cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13520859
title: Additional file 12 of A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species

description: Additional file 12: Table S12. ROH island regions (bins of one Mb) for the studied pig populations: details per chromosome. Within the Excel file, there is one tab for each chromosome with ROH island details per population. Cells are marked with an ‘x’ if an ROH island appeared in the specific region.
DOI: 10.6084/m9.figshare.13520856
title: Additional file 11 of A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species

description: Additional file 11: Table S11. ROH island regions (bins of one Mb) for the studied pig populations: overview for all chromosomes. Within each cell, the genomic region(s) with an ROH island are given (in Mb).
DOI: 10.6084/m9.figshare.13520853
title: Additional file 10 of A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species

description: Additional file 10: Table S10. ROH island regions (bins of one Mb) for the studied horse populations: details per chromosome. Within the Excel file, there is one tab for each chromosome with ROH island details per population. Cells are marked with an ‘x’ if an ROH island appeared in the specific region.
DOI: 10.6084/m9.figshare.13520713
title: Additional file 2 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 2: Table S1. The table presents an overview of the sequence count per ASV obtained after V3 V4 sequencing of mock community sample HM-783D following setup 1.
DOI: 10.6084/m9.figshare.13520713.v1
title: Additional file 2 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 2: Table S1. The table presents an overview of the sequence count per ASV obtained after V3 V4 sequencing of mock community sample HM-783D following setup 1.
DOI: 10.6084/m9.figshare.13520716
title: Additional file 3 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 3: Table S2. The table presents an overview of the sequence count per ASV obtained after V4 sequencing of mock community sample HM-783D following setup 2.
DOI: 10.6084/m9.figshare.13520716.v1
title: Additional file 3 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 3: Table S2. The table presents an overview of the sequence count per ASV obtained after V4 sequencing of mock community sample HM-783D following setup 2.
DOI: 10.6084/m9.figshare.13520719
title: Additional file 4 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 4: Table S3. The table presents an overview of the sequence count per ASV obtained after V4 sequencing of mock community sample HM-783D following setup 3.
DOI: 10.6084/m9.figshare.13520722
title: Additional file 5 of Exploring protocol bias in airway microbiome studies: one versus two PCR steps and 16S rRNA gene region V3 V4 versus V4

description: Additional file 5: Table S4. The table presents an overview of the relative abundance of ASVs mapping to Enterobacteriaceae per sample across all sample types and setups.
DOI: 10.18112/openneuro.ds003430.v1.0.3
title: Visual image reconstruction from human brain activity during selective attention unveils subjective visual images
DOI: 10.18112/openneuro.ds003430.v1.0.4
title: Visual image reconstruction from human brain activity during selective attention unveils subjective visual images
DOI: 10.21227/hjnw-gh28
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/b2a9-yd74
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/yxaw-x542
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/3hq2-6m92
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/3k0f-jr06
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/mvgv-wq61
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/g90y-4k66
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/9kdj-sv62
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/nr8z-vd40
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.21227/2859-ef08
title: Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks

description: Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures. To support the training and testing of deep learning segmentation models, we construct two fluorescence image datasets, ER and MITO, for the ER network and the mitochondrial network, respectively. We provide manual segmentation for these organelle networks in binary masks. The datasets have been used to evaluate and compare performance of the methods proposed in our article “Heuristic Optimization of Deep Learning Models for Segmentation of Intracellular Organelle Networks”.
DOI: 10.6084/m9.figshare.11894691
title: Processed HSPCs single-cell RNA-seq, Seurat object

description: Processed hematopoietic stem and progenitor cell (HSPC) single-cell RNAseq data from human samples. The file contains a Seurat object stored as an .rds file which can be read into R with the readRDS() function. It was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis
DOI: 10.6084/m9.figshare.11894691.v1
title: Processed HSPCs single-cell RNA-seq, Seurat object

description: Processed hematopoietic stem and progenitor cell (HSPC) single-cell RNAseq data to go along with a manuscript currently submitted for publication. The file contains a Seurat object stored as an .rds file which can be read into R with the readRDS() function. It was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis
DOI: 10.6084/m9.figshare.11894661
title: Processed bulk naive CD4 T cell RNA-seq, SingleCellExperiment & DESeqDataSet objects

description: Processed naive CD4 T cell RNAseq data from human samples. The contents are two .rds files, which can be read into R using readRDS(). One represents a SingleCellExperiment class object, and the other a DESeqDataSet object. These files were generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis
DOI: 10.6084/m9.figshare.11894661.v1
title: Processed bulk naive CD4 T cell RNA-seq, Seurat object

description: Processed naive CD4 T cell RNAseq data to go along with a manuscript currently submitted for publication. The file contains an .rds file, which can be read into R using readRDS(), and represents an RNAseq-class S4 object defined in the v0.3 version of the dittoSeq package (https://github.com/dtm2451/dittoSeq/tree/v0.3). This pre-Bioconductor, stable, version of the package is required for importing this data.<br>This file was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis<br>
DOI: 10.6084/m9.figshare.13072178
title: Alignment file for Annulotesta cochlephila

description: This is the alignment file for <i>Annulotesta cochlephila</i>, which was used in the study on "Taxonomic study of a new green alga, <i>Annulotesta cochlephila</i> gen. et sp. nov. (Kornmanniaceae, Ulvales, Ulvophyceae), growing on the shells of door snails".<br>In this study, we discovered green algae growing on the shells of six species of door snails (Clausiliidae) from nine localities in Japan. We propose a new genus and new species of shell-attached green alga, <i>Annulotesta cochlephila</i> based on the results of morphological observation and molecular phylogenetic analysis. There are very few reports of algae that grow on land snails. Interestingly, we found all algae forming visible colonies on the shells of different clausiliid species from different localities were the same species. This finding indicates the presence of specificity between the alga and clausiliid shells.<br><br>
DOI: 10.6084/m9.figshare.11894622
title: HSPCs single-cell RNA-seq, raw counts and annotation

description: Raw counts and sample annotations data for a single-cell RNA-seq dataset of hematopoietic stem and progenitor cells (HSPCs) of human samples. Includes both raw counts, generated by cellranger, and cells' sample annotations, generated with Demuxlet.
DOI: 10.6084/m9.figshare.11894622.v2
title: HSPCs single-cell RNA-seq, raw counts and annotation

description: Raw counts and sample annotations data for a single-cell RNA-seq dataset of hematopoietic stem and progenitor cells (HSPCs) of human samples. Includes both raw counts, generated by cellranger, and cells' sample annotations, generated with Demuxlet.
DOI: 10.6084/m9.figshare.11894622.v1
title: HSPCs single-cell RNA-seq, raw counts and annotation

description: Raw counts and sample annotations data for a single-cell RNA-seq dataset of hematopoietic stem and progenitor cells (HSPCs) to go along with a manuscript currently submitted for publication.
DOI: 10.6084/m9.figshare.11894589
title: bulk naive CD4 T cell RNA-seq, raw counts

description: Raw counts data from bulk RNA-sequencing of human naive CD4 T cell samples.
DOI: 10.6084/m9.figshare.11894589.v1
title: bulk naive CD4 T cell RNA-seq, raw counts

description: Raw counts data to go with a manuscript being submitted for publication
DOI: 10.6084/m9.figshare.11894577
title: Microarray data, annotated

description: Microarray transcriptome data from naive CD4 T cells and classical monocytes of human samples.
DOI: 10.6084/m9.figshare.11894577.v2
title: Microarray data, annotated

description: Microarray transcriptome data from naive CD4 T cells and classical monocytes of human samples.
DOI: 10.6084/m9.figshare.11894577.v1
title: Microarray data, annotated

description: Data in a processed form to go along with a manuscript submitted for publication
DOI: 10.6084/m9.figshare.11886891
title: Processed naive T cell single-cell RNA-seq, Seurat object

description: Processed naive CD4 and CD8 T cell single-cell RNAseq data from human samples. The file contains a Seurat object stored as an .rds file which can be read into R with the readRDS() function. It was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis
DOI: 10.6084/m9.figshare.11886891.v1
title: Processed naive T cell single-cell RNA-seq, Seurat object

description: Processed naive CD4 and CD8 T cell single-cell RNAseq data to go along with a manuscript currently submitted for publication. The file contains a Seurat object stored as an .rds file which can be read into R with the readRDS() function. It was generated using the raw data of similar name in this project, as well as the code stored here: https://github.com/dtm2451/ProgressiveHematopoiesis
DOI: 10.25919/5753-tt25
title: Parkes observations for project P1092 semester 2020OCTS_02

description: We propose to use the Parkes UWL receiver for confirmation and monitoring a sub-set of bright FAST-discovered pulsars, especially for those discoveries located outside the sky regions covered by the purchased PX-500 project. For special FAST discoveries that already confirmed by Parkes, such as MSPs or pulsars with interesting emission properties, we propose to do long-term monitoring for them as well. These monitoring aim to solve their parameters, binary orbit, and provide significant insight into the special emission properties of these new discoveries and facilitate science. The Parkes UWL system provides a unique capability for studying the pulsars over wide bandwidths, in term of both the timing precision and frequency-dependent observing properties.
DOI: 10.21258/1768231
title: Swiss national forest inventory - Result table No. 898021
DOI: 10.21258/1762907
title: Swiss national forest inventory - Result table No. 891681
DOI: 10.21258/1723214
title: Swiss national forest inventory - Result table No. 898517
DOI: 10.21258/1769456
title: Swiss national forest inventory - Result table No. 896986
DOI: 10.21258/1733994
title: Swiss national forest inventory - Result table No. 902249
DOI: 10.21258/1744042
title: Swiss national forest inventory - Result table No. 899466
DOI: 10.21258/1746548
title: Swiss national forest inventory - Result table No. 899505
DOI: 10.17632/hm6mtv97cf.1
title: Data for: Crustal melting in a protracted hot setting in the Altai Orogen (NW China): Evidence from Permian leucogranite dykes in the metamorphic belt

description: In this contribution, we provide integrated petrological, geochronological and geochemical results of the leucogranite dykes to decipher their ages and petrogenesis and thus provide new constraints on the tectono-thermal setting of the Chinese Altai orogeny. Our work, for the first time, dates the ages of the leucogranite dykes (279–267 Ma) and places these leucogranite dykes in temporal association with the high to hihg-/ ultrahigh-temperature metamorphism in the region. The petrologic and geochemical features indicate that the leucogranites were derived from fluid-absent muscovite dehydration melting mainly of metapelitic source. The generation of the leucogranite dykes may have been attributed to long-lasting high heat flow in early Permian.
DOI: 10.21258/1723465
title: Swiss national forest inventory - Result table No. 898781
DOI: 10.21258/1751366
title: Swiss national forest inventory - Result table No. 897108
DOI: 10.21258/1754318
title: Swiss national forest inventory - Result table No. 898736
DOI: 10.21258/1723834
title: Swiss national forest inventory - Result table No. 898759
DOI: 10.21258/1728966
title: Swiss national forest inventory - Result table No. 899506
DOI: 10.21258/1768679
title: Swiss national forest inventory - Result table No. 898791
DOI: 10.21258/1730578
title: Swiss national forest inventory - Result table No. 898270