cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25959/3p21-ek11
title: Usage Statistics for University of Tasmania EPrints Repository

description: The dataset is an active collection of access data to information items in the University of Tasmania's EPrints repository. Each night a task is scheduled to run, and this picks up in the Apache access logs from where it left off the previous night. Each download of an open access full-text item causes the generation of a database record in the MySQL database, together with a timestamp, and an approximate location of the computer system generating the download. This is achieved by looking up the IP address against the GeoIP database, with one significant difference. Downloads originating from a University of Tasmania IP address are separately identified, and removed from the Australia category. This eliminates vanity searches from achieving high significance. Countries are coded using the ISO3166 two-letter code.

The dataset has been used to analyse the usage made of the repository and to tune it to achieve maximal visibility for the University of Tasmania. Researchers with items in the repository have used it to identify the types of use being made of their work, and to find potential collaborators. The citation of a work in a journal or conference article, for example, causes a typical step in usage, and the citing article can be searched in Google or Google Scholar to identify the authors. This enhances the dissemination experience and its value.

The software was written in the University of Tasmania by Professor Arthur Sale (in php) based on earlier work by the University of Melbourne (with permission). Mr Christian McGee wrote some critical sections of the code in perl, and set up the cron scheduling.

The dataset is generated by a computer program written by Professor Arthur Sale. The software was a test bed for ideas, and subsequently resulted in an official software set included in the EPrints distribution. This set expanded on the concepts significantly
DOI: 10.5281/zenodo.7069128
title: CoexPub learning datasets

description: Learning datasets for CoexPub
DOI: 10.5281/zenodo.7069129
title: CoexPub learning datasets

description: Learning datasets for CoexPub
DOI: 10.5281/zenodo.7052115
title: Investigation of the southern hemisphere mid-high latitude thermospheric ∑O/N2 responses to the Space-X storm

description: This data sets are the data used to plot the figures in the above mentioned paper (Figure 3 to 6) All files are in dimension 288*144*6, 288 stands for longitudes number from -180 to 180 with a resolution of 1.25 144 stands for latitude numbers fro -88.75 to 88.75 with a resolution of 1.25. 6 stands for the time, 0:20, 2:20, 4:20, 7:20, 10:20 and 13:20 UT on DOY 34. dON2 stand for the percentage diff of column density ratio of O to N2 between DOY 34 and 32 UN stands for zonal wind, VN stands for meridional wind, TN stands for neutral temperature QJO stands for Joule heating rate per unit mass near 160 km POTEN stands for ionosphere potential
DOI: 10.5281/zenodo.7052114
title: Investigation of the southern hemisphere mid-high latitude thermospheric ∑O/N2 responses to the Space-X storm

description: This data sets are the data used to plot the figures in the above mentioned paper (Figure 3 to 6) All files are in dimension 288*144*6, 288 stands for longitudes number from -180 to 180 with a resolution of 1.25 144 stands for latitude numbers fro -88.75 to 88.75 with a resolution of 1.25. 6 stands for the time, 0:20, 2:20, 4:20, 7:20, 10:20 and 13:20 UT on DOY 34. dON2 stand for the percentage diff of column density ratio of O to N2 between DOY 34 and 32 UN stands for zonal wind, VN stands for meridional wind, TN stands for neutral temperature QJO stands for Joule heating rate per unit mass near 160 km POTEN stands for ionosphere potential
DOI: 10.34740/kaggle/dsv/4190977
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.21078067
title: Transcriptomics of circulating neutrophils in dairy cows with subclinical hypocalcemia

description: Transcriptome RNA-seq sequencing row data
DOI: 10.6084/m9.figshare.21078067.v1
title: Transcriptomics of circulating neutrophils in dairy cows with subclinical hypocalcemia

description: Transcriptome RNA-seq sequencing row data
DOI: 10.34740/kaggle/dsv/4191025
title: BBC News
DOI: 10.34740/kaggle/dsv/4191012
title: Random Rubik
DOI: 10.34740/kaggle/dsv/4191049
title: Random Rubik
DOI: 10.34740/kaggle/dsv/4191056
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.3886/e130282v1-126880
title: National Neighborhood Data Archive (NaNDA): Public Transit Stops by ZIP Code Tabulation Area, United States, 2016-2018

description: This dataset lists the number of public transit stops per United States ZIP code tabulation area (ZCTA) based on data from the National Transit Map (NTM). Each observation represents the count and density of transit stops within a ZCTA, as voluntarily reported to NTM by one of 270 regional transit agencies choosing to participate between 2016 and 2018.
description: Public transit stops within the fifty United States and US island territories, as voluntarily reported by NTM participating agencies between 2016 and 2018. (A list of agencies participating as of September 2019 is available as an appendix to the documentation.)<br>. Smallest Geographic Unit: ZIP code tabulation area
description: A ZIP code to ZCTA crosswalk must be used to combine this dataset with ZIP code geocoded data must use a ZIP code to ZCTA crosswalk. Such a crosswalk is available on the UDS Mapper website at https://udsmapper.org/zip-code-to-zcta-crosswalk/. Sample code for merging the UDS Mapper crosswalk with NaNDA datasets is available on the NaNDA repository at https://www.openicpsr.org/openicpsr/project/120088/.
DOI: 10.6084/m9.figshare.21078727.v1
title: Additional file 1 of Computational analysis of G-protein-coupled receptor kinase family members as potential targets for colorectal cancer therapy

description: Additional file 1: Co-expressed genes from query genes.
DOI: 10.6084/m9.figshare.21078727
title: Additional file 1 of Computational analysis of G-protein-coupled receptor kinase family members as potential targets for colorectal cancer therapy

description: Additional file 1: Co-expressed genes from query genes.
DOI: 10.6084/m9.figshare.21078736
title: Additional file 1 of Investigating causal relations between sleep duration and risks of adverse pregnancy and perinatal outcomes: linear and nonlinear Mendelian randomization analyses

description: Additional file 1: Table S1. Results extracted from recent systematic reviews. Table S2. Key characteristics of GWAS of self-report sleep duration. Table S3. SNP list and female-specific effect estimates of sleep duration SNPs identified in UK Biobank. Table S4. Definitions of outcomes. Table S5. Details of UKB women by sleep duration groups in one-sample MR. Table S6. Characteristics of the women in MoBa by sleep duration categories. Table S7. Associations of 78 SNPs with sleep duration and with outcomes in UKB, ALSPAC, BiB, and MoBa. Table S8. Two-sample MR estimates for causal effects of sleep duration on the outcomes. Table S9. MVreg associations of self-reported sleep duration categories with the outcomes in MoBa.
DOI: 10.6084/m9.figshare.21078736.v1
title: Additional file 1 of Investigating causal relations between sleep duration and risks of adverse pregnancy and perinatal outcomes: linear and nonlinear Mendelian randomization analyses

description: Additional file 1: Table S1. Results extracted from recent systematic reviews. Table S2. Key characteristics of GWAS of self-report sleep duration. Table S3. SNP list and female-specific effect estimates of sleep duration SNPs identified in UK Biobank. Table S4. Definitions of outcomes. Table S5. Details of UKB women by sleep duration groups in one-sample MR. Table S6. Characteristics of the women in MoBa by sleep duration categories. Table S7. Associations of 78 SNPs with sleep duration and with outcomes in UKB, ALSPAC, BiB, and MoBa. Table S8. Two-sample MR estimates for causal effects of sleep duration on the outcomes. Table S9. MVreg associations of self-reported sleep duration categories with the outcomes in MoBa.
DOI: 10.6084/m9.figshare.21078728
title: Additional file 10 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 10. Statistical analysis mdC data MOLM-13.
DOI: 10.6084/m9.figshare.21078731
title: Additional file 11 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 11. Statistical analysis m5C data MOLM-13.
DOI: 10.6084/m9.figshare.21078731.v1
title: Additional file 11 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 11. Statistical analysis m5C data MOLM-13.
DOI: 10.6084/m9.figshare.21078737.v1
title: Additional file 13 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 13. Statistical analysis flow cytometry cell death data MOLM-13.
DOI: 10.6084/m9.figshare.21078740
title: Additional file 14 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 14. Statistical analysis flow cytometry apoptotic cells MOLM-13.
DOI: 10.6084/m9.figshare.21078740.v1
title: Additional file 14 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 14. Statistical analysis flow cytometry apoptotic cells MOLM-13.
DOI: 10.6084/m9.figshare.21078743
title: Additional file 15 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 15. Statistical analysis mdC data HL-60.
DOI: 10.6084/m9.figshare.21078743.v1
title: Additional file 15 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 15. Statistical analysis mdC data HL-60.
DOI: 10.6084/m9.figshare.21078746
title: Additional file 16 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 16. Statistical analysis hmdC data HL-60.
DOI: 10.6084/m9.figshare.21078746.v1
title: Additional file 16 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 16. Statistical analysis hmdC data HL-60.
DOI: 10.6084/m9.figshare.21078752
title: Additional file 2 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 2. Proteomics data MOLM-13 1.0 μM AzaC.
DOI: 10.6084/m9.figshare.21078734.v1
title: Additional file 12 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 12. Statistical analysis MTT assay MOLM-13.
DOI: 10.6084/m9.figshare.21078752.v1
title: Additional file 2 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 2. Proteomics data MOLM-13 1.0 μM AzaC.
DOI: 10.6084/m9.figshare.21078755
title: Additional file 3 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 3. Proteomics data MOLM-13 2.5 μM AzaC.
DOI: 10.6084/m9.figshare.21078755.v1
title: Additional file 3 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 3. Proteomics data MOLM-13 2.5 μM AzaC.
DOI: 10.6084/m9.figshare.21078758
title: Additional file 4 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 4. Proteomics data MOLM-13 0.5 μM AzadC.
DOI: 10.6084/m9.figshare.21078758.v1
title: Additional file 4 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 4. Proteomics data MOLM-13 0.5 μM AzadC.
DOI: 10.6084/m9.figshare.21078761
title: Additional file 5 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 5. Proteomics data MOLM-13 comparison AzaC and AzadC.
DOI: 10.6084/m9.figshare.21078761.v1
title: Additional file 5 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 5. Proteomics data MOLM-13 comparison AzaC and AzadC.
DOI: 10.6084/m9.figshare.21078764
title: Additional file 6 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 6. Proteomics data MOLM-13 IPA.
DOI: 10.6084/m9.figshare.21078764.v1
title: Additional file 6 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 6. Proteomics data MOLM-13 IPA.
DOI: 10.6084/m9.figshare.21078767
title: Additional file 7 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 7. Proteomics data HL-60 5.0 μM AzaC.
DOI: 10.6084/m9.figshare.21078770
title: Additional file 8 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 8. Proteomics data HL-60 5.0 μM AzadC.
DOI: 10.6084/m9.figshare.21078770.v1
title: Additional file 8 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 8. Proteomics data HL-60 5.0 μM AzadC.
DOI: 10.6084/m9.figshare.21078728.v1
title: Additional file 10 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 10. Statistical analysis mdC data MOLM-13.
DOI: 10.6084/m9.figshare.21078734
title: Additional file 12 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 12. Statistical analysis MTT assay MOLM-13.
DOI: 10.6084/m9.figshare.21078773
title: Additional file 9 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 9. Proteomics data HL-60 IPA.
DOI: 10.6084/m9.figshare.21078773.v1
title: Additional file 9 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 9. Proteomics data HL-60 IPA.
DOI: 10.6084/m9.figshare.21078737
title: Additional file 13 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 13. Statistical analysis flow cytometry cell death data MOLM-13.
DOI: 10.6084/m9.figshare.21078767.v1
title: Additional file 7 of Comprehensive comparison between azacytidine and decitabine treatment in an acute myeloid leukemia cell line

description: Additional file 7. Proteomics data HL-60 5.0 μM AzaC.
DOI: 10.6084/m9.figshare.16875904
title: OrthoSNAP: a tree splitting and pruning algorithm for retrieving single-copy orthologs from gene family trees

description: Molecular evolution studies, such as phylogenomic studies and genome-wide surveys of selection, often rely on gene families of single-copy orthologs (SC-OGs). Large gene families with multiple homologs in one or more species—a phenomenon observed among several important families of genes such as transporters and transcription factors—are often ignored because identifying and retrieving SC-OGs nested within them is challenging. To address this issue and increase the number of markers used in molecular evolution studies, we developed OrthoSNAP, a software that uses a phylogenetic framework to simultaneously split gene families into SC-OGs and prune species-specific inparalogs. We term SC-OGs identified by OrthoSNAP as SNAP-OGs because they are identified using a <em>s</em>plitti<em>n</em>g <em>a</em>nd <em>p</em>runing procedure analogous to snapping branches on a tree. From 415,129 orthologous groups of genes inferred across seven eukaryotic phylogenomic datasets, we identified 9,821 SC-OGs; using OrthoSNAP on the remaining 405,308 orthologous groups of genes, we identified an additional 10,704 SNAP-OGs. Comparison of SNAP-OGs and SC-OGs revealed that their phylogenetic information content was similar, even in complex datasets that contain a whole genome duplication, complex patterns of duplication and loss, transcriptome data where each gene typically has multiple transcripts, and contentious branches in the tree of life. OrthoSNAP is useful for increasing the number of markers used in molecular evolution data matrices, a critical step for robustly inferring and exploring the tree of life.
DOI: 10.6084/m9.figshare.16875904.v1
title: OrthoSNAP: a tree splitting and pruning algorithm for retrieving single-copy orthologs from gene family trees

description: Molecular evolution studies, such as phylogenomic studies and genome-wide surveys of selection, often rely on gene families of single-copy orthologs (SC-OGs). Large gene families with multiple homologs in one or more species—a phenomenon observed among several important families of genes such as transporters and transcription factors—are often ignored because identifying and retrieving SC-OGs nested within them is challenging. To address this issue and increase the number of markers used in molecular evolution studies, we developed OrthoSNAP, a software that uses a phylogenetic framework to simultaneously split gene families into SC-OGs and prune species-specific inparalogs. We term SC-OGs identified by OrthoSNAP as SNAP-OGs because they are identified using a <em>s</em>plitti<em>n</em>g <em>a</em>nd <em>p</em>runing procedure analogous to snapping branches on a tree. From 415,129 orthologous groups of genes inferred across seven eukaryotic phylogenomic datasets, we identified 9,821 SC-OGs; using OrthoSNAP on the remaining 405,308 orthologous groups of genes, we identified an additional 10,704 SNAP-OGs. Comparison of SNAP-OGs and SC-OGs revealed that their phylogenetic information content was similar, even in complex datasets that contain a whole genome duplication, complex patterns of duplication and loss, transcriptome data where each gene typically has multiple transcripts, and contentious branches in the tree of life. OrthoSNAP is useful for increasing the number of markers used in molecular evolution data matrices, a critical step for robustly inferring and exploring the tree of life.
DOI: 10.34740/kaggle/dsv/4191297
title: BART Ridership
DOI: 10.5281/zenodo.7068402
title: Physical oceanography, biotic and abiotic carbon data in the deep sea on the northern slope of the South China Sea in June 2015

description: The datasets are used for the study of the effects of mesoscale eddies on biological carbon pumps in deep sea water in the northern South China Sea. Thus the physical oceanography, biotic and abiotic carbon data are included in the datasets.
DOI: 10.5281/zenodo.7068401
title: Physical oceanography, biotic and abiotic carbon data in the deep sea on the northern slope of the South China Sea in June 2015

description: The datasets are used for the study of the effects of mesoscale eddies on biological carbon pumps in deep sea water in the northern South China Sea. Thus the physical oceanography, biotic and abiotic carbon data are included in the datasets.
DOI: 10.6084/m9.figshare.13030898
title: Supporting information

description: Data obtiened from analysis using weka segmentation from around 7500 pictures obteined with different stainings Live/Dead Cell Imaging Kit, Autophagy Cell Imaging Kit, Caspase-3 and -7 Cell Imaging Kit, and Propidium iodide. The file contains data from which Figure 3, Figure 4, Figure 5 and Figure 6 were depicted.
DOI: 10.6084/m9.figshare.13030898.v5
title: Supporting information

description: Data obtiened from analysis using weka segmentation from around 7500 pictures obteined with different stainings Live/Dead Cell Imaging Kit, Autophagy Cell Imaging Kit, Caspase-3 and -7 Cell Imaging Kit, and Propidium iodide. The file contains data from which Figure 3, Figure 4, Figure 5 and Figure 6 were depicted.
DOI: 10.6084/m9.figshare.13030898.v4
title: Supplementary information

description: Data obtiened from analysis using weka segmentation from around 7500 pictures obteined with different stainings Live/Dead Cell Imaging Kit, Autophagy Cell Imaging Kit, Caspase-3 and -7 Cell Imaging Kit, and Propidium iodide. The file contains data from which Figure 3, Figure 4, Figure 5 and Figure 6 were depicted.
DOI: 10.6084/m9.figshare.20092547
title: Academic Medical Center Degree Distribution

description: Degree distribution data and plots for 40 academic medical center website topologies.
DOI: 10.6084/m9.figshare.20092547.v2
title: Academic Medical Center Degree Distribution

description: Degree distribution plots for 40 academic medical center websites.
DOI: 10.6084/m9.figshare.20092547.v3
title: Academic Medical Center Degree Distribution

description: Degree distribution data and plots for 40 academic medical center website topologies.
DOI: 10.34740/kaggle/dsv/4191452
title: Russian Invasion of Ukraine
DOI: 10.5281/zenodo.7069403
title: Supporting information: VISA Metabolomics Profiling Metadata

description: Metadata for metabolomics profiling of vancomycin-intermediate S. aureus (VISA) strains: Mu50Ω (VSSA), Mu50Ω-<em>vraS</em>m (VISA) and Mu50Ω-<em>vraS</em>m-<em>graR</em>m (VISA)
DOI: 10.5281/zenodo.7069402
title: Supporting information: VISA Metabolomics Profiling Metadata

description: Metadata for metabolomics profiling of vancomycin-intermediate S. aureus (VISA) strains: Mu50Ω (VSSA), Mu50Ω-<em>vraS</em>m (VISA) and Mu50Ω-<em>vraS</em>m-<em>graR</em>m (VISA)
DOI: 10.6084/m9.figshare.19532131
title: Data for "Pore-scale modelling of multiphase flow in porous media using a conditional generative adversarial network (cGAN)"

description: The data set contains the invasion morphologies at percolation of quasi-static fluid-fluid displacement processes for different contact angles and particle shapes.<br>Simulations are generated using the interface tracking algorithm, which are then binarized into 256x256 size images. These images are used to train the cGAN network for predicting multiphase flow. See the manuscript for more detial.<br>File name "ITA_XXX_CAYYY.mat" represents the data set for porous media with particle shape "XXX" with contact angle "YYY". Files can be opened by MATLAB. Each data contains 5000 pairs of data, with have been divided into two parts, i.e., 4000 pairs for training, and 1000 pairs for test.
DOI: 10.6084/m9.figshare.19532131.v2
title: Data for "Pore-scale modelling of multiphase flow in porous media using a conditional generative adversarial network (cGAN)"

description: The data set contains the invasion morphologies at percolation of quasi-static fluid-fluid displacement processes for different contact angles and particle shapes.<br>Simulations are generated using the interface tracking algorithm, which are then binarized into 256x256 size images. These images are used to train the cGAN network for predicting multiphase flow. See the manuscript for more detial.<br>File name "ITA_XXX_CAYYY.mat" represents the data set for porous media with particle shape "XXX" with contact angle "YYY". Files can be opened by MATLAB. Each data contains 5000 pairs of data, with have been divided into two parts, i.e., 4000 pairs for training, and 1000 pairs for test.