cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3119248
title: U133A_combat.h5

description: We compiled a large cohort of breast cancer samples from NCBI's Gene Expression Omnibus (GEO) (see Table 1) as it was suggested in (Györffy and Schäfer, 2009). We only took samples from the U133A platform into account and removed duplicate samples, that is, samples that occur in several studies under the same GEO id. Array quality checks were executed for all samples belonging to the same study by the R package<code>arrayQualityMetrics</code>. Due to high memory demands of this package, studies containing more than 400 samples had to be divided into two parts. Samples that were classified as outliers in the RLE or NUSE analysis were discarded. Finally, all samples across all studies were normalized together using R's <code>justRMA</code> function yielding for each sample and each probe a log(intensity) value. This normalization also included a quantile normalization step. Subsequently, probe intensities were mean centered, yielding for each sample and each probe<i>p</i> a log(intensityμ(intensityp))log(intensityμ(intensityp)) value.We found batch effects within single studies, where samples have been collected from different locations and batch effects between studies. Specifically for breast cancer, samples also form batches according to the five subtypes of breast cancer: luminal A, luminal B, Her2 enriched, normal like and basal like. To account for these effects we employed R's <code>combat</code>, where the cancer subtype was modeled as an additional covariate to maintain the variance associated with the subtypes. To do so we needed to stratify the patients according to the subtype. Since this variable is not always available in the annotation of the patients, we predict the subtype employing the PAM50 marker genes as documented in R's <code>genefu</code> package.Principal component analysis of the batch corrected data revealed pairs of samples with a very high correlation (&gt;0.9). Those pairs were regarded as replicate samples. For each pair of replicate samples one sample was removed randomly. Affymetrix probe IDs were mapped to Entrez Gene IDs via the mapping files provided by Affymetrix. Only probes that mapped to exactly one Gene ID were taken into account and probes starting with AFFX were discarded. If an Entrez Gene ID mapped to several Affymetrix probe IDs, probes were considered in the following order according to their suffix (Gohlmann and Talloen, 2010): “_at,” “s_at,” “x_at,” “i_at,” and “a_at.” When there were still several probes valid for one Gene ID, the Affymetrix probe with the higher variance of expression values was chosen.The patients' class labels corresponding to recurrence free or distant metastasis free survival were calculated with respect to a 5-year threshold. The final cohort is shown in Table 1. We derived two data sets: one labeled according to recurrence free survival (RFS) and one labeled according to distant metastasis free survival (DMFS). Note, that the DMFS data set is a subset of the RFS data set.
DOI: 10.6084/m9.figshare.3119590.v1
title: raw data

description: data from "Altered activation in cerebellum contralateral to unilateral thalamotomy may mediate tremor suppression in Parkinson’s disease: A short-term regional homogeneity fMRI study".NCXXX and nXX are healthy subjectssubXX is patients with PDa represents pre-surgeryb represents post-surgery
DOI: 10.6084/m9.figshare.3119248.v1
title: U133A_combat.h5

description: We compiled a large cohort of breast cancer samples from NCBI's Gene Expression Omnibus (GEO) (see Table 1) as it was suggested in (Györffy and Schäfer, 2009). We only took samples from the U133A platform into account and removed duplicate samples, that is, samples that occur in several studies under the same GEO id. Array quality checks were executed for all samples belonging to the same study by the R package<code>arrayQualityMetrics</code>. Due to high memory demands of this package, studies containing more than 400 samples had to be divided into two parts. Samples that were classified as outliers in the RLE or NUSE analysis were discarded. Finally, all samples across all studies were normalized together using R's <code>justRMA</code> function yielding for each sample and each probe a log(intensity) value. This normalization also included a quantile normalization step. Subsequently, probe intensities were mean centered, yielding for each sample and each probe<i>p</i> a log(intensityμ(intensityp))log(intensityμ(intensityp)) value.We found batch effects within single studies, where samples have been collected from different locations and batch effects between studies. Specifically for breast cancer, samples also form batches according to the five subtypes of breast cancer: luminal A, luminal B, Her2 enriched, normal like and basal like. To account for these effects we employed R's <code>combat</code>, where the cancer subtype was modeled as an additional covariate to maintain the variance associated with the subtypes. To do so we needed to stratify the patients according to the subtype. Since this variable is not always available in the annotation of the patients, we predict the subtype employing the PAM50 marker genes as documented in R's <code>genefu</code> package.Principal component analysis of the batch corrected data revealed pairs of samples with a very high correlation (&gt;0.9). Those pairs were regarded as replicate samples. For each pair of replicate samples one sample was removed randomly. Affymetrix probe IDs were mapped to Entrez Gene IDs via the mapping files provided by Affymetrix. Only probes that mapped to exactly one Gene ID were taken into account and probes starting with AFFX were discarded. If an Entrez Gene ID mapped to several Affymetrix probe IDs, probes were considered in the following order according to their suffix (Gohlmann and Talloen, 2010): “_at,” “s_at,” “x_at,” “i_at,” and “a_at.” When there were still several probes valid for one Gene ID, the Affymetrix probe with the higher variance of expression values was chosen.The patients' class labels corresponding to recurrence free or distant metastasis free survival were calculated with respect to a 5-year threshold. The final cohort is shown in Table 1. We derived two data sets: one labeled according to recurrence free survival (RFS) and one labeled according to distant metastasis free survival (DMFS). Note, that the DMFS data set is a subset of the RFS data set.
DOI: 10.6084/m9.figshare.3119029
title: Enrichment maps of gene sets enriched for signals of positive selection (Figure S2-S5)

description: Enrichment map of the gene sets enriched for signals of positive selection in the Homininae, Hominidae, Hominoidae and Catarrhini branch. The nodes represent the pathways with q-values &lt; 0.2 before pruning (removal of overlapping genes). The node color scale represents gene set p-values. Edges represent mutual overlap; nodes are connected if one of the sets has at least 33% of its genes in common with the other gene set. The widths of the edges scale with the similarity between nodes. The pathways that remained significant after pruning are marked with a black circle.
DOI: 10.6084/m9.figshare.3119188.v1
title: Evaluación del Índice de Revistas Mexicanas de Divulgación Científica en la era digital

description: V Conference on Social Sciences, Guadalajara, Mexico.<br>
DOI: 10.6084/m9.figshare.3119011
title: Insula and somatosensory cortical myelination and iron markers underlie individual differences in empathy

description: The behavioral data with demographics (age, gender) and interpersonal reactivity index (IRI) scores for all participants. <br>Paper Abstract: Empathy is a key component of our ability to engage and interact with others. In recent years, the neural mechanisms underlying affective and cognitive empathy have garnered intense interest. This work demonstrates that empathy for others depends upon similar neural mechanisms as when we ourselves experience an empathized-with emotion (e.g., pain). Individuals vary markedly in their ability to empathize with others, which predicts the tendency to help others and relates to individual differences in neuroanatomy. Here, we use a newly developed, high-resolution (800 µm isotropic), quantitative MRI technique to better elucidate the neurobiological mechanisms underpinning individual differences in empathy and neuroanatomy. Our findings extend previous studies of the neuroanatomical correlates of cognitive and affective empathy. In particular, individual differences in cognitive empathy were associated with markers of myeloarchitectural integrity of the insular cortex while affective empathy was predicted by a marker of iron content in second somatosensory cortex. These results indicate novel biomarkers of empathy, suggesting that microstructural features of brain areas previously associated with interoceptive and bodily self-awareness are crucial for understanding the mental and emotional states of others. Further, these results may indicate a previously unappreciated neurobiological mechanism underlying socio-economic or stress-related factors in the development of empathy.
DOI: 10.6084/m9.figshare.3119026
title: Heat maps showing branch specific ΔlnL4 scores of genes in pathways that score significant in enrichment test (Figure S6)

description: Heat maps showing branch specific ΔlnL4 scores of genes in pathways that score significant (q &lt; 0.2) in the gene set enrichment test after removing overlap between sets ('pruning'). The ΔlnL4 score is computed as the fourth root of the log-likelihood ratio in the branch site test for positive selection. Branches where a pathway scores significant are marked with a '*'. The genes are grouped by hierarchical clustering to visualize blocks with similar signals among branches. Genes for which ΔlnL4 scores were not available (NA) in a certain branch are depicted in grey. Genes are merged (horizontally) with their paralog(s) into an 'ancestral gene' in the branches preceding a duplication and their scores were included only once in the calculation of the SUMSTAT score for these branches. Genes with (vertically) merged branches represent cases where the sequence of one or more species is missing or excluded, resulting in a single 'average' ΔlnL4 score over multiple branches. We used this score when testing each branch separately.
DOI: 10.6084/m9.figshare.3119029.v1
title: Enrichment maps of gene sets enriched for signals of positive selection (Figure S2-S5)

description: Enrichment map of the gene sets enriched for signals of positive selection in the Homininae, Hominidae, Hominoidae and Catarrhini branch. The nodes represent the pathways with q-values &lt; 0.2 before pruning (removal of overlapping genes). The node color scale represents gene set p-values. Edges represent mutual overlap; nodes are connected if one of the sets has at least 33% of its genes in common with the other gene set. The widths of the edges scale with the similarity between nodes. The pathways that remained significant after pruning are marked with a black circle.
DOI: 10.6084/m9.figshare.3118759
title: Investigation of the mechanisms of action behind Electromotive Drug Administration (EMDA)

description: The fileset contains the original raw data for paper titled "Investigation of the mechanisms of action behind Electromotive Drug Administration (EMDA)".<br>The fileset contains the original segmentation of the abdominal area, COMSOL Multiphysics model, conductivity data for the model and computed results with ParaView visualizations.
DOI: 10.6084/m9.figshare.3119026.v1
title: Heat maps showing branch specific ΔlnL4 scores of genes in pathways that score significant in enrichment test (Figure S6)

description: Heat maps showing branch specific ΔlnL4 scores of genes in pathways that score significant (q &lt; 0.2) in the gene set enrichment test after removing overlap between sets ('pruning'). The ΔlnL4 score is computed as the fourth root of the log-likelihood ratio in the branch site test for positive selection. Branches where a pathway scores significant are marked with a '*'. The genes are grouped by hierarchical clustering to visualize blocks with similar signals among branches. Genes for which ΔlnL4 scores were not available (NA) in a certain branch are depicted in grey. Genes are merged (horizontally) with their paralog(s) into an 'ancestral gene' in the branches preceding a duplication and their scores were included only once in the calculation of the SUMSTAT score for these branches. Genes with (vertically) merged branches represent cases where the sequence of one or more species is missing or excluded, resulting in a single 'average' ΔlnL4 score over multiple branches. We used this score when testing each branch separately.
DOI: 10.6084/m9.figshare.3119011.v2
title: Insula and somatosensory cortical myelination and iron markers underlie individual differences in empathy

description: The behavioral data with demographics (age, gender) and interpersonal reactivity index (IRI) scores for all participants. <br>Paper Abstract: Empathy is a key component of our ability to engage and interact with others. In recent years, the neural mechanisms underlying affective and cognitive empathy have garnered intense interest. This work demonstrates that empathy for others depends upon similar neural mechanisms as when we ourselves experience an empathized-with emotion (e.g., pain). Individuals vary markedly in their ability to empathize with others, which predicts the tendency to help others and relates to individual differences in neuroanatomy. Here, we use a newly developed, high-resolution (800 µm isotropic), quantitative MRI technique to better elucidate the neurobiological mechanisms underpinning individual differences in empathy and neuroanatomy. Our findings extend previous studies of the neuroanatomical correlates of cognitive and affective empathy. In particular, individual differences in cognitive empathy were associated with markers of myeloarchitectural integrity of the insular cortex while affective empathy was predicted by a marker of iron content in second somatosensory cortex. These results indicate novel biomarkers of empathy, suggesting that microstructural features of brain areas previously associated with interoceptive and bodily self-awareness are crucial for understanding the mental and emotional states of others. Further, these results may indicate a previously unappreciated neurobiological mechanism underlying socio-economic or stress-related factors in the development of empathy.
DOI: 10.6084/m9.figshare.3118759.v1
title: Investigation of the mechanisms of action behind Electromotive Drug Administration (EMDA)

description: The fileset contains the original raw data for paper titled "Investigation of the mechanisms of action behind Electromotive Drug Administration (EMDA)".<br>The fileset contains the original segmentation of the abdominal area, COMSOL Multiphysics model, conductivity data for the model and computed results with ParaView visualizations.
DOI: 10.6084/m9.figshare.3119011.v1
title: Insula and somatosensory cortical myeloarchitecture and iron underlie individual differences in empathy

description: The behavioral data with demographics (age, gender) and interpersonal reactivity index (IRI) scores for all participants. <br>Paper Abstract:<br>Empathy is a key component of our ability to engage and interact with others. In recent years, the neural mechanisms underlying affective and cognitive empathy have garnered intense interest. This work demonstrates that empathy for others depends upon similar neural mechanisms as when we ourselves experience an empathized-with emotion (e.g., pain). Furthermore, individuals vary markedly in their ability to empathize with others, and this variation depends upon individual differences in neuroanatomy and predicts the tendency to help others. Here, we use a newly developed, high-resolution (800 µm isotropic), quantitative imaging technique to better elucidate the neurobiological mechanisms underpinning individual differences in empathy and neuroanatomy. Our findings replicate and extend previous studies of the neuroanatomical correlates of cognitive and affective empathy; with the latter being associated with the micro-architectural integrity of the insular cortex and the former with the concentration of iron in second somatosensory cortex. These results illustrate novel biomarkers of empathy, suggesting that micro-architectural features of brain areas previously associated with interoceptive and bodily self-awareness are crucial for understanding the mental and emotional states of others. Further, these results may indicate a previously unappreciated neurobiological mechanism underlying socio-economic or stress-related factors in the development of empathy. <br><br>
DOI: 10.6084/m9.figshare.3118654
title: Comprehensive European dietary exposure model (CEDEM) for food additives

description: European methods for assessing dietary exposures to nutrients, additives and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed comprehensive European dietary exposure model (CEDEM) applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach can predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.
DOI: 10.6084/m9.figshare.3118654.v2
title: Comprehensive European dietary exposure model (CEDEM) for food additives

description: European methods for assessing dietary exposures to nutrients, additives and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed comprehensive European dietary exposure model (CEDEM) applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach can predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.
DOI: 10.6084/m9.figshare.3118654.v1
title: A Comprehensive European Dietary Exposure Model for Food Additives

description: European methods for assessing dietary exposures to nutrients, additives, and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed Comprehensive European Dietary Exposure Model applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach is able to predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.
DOI: 10.6084/m9.figshare.3118600
title: Hong_Microarray Dataset

description: The compositional variation of the Formula Diet fed to the experimental mice (Additional table file), Differentially expressed genes by effect of RS, DJ, and DJ526 (Additional table file), The most highly significant up-regulated and down-regulated pathways in the livers of mice on RS, DJ and DJ526 towards those on Ctrl groups (Additional table file), Globally normalized data (Fold change raw data file), Z transformed data (Z-ratio raw data file), and GSEA results (GSEA results data file)
DOI: 10.6084/m9.figshare.3118603
title: Medicinal Chemistry in Common View.doc

description: To be brief, medicinal chemistry is a new drug discovery and invention, which requires a wide range of applications. Therefore, a solid foundation for medicinal chemistry development should be created, in order to obtain new chemical entity development of new medicines. Certainly, such view is merely personal opinion, meaning such issue still demands more efforts. Source(Read full text):http://alfachemistry.blog.com/2016/02/04/medicinal-chemistry-in-common-view/ <br><br>
DOI: 10.6084/m9.figshare.3118600.v2
title: Hong_Microarray Dataset

description: The compositional variation of the Formula Diet fed to the experimental mice (Additional table file), Differentially expressed genes by effect of RS, DJ, and DJ526 (Additional table file), The most highly significant up-regulated and down-regulated pathways in the livers of mice on RS, DJ and DJ526 towards those on Ctrl groups (Additional table file), Globally normalized data (Fold change raw data file), Z transformed data (Z-ratio raw data file), and GSEA results (GSEA results data file)
DOI: 10.6084/m9.figshare.3118549
title: CRP-PCT-BMC.dta

description: Procalcitonin and CRP values from well characterised samples from fever studies in Southeast Asia<br>
DOI: 10.6084/m9.figshare.3118546
title: Pulse Wave Velocity at Early Adulthood: Breastfeeding and Nutrition during Pregnancy and Childhood

description: Data from "Pulse Wave Velocity at Early Adulthood: Breastfeeding and Nutrition during Pregnancy and Childhood"
DOI: 10.6084/m9.figshare.3118600.v1
title: Hong_Microarray Dataset

description: The compositional variation of the Formula Diet fed to the experimental mice (Additional table file), Differentially expressed genes by effect of RS, DJ, and DJ526 (Additional table file), The most highly significant up-regulated and down-regulated pathways in the livers of mice on RS, DJ and DJ526 towards those on Ctrl groups (Additional table file), Globally normalized data (Fold change raw data file), Z transformed data (Z-ratio raw data file), and GSEA results (GSEA results data file)
DOI: 10.6084/m9.figshare.3118549.v1
title: CRP-PCT-BMC.dta

description: Procalcitonin and CRP values from well characterised samples from fever studies in Southeast Asia<br>
DOI: 10.6084/m9.figshare.3118546.v2
title: Pulse Wave Velocity at Early Adulthood: Breastfeeding and Nutrition during Pregnancy and Childhood

description: Data from "Pulse Wave Velocity at Early Adulthood: Breastfeeding and Nutrition during Pregnancy and Childhood"
DOI: 10.4225/03/56eb78f1cfbed
title: 3D + time evolution of the Mount Painter Province

description: 3D plus time animation of the evolution of the Mount Painter Province in South Australia since the Mesoproterozoic.
DOI: 10.6084/m9.figshare.3118546.v1
title: PONE-D-15-25303 A.xlsx

description: Data from "Pulse Wave Velocity at Early Adulthood: Breastfeeding and Nutrition during Pregnancy and Childhood"
DOI: 10.6084/m9.figshare.3118528
title: Combining Functional Data Registration and Factor Analysis

description: We extend the definition of functional data registration to encompass a larger class of registration models. In contrast to traditional registration models, we allow for registered functions that have more than one primary direction of variation. The proposed Bayesian hierarchical model simultaneously registers the observed functions and estimates the two primary factors that characterize variation in the registered functions. Each registered function is assumed to be predominantly composed of a linear combination of these two primary factors, and the function-specific weights for each observation are estimated within the registration model. We show how these estimated weights can easily be used to classify functions after registration using both simulated data and a juggling data set.