cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2252938.v1
title: Calibrated Bayes Factors Should Not Be Used: A Reply to Hoijtink, van Kooten, and Hulsker

description: Hoijtink, Kooten, and Hulsker (2016) present a method for choosing the prior distribution for an analysis with Bayes factor that is based on controlling error rates, which they advocate as an alternative to our more subjective methods (Morey & Rouder, 2014; Rouder, Speckman, Sun, Morey, & Iverson, 2009; Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011). We show that the method they advocate amounts to a simple significance test, and that the resulting Bayes factors are not interpretable. Additionally, their method fails in common circumstances, and has the potential to yield arbitrarily high Type II error rates. After critiquing their method, we outline the position on subjectivity that underlies our advocacy of Bayes factors.
DOI: 10.6084/m9.figshare.2885308.v279
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v286
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2247616
title: ASAPbio preprint survey results

description: This file contains raw survey responses from the google form hosted on asapbio.org/survey as well as derivative charts.
DOI: 10.6084/m9.figshare.2248474
title: Risky Choice in Virtual Reality Predicts Real-Life Risk-Taking Propensity

description: This dataset contains behavioral outcomes on risky decision making on a standard lottery task and on a virtual reality driving task. Most importantly, the number of risky choices on both tasks are entered for all participants. In addition, this dataset includes self-reports of real-life risk-taking propensity as measured by the Domain Specific Risk Taking Scale (DOSPERT)
DOI: 10.6084/m9.figshare.2247616.v1
title: ASAPbio preprint survey results

description: This file contains raw survey responses from the google form hosted on asapbio.org/survey as well as derivative charts.
DOI: 10.6084/m9.figshare.2885308.v280
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v287
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2951263.v288
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2885308.v281
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2245882
title: S-1-based regimens and the risk of leucopenic complications; a Meta-analysis with comparison to other fluoropyrimidines and non fluoropyrimidines

description: <b>Background</b>: We performed a meta-analysis oriented at the risk of leucopenic complications associated with S-1-based regimens. <b>Patients and methods</b>: The studies that were granted eligibility for inclusion include randomized phase II and III trials of patients with solid tumors on S-1; that entailed details of events of febrile neutropenia, all-grade and high-grade neutropenia and leucopenia. <b>Results</b>: After rejecting ineligible studies, a total of 28 clinical trials were elected eligible for further quantitative analysis. The RR of febrile neutropenia, all-grade and high-grade neutropenia for S-1 vs.non fluoropyrimidine controls was 0.27 [95% CI 0.16, 0.46; P <b>Conclusions</b>: The risk of febrile neutropenia, all-grade and high-grade neutropenia and leucopenia is less in S-1-based therapy than in non fluoropyrimidine regimens; yet comparable to the risk associated with infusional 5FU or capecitabine-based regimens.
DOI: 10.6084/m9.figshare.2951263.v289
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2245882.v1
title: S-1-based regimens and the risk of leucopenic complications; a Meta-analysis with comparison to other fluoropyrimidines and non fluoropyrimidines

description: <b>Background</b>: We performed a meta-analysis oriented at the risk of leucopenic complications associated with S-1-based regimens. <b>Patients and methods</b>: The studies that were granted eligibility for inclusion include randomized phase II and III trials of patients with solid tumors on S-1; that entailed details of events of febrile neutropenia, all-grade and high-grade neutropenia and leucopenia. <b>Results</b>: After rejecting ineligible studies, a total of 28 clinical trials were elected eligible for further quantitative analysis. The RR of febrile neutropenia, all-grade and high-grade neutropenia for S-1 vs.non fluoropyrimidine controls was 0.27 [95% CI 0.16, 0.46; P <b>Conclusions</b>: The risk of febrile neutropenia, all-grade and high-grade neutropenia and leucopenia is less in S-1-based therapy than in non fluoropyrimidine regimens; yet comparable to the risk associated with infusional 5FU or capecitabine-based regimens.
DOI: 10.6084/m9.figshare.2951263.v290
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2245810
title: Dataset from Bohol

description: Dataset contains following files:overlap.csv-Activity records of selected species of terrestrial vertebrates camera-trapped during survey on Bohol Island, Philippines. Columns represent sites of survey (Zone), species captured (Sps) and time of record in 0-1 format (Time) <br> RAI.xls-number of independent events of all camera-trapped taxa on Bohol island and calculation of the relative abundance indices <br> estimatesS_data.xls-matrix of detection (0-1 format) of 18 recorded vertebrate species (columns) on Bohol in 2885 trap days (raws) for computation of species accumulation curve by EstimateS.
DOI: 10.6084/m9.figshare.2885308.v282
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2885308.v283
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v291
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2885308.v284
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v292
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2244760
title: Relative abundance index

description: Number of independent events of all camera-trapped taxa on Bohol island and calculation of the relative abundance indices.
DOI: 10.6084/m9.figshare.2244838
title: Data of species detection for EstimateS

description: Matrix of detection (0-1 format) of 18 recorded vertebrate species (columns) on Bohol in 2885 trap days (raws) for computation of species accumulation curve by EstimateS.
DOI: 10.6084/m9.figshare.2244838.v1
title: Data of species detection for EstimateS

description: Matrix of detection (0-1 format) of 18 recorded vertebrate species (columns) on Bohol in 2885 trap days (raws) for computation of species accumulation curve by EstimateS.
DOI: 10.6084/m9.figshare.2885308.v285
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2244601
title: Overlap data

description: Activity records of selected species of terrestrial vertebrates camera-trapped during survey on Bohol Island, Philippines. Columns represent sites of survey (Zone), species captured (Sps) and time of record in 0-1 format (Time).
DOI: 10.6084/m9.figshare.2245630.v1
title: Einstellungen zum Inklusiven Schulsystem (EZIS)

description: Skala zur Erfassung der "Einstellungen zum Inklusiven Schulsystem" mit 20 Items und 3 Subskalen.<br>
DOI: 10.6084/m9.figshare.2951263.v293
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2244601.v1
title: Overlap data

description: Activity records of selected species of terrestrial vertebrates camera-trapped during survey on Bohol Island, Philippines. Columns represent sites of survey (Zone), species captured (Sps) and time of record in 0-1 format (Time).
DOI: 10.6084/m9.figshare.2245630.v2
title: Einstellungen zum Inklusiven Schulsystem (EZIS)

description: Skala zur Erfassung der "Einstellungen zum Inklusiven Schulsystem" mit 20 Items und 3 Subskalen.<br>
DOI: 10.6084/m9.figshare.2885308.v286
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v294
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2885308.v287
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v295
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2885308.v288
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v296
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2242447
title: Data package from: Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: evidence of widespread facilitation in south Andean plant communities (Tur et al. 2016)

description: This is a fileset containing data and R scripts for analyses published in:<br><br>C. Tur, A. Sáez, A. Traveset & M.A. Aizen (2016) Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: evidence of widespread facilitation in south Andean plant communities. Ecology Letters. doi: 10.1111/ele.12594<br><br>Files included in the data package are:<br><br>(1) Data_Tur_et_al_2016_EcolLet.txt. File with data of pollen deposition on stigmas of high-Andean species. The dataframe contains the following columns:<br> <br> code -&gt; sample ID<br> altitude -&gt; altitudinal community (1600, 1800, 2000 m)<br> date -&gt; sampling date<br> plant -&gt; plant ID<br> stigma -&gt; number identifying the stigma sampled<br> receptor -&gt; stigma's plant species<br> donor -&gt; pollen's plant species <br> germ -&gt; number of germinated pollen grains on stigma<br> non.germ -&gt; number of non-germinated pollen grains on stigma<br> total -&gt; number of total pollen grains on stigma<br><br>(2) Species_info_Tur_et_al_2016_EcolLet.txt. Table with information on complete species names and plant families matching species abbreviations used in the previous data file.<br><br>(3) Tur_et_al_2016_EcolLet_script.r. R script to perform the analyses included in Tur et al. 2016<br><br>When using these data please cite:<br>C. Tur, A. Sáez, A. Traveset & M.A. Aizen (2016) Evaluating the effects of pollinator-mediated interactions using pollen transfer networks: evidence of widespread facilitation in south Andean plant communities. Ecology Letters. doi: 10.1111/ele.12594.<br><br>For further information or questions please contact to: cris.tur.espinosa@gmail.com<br>
DOI: 10.6084/m9.figshare.2885308.v289
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v297
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2238865
title: Data from: Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

description: Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades-off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach to the standard lasso and ten other competitors: AIC, AICC, BIC, eBIC, HQIC, RIC, one-standard-error rule, adaptive lasso, stability selection and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC and the adaptive lasso which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted.<br>
DOI: 10.6084/m9.figshare.2885308.v290
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v298
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2238865.v1
title: Data from: Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models

description: Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades-off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach to the standard lasso and ten other competitors: AIC, AICC, BIC, eBIC, HQIC, RIC, one-standard-error rule, adaptive lasso, stability selection and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC and the adaptive lasso which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted.<br>
DOI: 10.6084/m9.figshare.2951263.v299
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2885308.v291
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v300
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.
DOI: 10.6084/m9.figshare.2229988
title: 7T TRT - Brain masks

description: Brain mask derived from structural (T1) images using CBSTools (https://www.nitrc.org/projects/cbs-tools/) SPECTRE 2010 tool. Code available here: https://github.com/nipy/nipype/blob/0.11.0/examples/smri_cbs_skullstripping.py<br>WARNING: some masks will require manual corrections.<br>Raw dataset described in http://www.nature.com/articles/sdata201454
DOI: 10.6084/m9.figshare.2885308.v292
title: UNH-RVAT 3-D SST RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions using the k-omega SST RANS model in OpenFOAM 2.3.x (590d57f32fed). See the link below for case files and download instructions.
DOI: 10.6084/m9.figshare.2951263.v301
title: UNH-RVAT 3-D Spalart--Allmaras RANS simulation results

description: Results from simulating the UNH-RVAT cross-flow turbine in three dimensions with the Spalart--Allmaras RANS model in OpenFOAM 2.3.x (590d57f32fed). See link below for case files (note: use the `SpalartAllmaras` branch), which include a script for downloading and extracting, as well as scripts for post-processing.