cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13145/bacdive145402.20190402.4
title: Streptococcus sanguinis

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive145403.20190402.4
title: Staphylococcus haemolyticus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146194.20190402.4
title: Campylobacter helveticus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146195.20190402.4
title: Enterococcus avium

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146196.20190402.4
title: Enterococcus malodoratus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146197.20190402.4
title: Enterococcus mundtii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146198.20190402.4
title: Lactobacillus delbrueckii subsp. bulgaricus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146199.20190402.4
title: Streptococcus salivarius subsp. thermophilus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146200.20190402.4
title: Serratia marcescens

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146201.20190402.4
title: Legionella pneumophila

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146913.20190402.4
title: Staphylococcus simulans

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146914.20190402.4
title: Acinetobacter lwoffii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146915.20190402.4
title: Pseudomonas fluorescens

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146916.20190402.4
title: Aggregatibacter aphrophilus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146917.20190402.4
title: Bordetella parapertussis

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146918.20190402.4
title: Rothia dentocariosa

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146919.20190402.4
title: Streptococcus salivarius

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146920.20190402.4
title: Streptococcus anginosus subsp. whileyi

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive146921.20190402.4
title: Schaalia odontolytica

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147676.20190402.4
title: Staphylococcus haemolyticus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147677.20190402.4
title: Staphylococcus aureus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147678.20190402.4
title: Actinomyces naeslundii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147679.20190402.4
title: Rothia dentocariosa

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147680.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147681.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147682.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147683.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive147684.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148441.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148442.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148443.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148444.20190402.4
title: Actinomyces viscosus

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive148445.20190402.4
title: Actinomyces israelii

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.25345/c5xr1v
title: MassIVE MSV000086259 - AFmetabolomics project for charting the metabolic diversity
title: AFmetabolomics project for charting the metabolic diversity
DOI: 10.5281/zenodo.5553287
title: BISICLES Pine Island Glacier simulations with linear friction

description: Model simulations of the ice sheet model BISICLES for 100 years on a set of topographies, sampled form a GP (plus BM2 and BedMachine) with linear Weertman friction law. See chapter 5 of https://doi.org/10.21954/ou.ro.0001223d for more information
description: {"references": ["Wernecke, Andreas. Quantifying Century-Scale Uncertainties Of The Global Mean Sea Level Rise Contribution From The Amundsen Sea Sector, West Antarctica. Diss. The Open University, 2020."]}
DOI: 10.6084/m9.figshare.16634977
title: Supplementary data

description: Average transmittance curves of whole eyes, corneas and lenses for all bird species used
DOI: 10.6084/m9.figshare.16634977.v1
title: Supplementary data

description: Average transmittance curves of whole eyes, corneas and lenses for all bird species used
DOI: 10.6084/m9.figshare.13265561.v4
title: Arctic COSMO-CLM hindcast 2015

description: COSMO-CLM Russian Arctic climate dataset
DOI: 10.3886/e152621
title: The original data

description: This project is the introduction of the data source and processing method of the paper. The data includes two operation codes and the final original data. The original data mainly comes from the network and books, mainly from the network, supplemented by manual supplement. The code of network capture is implemented in Python. Capture the lyrics and song titles information based on the song list. subsequently, the song list information was sorted, the geographic information data (longitude and latitude) were supplemented, and the nuclear density was analyzed by ArcGIS. After that, TF IDF is calculated based on the song titles information, and the calculation code has been uploaded. The data analysis steps and results are explained in detail in the paper.<br>
DOI: 10.3886/e152621v1
title: The original data

description: This project is the introduction of the data source and processing method of the paper. The data includes two operation codes and the final original data. The original data mainly comes from the network and books, mainly from the network, supplemented by manual supplement. The code of network capture is implemented in Python. Capture the lyrics and song titles information based on the song list. subsequently, the song list information was sorted, the geographic information data (longitude and latitude) were supplemented, and the nuclear density was analyzed by ArcGIS. After that, TF IDF is calculated based on the song titles information, and the calculation code has been uploaded. The data analysis steps and results are explained in detail in the paper.<br>
DOI: 10.6084/m9.figshare.16822918
title: Cell Populations Expressing Stemness-Associated Markers in Lung Adenocarcinoma

description: <b>Abstract:</b><br>The stemness-associated markers OCT4, NANOG, SOX2, KLF4 and c-MYC are expressed in numerous cancer types suggesting the presence of cancer stem cells (CSCs). Immunohistochemical (IHC) staining performed on 12 lung adenocarcinoma (LA) tissue samples showed protein expression of OCT4, NANOG, SOX2, KLF4 and c-MYC, and the CSC marker CD44. In situ hybridization (ISH) performed on six of the LA tissue samples showed mRNA expression of OCT4, NANOG, SOX2, KLF4 and c-MYC. Immunofluorescence staining performed on three of the tissue samples showed co-expression of OCT4 and c-MYC with NANOG, SOX2 and KLF4 by tumor gland cells, and expression of OCT4 and c-MYC exclusively by cells within the stroma. RT-qPCR performed on five LA-derived primary cell lines showed mRNA expression of all the markers except SOX2. Western blotting performed on four LA-derived primary cell lines demonstrated protein expression of all the markers except SOX2 and NANOG. Initial tumorsphere assays performed on four LA-derived primary cell lines demonstrated 0–80% of tumorspheres surpassing the 50 µm threshold. The expression of the stemness-associated markers OCT4, SOX2, NANOG, KFL4 and c-MYC by LA at the mRNA and protein level, and the unique expression patterns suggest a putative presence of CSC subpopulations within LA, which may be a novel therapeutic target for this cancer. Further functional studies are required to investigate the possession of stemness traits.
DOI: 10.6084/m9.figshare.16822918.v3
title: Cell Populations Expressing Stemness-Associated Markers in Lung Adenocarcinoma

description: <b>Abstract:</b><br>The stemness-associated markers OCT4, NANOG, SOX2, KLF4 and c-MYC are expressed in numerous cancer types suggesting the presence of cancer stem cells (CSCs). Immunohistochemical (IHC) staining performed on 12 lung adenocarcinoma (LA) tissue samples showed protein expression of OCT4, NANOG, SOX2, KLF4 and c-MYC, and the CSC marker CD44. In situ hybridization (ISH) performed on six of the LA tissue samples showed mRNA expression of OCT4, NANOG, SOX2, KLF4 and c-MYC. Immunofluorescence staining performed on three of the tissue samples showed co-expression of OCT4 and c-MYC with NANOG, SOX2 and KLF4 by tumor gland cells, and expression of OCT4 and c-MYC exclusively by cells within the stroma. RT-qPCR performed on five LA-derived primary cell lines showed mRNA expression of all the markers except SOX2. Western blotting performed on four LA-derived primary cell lines demonstrated protein expression of all the markers except SOX2 and NANOG. Initial tumorsphere assays performed on four LA-derived primary cell lines demonstrated 0–80% of tumorspheres surpassing the 50 µm threshold. The expression of the stemness-associated markers OCT4, SOX2, NANOG, KFL4 and c-MYC by LA at the mRNA and protein level, and the unique expression patterns suggest a putative presence of CSC subpopulations within LA, which may be a novel therapeutic target for this cancer. Further functional studies are required to investigate the possession of stemness traits.
DOI: 10.6084/m9.figshare.16822918.v2
title: Cell Populations Expressing Stemness-Associated Markers in Lung Adenocarcinoma

description: <b>Abstract:</b><br>The stemness-associated markers OCT4, NANOG, SOX2, KLF4 and c-MYC are expressed in numerous cancer types suggesting the presence of cancer stem cells (CSCs). Immunohistochemical (IHC) staining performed on 12 lung adenocarcinoma (LA) tissue samples showed protein expression of OCT4, NANOG, SOX2, KLF4 and c-MYC, and the CSC marker CD44. In situ hybridization (ISH) performed on six of the LA tissue samples showed mRNA expression of OCT4, NANOG, SOX2, KLF4 and c-MYC. Immunofluorescence staining performed on three of the tissue samples showed co-expression of OCT4 and c-MYC with NANOG, SOX2 and KLF4 by tumor gland cells, and expression of OCT4 and c-MYC exclusively by cells within the stroma. RT-qPCR performed on five LA-derived primary cell lines showed mRNA expression of all the markers except SOX2. Western blotting performed on four LA-derived primary cell lines demonstrated protein expression of all the markers except SOX2 and NANOG. Initial tumorsphere assays performed on four LA-derived primary cell lines demonstrated 0–80% of tumorspheres surpassing the 50 µm threshold. The expression of the stemness-associated markers OCT4, SOX2, NANOG, KFL4 and c-MYC by LA at the mRNA and protein level, and the unique expression patterns suggest a putative presence of CSC subpopulations within LA, which may be a novel therapeutic target for this cancer. Further functional studies are required to investigate the possession of stemness traits.
DOI: 10.6084/m9.figshare.16823029
title: large-eddy simulation data and the ocean mixed-layer model results

description: These are the large-eddy simulation data, the ocean mixed-layer model results, and the inverse estimation results, which are used in a paper draft "Improvement of the Ocean Mixed-Layer Model via Large-Eddy Simulation and Inverse Estimation".<br>The large-eddy simulation data are obtained via PALM (https://palm.muk.uni-hannover.de/trac).The ocean mixed-layer model (Noh and Kim 1999, Noh et al. 2016) results are obtained using its original, perturbed (for sensitivity tests), and optimized versions.<br>The ocean mixed-layer model data include the temperature, horizontal velocities, turbulent kinetic energy (TKE), turbulent vertical fluxes, the mixed layer depths, and theTKE budget terms. The LES data include the mixed layer depths and the TKE budget terms. The data are in netCDF and text formats.<br>Most large-eddy simulations have been carried out on the supercomputer system supported by the National Center for Meteorological Supercomputer of Korea Meteorological Administration (KMA).
DOI: 10.6084/m9.figshare.16823029.v1
title: large-eddy simulation data and the ocean mixed-layer model results

description: These are the large-eddy simulation data, the ocean mixed-layer model results, and the inverse estimation results, which are used in a paper draft "Improvement of the Ocean Mixed-Layer Model via Large-Eddy Simulation and Inverse Estimation".<br>The large-eddy simulation data are obtained via PALM (https://palm.muk.uni-hannover.de/trac).The ocean mixed-layer model (Noh and Kim 1999, Noh et al. 2016) results are obtained using its original, perturbed (for sensitivity tests), and optimized versions.<br>The ocean mixed-layer model data include the temperature, horizontal velocities, turbulent kinetic energy (TKE), turbulent vertical fluxes, the mixed layer depths, and theTKE budget terms. The LES data include the mixed layer depths and the TKE budget terms. The data are in netCDF and text formats.<br>Most large-eddy simulations have been carried out on the supercomputer system supported by the National Center for Meteorological Supercomputer of Korea Meteorological Administration (KMA).
DOI: 10.6084/m9.figshare.16823020.v1
title: nkx2.1.incluster13_distalPeaks_PAL.bed.highestScoreMotifs

description: NKX2-1 transcription factor binding site (TFBS) analysis. First, we used HOMER to identify predicted NKX2-1 binding sites (using the HOMER built in matrix “nkx2.1.motif”) across the GRCh38 genome, and removed any site within the ENCODE blacklist. For normal lung cell analysis, we intersected this list with 6,754 ATAC-seq peaks identified in the pneumocyte (PAL) cluster 13 CREs from (6) (downloaded from supplemental table 6 of that paper “Table_S6_Union_set_of_cCREs.xlsx”). We then selected 5,974 peaks that overlapped a predicted NKX2-1 TFBS, and centered each on the predicted NXK2-1 TFBS. If multiple TFBS were present in the peak, we took the motif with the highest HOMER log-odds match score.
DOI: 10.6084/m9.figshare.16822990.v1
title: raw-modifcation-calls-fixed-format.zip

description: Methylation calling of nanopore sWGS data. 5mC was called using DeepSignal Version: 0.1.8, with model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt, which was downloaded from the DeepSignal Google Drive (https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P). We used the DeepSignal call_mods (mofidication_call) output tsv file. To aid in combined methylation and fragmentomic analysis, we added additional columns to this file The final 14 fields were extracted from Minimap2 alignment files, matched by read id. They are as follows:Column 11: flag, 12: contig, 13: start, 14: end, 15: mapping quality of the chosen alignment, 16: mapping quality of the alignment with the best mapping quality (check), 17: mapping quality of the alignment with the "second" best mapping quality (check), 18: number of alignments for that read, 19: number of alignments for those alignments with mapping quality &gt; 0, 20: left hard clipped bases, 21: left soft clipped bases, 22: read length (from CIGAR, not including soft clipped bases), 23: right soft clipped bases, 24: right hard clipped basesAll genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16822996
title: bedsFromBAMsForGEO.zip

description: Filtering of alignments for ichorCNA and fragmentomic analysis. Samtools (Version 1.9) was used to filter BAM alignments, unmapped reads, secondary and supplementary reads, reads with Minimap2 mapping quality less than 20 as in https://doi.org/10.1101/2020.12.28.424602, and reads longer than 700bp. Bedtobam was used to create bed files. All genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16822996.v1
title: bedsFromBAMsForGEO.zip

description: Filtering of alignments for ichorCNA and fragmentomic analysis. Samtools (Version 1.9) was used to filter BAM alignments, unmapped reads, secondary and supplementary reads, reads with Minimap2 mapping quality less than 20 as in https://doi.org/10.1101/2020.12.28.424602, and reads longer than 700bp. Bedtobam was used to create bed files. All genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16823002
title: SegmentationResultsMartignano2021.zip

description: Segmentation results from our previous CNV analysis (https://doi.org/10.1186/s12943-021-01327-5) were converted from GRCh37 to GRCh38 using NCBI remap API and divided into non-overlapping 10Mb bins. All genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16823020
title: nkx2.1.incluster13_distalPeaks_PAL.bed.highestScoreMotifs

description: NKX2-1 transcription factor binding site (TFBS) analysis. First, we used HOMER to identify predicted NKX2-1 binding sites (using the HOMER built in matrix “nkx2.1.motif”) across the GRCh38 genome, and removed any site within the ENCODE blacklist. For normal lung cell analysis, we intersected this list with 6,754 ATAC-seq peaks identified in the pneumocyte (PAL) cluster 13 CREs from (6) (downloaded from supplemental table 6 of that paper “Table_S6_Union_set_of_cCREs.xlsx”). We then selected 5,974 peaks that overlapped a predicted NKX2-1 TFBS, and centered each on the predicted NXK2-1 TFBS. If multiple TFBS were present in the peak, we took the motif with the highest HOMER log-odds match score.
DOI: 10.6084/m9.figshare.16823002.v1
title: SegmentationResultsMartignano2021.zip

description: Segmentation results from our previous CNV analysis (https://doi.org/10.1186/s12943-021-01327-5) were converted from GRCh37 to GRCh38 using NCBI remap API and divided into non-overlapping 10Mb bins. All genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16822990
title: raw-modifcation-calls-fixed-format.zip

description: Methylation calling of nanopore sWGS data. 5mC was called using DeepSignal Version: 0.1.8, with model.CpG.R9.4_1D.human_hx1.bn17.sn360.v0.1.7+/bn_17.sn_360.epoch_9.ckpt, which was downloaded from the DeepSignal Google Drive (https://drive.google.com/open?id=1zkK8Q1gyfviWWnXUBMcIwEDw3SocJg7P). We used the DeepSignal call_mods (mofidication_call) output tsv file. To aid in combined methylation and fragmentomic analysis, we added additional columns to this file The final 14 fields were extracted from Minimap2 alignment files, matched by read id. They are as follows:Column 11: flag, 12: contig, 13: start, 14: end, 15: mapping quality of the chosen alignment, 16: mapping quality of the alignment with the best mapping quality (check), 17: mapping quality of the alignment with the "second" best mapping quality (check), 18: number of alignments for that read, 19: number of alignments for those alignments with mapping quality &gt; 0, 20: left hard clipped bases, 21: left soft clipped bases, 22: read length (from CIGAR, not including soft clipped bases), 23: right soft clipped bases, 24: right hard clipped basesAll genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16823005
title: grouped-beta-value_bedgraph.zip

description: From the modified DeepSignal modification_call output described above (in “raw-modifcation-calls-fixed-format.zip”), we then extracted the methylation calls for each (strand-specific) CpG from column 9 (called_label field), and calculated a methylation beta value by taking the number of methylated reads (value 1) divided by the total number of reads (value 0 or value 1). These were collapsed into a bedgraph file with a value between 0-1 for every CpG covered. All genomic coordinates are in GRCh38.
DOI: 10.6084/m9.figshare.16823005.v1
title: grouped-beta-value_bedgraph.zip

description: From the modified DeepSignal modification_call output described above (in “raw-modifcation-calls-fixed-format.zip”), we then extracted the methylation calls for each (strand-specific) CpG from column 9 (called_label field), and calculated a methylation beta value by taking the number of methylated reads (value 1) divided by the total number of reads (value 0 or value 1). These were collapsed into a bedgraph file with a value between 0-1 for every CpG covered. All genomic coordinates are in GRCh38.