762,000 devices- 88,000 grid locations (600m * 600m), summarised as a point- average speed 33.7Mbps (down), 12.4M (up)- Max speed 724Mbps- data is for 600m * 600m grids, showing average speed up/down, number of tests, and number of users (IP). Added centroid, and now lat/long.<br>See tweet of image of centroids also attached.<br>Versions:v15/16. Add Hist comparing Q1-21 vs Q2-20. Inc ipynb to calc.v14 Add AUS Speedtest Q1 2021 geojson.(79k lines avg d/l 45.4Mbps)v13 - Added three colour MELB map (less than 20Mbps, over 90Mbps, 20-90Mbps)v12 - Added AUS - Syd - Mel Line Chart Q320.v11 - Add line chart compare Q2, Q3, Q4 plus Melb - result virtually indistinguishable. Add line chart to compare Syd - Melb Q3. Also virtually indistinguishable. Add HIST compare Syd - Melb Q3. Add new Jupyter with graph calcs (nbn-AUS-v1.3). Some ERRATA document in Notebook. Issue with resorting table, and graphing only part of table. Not an issue if all lines of table graphed.v10 - Load AURIN sample pics. Speedtest data loaded to AURIN geo-analytic platform; requires edu.au login.v9 - Add comparative Q2, Q3, Q4 Hist pic.v8 - Added Q4 data geojson. Add Q3, Q4 Hist pic.v7 - Rename to include Q2, Q3 in Title.v6 - Add Q3 20 data. Rename geojson AUS data as Q2. Add comparative Histogram. Calc in International.ipynb.v5 - add Jupyter Notebook inc Histograms. Hist is count of geo-locations avg download speed (unweighted by tests).v4 - added Melb choropleth (png 50Mpix) inc legend. (To do - add Melb.geojson). Posted Link to AURIN description of Speedtest data.v3 - Add super fast data (&gt;100Mbps) less than 1% of data - 697 lines. Includes png of superfast.plot(). Link below to Google Maps version of superfast data points. Also Google map of first 100 data points - sample data. Geojson format for loading into GeoPandas, per Jupyter Notebook. New version of Jupyter Notebook, v.1.1.v2 - add centroids image.v1 - initial data load.<br>** Future Work- combine Speedtest data with NBN Technology by location data (national map.gov.au); https://www.data.gov.au/dataset/national-broadband-network-connections-by-technology-type- combine Speedtest data with SEIFA data - socioeconomic categories - to discuss with AURIN.- Further international comparisons- discussed collaboration with Assoc Prof Tooran Alizadeh, USyd.
DOI: 10.6084/m9.figshare.14516084
title: Additional file 2 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 2 Supplementary table 1. It includes enrichment analysis results of LPS-dendritic cell dataset.
DOI: 10.6084/m9.figshare.14516087
title: Additional file 3 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 3 Supplementary table 2. It includes enrichment analysis results of pancreatic beta cell maturation dataset.
DOI: 10.6084/m9.figshare.14516087.v1
title: Additional file 3 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 3 Supplementary table 2. It includes enrichment analysis results of pancreatic beta cell maturation dataset.
DOI: 10.6084/m9.figshare.14516084.v1
title: Additional file 2 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 2 Supplementary table 1. It includes enrichment analysis results of LPS-dendritic cell dataset.
DOI: 10.6084/m9.figshare.14516093
title: Additional file 4 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 4 Supplementary table 3. It includes enrichment analysis results of mouse bone marrow dataset.
DOI: 10.6084/m9.figshare.14516093.v1
title: Additional file 4 of PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data

description: Additional file 4 Supplementary table 3. It includes enrichment analysis results of mouse bone marrow dataset.
DOI: 10.6084/m9.figshare.14516164
title: Additional file 2 of Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils

description: Additional file 2: Table S1. Summary of total mapped reads of inoculated microcosms exposed to toluene. Table S2. Subset of genes whose expression levels responded to inoculation into glass beads (APM) versus liquid (FDR
DOI: 10.6084/m9.figshare.14516164.v1
title: Additional file 2 of Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils

description: Additional file 2: Table S1. Summary of total mapped reads of inoculated microcosms exposed to toluene. Table S2. Subset of genes whose expression levels responded to inoculation into glass beads (APM) versus liquid (FDR
DOI: 10.6084/m9.figshare.14516167
title: Additional file 3 of Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils

description: Additional file 3: DATA 1. Complete list of differentially expressed genes in APM-1H versus liquid-1H (FDR
DOI: 10.6084/m9.figshare.14516167.v1
title: Additional file 3 of Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils

description: Additional file 3: DATA 1. Complete list of differentially expressed genes in APM-1H versus liquid-1H (FDR
DOI: 10.6084/m9.figshare.14516186
title: Additional file 1 of The Turkish version of the SPPIC validated among informal caregivers with a Turkish immigrant background

description: Additional file 1: Appendix 1. SPPIC questionnaire (Dutch and English). Appendix 2. Con characteristics relating to language skills. Appendix 3. Missing values, mean, skewness and kurtosis for the Turkish translation of the SPPIC per item. Appendix 4. Items in order of proportion that agreed with the items.
DOI: 10.6084/m9.figshare.14516186.v1
title: Additional file 1 of The Turkish version of the SPPIC validated among informal caregivers with a Turkish immigrant background

description: Additional file 1: Appendix 1. SPPIC questionnaire (Dutch and English). Appendix 2. Con characteristics relating to language skills. Appendix 3. Missing values, mean, skewness and kurtosis for the Turkish translation of the SPPIC per item. Appendix 4. Items in order of proportion that agreed with the items.
DOI: 10.6084/m9.figshare.14516227
title: Additional file 1 of Factors associated with dietary diversity among adolescents in Woldia, Northeast Ethiopia

description: Additional file 1. Peer Review Reports.
DOI: 10.6084/m9.figshare.14516227.v1
title: Additional file 1 of Factors associated with dietary diversity among adolescents in Woldia, Northeast Ethiopia

description: Additional file 1. Peer Review Reports.
DOI: 10.6084/m9.figshare.14516273.v1
title: Additional file 2 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 2 List of 596 protein-coding genes defined by the “CAF signature”, including: gene symbol; gene description; ENSEMBL gene id; and statistical parameters provided by the SAM algorithm (i.e. fold change in log2 scale, statistic parameter d value, and p value) in the analysis of differential expression.
DOI: 10.6084/m9.figshare.14516276
title: Additional file 3 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 3. Functional enrichment analysis with GO-BP and GO-MF of the genes of the “CAF signature” within the database MSigDB. The table shows the most relevant GO terms (top 20 in GO-BP and in GO-MF) associated with genes from the CAF-derived signature.
DOI: 10.6084/m9.figshare.14516279
title: Additional file 4 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 4. List of the 67 genes included in the “CAF-derived exosomes signature”. For each gene the table gives the gene symbol and the ENSEMBL gene id (ENSG).
DOI: 10.6084/m9.figshare.14516282
title: Additional file 5 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 5. Functional enrichment analysis with GO-BP and GO-MF of the genes of the “CAF-derived exosomes signature” within the database MSigDB. The table shows the most relevant GO terms (top 20 in GO-BP and in GO-MF) associated with genes from this signature.
DOI: 10.6084/m9.figshare.14516273
title: Additional file 2 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 2 List of 596 protein-coding genes defined by the “CAF signature”, including: gene symbol; gene description; ENSEMBL gene id; and statistical parameters provided by the SAM algorithm (i.e. fold change in log2 scale, statistic parameter d value, and p value) in the analysis of differential expression.
DOI: 10.6084/m9.figshare.14516279.v1
title: Additional file 4 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 4. List of the 67 genes included in the “CAF-derived exosomes signature”. For each gene the table gives the gene symbol and the ENSEMBL gene id (ENSG).
DOI: 10.6084/m9.figshare.14516285
title: Additional file 6 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 6. Top 50 genes of each signature (CAF-signature in blue and CAF-derived exosomes signature in green) and the beta-values corresponding to each gene provided by Cox. * non-coding genes.
DOI: 10.6084/m9.figshare.14516285.v1
title: Additional file 6 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 6. Top 50 genes of each signature (CAF-signature in blue and CAF-derived exosomes signature in green) and the beta-values corresponding to each gene provided by Cox. * non-coding genes.
DOI: 10.6084/m9.figshare.14516282.v1
title: Additional file 5 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 5. Functional enrichment analysis with GO-BP and GO-MF of the genes of the “CAF-derived exosomes signature” within the database MSigDB. The table shows the most relevant GO terms (top 20 in GO-BP and in GO-MF) associated with genes from this signature.
DOI: 10.6084/m9.figshare.14516276.v1
title: Additional file 3 of Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients

description: Additional file 3. Functional enrichment analysis with GO-BP and GO-MF of the genes of the “CAF signature” within the database MSigDB. The table shows the most relevant GO terms (top 20 in GO-BP and in GO-MF) associated with genes from the CAF-derived signature.
DOI: 10.6084/m9.figshare.14516306
title: Additional file 2 of Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

description: Additional file 2. Table with annotation of the 89,118 mimQTLs found across the Oslo2 and TCGA cohorts. Genome locations are based on the hg19 build.
DOI: 10.6084/m9.figshare.14516309
title: Additional file 3 of Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

description: Additional file 3. a) Overview and annotation of the 119 mimQTL miRNAs. b) Overview and annotation of the 26,746 mimQTL CpGs. c) Genes found positively or negatively correlated to miRNAs of each cluster (mRNA-miRNA Spearman correlation &gt; 0.4 or
DOI: 10.6084/m9.figshare.14516309.v1
title: Additional file 3 of Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

description: Additional file 3. a) Overview and annotation of the 119 mimQTL miRNAs. b) Overview and annotation of the 26,746 mimQTL CpGs. c) Genes found positively or negatively correlated to miRNAs of each cluster (mRNA-miRNA Spearman correlation &gt; 0.4 or
DOI: 10.6084/m9.figshare.14516306.v1
title: Additional file 2 of Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

description: Additional file 2. Table with annotation of the 89,118 mimQTLs found across the Oslo2 and TCGA cohorts. Genome locations are based on the hg19 build.
DOI: 10.6084/m9.figshare.14516336
title: Additional file 2 of Application of a framework to guide genetic testing communication across clinical indications

description: Additional file 2: Supplementary Table S1. All CADRe communication recommendations for the standard testing indications.
DOI: 10.6084/m9.figshare.14516339
title: Additional file 3 of Application of a framework to guide genetic testing communication across clinical indications

description: Additional file 3: Supplementary Table S2. CADRe communication recommendations for unique testing indications.
DOI: 10.6084/m9.figshare.14516339.v1
title: Additional file 3 of Application of a framework to guide genetic testing communication across clinical indications

description: Additional file 3: Supplementary Table S2. CADRe communication recommendations for unique testing indications.
DOI: 10.6084/m9.figshare.14516336.v1
title: Additional file 2 of Application of a framework to guide genetic testing communication across clinical indications

description: Additional file 2: Supplementary Table S1. All CADRe communication recommendations for the standard testing indications.
DOI: 10.6084/m9.figshare.14516335
title: Additional file 10 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 10: Table S9. General classification profiles of PDXs.
DOI: 10.6084/m9.figshare.14516335.v1
title: Additional file 10 of Evaluating the transcriptional fidelity of cancer models

description: Additional file 10: Table S9. General classification profiles of PDXs.