cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/98y6-mh43
title: Telugu Handwritten Character Dataset

description: The dataset consists all the Telugu characters that contains Vowels, Consonants and combine characters such as Othulu (Consonant-Consonant) and Guninthamulu (Consonant-Volwels). The main objective of this dataset to recognize handwritten Telugu characters, from that convert handwritten document into editable electronic copy. There is a significant difference between Indian literature and English literature, i.e., if we see English literature only 26 Characters, but where in Telugu total number of characters are 1,924 (Achulu (Vowels)- 16, Hallulu (consonants)- 36, Othulu – 36 and Guninthamulu – 34*16=544). Hence, problem of recognition of Telugu characters are complex in compare to English. Furthermore, no dataset of Telugu characters that covers all characters in Telugu literature and even the worldwide encoding standard “Unicode” have not covering all Alphabet in Telugu. The objective of this work is to present a Handwritten Telugu character dataset with all Telugu Alphabets, assigning unique label to each character from there assign ‘Unicode’ to each label. If we could bring all Telugu and other Indian language characters into ‘Unicode’, it will resolve compatibility issues of all major Operating Systems and Word Processors. The dataset is designed to recognize all short of handwriting styles. Hence, we create dataset from different distinct writers in that some are from schools (under 15 years), some are from above 45 years and some are engineering graduates at the age between 18-24. Moreover, we gave unique labels for each character in Telugu literature. Hence, these labels are also support other Indian languages because of similarity in Phonics.
DOI: 10.21227/fp04-fq60
title: Telugu Handwritten Character Dataset

description: The dataset consists all the Telugu characters that contains Vowels, Consonants and combine characters such as Othulu (Consonant-Consonant) and Guninthamulu (Consonant-Volwels). The main objective of this dataset to recognize handwritten Telugu characters, from that convert handwritten document into editable electronic copy. There is a significant difference between Indian literature and English literature, i.e., if we see English literature only 26 Characters, but where in Telugu total number of characters are 1,924 (Achulu (Vowels)- 16, Hallulu (consonants)- 36, Othulu – 36 and Guninthamulu – 34*16=544). Hence, problem of recognition of Telugu characters are complex in compare to English. Furthermore, no dataset of Telugu characters that covers all characters in Telugu literature and even the worldwide encoding standard “Unicode” have not covering all Alphabet in Telugu. The objective of this work is to present a Handwritten Telugu character dataset with all Telugu Alphabets, assigning unique label to each character from there assign ‘Unicode’ to each label. If we could bring all Telugu and other Indian language characters into ‘Unicode’, it will resolve compatibility issues of all major Operating Systems and Word Processors. The dataset is designed to recognize all short of handwriting styles. Hence, we create dataset from different distinct writers in that some are from schools (under 15 years), some are from above 45 years and some are engineering graduates at the age between 18-24. Moreover, we gave unique labels for each character in Telugu literature. Hence, these labels are also support other Indian languages because of similarity in Phonics.
DOI: 10.17632/syg24p87mt.1
title: Data for: A Metasomatized 18O-rich Veined Lithospheric Mantle Source for Ultrapotassic Magmas
DOI: 10.15468/wx4w4z
title: Revision of the endemic Afrotropical genus Tetractenion (Hymenoptera, Ichneumonidae) with an identification key to genera of Banchinae for the region

description: This dataset contains the digitized treatments in Plazi based on the original journal article Berry, Terry Reynolds, Noort, Simon van (2020): Revision of the endemic Afrotropical genus Tetractenion (Hymenoptera, Ichneumonidae) with an identification key to genera of Banchinae for the region. ZooKeys 1007: 49-84, DOI: http://dx.doi.org/10.3897/zookeys.1007.55543, URL: http://dx.doi.org/10.3897/zookeys.1007.55543
DOI: 10.15468/pke6k4
title: Three new species of Exocelina Broun, 1886 from the southern slopes of the New Guinea central range, with introduction of the Exocelina skalei group (Coleoptera, Dytiscidae, Copelatinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Shaverdo, Helena, Surbakti, Suriani, Sumoked, Bob, Balke, Michael (2020): Three new species of Exocelina Broun, 1886 from the southern slopes of the New Guinea central range, with introduction of the Exocelina skalei group (Coleoptera, Dytiscidae, Copelatinae). ZooKeys 1007: 129-143, DOI: http://dx.doi.org/10.3897/zookeys.1007.59351, URL: http://dx.doi.org/10.3897/zookeys.1007.59351
DOI: 10.15468/gctred
title: Pimpla Fabricius, 1804 (Ichneumonidae, Pimplinae) from Uruguay: a replacement name, new records, and an identification key to the species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Padua, Diego G., Fernandes, Daniell R. R., Saeaeksjaervi, Ilari E. (2020): Pimpla Fabricius, 1804 (Ichneumonidae, Pimplinae) from Uruguay: a replacement name, new records, and an identification key to the species. ZooKeys 1007: 23-47, DOI: http://dx.doi.org/10.3897/zookeys.1007.56328, URL: http://dx.doi.org/10.3897/zookeys.1007.56328
DOI: 10.15468/npcncu
title: Shards, sequences, and shorelines: two new species of Bembidion from North America (Coleoptera, Carabidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Maddison, David R. (2020): Shards, sequences, and shorelines: two new species of Bembidion from North America (Coleoptera, Carabidae). ZooKeys 1007: 85-128, DOI: http://dx.doi.org/10.3897/zookeys.1007.60012, URL: http://dx.doi.org/10.3897/zookeys.1007.60012
DOI: 10.5281/zenodo.4304786
title: Iterative Bleaching Extends multi-pleXity (IBEX) imaging method, mouse spleen

description: This dataset was acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in: “IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., 2020, doi:10.1073/pnas.2018488117. It is comprised of a three cycle IBEX experiment performed on mouse spleen sections labeled with the nuclear marker JOJO-1 and membrane label CD4 AF594. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 mm), y (0.284 mm), and z (1 mm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976). Markers per channel in each of the three cycles: spleen_panel1.nrrd (6 channels): B220 PE, CD8 BV421, IgD AF700, CD4 AF594, JOJO, Foxp3 eF660 spleen_panel2.nrrd (7 channels): CD169 PE, F480 BV421, MHCII AF700, CollIV AF488, JOJO, CD11c AF647, CD4 AF594 spleen_panel3.nrrd (7 channels): CD31 PE, CD68 BV421, Ki67 AF700, CD45 AF488, CD4 AF594, JOJO, CD3 AF647 The panels can be registered using the code available on github: https://github.com/niaid/sitk-ibex To view these multi-channel images, in nrrd format, use the Fiji viewer. The data is stored in XYZC order.
description: {"references": ["A. Radtke et al., \"IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues\", 2020."]}
DOI: 10.5281/zenodo.4304785
title: Iterative Bleaching Extends multi-pleXity (IBEX) imaging method, mouse spleen

description: This dataset was acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in: “IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues“, A. Radtke et al., 2020, doi:10.1073/pnas.2018488117. It is comprised of a three cycle IBEX experiment performed on mouse spleen sections labeled with the nuclear marker JOJO-1 and membrane label CD4 AF594. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 mm), y (0.284 mm), and z (1 mm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976). Markers per channel in each of the three cycles: spleen_panel1.nrrd (6 channels): B220 PE, CD8 BV421, IgD AF700, CD4 AF594, JOJO, Foxp3 eF660 spleen_panel2.nrrd (7 channels): CD169 PE, F480 BV421, MHCII AF700, CollIV AF488, JOJO, CD11c AF647, CD4 AF594 spleen_panel3.nrrd (7 channels): CD31 PE, CD68 BV421, Ki67 AF700, CD45 AF488, CD4 AF594, JOJO, CD3 AF647 The panels can be registered using the code available on github: https://github.com/niaid/sitk-ibex To view these multi-channel images, in nrrd format, use the Fiji viewer. The data is stored in XYZC order.
description: {"references": ["A. Radtke et al., \"IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues\", 2020."]}
DOI: 10.15468/np3ycd
title: Preliminary list of mosses of the Sengilen Range (the Republic of Tuva)

description: The dataset includes a list of moss species registered in sum at three key plots located in the steppe belt (1300-1450 m altitude), forest-steppe belt (1880-2150 m altitude), and high-mountains (2200-2560 m altitude) of the range. The surveyed area in each case was about 3x3 km. Samples are in NSK (USU 440537). The work is under support of RFBR ? 18-04-00822.
DOI: 10.25919/bap2-zr31
title: Parkes observations for project P885 semester 2020OCTS_02

description: We propose to continue observing the magnetars 1E 1547.0?5408 and PSR J1622?4950 weekly, to track their rotation, flux density, and polarisation. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810?197, again a radio magnetar after 10 years dormant, and of the hot-off-the-press magnetar Swift J1818.0?1607, for which our observations with the UWL receiver are providing unique and tantalizing measurements of radio spectrum and polarisation.
DOI: 10.21227/g0xs-gr79
title: Measurement data for Compact MIMO Antenna Systems for Sub-6GHz 5G and V2X Communications

description: Various multiple-input multiple-output (MIMO) antenna systems are presented in this paper using low profile wideband Planar Inverted-F antenna (PIFA) and compact wideband monopole for automotive application in the sub-6GHz 5G systems and Vehicle-to-Everything (V2X) communications that operate on the frequency range from 617MHz to 6GHz. The envelope correlation coefficient (ECC) and diversity gain (DG) are calculated in each MIMO configuration as they represent the two key factors in the MIMO performance. Simulation results are presented along with measured data on Ground plane (GND) and on vehicle from properly cut metal sheet prototypes. The results are discussed in terms of return loss, passive isolation between elements, combined radiation patterns, port- efficiencies, correlation coefficient and diversity gain.
DOI: 10.21227/vgnd-2m09
title: Measurement data for Compact MIMO Antenna Systems for Sub-6GHz 5G and V2X Communications

description: Various multiple-input multiple-output (MIMO) antenna systems are presented in this paper using low profile wideband Planar Inverted-F antenna (PIFA) and compact wideband monopole for automotive application in the sub-6GHz 5G systems and Vehicle-to-Everything (V2X) communications that operate on the frequency range from 617MHz to 6GHz. The envelope correlation coefficient (ECC) and diversity gain (DG) are calculated in each MIMO configuration as they represent the two key factors in the MIMO performance. Simulation results are presented along with measured data on Ground plane (GND) and on vehicle from properly cut metal sheet prototypes. The results are discussed in terms of return loss, passive isolation between elements, combined radiation patterns, port- efficiencies, correlation coefficient and diversity gain.
DOI: 10.6084/m9.figshare.13506711
title: Raw IMU Data from Experiments

description: Raw IMU data from the different experimental conditions used in the thesis (mechanical, mechanical with soft-tissue, and human)
DOI: 10.5281/zenodo.4398992
title: Data of q-flux Green's Function experiments and abrupt 4xCO2 simulation with NCAR CAM5_SOM model

description: This repository contains processed data for a suite of q-flux patches experiments and the abrupt4×CO<sub>2 </sub>simulation in NCAR CAM5 coupled with a slab-ocean model. See README file for more information.
DOI: 10.6084/m9.figshare.13506966
title: Data_S1.zip

description: Sequence trace of genome edited regions of mouse Stra8 promoter.
DOI: 10.6084/m9.figshare.13506984.v1
title: Additional file 1 of Correction to: The low-copy nuclear gene Agt1 as a novel DNA barcoding marker for Bromeliaceae

description: Additional file 1: Incorrect version of Additional file 1.
DOI: 10.6084/m9.figshare.13506984
title: Additional file 1 of Correction to: The low-copy nuclear gene Agt1 as a novel DNA barcoding marker for Bromeliaceae

description: Additional file 1: Incorrect version of Additional file 1.
DOI: 10.6084/m9.figshare.13506987
title: Additional file 2 of Correction to: The low-copy nuclear gene Agt1 as a novel DNA barcoding marker for Bromeliaceae

description: Additional file 2: Correct version of Additional file 1.
DOI: 10.6084/m9.figshare.13506987.v1
title: Additional file 2 of Correction to: The low-copy nuclear gene Agt1 as a novel DNA barcoding marker for Bromeliaceae

description: Additional file 2: Correct version of Additional file 1.
DOI: 10.6084/m9.figshare.13467291
title: Additional file 3 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 3: Table S2. Full linear model results for Fig. 3.
DOI: 10.6084/m9.figshare.13467297
title: Additional file 4 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 4: Summary of metadata for study population and samples collected for S. aureus microbiome profiling.
DOI: 10.6084/m9.figshare.13467291.v1
title: Additional file 3 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 3: Table S2. Full linear model results for Fig. 3.
DOI: 10.6084/m9.figshare.13467297.v1
title: Additional file 4 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 4: Summary of metadata for study population and samples collected for S. aureus microbiome profiling.
DOI: 10.6084/m9.figshare.13467301
title: Additional file 5 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 5: Table S4. Summary of S. aureus variables for study population and samples collected for S. aureus microbiome profiling.
DOI: 10.6084/m9.figshare.13467289.v1
title: Additional file 2 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 2: Table S1. Sample sizes for boxplots.
DOI: 10.6084/m9.figshare.13467307
title: Additional file 7 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 7: Table S6. Subject metadata.
DOI: 10.6084/m9.figshare.13467315
title: Additional file 9 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 9: Table S8. HUMAnN2 functional profiles.
DOI: 10.6084/m9.figshare.13467289
title: Additional file 2 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 2: Table S1. Sample sizes for boxplots.
DOI: 10.6084/m9.figshare.13467307.v1
title: Additional file 7 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 7: Table S6. Subject metadata.
DOI: 10.6084/m9.figshare.13467315.v1
title: Additional file 9 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 9: Table S8. HUMAnN2 functional profiles.
DOI: 10.6084/m9.figshare.13467311.v1
title: Additional file 8 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 8: Table S7. MetaPhlAn2 taxonomic profiles.
DOI: 10.6084/m9.figshare.13467303
title: Additional file 6 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 6: Table S5. Sample sizes for random forest models.
DOI: 10.6084/m9.figshare.13467303.v1
title: Additional file 6 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 6: Table S5. Sample sizes for random forest models.
DOI: 10.6084/m9.figshare.13467311
title: Additional file 8 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 8: Table S7. MetaPhlAn2 taxonomic profiles.
DOI: 10.6084/m9.figshare.13467301.v1
title: Additional file 5 of Determinants of Staphylococcus aureus carriage in the developing infant nasal microbiome

description: Additional file 5: Table S4. Summary of S. aureus variables for study population and samples collected for S. aureus microbiome profiling.
DOI: 10.6084/m9.figshare.13506996
title: Additional file 11 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 11: Table S11. Primer sequences for qRT-PCR.
DOI: 10.6084/m9.figshare.13506999.v1
title: Additional file 1 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 1: Table S1. Summary of RNA-Seq results.
DOI: 10.6084/m9.figshare.13506996.v1
title: Additional file 11 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 11: Table S11. Primer sequences for qRT-PCR.
DOI: 10.6084/m9.figshare.13507005.v1
title: Additional file 3 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 3: Table S3. GO enrichment analysis of the DEGs in ST/SS at different time points.
DOI: 10.6084/m9.figshare.13507008.v1
title: Additional file 4 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 4: Table S4. Analysis of enriched GO terms common in ST and SS at different time points.
DOI: 10.6084/m9.figshare.13507002.v1
title: Additional file 2 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 2: Table S2. Significantly enriched KEGG pathways of the DEGs in ST and SS in response to salt stress (Corrected P-Value
DOI: 10.6084/m9.figshare.13506993.v1
title: Additional file 10 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 10: Table S10. Enriched KEGG pathways of the core DEGs in quinoa.
DOI: 10.6084/m9.figshare.13507014
title: Additional file 6 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 6: Table S6. GO terms significantly enriched amongst the DEGs exclusively in ST genotype.
DOI: 10.6084/m9.figshare.13507014.v1
title: Additional file 6 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 6: Table S6. GO terms significantly enriched amongst the DEGs exclusively in ST genotype.
DOI: 10.6084/m9.figshare.13506999
title: Additional file 1 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 1: Table S1. Summary of RNA-Seq results.
DOI: 10.6084/m9.figshare.13507023.v1
title: Additional file 9 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 9: Table S9. GO terms significantly enriched amongst the core DEGs in quinoa.
DOI: 10.6084/m9.figshare.13507017
title: Additional file 7 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 7: Table S7. KEGG pathways analysis of the DEGs exclusively in ST genotype.
DOI: 10.6084/m9.figshare.13507005
title: Additional file 3 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 3: Table S3. GO enrichment analysis of the DEGs in ST/SS at different time points.
DOI: 10.6084/m9.figshare.13507017.v1
title: Additional file 7 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 7: Table S7. KEGG pathways analysis of the DEGs exclusively in ST genotype.
DOI: 10.6084/m9.figshare.13507020
title: Additional file 8 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 8: Table S8. List of the core genes set involved in quinoa response to salt stress.
DOI: 10.6084/m9.figshare.13507011.v1
title: Additional file 5 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 5: Table S5. List of genes that were exclusively up-regulated in ST genotype at all salt stress time points.
DOI: 10.6084/m9.figshare.13507008
title: Additional file 4 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 4: Table S4. Analysis of enriched GO terms common in ST and SS at different time points.
DOI: 10.6084/m9.figshare.13507011
title: Additional file 5 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 5: Table S5. List of genes that were exclusively up-regulated in ST genotype at all salt stress time points.
DOI: 10.6084/m9.figshare.13506993
title: Additional file 10 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 10: Table S10. Enriched KEGG pathways of the core DEGs in quinoa.
DOI: 10.6084/m9.figshare.13507002
title: Additional file 2 of Transcriptome analysis and differential gene expression profiling of two contrasting quinoa genotypes in response to salt stress

description: Additional file 2: Table S2. Significantly enriched KEGG pathways of the DEGs in ST and SS in response to salt stress (Corrected P-Value