cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15020417.v1
title: Additional file 1 of Glacier ice archives nearly 15,000-year-old microbes and phages

description: Additional file1: Table S1 Relative abundance and taxonomy of 33 most abundant OTUs in Inner, Wash and Cut parts of two glacier-ice sections. Table S2 Microbial community composition of eight glacier-ice samples at genus level. Table S3 Physical and chemical characteristics of ice samples from the Guliya ice cap. Table S4 Significance of each parameter's influence on the distribution of microbial communities by Mantel tests. Table S5 General statistics for two viromes. Table S6 Taxonomic assignments and viral cluster information of 33 glacier-ice vOTUs and 2,304 RefSeq genomes. Table S7 Adjusted coverage of 33 vOTUs per gigabase of MetaG for two glacier-ice viromes. Table S8 Predicted hosts taxonomy by sequence similarity method (Blastn). Table S9 Predicted hosts taxonomy by sequence composition method (VirHostMatcher). Table S10 Predicted hosts taxonomy by CRISPR similarity method. Table S11 Temperate viruses predicted by VIBRANT from the 33 vOTUs. Table S12 Methylobacterium viruses identified from glacier ice, bacterial genomes in RefSeq database and environmental metagenomes. Table S13 Annotations of all genes from 33 vOTUs. Table S14 Four AMGs identified from glacier-ice viruses. Table S15 Annotations of 70 viral contigs’ genes from the Global Ocean Viromes 2.0 dataset.
DOI: 10.6084/m9.figshare.15020435
title: Additional file 10 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 10: Table S5. All significant outputs of the Kruskal-Wallis analysis measuring the effect of taxonomic group on ABC family size are listed.
DOI: 10.6084/m9.figshare.15020435.v1
title: Additional file 10 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 10: Table S5. All significant outputs of the Kruskal-Wallis analysis measuring the effect of taxonomic group on ABC family size are listed.
DOI: 10.6084/m9.figshare.15020438
title: Additional file 11 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 11: Table S6. A transcriptomic analysis of H. armigera midgut, Malpighian tubules and central nervous system was undertaken to examine P-glycoprotein expression in these epithelial tissues. H. armigera genes are listed with their official gene set (OGS) codes, corresponding NCBI accession numbers, and their expression values in each tissue displayed in transcripts per million (TPM).
DOI: 10.6084/m9.figshare.15020441
title: Additional file 12 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 12: Table S7. All predicted ABC-H genes in N. viridula are listed along with transcriptomic data derived from [47].
DOI: 10.6084/m9.figshare.15020441.v1
title: Additional file 12 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 12: Table S7. All predicted ABC-H genes in N. viridula are listed along with transcriptomic data derived from [47].
DOI: 10.6084/m9.figshare.15020438.v1
title: Additional file 11 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 11: Table S6. A transcriptomic analysis of H. armigera midgut, Malpighian tubules and central nervous system was undertaken to examine P-glycoprotein expression in these epithelial tissues. H. armigera genes are listed with their official gene set (OGS) codes, corresponding NCBI accession numbers, and their expression values in each tissue displayed in transcripts per million (TPM).
DOI: 10.6084/m9.figshare.15020459
title: Additional file 6 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 6: Table S1. This table shows all the metadata associated with every species included in the analysis. ABC counts are missing for species with a BUSCO scores of below � Single copy orthologues.
DOI: 10.6084/m9.figshare.15020465
title: Additional file 8 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 8: Table S3. All primers used in the N. viridula RNAi screen are shown.
DOI: 10.6084/m9.figshare.15020459.v1
title: Additional file 6 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 6: Table S1. This table shows all the metadata associated with every species included in the analysis. ABC counts are missing for species with a BUSCO scores of below � Single copy orthologues.
DOI: 10.6084/m9.figshare.15020462.v1
title: Additional file 7 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 7: Table S2. Species level phylogenies used in CAFE were constructed with the species shown in this table. Outgroups are highlighted in yellow.
DOI: 10.6084/m9.figshare.15020465.v1
title: Additional file 8 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 8: Table S3. All primers used in the N. viridula RNAi screen are shown.
DOI: 10.6084/m9.figshare.15020462
title: Additional file 7 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 7: Table S2. Species level phylogenies used in CAFE were constructed with the species shown in this table. Outgroups are highlighted in yellow.
DOI: 10.6084/m9.figshare.15020468
title: Additional file 9 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 9: Table S4. The raw data for Figure S2 is shown, which contains the total number of ABC transporters identified in ABC_scan and the literature for a subset of species. Also included is the source of the literature derived counts.
DOI: 10.6084/m9.figshare.15020468.v1
title: Additional file 9 of Comparative and functional genomics of the ABC transporter superfamily across arthropods

description: Additional file 9: Table S4. The raw data for Figure S2 is shown, which contains the total number of ABC transporters identified in ABC_scan and the literature for a subset of species. Also included is the source of the literature derived counts.
DOI: 10.6084/m9.figshare.15020561.v1
title: Additional file 2 of Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy

description: Additional file 2. The change facilitation strategies conducted by change facilitators, grouped into the 16 categories.
DOI: 10.6084/m9.figshare.15020561
title: Additional file 2 of Data-driven approach for tailoring facilitation strategies to overcome implementation barriers in community pharmacy

description: Additional file 2. The change facilitation strategies conducted by change facilitators, grouped into the 16 categories.
DOI: 10.6084/m9.figshare.15020567
title: Additional file 1 of Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP

description: Additional file 1. Predictive densities for US States and Jurisdictions.
DOI: 10.6084/m9.figshare.15020567.v1
title: Additional file 1 of Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP

description: Additional file 1. Predictive densities for US States and Jurisdictions.
DOI: 10.6084/m9.figshare.15020618
title: Additional file 2 of Transcriptomic analyses of cacao flavonoids produced in photobioreactors

description: Additional file 2: Table S1. Mapping of Theobroma cacao RNA-seq library reads to the Cacao criollo reference genome database. Table S2. KEGG enrichment annotation for the three pairwaise comparisons in White and Blue conditions. Table S3. List of Transcription factors identified in the DEGs of cacao cells in light-treated Bioreactors. Table S4. Annotation and statistical information of photoreceptors identified in the cacao cell bioreactors. Table S5. KEGG metabolic enrichment analysis for ROS markers in cacao DEGs. Table S6. Up and down regulated anotated ROS markers in cacao DEGs for the three pairwise comparisons. Red shadow locus: Upregulated. Blue shadow locus: Downregulated. Table S7. Cacao transcription factor identified in each of three ROS types. Table S8. Primers used for qRT-PCR analysis of flavonoid biosynthesis related genes
DOI: 10.6084/m9.figshare.15020618.v1
title: Additional file 2 of Transcriptomic analyses of cacao flavonoids produced in photobioreactors

description: Additional file 2: Table S1. Mapping of Theobroma cacao RNA-seq library reads to the Cacao criollo reference genome database. Table S2. KEGG enrichment annotation for the three pairwaise comparisons in White and Blue conditions. Table S3. List of Transcription factors identified in the DEGs of cacao cells in light-treated Bioreactors. Table S4. Annotation and statistical information of photoreceptors identified in the cacao cell bioreactors. Table S5. KEGG metabolic enrichment analysis for ROS markers in cacao DEGs. Table S6. Up and down regulated anotated ROS markers in cacao DEGs for the three pairwise comparisons. Red shadow locus: Upregulated. Blue shadow locus: Downregulated. Table S7. Cacao transcription factor identified in each of three ROS types. Table S8. Primers used for qRT-PCR analysis of flavonoid biosynthesis related genes
DOI: 10.6084/m9.figshare.15020648.v1
title: Additional file 1 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 1. The effects of TBI and complement inhibition on neuroinflammation transcriptome. Table showing for each gene in the NanoString neuroinflammation panel, the effects of TBI on gene expression at days 3, 7 and 28 post injury (fold change and FDR), the temporal patterns of differential expression and peak change in expression, the effects of complement inhibition on gene expression after TBI, and the pathway annotations.
DOI: 10.6084/m9.figshare.15020648
title: Additional file 1 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 1. The effects of TBI and complement inhibition on neuroinflammation transcriptome. Table showing for each gene in the NanoString neuroinflammation panel, the effects of TBI on gene expression at days 3, 7 and 28 post injury (fold change and FDR), the temporal patterns of differential expression and peak change in expression, the effects of complement inhibition on gene expression after TBI, and the pathway annotations.
DOI: 10.6084/m9.figshare.15020651
title: Additional file 2 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 2. Response of Complement Genes to TBI and Aging. Table showing for each gene in the custom-built NanoString complement panel, the effects of TBI on gene expression at day 3, day 7, day 28, year 1, and year 2 post injury (fold change and FDR). It also shows the change in complement gene expression with aging.
DOI: 10.6084/m9.figshare.15020651.v1
title: Additional file 2 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 2. Response of Complement Genes to TBI and Aging. Table showing for each gene in the custom-built NanoString complement panel, the effects of TBI on gene expression at day 3, day 7, day 28, year 1, and year 2 post injury (fold change and FDR). It also shows the change in complement gene expression with aging.
DOI: 10.6084/m9.figshare.15020663
title: Additional file 6 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 6. R code. R code written to analyze data exported from nSolver containing fold change and p-values for comparisons between experimental groups. This code can be run on data from additional file 7 to reproduce all the graphs and tables in the paper (in addition to gene expression heatmaps that were excluded for brevity).
DOI: 10.6084/m9.figshare.15020660
title: Additional file 5 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 5. Consistently Dysregulated Genes Resistant to Complement Inhibition. Table containing a list of consistently upregulated and downregulated genes that were not affected by complement inhibition. This list was used for a Reactome pathway analysis.
DOI: 10.6084/m9.figshare.15020660.v1
title: Additional file 5 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 5. Consistently Dysregulated Genes Resistant to Complement Inhibition. Table containing a list of consistently upregulated and downregulated genes that were not affected by complement inhibition. This list was used for a Reactome pathway analysis.
DOI: 10.6084/m9.figshare.15020663.v1
title: Additional file 6 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 6. R code. R code written to analyze data exported from nSolver containing fold change and p-values for comparisons between experimental groups. This code can be run on data from additional file 7 to reproduce all the graphs and tables in the paper (in addition to gene expression heatmaps that were excluded for brevity).
DOI: 10.6084/m9.figshare.15020666
title: Additional file 7 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 7. Data for R code. This file contains several excel sheets that need to be separated into separate files with the right .csv and .xlsx extensions (follow sheet name) before using the R code in additional file 6. The .csv sheets were exported from NanoString’s nSolver software and contain the fold change and p-value from multiple comparisons. The .xlsx sheets contain gene metadata for the NanoString panels. NI and COMPH in the sheet names stand for “neuroinflammation panel” and “complement panel”, respectively. Sheet names should not be modified in order for the R code to find them in the working directory.
DOI: 10.34810/data72/425
title: 01CO_1.MR.0004.0134.2016.02.04.15.44.11.109375.67139264.IMA
DOI: 10.6084/m9.figshare.15020666.v1
title: Additional file 7 of Chronic complement dysregulation drives neuroinflammation after traumatic brain injury: a transcriptomic study

description: Additional file 7. Data for R code. This file contains several excel sheets that need to be separated into separate files with the right .csv and .xlsx extensions (follow sheet name) before using the R code in additional file 6. The .csv sheets were exported from NanoString’s nSolver software and contain the fold change and p-value from multiple comparisons. The .xlsx sheets contain gene metadata for the NanoString panels. NI and COMPH in the sheet names stand for “neuroinflammation panel” and “complement panel”, respectively. Sheet names should not be modified in order for the R code to find them in the working directory.
DOI: 10.6084/m9.figshare.15020675
title: Additional file 2 of Knowledge and attitude on prevention of COVID-19 among community health workers in Nepal-a cross-sectional study

description: Additional file 2: COVID-19 database SPSS.
DOI: 10.6084/m9.figshare.15020675.v1
title: Additional file 2 of Knowledge and attitude on prevention of COVID-19 among community health workers in Nepal-a cross-sectional study

description: Additional file 2: COVID-19 database SPSS.
DOI: 10.6084/m9.figshare.15020681
title: Additional file 1 of A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

description: Additional file 1.
DOI: 10.6084/m9.figshare.15020681.v1
title: Additional file 1 of A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

description: Additional file 1.
DOI: 10.6084/m9.figshare.15020693
title: Additional file 3 of Transcriptomic changes in peripheral blood mononuclear cells with weight loss: systematic literature review and primary data synthesis

description: Additional file 3: Significant genes lists for included studies. Tables of genes significantly differentially expressed in response to the intervention and between HR and LR at baseline for the studies which yielded significant genes.
DOI: 10.6084/m9.figshare.15020693.v1
title: Additional file 3 of Transcriptomic changes in peripheral blood mononuclear cells with weight loss: systematic literature review and primary data synthesis

description: Additional file 3: Significant genes lists for included studies. Tables of genes significantly differentially expressed in response to the intervention and between HR and LR at baseline for the studies which yielded significant genes.
DOI: 10.6084/m9.figshare.15020759.v1
title: Additional file 2 of The core autophagy machinery is not required for chloroplast singlet oxygen-mediated cell death in the Arabidopsis thaliana plastid ferrochelatase two mutant

description: Additional file 2: Table S1.List of autophagy-related genes and expression data used in heatmap generation.Table S2. List of predicted microautophagy-related genes and expressiondata used in heatmap generation. Table S3. Mutants used in this study. TableS4. Primers use in this study.
DOI: 10.6084/m9.figshare.15020759
title: Additional file 2 of The core autophagy machinery is not required for chloroplast singlet oxygen-mediated cell death in the Arabidopsis thaliana plastid ferrochelatase two mutant

description: Additional file 2: Table S1.List of autophagy-related genes and expression data used in heatmap generation.Table S2. List of predicted microautophagy-related genes and expressiondata used in heatmap generation. Table S3. Mutants used in this study. TableS4. Primers use in this study.
DOI: 10.6084/m9.figshare.15020765
title: Additional file 2 of Evaluation of compost quality from municipal solid waste integrated with organic additive in Mizan–Aman town, Southwest Ethiopia

description: Additional file 2: Compost dataset and analysis
DOI: 10.6084/m9.figshare.15020813.v1
title: Additional file 1 of Emergency department impaired adherence to personal protective equipment donning and doffing protocols during the COVID-19 pandemic

description: Additional file 1.
DOI: 10.6084/m9.figshare.15020813
title: Additional file 1 of Emergency department impaired adherence to personal protective equipment donning and doffing protocols during the COVID-19 pandemic

description: Additional file 1.
DOI: 10.6084/m9.figshare.15020858
title: Additional file 3 of Supplementation of a lacto-fermented rapeseed-seaweed blend promotes gut microbial- and gut immune-modulation in weaner piglets

description: Additional file 3: Table S1. The PCR primer sequences for ONT V1-V8 region 16S rRNA gene sequencing.
DOI: 10.6084/m9.figshare.15020858.v1
title: Additional file 3 of Supplementation of a lacto-fermented rapeseed-seaweed blend promotes gut microbial- and gut immune-modulation in weaner piglets

description: Additional file 3: Table S1. The PCR primer sequences for ONT V1-V8 region 16S rRNA gene sequencing.
DOI: 10.34810/data72/426
title: 01CO_1.MR.0004.0061.2016.02.04.15.44.11.109375.67136337.IMA
DOI: 10.34810/data72/427
title: 01CO_1.MR.0004.0172.2016.02.04.15.44.11.109375.67140788.IMA
DOI: 10.34810/data72/428
title: 01CO_1.MR.0004.0084.2016.02.04.15.44.11.109375.67137260.IMA
DOI: 10.34810/data72/429
title: 01CO_1.MR.0004.0138.2016.02.04.15.44.11.109375.67139425.IMA
DOI: 10.34810/data72/430
title: 01CO_1.MR.0004.0127.2016.02.04.15.44.11.109375.67138984.IMA
DOI: 10.34810/data72/431
title: 01CO_1.MR.0004.0079.2016.02.04.15.44.11.109375.67137059.IMA
DOI: 10.5281/zenodo.5115136
title: Source discrimination - Mount St. Helens dataset

description: Explosions and earthquakes are effectively discriminated by <em>P</em>/<em>S</em> amplitude ratios for moderate magnitude events (M ≥ 4) observed at regional to teleseismic distances (≥200 km). It is less clear if <em>P</em>/<em>S</em> ratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests of <em>P</em>/<em>S</em> discrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single-fired borehole explosions (M<sub>L</sub> 0.9–2.3) and 406 earthquakes (M<sub>L</sub> 1–3.3). The array provides up to 95 three-component broadband seismographs, and most source-receiver distances are &lt;120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distance <em>P</em>/<em>S</em> ratios are investigated, including frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10–18 Hz performs better than lower or narrower bands because explosion-induced <em>S</em> wave amplitudes diminish relative to <em>P</em> for higher frequencies. Source depth and magnitude exhibited weak influences on <em>P</em>/<em>S</em> ratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustal <em>V</em><sub><em>p</em></sub> and <em>V</em><sub><em>s</em></sub> from traveltime tomography. Overall, the results indicate high potential for local distance <em>P</em>/<em>S</em> explosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.
DOI: 10.5281/zenodo.5115135
title: Source discrimination - Mount St. Helens dataset

description: Explosions and earthquakes are effectively discriminated by <em>P</em>/<em>S</em> amplitude ratios for moderate magnitude events (M ≥ 4) observed at regional to teleseismic distances (≥200 km). It is less clear if <em>P</em>/<em>S</em> ratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests of <em>P</em>/<em>S</em> discrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single-fired borehole explosions (M<sub>L</sub> 0.9–2.3) and 406 earthquakes (M<sub>L</sub> 1–3.3). The array provides up to 95 three-component broadband seismographs, and most source-receiver distances are &lt;120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distance <em>P</em>/<em>S</em> ratios are investigated, including frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10–18 Hz performs better than lower or narrower bands because explosion-induced <em>S</em> wave amplitudes diminish relative to <em>P</em> for higher frequencies. Source depth and magnitude exhibited weak influences on <em>P</em>/<em>S</em> ratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustal <em>V</em><sub><em>p</em></sub> and <em>V</em><sub><em>s</em></sub> from traveltime tomography. Overall, the results indicate high potential for local distance <em>P</em>/<em>S</em> explosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.