cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.707016.v2
title: Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets

description: A presentation of our NeuroImage paper "Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets" Abstract The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data. Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study, 1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of 5%, significant activity was found in 1%-70% of the 1484 rest datasets, depending on repetition time, paradigm and parameter settings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason for the high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra of the residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametric fMRI analysis in general, other software packages may give different results. By using the computational power of the graphics processing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was then found in 1%-19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.
DOI: 10.6084/m9.figshare.707007.v1
title: Ignite! talk on Complexity in science

description: 5 minute talk styled after talks in the Ignite! series, created for the SciFund class (May, 2013).
DOI: 10.6084/m9.figshare.707010.v1
title: Searchlight Goes GPU - Fast Multi-Voxel Pattern Analysis of fMRI Data

description: The poster for our ISMRM abstract "Searchlight Goes GPU - Fast Multi-Voxel Pattern Analysis of fMRI Data"
DOI: 10.6084/m9.figshare.707016.v1
title: Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets

description: A presentation of our NeuroImage paper "Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets"
DOI: 10.6084/m9.figshare.706992.v1
title: “Culture Vivarium”, an industrial cultural plant in the Southern Italy, based on a network of CULTLabs and an HPW technological ecosystem where modelling innovative manufacturing based on the Cultural Heritage

description: The project named “Culture Vivarium” aims to create an “industrial cultural district”, i.e. a vibrant aggregating hub of creative and cultural industries (CCIs) using and reusing the cultural heritage of the Campania region to start and successfully develop high impact cultural and creative business models. To achieve this aims, the project builds on two logical functional pillars: #1. a digital technological infrastructure, and #2. a physical network named “Culture Vivarium”, a broad ecosystem for incubating, accelerating, establishment and growth [Culture] of industrial prototyps and their products.
DOI: 10.6084/m9.figshare.706825
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706826
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706824
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706825.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706822
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706823
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706824.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706823.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706826.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706820
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706821
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706822.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706818
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706819
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706820.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706821.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706816
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706816.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706817
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706818.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706819.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706812
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706813
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706814
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706817.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706811
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706812.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706814.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706813.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706811.v1
title: Gaussian Job Archive for C15H25NO3

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706791
title: Carbohydrate active enzyme domains in Chalara fraxinea

description: Carbohydrate active enzyme domain analysis in Chalara fraxinea draft assembly
DOI: 10.6084/m9.figshare.706791.v1
title: Carbohydrate active enzyme domains in Chalara fraxinea

description: Carbohydrate active enzyme domain analysis in Chalara fraxinea draft assembly
DOI: 10.6084/m9.figshare.706756.v1
title: Gaussian Job Archive for C4H6NO3S(1-)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.706369
title: PhyloSift: Phylogenetic analysis of genomes and metagenomes

description: Poster presented at the 2013 Annual Meeting of the American Society for Microbiology (May 18-21, Denver, Colorado). For software download and documentation please visit http://phylosift.wordpress.com
DOI: 10.6084/m9.figshare.706367
title: Analytical Data of three Microcystins from Planktothrix

description: Complete NMR and MS raw data of three microcystins as described in the manuscript "Isolation of protein phosphatase 1 inhibiting microcystins from the cyanobacterium Planktothrix rubescens strain No80", THJ Niedermeyer et al., Nat. Prod. Bioprosp., 2014
DOI: 10.6084/m9.figshare.706367.v1
title: Analytical Data of three Microcystins from Planktothrix

description: Complete NMR and MS raw data of three microcystins as described in the manuscript "Isolation of protein phosphatase 1 inhibiting microcystins from the cyanobacterium Planktothrix rubescens strain No80", THJ Niedermeyer et al., Nat. Prod. Bioprosp., 2014
DOI: 10.6084/m9.figshare.706347
title: Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events

description: Data and code for "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events" ######### The simulation model in in the file Monte.Carlo.topub.r. Within the file, all input and output are described in detail. You can simply source the file: source("Monte.Carlo.topub.r") ########################### the file ext.RDS can be loaded in R by typing ext.df = readRDS("ext.RDS") after having moved the file to you current directory ######## In the data set ext.RDS, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the "sampling window" addvar.mean.10.rand. - mean additive genetic variance in the "sampling window" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0) ############# For some of the analyses and plots in Vincenzi Point Extremes.r, the full dataset is required Please download sum.ris1.RDS and sum.ris2.RDS and move them to the current directory of R Then: sum.ris1 = readRDS("sum.ris1.RDS") sum.ris2 = readRDS("sum.ris2.RDS") sum.ris = c(sum.ris1,sum.ris2) rm(list=c("sum.ris1","sum.ris2")) The list sum.ris contains some of the measured variables for all the 25 600 replicates. In particular: extinct - pop going extinct or not<br>addvar - additive genetic variance at each time step<br>phenvar - phenotypic variance at each time step<br>yearextinct - year of extinction (299 if not extinct)<br>phenomean - phenotypic mean at each time step<br>sdopt - increase in variability of the optimum<br>meanopt - directional trend<br>selstrength - strength of selection<br>mutalfa - mutation amplitude<br>fitness.mean - mean fitness at each time step<br>optimum - optimum at each time step<br>popsize.post - population size after mortality at each time step Please write to simon.vincenz@gmail.com in the case help or clarifications are needed
DOI: 10.6084/m9.figshare.706346
title: Code for some analyses in the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events"

description: Code for some analyses in the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events". In particular, code to (i) compute gam, glm and ols models in Table 2 and Table 3, (ii) cutoff for models in Table 3, (iii) plot predictions of glm model in Table 3
DOI: 10.6084/m9.figshare.705915
title: Coral Provinces (Keith et al, 2013)

description: <strong>PLEASE USE FULL CITATION (not figshare citation):</strong> Keith, S.A., Baird, A.H., Madin, J.S., Hughes, T.P. & Connolly, S.R. (2013) Faunal breaks and species composition of Indo-Pacific corals: the role of plate tectonics, environment, and habitat distribution. <em>Proceedings of the Royal Society B: Biological Sciences</em> <strong>280</strong>(1763) : 20130818 ESRI Shapefile (polygons) of 11 faunal provinces, projection centred on Hawaii (prime meridian -160)
DOI: 10.6084/m9.figshare.706329
title: Inferring global protein activity profiles by network based analysis of gene expression signatures

description: This file contains all the normalized datasets and R-scripts used in the "Inferring global protein activity profiles<br>by network based analysis of gene expression signatures" manuscript by Mariano J. Alvarez, Yao Shen, B. Belinda Ding, B. Hilda Ye and Andrea Califano.
DOI: 10.6084/m9.figshare.706339
title: Data for the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events"

description: Data for the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events" by Simone Vincenzi the file ext.RDS can be loaded in R by typing ext.df = readRDS("ext.RDS") after having moved the file to you current directory In the data set, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the "sampling window" addvar.mean.10.rand. - mean additive genetic variance in the "sampling window" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0)
DOI: 10.6084/m9.figshare.706339.v1
title: Data for the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events"

description: Data for the manuscript "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events" by Simone Vincenzi the file ext.RDS can be loaded in R by typing ext.df = readRDS("ext.RDS") after having moved the file to you current directory In the data set, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the "sampling window" addvar.mean.10.rand. - mean additive genetic variance in the "sampling window" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0)
DOI: 10.6084/m9.figshare.706347.v1
title: Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events

description: Data and code for "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events" ######### the file ext.RDS can be loaded in R by typing ext.df = readRDS("ext.RDS") after having moved the file to you current directory ######## In the data set ext.RDS, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the "sampling window" addvar.mean.10.rand. - mean additive genetic variance in the "sampling window" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0) ############# For some of the analyses and plots in Vincenzi Point Extremes.r, the full dataset is required Please download sum.ris1.RDS and sum.ris2.RDS and move them to the current directory of R Then: sum.ris1 = readRDS("sum.ris1.RDS") sum.ris2 = readRDS("sum.ris2.RDS") sum.ris = c(sum.ris1,sum.ris2) rm(list=c("sum.ris1","sum.ris2")) The list sum.ris contains some of the measured variables for all the 25 600 replicates. In particular: extinct - pop going extinct or not<br>addvar - additive genetic variance at each time step<br>phenvar - phenotypic variance at each time step<br>yearextinct - year of extinction (299 if not extinct)<br>phenomean - phenotypic mean at each time step<br>sdopt - increase in variability of the optimum<br>meanopt - directional trend<br>selstrength - strength of selection<br>mutalfa - mutation amplitude<br>fitness.mean - mean fitness at each time step<br>optimum - optimum at each time step<br>popsize.post - population size after mortality at each time step Please write to simon.vincenz@gmail.com in the case help or clarifications are needed
DOI: 10.6084/m9.figshare.705890
title: Time-averaged surface pressure on a flying-snake cross-section

description: This file bundle includes two plots of time-averaged pressure along the surface of the cross-section of a flying snake. It also includes de data sets and the plotting scripts (in Matlab). The values of the surface pressure for various angles of attack at Reynolds number 1000 and 2000 are listed in the first column of the files in the folder 'averageSurfacePressue/surfPres'. The x- and y-coordinates of the locations at which the pressure was measured are listed in columns three and four. The area under the plots of the average surface pressure vs. the x-coordinate of the measurement locations gives the pressure contribution to the lift on the body. These plots can be generated using MATLAB running the scripts: surfPresRe1000.m and surfPresRe2000.m
DOI: 10.6084/m9.figshare.706347.v3
title: Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events

description: Data and code for "Extinction risk and evolution of a quantitative trait in a variable environment with increasing frequency of extreme events" ######### The simulation model in in the file Monte.Carlo.topub.r. Within the file, all input and output are described in detail. You can simply source the file: source("Monte.Carlo.topub.r") ########################### the file ext.RDS can be loaded in R by typing ext.df = readRDS("ext.RDS") after having moved the file to you current directory ######## In the data set ext.RDS, I included the following variables and outcomes of simulations: sel - strength of selection cat.freq - probability of occurrence of point extremes pop.ext10.rand - mean population size in the "sampling window" addvar.mean.10.rand. - mean additive genetic variance in the "sampling window" meanopt - directional trend mut - mutation amplitude mean.pheno - mean phenotype at the end of simulation time (interesting for replicates in which ext = 1) cat.bef - number of point extremes in the five years before extinction (interesting for replicates in which ext = 1) ext_year - extinction time (299 for replicates that persisted up to the end of simulation time) ext - extinct (1) or not (0) ############# For some of the analyses and plots in Vincenzi Point Extremes.r, the full dataset is required Please download sum.ris1.RDS and sum.ris2.RDS and move them to the current directory of R Then: sum.ris1 = readRDS("sum.ris1.RDS") sum.ris2 = readRDS("sum.ris2.RDS") sum.ris = c(sum.ris1,sum.ris2) rm(list=c("sum.ris1","sum.ris2")) The list sum.ris contains some of the measured variables for all the 25 600 replicates. In particular: extinct - pop going extinct or not<br>addvar - additive genetic variance at each time step<br>phenvar - phenotypic variance at each time step<br>yearextinct - year of extinction (299 if not extinct)<br>phenomean - phenotypic mean at each time step<br>sdopt - increase in variability of the optimum<br>meanopt - directional trend<br>selstrength - strength of selection<br>mutalfa - mutation amplitude<br>fitness.mean - mean fitness at each time step<br>optimum - optimum at each time step<br>popsize.post - population size after mortality at each time step Please write to simon.vincenz@gmail.com in the case help or clarifications are needed
DOI: 10.6084/m9.figshare.705890.v1
title: Time-averaged surface pressure on a flying-snake cross-section

description: This file bundle includes two plots of time-averaged pressure along the surface of the cross-section of a flying snake. It also includes de data sets and the plotting scripts (in Matlab). The values of the surface pressure for various angles of attack at Reynolds number 1000 and 2000 are listed in the first column of the files in the folder 'averageSurfacePressue/surfPres'. The x- and y-coordinates of the locations at which the pressure was measured are listed in columns three and four. The area under the plots of the average surface pressure vs. the x-coordinate of the measurement locations gives the pressure contribution to the lift on the body. These plots can be generated using MATLAB running the scripts: surfPresRe1000.m and surfPresRe2000.m
DOI: 10.6084/m9.figshare.705915.v1
title: Coral Provinces (Keith et al, 2013)

description: <strong>PLEASE USE FULL CITATION (not figshare citation):</strong> Keith, S.A., Baird, A.H., Madin, J.S., Hughes, T.P. & Connolly, S.R. (2013) Faunal breaks and species composition of Indo-Pacific corals: the role of plate tectonics, environment, and habitat distribution. <em>Proceedings of the Royal Society B: Biological Sciences</em> <strong>280</strong>(1763) : 20130818 ESRI Shapefile (polygons) of 11 faunal provinces, projection centred on Hawaii (prime meridian -160)