cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7069835
title: Use of hyperspectral data to identify and discriminate wheat from a glyphosate resistant ryegrass biotype

description: Data employed for weed-crop discrimination analysis<br>
DOI: 10.6084/m9.figshare.7069835.v1
title: Use of hyperspectral data to identify and discriminate wheat from a glyphosate resistant ryegrass biotype

description: Data employed for weed-crop discrimination analysis<br>
DOI: 10.6084/m9.figshare.7069820
title: Data from: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley

description: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley. India, Spain, Turkey, UK, USA.
DOI: 10.6084/m9.figshare.7069772
title: Extensive network

description: Table with the interactions in the extensive network.
DOI: 10.6084/m9.figshare.7069820.v1
title: Data from: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley

description: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley. India, Spain, Turkey, UK, USA.
DOI: 10.6084/m9.figshare.7069820.v2
title: Data from: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley

description: Does female-authored research have more educational impact than male-authored research? Evidence from Mendeley. India, Spain, Turkey, UK, USA.
DOI: 10.6084/m9.figshare.7069769
title: Interactions filtered by controls

description: Table with all interactions after removing identifications from control experiments.
DOI: 10.6084/m9.figshare.7069766
title: Experiment overview

description: Table giving an overview of all Virotrap LC-MS/MS runs.
DOI: 10.6084/m9.figshare.7069769.v1
title: Interactions filtered by controls

description: Table with all interactions after removing identifications from control experiments.
DOI: 10.6084/m9.figshare.7069760
title: Overview filtering

description: Table showing the overlap with BioGRID after each filtering step.
DOI: 10.6084/m9.figshare.7069763
title: Spectral counts

description: Table with spectral counts for every identified protein for every experiment.
DOI: 10.6084/m9.figshare.7069766.v1
title: Experiment overview

description: Table giving an overview of all Virotrap LC-MS/MS runs.
DOI: 10.6084/m9.figshare.7069757
title: A20 iMixPro AP-MS result

description: Table with the results of the iMixPro affinity purification-mass spectrometry experiment for the A20 protein.
DOI: 10.6084/m9.figshare.7069760.v1
title: Overview filtering

description: Table showing the overlap with BioGRID after each filtering step.
DOI: 10.6084/m9.figshare.7069763.v1
title: Spectral counts

description: Table with spectral counts for every identified protein for every experiment.
DOI: 10.6084/m9.figshare.7069754
title: Novel interactors with link

description: Table with references linking novel interacting proteins to NF-kB and/or cell death signaling.
DOI: 10.6084/m9.figshare.7069757.v1
title: A20 iMixPro AP-MS result

description: Table with the results of the iMixPro affinity purification-mass spectrometry experiment for the A20 protein.
DOI: 10.6084/m9.figshare.7069751
title: Plasmids

description: Table with all plasmids used for Virotrap experiments.
DOI: 10.6084/m9.figshare.7069751.v1
title: Plasmids

description: Table with all plasmids used for Virotrap experiments.
DOI: 10.6084/m9.figshare.7069745
title: Lean network

description: Table with the interactions of the lean network.
DOI: 10.6084/m9.figshare.7069742
title: Lean network cytoscape file

description: Lean network cytoscape file.
DOI: 10.6084/m9.figshare.7069745.v1
title: Lean network

description: Table with the interactions of the lean network.
DOI: 10.6084/m9.figshare.7069718
title: Schwartz_CompassionateCare_Retest.xlsx

description: Retest dataset for psychometric validation of a new measure<br>
DOI: 10.6084/m9.figshare.7069718.v1
title: Schwartz_CompassionateCare_Retest.xlsx

description: Retest dataset for psychometric validation of a new measure<br>
DOI: 10.6084/m9.figshare.7069742.v1
title: Lean network cytoscape file

description: Lean network cytoscape file.
DOI: 10.6084/m9.figshare.7069709
title: Schwartz_CompassionateCare_Test.xlsx

description: Test dataset for psychometric validation of a new measure
DOI: 10.6084/m9.figshare.7069709.v1
title: Schwartz_CompassionateCare_Test.xlsx

description: Test dataset for psychometric validation of a new measure
DOI: 10.6084/m9.figshare.7069673
title: Supplemental material for Lin et al., 2018

description: Supplemental Table 1 Complete correlation data of cardiac transcripts with ISO-induced left ventricular dilation.Supplemental Table 2 Individual clinical data of METSIM participants included in this study.Supplemental Table 3 Clinical characteristics of patients with heart failure and control subjects
DOI: 10.6084/m9.figshare.7069673.v1
title: Supplemental material for Lin et al., 2018

description: Supplemental Table 1 Complete correlation data of cardiac transcripts with ISO-induced left ventricular dilation.Supplemental Table 2 Individual clinical data of METSIM participants included in this study.Supplemental Table 3 Clinical characteristics of patients with heart failure and control subjects
DOI: 10.6084/m9.figshare.7068857.v1
title: M. oryzae transcriptomic data matrix covering a range of experiments

description: Normalized (same total expression) <i>M. oryzae </i>expression (Log2 values) from a range of experiments with the fungus infecting a plant. Column labels are experiment codes from the database found at https://www.ncbi.nlm.nih.gov/geo
DOI: 10.6084/m9.figshare.7068860.v1
title: F. graminearum transcriptomic data matrix covering a range of in planta experiments

description: Normalized (Same total expression) <i>F graminearum</i> expression (Log2 values) from a range of experiments with the fungus infecting a plant. Column labels are experiment codes from the database http://www.plexdb.org/modules/PD_general/downloadphp?species=Fusarium
DOI: 10.6084/m9.figshare.7068773
title: Code for "Measurement error in continuous endpoints in randomised trials: problems and solutions"

description: This is the R-code used in the simulation study of the paper 'Measurement error in continuous endpoints in randomised trials: problems and solutions". It generates data, calculates results and makes tables and figures which are shown in the above mentioned paper.<br>By running 0_setup.R, all needed variables and functions are loaded in your R environment.By using 1_generateDatasets.R, datasets (one row for the results of each iteration) are generated. You could either use the provided seed (seed.Rdata) or generate a different seed to generate new results. Generating these datasets will take several days, therefore, the datasets are placed in the other subfolder of this project on figshare.By running 2_loadDatasets.R the datasets will be loaded in your R environment (first save the datasets on your computer, preferably in your working directory, or code the location of the datasets). Save the datasets (in your working directory) in 4 different folders: SME, DME, NAIVE and THETAS (or change the script in order for R to find the saved datasets).By running 3_calcResults.R the results from the datasets are calculated (as shown in corresponding paper). By running visualizeResults.R the results calculated in step 3 are visualized (i.e. plots are made). <br>script.R runs all former scripts and includes code to form the tables and plots (as shown in corresponding paper).<br>
DOI: 10.6084/m9.figshare.7068734.v1
title: Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa

description: R scripts and data tables needed to reproduce figures in the manuscript: Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa.
DOI: 10.6084/m9.figshare.7068695
title: Simulated datasets of "Measurement error in continuous endpoints in randomised trials: problems and solutions"

description: This contains files with results of the simulation study in "Measurement error in continuous endpoints: problems and solutions". <br>Five different types of files are saved in this folder, all consisting of 10,000 rows (number of replicates for each scenario):<b>1.</b> Results for the systematic measurement error scenario (prefix = sme). Each file has 12 columns: alpha_hat (estimated regression intercept after correction), beta_hat (estimated regression slope after correction), ci.zv.l (zero variance confidence interval lower limit), ci.zv.u (zero variance confidence interval upper limit), ci.delta.l (delta ci l limit), ci.delta.u (delta ci u limit), ci.fieller.l (fieller ci l limit), ci.fieller.u (fieller ci u limit), ci.bootstrap.l (bootstrap ci l limit), ci.bootstrap.u (bootstrap ci u limit), boostrap.R (number of replicates of bootstrap), seednr (number of seed).<br><b>2.</b> Results for the differential measurement error scenario (prefix = dme). Each file has 12 columns identical to the sme scenario.<b>3.</b> Results for the naive scenario of both systematic and differential measurement error (no measurement error correction applied) (prefix = naive_dme or naive_sme). Each file has 4 columns: alpha_hat, beta_hat, ci.l, ci.u and seednr.<b>4.</b> Results containing the estimated theta's that are used to correct for systematic measurement error (prefix = sme_thetas). Each file has 3 rows: theta0 (estimated theta0 = intercept of sme model), theta1 (estimated theta1 = intercept of sme model), seednr.<br>File names are constructed as: prefix_k_rsquared, with prefix as above, k the number of people in the external calibration set and rsquared the r-squared between y* (y with error) and y. An additional _2 is used for sme_k_08_2, indicating the second scenario where the bias in the naive estimator is bigger (theta1 = 1.25, whereas in all other scenarios theta1 = 1.05). <br><br>
DOI: 10.6084/m9.figshare.7068680
title: Newbury and Hodges bobcat diet data.xlsx

description: Winter diet data for bobcats in northwest Montana, includes proportion of prey type in sample, and dry weight in sample.<br>
DOI: 10.6084/m9.figshare.7068680.v1
title: Newbury and Hodges bobcat diet data.xlsx

description: Winter diet data for bobcats in northwest Montana, includes proportion of prey type in sample, and dry weight in sample.<br>
DOI: 10.6084/m9.figshare.7068668
title: Files used for dimensioning of photovoltaic systems in buildings, Coimbra

description: Files used to create a tool of dimensioning of photovoltaic systems in buildings. It uses EnergyPlus as energy simulator and GenOpt as the optimization program. The folder contains 7 different scenarios of optimization, depending on the areas selected and PV structure. It was also studied the inflation effect on them. Each situation contains a Backup folder, which contains the reduced IDF of the original building, named TEST, and respective outputs, Purchased and Surplus Energies. Furthermore, it has all the GenOpt files involved: commands, algorithm, outputs and file optimized, and even the data used as an approach to solar trackers' positioning in EnergyPlus, for Coimbra. As variables for optimization, the number of solar panels and respective orientation, positioning and tilt were used. It was also considered the inter-row shading, outside losses (that EnergyPlus doesn't consider) and the main economic factors: equipment costs (PV, structure, inverter and battery) and respective installation, maintenance and renovation. This work was part of the Ren4EEnIEQ project, in order to create an BIM add-on tool for the improvement of energy efficiency and indoor environment quality in renovation of buildings
DOI: 10.17028/rd.lboro.7068413
title: People in pain make poorer decisions: tasks, data files and SPSS analysis syntax

description: Tasks, data files, and SPSS analysis scripts for the paper "People in pain make poorer decisions".
DOI: 10.17028/rd.lboro.7068413.v1
title: People in pain make poorer decisions: tasks, data files and SPSS analysis syntax

description: Tasks, data files, and SPSS analysis scripts for the paper "People in pain make poorer decisions".
DOI: 10.6084/m9.figshare.7068398
title: spatial model from A spatio-temporal model reveals self-limiting Fc<i>ɛ</i>RI crosslinking by multivalent antigens

description: Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signaling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (Fc<i>ɛ</i>RI) in the plasma membrane of mast cells. However, understanding of the mechanisms of Fc<i>ɛ</i>RI aggregation remains incomplete. Experimental reports indicate that Fc<i>ɛ</i>RI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor crosslinking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse Fc<i>ɛ</i>RI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model-predicted sharp decay in species mobility in the plasma membrane in response receptor crosslinking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.
DOI: 10.6084/m9.figshare.7068398.v1
title: spatial model from A spatio-temporal model reveals self-limiting Fc<i>ɛ</i>RI crosslinking by multivalent antigens

description: Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signaling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (Fc<i>ɛ</i>RI) in the plasma membrane of mast cells. However, understanding of the mechanisms of Fc<i>ɛ</i>RI aggregation remains incomplete. Experimental reports indicate that Fc<i>ɛ</i>RI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor crosslinking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse Fc<i>ɛ</i>RI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model-predicted sharp decay in species mobility in the plasma membrane in response receptor crosslinking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.
DOI: 10.6084/m9.figshare.7068386
title: nonspatial model from A spatio-temporal model reveals self-limiting Fc<i>ɛ</i>RI crosslinking by multivalent antigens

description: Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signaling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (Fc<i>ɛ</i>RI) in the plasma membrane of mast cells. However, understanding of the mechanisms of Fc<i>ɛ</i>RI aggregation remains incomplete. Experimental reports indicate that Fc<i>ɛ</i>RI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor crosslinking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse Fc<i>ɛ</i>RI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model-predicted sharp decay in species mobility in the plasma membrane in response receptor crosslinking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.
DOI: 10.6084/m9.figshare.7068383
title: Supplementary Table 1 - Mei2 peaks from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: Mei2 CLIP-Seq peaks determined by HOMER
DOI: 10.6084/m9.figshare.7068386.v1
title: nonspatial model from A spatio-temporal model reveals self-limiting Fc<i>ɛ</i>RI crosslinking by multivalent antigens

description: Aggregation of cell surface receptor proteins by multivalent antigens is an essential early step for immune cell signaling. A number of experimental and modelling studies in the past have investigated multivalent ligand-mediated aggregation of IgE receptors (Fc<i>ɛ</i>RI) in the plasma membrane of mast cells. However, understanding of the mechanisms of Fc<i>ɛ</i>RI aggregation remains incomplete. Experimental reports indicate that Fc<i>ɛ</i>RI forms relatively small and finite-sized clusters when stimulated by a multivalent ligand. By contrast, modelling studies have shown that receptor crosslinking by a trivalent ligand may lead to the formation of large receptor superaggregates that may potentially give rise to hyperactive cellular responses. In this work, we have developed a Brownian dynamics-based spatio-temporal model to analyse Fc<i>ɛ</i>RI aggregation by a trivalent antigen. Unlike the existing models, which implemented non-spatial simulation approaches, our model explicitly accounts for the coarse-grained site-specific features of the multivalent species (molecules and complexes). The model incorporates membrane diffusion, steric collisions and sub-nanometre-scale site-specific interaction of the time-evolving species of arbitrary structures. Using the model, we investigated temporal evolution of the species and their diffusivities. Consistent with a recent experimental report, our model-predicted sharp decay in species mobility in the plasma membrane in response receptor crosslinking by a multivalent antigen. We show that, due to such decay in the species mobility, post-stimulation receptor aggregation may become self-limiting. Our analysis reveals a potential regulatory mechanism suppressing hyperactivation of immune cells in response to multivalent antigens.
DOI: 10.6084/m9.figshare.7068380
title: Supplementary Table 3 - Msa1 peaks from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: Msa1 CLIP-Seq peaks determined by HOMER
DOI: 10.6084/m9.figshare.7068383.v1
title: Supplementary Table 1 - Mei2 peaks from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: Mei2 CLIP-Seq peaks determined by HOMER
DOI: 10.6084/m9.figshare.7068377
title: Supplementary Table 2 - Mei2 CLIP targets from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: High-confidence targets of Mei2 determined by CLIP_Seq
DOI: 10.6084/m9.figshare.7068377.v1
title: Supplementary Table 2 - Mei2 CLIP targets from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: High-confidence targets of Mei2 determined by CLIP_Seq
DOI: 10.6084/m9.figshare.7068380.v1
title: Supplementary Table 3 - Msa1 peaks from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: Msa1 CLIP-Seq peaks determined by HOMER
DOI: 10.6084/m9.figshare.7068362
title: Supplementary Table 4 - Strains and primers from <i>mmi1</i> and <i>rep2</i> mRNAs are novel RNA targets of the Mei2 RNA binding protein during early meiosis in <i>Schizosaccharomyces pombe</i>

description: The RNA-binding protein Mei2 is crucial for meiosis in <i>Schizosaccharomyces pombe.</i> In <i>mei2</i> mutants, pre-meiotic S-phase is blocked, along with meiosis. Mei2 binds a long non-coding RNA (lncRNA) called meiRNA, which is a ‘sponge RNA’ for the meiotic inhibitor protein Mmi1. The interaction between Mei2, meiRNA and Mmi1 protein is essential for meiosis. But <i>mei2</i> mutants have stronger and different phenotypes than meiRNA mutants, since <i>mei2Δ</i> arrests before pre-meiotic S, while the meiRNA mutant arrests after pre-meiotic S but before meiosis. This suggests Mei2 may bind additional RNAs. To identify novel RNA targets of Mei2, which might explain how Mei2 regulates pre-meiotic S, we used RNA Immunoprecipitation and Cross-linking Immunoprecipitation. In addition to meiRNA, we found the mRNAs for <i>mmi1</i> (which encodes Mmi1) and for the S-phase transcription factor <i>rep2</i>. There were also three other RNAs of uncertain relevance. We suggest that at meiotic initiation, Mei2 may sequester <i>rep2</i> mRNA to help allow pre-meiotic S, and then may bind both meiRNA and <i>mmi1</i> mRNA to inactivate Mmi1 at two levels, the protein level (as previously known), and also the mRNA level, allowing meiosis. We call Mei2–meiRNA a ‘double sponge’ (i.e. binding both an mRNA, and its encoded protein).