cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4629619.v1
title: Sup_Material_E_and_H_of_all_indents.xls: Reduced modulus and hardness for each single indentation of this study in .xls format from Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone

description: Studies investigating micromechanical properties in mouse cortical bone often solely focus on the mechanical behaviour along the long axis of the bone. Therefore, data on the anisotropy of mouse cortical bone is scarce. The aim of this study is the first-time evaluation of the anisotropy ratio between the longitudinal and transverse directions of reduced modulus and hardness in mouse femurs by using the nanoindentation technique. For this purpose, nine 22-week-old mice (C57BL/6) were sacrificed and all femurs extracted. A total of 648 indentations were performed with a Berkovich tip in the proximal (P), central (C) and distal (D) regions of the femoral shaft in the longitudinal and transverse directions. Higher values for reduced modulus are obtained for indentations in the longitudinal direction, with anisotropy ratios of 1.72 ± 0.40 (P), 1.75 ± 0.69 (C) and 1.34 ± 0.30 (D). Hardness is also higher in the longitudinal direction, with anisotropic ratios of 1.35 ± 0.27 (P), 1.35 ± 0.47 (C) and 1.17 ± 0.19 (D). We observed a significant anisotropy in the micromechanical properties of the mouse femur, but the correlation for reduced modulus and hardness between the two directions is low (<i>r</i><sup>2</sup>
DOI: 10.6084/m9.figshare.4629622
title: Figure S1 from Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone.

description: Studies investigating micromechanical properties in mouse cortical bone often solely focus on the mechanical behaviour along the long axis of the bone. Therefore, data on the anisotropy of mouse cortical bone is scarce. The aim of this study is the first-time evaluation of the anisotropy ratio between the longitudinal and transverse directions of reduced modulus and hardness in mouse femurs by using the nanoindentation technique. For this purpose, nine 22-week-old mice (C57BL/6) were sacrificed and all femurs extracted. A total of 648 indentations were performed with a Berkovich tip in the proximal (P), central (C) and distal (D) regions of the femoral shaft in the longitudinal and transverse directions. Higher values for reduced modulus are obtained for indentations in the longitudinal direction, with anisotropy ratios of 1.72 ± 0.40 (P), 1.75 ± 0.69 (C) and 1.34 ± 0.30 (D). Hardness is also higher in the longitudinal direction, with anisotropic ratios of 1.35 ± 0.27 (P), 1.35 ± 0.47 (C) and 1.17 ± 0.19 (D). We observed a significant anisotropy in the micromechanical properties of the mouse femur, but the correlation for reduced modulus and hardness between the two directions is low (<i>r</i><sup>2</sup>
DOI: 10.6084/m9.figshare.4629622.v1
title: Sup_Material_E_and_H_of_all_indents.sav: Reduced modulus and hardness for each single indentation of this study in .sav format (SPSS file). We also included a Supplementary Figure and Table which are uploaded and described in the File Upload session. from Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone

description: Studies investigating micromechanical properties in mouse cortical bone often solely focus on the mechanical behaviour along the long axis of the bone. Therefore, data on the anisotropy of mouse cortical bone is scarce. The aim of this study is the first-time evaluation of the anisotropy ratio between the longitudinal and transverse directions of reduced modulus and hardness in mouse femurs by using the nanoindentation technique. For this purpose, nine 22-week-old mice (C57BL/6) were sacrificed and all femurs extracted. A total of 648 indentations were performed with a Berkovich tip in the proximal (P), central (C) and distal (D) regions of the femoral shaft in the longitudinal and transverse directions. Higher values for reduced modulus are obtained for indentations in the longitudinal direction, with anisotropy ratios of 1.72 ± 0.40 (P), 1.75 ± 0.69 (C) and 1.34 ± 0.30 (D). Hardness is also higher in the longitudinal direction, with anisotropic ratios of 1.35 ± 0.27 (P), 1.35 ± 0.47 (C) and 1.17 ± 0.19 (D). We observed a significant anisotropy in the micromechanical properties of the mouse femur, but the correlation for reduced modulus and hardness between the two directions is low (<i>r</i><sup>2</sup>
DOI: 10.6084/m9.figshare.4629622.v2
title: Figure S1 from Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone.

description: Studies investigating micromechanical properties in mouse cortical bone often solely focus on the mechanical behaviour along the long axis of the bone. Therefore, data on the anisotropy of mouse cortical bone is scarce. The aim of this study is the first-time evaluation of the anisotropy ratio between the longitudinal and transverse directions of reduced modulus and hardness in mouse femurs by using the nanoindentation technique. For this purpose, nine 22-week-old mice (C57BL/6) were sacrificed and all femurs extracted. A total of 648 indentations were performed with a Berkovich tip in the proximal (P), central (C) and distal (D) regions of the femoral shaft in the longitudinal and transverse directions. Higher values for reduced modulus are obtained for indentations in the longitudinal direction, with anisotropy ratios of 1.72 ± 0.40 (P), 1.75 ± 0.69 (C) and 1.34 ± 0.30 (D). Hardness is also higher in the longitudinal direction, with anisotropic ratios of 1.35 ± 0.27 (P), 1.35 ± 0.47 (C) and 1.17 ± 0.19 (D). We observed a significant anisotropy in the micromechanical properties of the mouse femur, but the correlation for reduced modulus and hardness between the two directions is low (<i>r</i><sup>2</sup>
DOI: 10.6084/m9.figshare.4806568
title: 8d synthetic dataset from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806568.v2
title: 8d synthetic dataset from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806568.v1
title: 8d synthetic dataset from Clustering: how much bias do we need?

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806571
title: 8d synthetic dataset labels from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806571.v2
title: 8d synthetic dataset labels from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806571.v1
title: 8d synthetic dataset labels from Clustering: how much bias do we need?

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806577
title: 2d synthetic dataset from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806577.v2
title: 2d synthetic dataset from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806577.v1
title: 2d synthetic dataset from Clustering: how much bias do we need?

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806574
title: 2d synthetic dataset labels from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806574.v2
title: 2d synthetic dataset labels from Clustering: how much bias do we need?.

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4806574.v1
title: 2d synthetic dataset labels from Clustering: how much bias do we need?

description: Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projections destroys essential relationships in the data. Similarly, traditional clustering algorithms introduce data bias that prevents detection of natural structures expected from generic nonlinear processes. We examine how these problems can best be addressed, where in particular we focus on two recent clustering approaches, Phenograph and Hebbian learning clustering, applied to synthetic and natural data examples. Our results reveal that already for very basic questions, minimizing clustering bias is essential, but that results can benefit further from biased post-processing.
DOI: 10.6084/m9.figshare.4829032
title: Table S1 from Comparative behavioural observations demonstrate the ‘cleaner’ shrimp <i>Periclimenes yucatanicus</i> engages in true symbiotic cleaning interactions.

description: Raw data used in this study
DOI: 10.6084/m9.figshare.4829032.v2
title: Table S1 from Comparative behavioural observations demonstrate the ‘cleaner’ shrimp <i>Periclimenes yucatanicus</i> engages in true symbiotic cleaning interactions.

description: Raw data used in this study
DOI: 10.6084/m9.figshare.4829032.v1
title: Table S1 from Comparative behavioural observations demonstrate the ‘cleaner’ shrimp <i>Periclimenes yucatanicus</i> engages in true symbiotic cleaning interactions

description: Raw data used in this study
DOI: 10.6084/m9.figshare.4833974
title: Data Used in Analyses from Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore.

description: Data tables
DOI: 10.6084/m9.figshare.4833974.v1
title: Data Used in Analyses from Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore

description: Data tables
DOI: 10.6084/m9.figshare.4833974.v2
title: Data Used in Analyses from Wind and rain are the primary climate factors driving changing phenology of an aerial insectivore.

description: Data tables
DOI: 10.6084/m9.figshare.4868249
title: SRA datasets from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: SRA datasets for human, mouse and cow samples used.
DOI: 10.6084/m9.figshare.4868249.v1
title: SRA datasets from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development

description: SRA datasets for human, mouse and cow samples used.
DOI: 10.6084/m9.figshare.4868249.v2
title: SRA datasets from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: SRA datasets for human, mouse and cow samples used.
DOI: 10.6084/m9.figshare.4868237
title: Expression data for genes around NANOGNB from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: Raw FPKM data for human, mouse and cow for genes in the chromosomal region around NANOGNB.
DOI: 10.6084/m9.figshare.4868237.v2
title: Expression data for genes around NANOGNB from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: Raw FPKM data for human, mouse and cow for genes in the chromosomal region around NANOGNB.
DOI: 10.6084/m9.figshare.4868237.v1
title: Expression data for genes around NANOGNB from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development

description: Raw FPKM data for human, mouse and cow for genes in the chromosomal region around NANOGNB.
DOI: 10.6084/m9.figshare.4868273
title: Expression analysis of NANOGNB-responsive genes from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: DESeq2 output for NANOGNB up- and down-regulated genes.
DOI: 10.6084/m9.figshare.4868273.v2
title: Expression analysis of NANOGNB-responsive genes from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development.

description: DESeq2 output for NANOGNB up- and down-regulated genes.
DOI: 10.6084/m9.figshare.4868273.v1
title: Expression analysis of NANOGNB-responsive genes from A sister of <i>NANOG</i> regulates genes expressed in pre-implantation human development

description: DESeq2 output for NANOGNB up- and down-regulated genes.
DOI: 10.5281/zenodo.4090378
title: Verification data for the Leipzig neutral ABL benchmark

description: This dataset has been extracted from Letau (1950) to verify ABL models in neutral conditions.
DOI: 10.5281/zenodo.4090377
title: Verification data for the Leipzig neutral ABL benchmark

description: This dataset has been extracted from Letau (1950) to verify ABL models in neutral conditions.
DOI: 10.25505/fiz.icsd.cc20fxzc
title: CSD 1860433: Experimental Crystal Structure Determination

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DOI: 10.25505/fiz.icsd.cc20g8j9
title: CSD 1860760: Experimental Crystal Structure Determination

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DOI: 10.25505/fiz.icsd.cc20g8kb
title: CSD 1860761: Experimental Crystal Structure Determination

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title: CSD 1860762: Experimental Crystal Structure Determination

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title: CSD 1860763: Experimental Crystal Structure Determination

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title: CSD 1860764: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc20g9b4
title: CSD 1860785: Experimental Crystal Structure Determination

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title: CSD 1862147: Experimental Crystal Structure Determination

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title: CSD 1862882: Experimental Crystal Structure Determination

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title: CSD 1862884: Experimental Crystal Structure Determination

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