cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11769183.v1
title: Additional file 1 of Genome-wide identification and expression of GRAS gene family members in cassava

description: Additional file 1: Table S1. List of the GRAS protein sequences in cassava. Table S2. A catalog of MeGRAS proteins with their HMM profiles. Table S3. Protein property and subcellular localization of MeGRAS proteins. Table S4. Secondary structure of amino acid sequences in cassava. Table S5. GRAS genes used in Arabidopsis, rice, castor bean, Populus, tomato, and tea plant. Table S6. The structural features of motif 1â€“20. Table S7. The expression data of the cassava GRAS genes in different tissues. Table S8. The accession number of transcriptomic data in NCBI. Table S9. Sequences of primers used in qRT-PCR.
DOI: 10.6084/m9.figshare.11768877
title: Global climate and human health effects of the gasoline and diesel vehicle fleets

description: We quantify the impacts of global gasoline and diesel emissions on air quality, climate change and human health, by employing the NCAR CESM CAM5-Chem model at the horizontal resolution of 0.9 degrees latitude by 1.25 degrees longitude as well as integrated exposure response functions in combinations with the most recent 2017 Global Burden of Disease. We run three sets of CAM5-Chem model simulations: (1) the control model simulation with anthropogenic emissions from IIASA GAINS ECLIPSE V5a for the year 2015 (hereafter referred to as CRL2015); (2) one sensitivity simulation identical to the control but with global gasoline emissions off (referred to as GSL2015); (3) one sensitivity simulation identical to the control but with global diesel emissions off (referred to as DS2015). The differences between CRL2015 and GSL2015, and between CRL2015 and DS2015, will be the net impacts of global gasoline and diesel sectors, respectively.
DOI: 10.6084/m9.figshare.11768826
title: Evaporation monthly

description: 432 month (36 years) between January 1980 and December 2015
DOI: 10.6084/m9.figshare.11766762
title: Tree

description: fasta file, alignment, IQtree log file, uncalibrated + calibrated tree
DOI: 10.6084/m9.figshare.11766747
title: SEDAC variables

description: Anthropogenic variables downloaded from SEDAC
DOI: 10.6084/m9.figshare.11766762.v1
title: Tree

description: fasta file, alignment, IQtree log file, uncalibrated + calibrated tree
DOI: 10.6084/m9.figshare.11766729
title: Categorical variables

description: biomes and ecoregions downloaded from the WWF, Köppen-Geiger classifications from koeppen-geiger.vu-wien.ac.at
DOI: 10.6084/m9.figshare.11766747.v2
title: SEDAC variables

description: Anthropogenic variables downloaded from SEDAC
DOI: 10.6084/m9.figshare.11766747.v1
title: SEDAC variables

description: Anthropogenic variables downloaded from SEDAC
DOI: 10.6084/m9.figshare.11766702
title: Frog occurrences and range maps

description: GBIF.csv - occurrence data downloaded from GBIF on Jan 16 2020 (search parameters: https://doi.org/10.15468/dl.akr84v)<br>SA_shape - txt and tiff files used to define South America study area for analysis<br>ANURA.shp - frog species range shapefiles downloaded from the IUCN (version 2019-3)
DOI: 10.6084/m9.figshare.11766729.v1
title: Categorical variables

description: biomes and ecoregions downloaded from the WWF, Köppen-Geiger classifications from koeppen-geiger.vu-wien.ac.at
DOI: 10.6084/m9.figshare.11766699
title: WorldClim2 variables

description: climatic variables downloaded from the WorldClim2 dataset at 30 second resolution
DOI: 10.6084/m9.figshare.11766702.v1
title: Frog occurrences and shapefiles

description: GBIF.csv - occurrence data downloaded from GBIF on Jan 16 2020 (search parameters: https://doi.org/10.15468/dl.akr84v)<br>SA_shape - txt and tiff files used to define South America study area for analysis<br>ANURA.shp - frog species range shapefiles downloaded from the IUCN (version 2019-3)
DOI: 10.6084/m9.figshare.11766699.v2
title: WorldClim2 variables

description: climatic variables downloaded from the WorldClim2 dataset at 30 second resolution
DOI: 10.6084/m9.figshare.11766699.v1
title: WorldClim2 variables

description: climatic variables downloaded from the WorldClim2 dataset at 30 second resolution
DOI: 10.6084/m9.figshare.11766702.v2
title: Frog occurrences and range maps

description: GBIF.csv - occurrence data downloaded from GBIF on Jan 16 2020 (search parameters: https://doi.org/10.15468/dl.akr84v)<br>SA_shape - txt and tiff files used to define South America study area for analysis<br>ANURA.shp - frog species range shapefiles downloaded from the IUCN (version 2019-3)
DOI: 10.6084/m9.figshare.11766639
title: GBIF_occurrences

description: GBIF occurrences recovered Jan 16 2020 (doi: https://doi.org/10.15468/dl.akr84v)<br>
DOI: 10.6084/m9.figshare.11766639.v2
title: GBIF_occurrences

description: GBIF occurrences recovered Jan 16 2020 (doi: https://doi.org/10.15468/dl.akr84v)<br>
DOI: 10.6084/m9.figshare.11766639.v1
title: GBIF_occurrences

description: GBIF occurrences recovered Jan 16 2020 (doi: https://doi.org/10.15468/dl.akr84v)<br>
DOI: 10.6084/m9.figshare.11766594
title: METADATA - file naming system and mesofauna counts

description: Spreadsheet including file naming system for mesofauna slide images. Additionally includes deployment dates, collection dates, treatment information, and mesofauna counts for litterbags.
DOI: 10.6084/m9.figshare.11766594.v1
title: METADATA - file naming system and mesofauna counts

description: Spreadsheet including file naming system for mesofauna slide images. Additionally includes deployment dates, collection dates, treatment information, and mesofauna counts for litterbags.
DOI: 10.6084/m9.figshare.11766486
title: Figure 5 raw data

description: Experimental and computational raw data presented in Figure 5.<br><b>Figure 5.</b> Rab5 activation wave spreads on membrane in presence of GAP. (A) Micrographs of CF488A-Rab5 activation wave spreading across the membrane. Scale bar is 20 µm. Times indicate relative duration after start of acquisition, not time after addition of GEF complex. (B) Fluorescence intensity time profile of the indicated area in A. (C) Kymograph of the indicated area in A and mean wave velocity. Wave velocity was determined from the slope of fluorescence increase in generated kymographs (N = 6). (D) Simulated Rab5 activation front with diffusion inclusion in the phenomenological model (see SI Appendix, Supplementary Text Eq. 6). (F) Solution in terms of the dimensionless distance x/√(D/a_2 ). <br>
DOI: 10.6084/m9.figshare.11766486.v1
title: Figure 5 raw data

description: Experimental and computational raw data presented in Figure 5.<br><b>Figure 5.</b> Rab5 activation wave spreads on membrane in presence of GAP. (A) Micrographs of CF488A-Rab5 activation wave spreading across the membrane. Scale bar is 20 µm. Times indicate relative duration after start of acquisition, not time after addition of GEF complex. (B) Fluorescence intensity time profile of the indicated area in A. (C) Kymograph of the indicated area in A and mean wave velocity. Wave velocity was determined from the slope of fluorescence increase in generated kymographs (N = 6). (D) Simulated Rab5 activation front with diffusion inclusion in the phenomenological model (see SI Appendix, Supplementary Text Eq. 6). (F) Solution in terms of the dimensionless distance x/√(D/a_2 ). <br>
DOI: 10.6084/m9.figshare.11766483
title: Omnibus test for normality based on the Edgeworth expansion

description: This is a matrix 5000x1000 of generated pseudorandom numbers from the N(0,1) distribution with the 'randn' routine in Matlab 2019a.
DOI: 10.6084/m9.figshare.11766483.v1
title: Omnibus test for normality based on the Edgeworth expansion

description: This is a matrix 5000x1000 of generated pseudorandom numbers from the N(0,1) distribution with the 'randn' routine in Matlab 2019a.
DOI: 10.6084/m9.figshare.11766231
title: Vetter_et_al_GCB_2020_SIGNAL.xlsx

description: This data was collected during the SIGNAL experiment in 2014 and 2015. The data was collected in a multisite invader x biodiversity x drought manipulation experiment to examine the combined effects of biodiversity and invasion on drought resistance and recovery at three semi-natural grassland sites across Europe. The stability of biomass production to an extreme drought manipulation (100% rainfall reduction) was quantified in field mesocosms with a richness gradient of 1, 3, and 6 species and three invasion treatments (no invader, <i>Lupinus polyphyllus</i>, <i>Senecio inaequidens</i>).<br>A detailed description of the data, the methodology to analyze the data as well as the results gained can be found in the paper:<br>Vetter et al. (2020): Invader presence disrupts the stabilizing effect of species richness in plant community recovery after drought. Global Change Biology.
DOI: 10.6084/m9.figshare.11765649
title: GREAT Export

description: Raw output from GREAT analysis (http://great.stanford.edu/public/html/) for the regions surrounding the 203 genome-wide significant lead SNPs.
DOI: 10.6084/m9.figshare.11765649.v1
title: GREAT Export

description: Raw output from GREAT analysis (http://great.stanford.edu/public/html/) for the regions surrounding the 203 GWAS signals.
DOI: 10.6084/m9.figshare.11765649.v2
title: GREAT Export

description: Raw output from GREAT analysis (http://great.stanford.edu/public/html/) for the regions surrounding the 203 genome-wide significant lead SNPs.
DOI: 10.6084/m9.figshare.11764551
title: Figure 4 raw data

description: Raw experimental and computational data presented in Figure 4.<br><b>Figure 4. </b>GAP activity reveals bistability of the reconstituted network. (A) Effect of RabGAP-5 on Rab5 activation. Different concentrations of RabGAP-5 were present in experiments with 500 nM CF488A-Rab5:GDI and 2 μM GDI, before adding 80 nM Rabex5:Rabaptin5. Shown are time courses at increasing GAP concentrations. (B) Maximal rates kmax of Rab5 activation for curves shown in A. Without detectable activation within 150 min, the activation rate was set to 0 and the corresponding points are depicted in orange. Error bars are SD. (C) Activation delay Ti for data presented in A. Without detectable activation, the times to inflection point are denoted as &gt;150 min (orange). (D) GAP addition after activation with 80 nM GEF. RabGAP-5 was added to 500 nM and 2 µM final concentration, respectively. The traces were offset to the point of GAP addition. (E) GAP titration response curve. The fold change was calculated by dividing the fluorescence intensity at steady state with the average fluorescence signal 10 min before GEF addition. +GAP → +GEF fold values (circles) are calculated based on traces in A and +GEF → +GAP values are taken from D. Activation is depicted in blue and inactive reactions in orange (fold &lt;10). (F) Changing the dissociation rate in opposite directions reveals hysteresis in switching of the phenomenological model after 150 minutes. Shown are means of 20 simulations, error bars are SD.<br>
DOI: 10.6084/m9.figshare.11764551.v1
title: Figure 4 raw data

description: Raw experimental and computational data presented in Figure 4.<br><b>Figure 4. </b>GAP activity reveals bistability of the reconstituted network. (A) Effect of RabGAP-5 on Rab5 activation. Different concentrations of RabGAP-5 were present in experiments with 500 nM CF488A-Rab5:GDI and 2 μM GDI, before adding 80 nM Rabex5:Rabaptin5. Shown are time courses at increasing GAP concentrations. (B) Maximal rates kmax of Rab5 activation for curves shown in A. Without detectable activation within 150 min, the activation rate was set to 0 and the corresponding points are depicted in orange. Error bars are SD. (C) Activation delay Ti for data presented in A. Without detectable activation, the times to inflection point are denoted as &gt;150 min (orange). (D) GAP addition after activation with 80 nM GEF. RabGAP-5 was added to 500 nM and 2 µM final concentration, respectively. The traces were offset to the point of GAP addition. (E) GAP titration response curve. The fold change was calculated by dividing the fluorescence intensity at steady state with the average fluorescence signal 10 min before GEF addition. +GAP → +GEF fold values (circles) are calculated based on traces in A and +GEF → +GAP values are taken from D. Activation is depicted in blue and inactive reactions in orange (fold &lt;10). (F) Changing the dissociation rate in opposite directions reveals hysteresis in switching of the phenomenological model after 150 minutes. Shown are means of 20 simulations, error bars are SD.<br>
DOI: 10.15131/shef.data.11764446
title: Data used in the study "ChronoMID - cross-modal neural networks for 3-D temporal medical imaging data"

description: Link to the data used in:<br>Alexander G. Rakowski, Petar Velickovic, Enrico Dall’Ara, Pietro Liò<br>from:-) Computer Laboratory, University of Cambridge, Cambridge, Cambs, United Kingdom-) Insigneo Institute for in silico medicine, University of Sheffield, Sheffield, SouthYorkshire, United Kingdom<br><i>ChronoMID - cross-modal neural networks for 3-D temporal </i><i>medical imaging data;</i> Plos One 2020.<br>Provided here are a selection of images from the study. The images have been resized to have consistent dimensions, but otherwise have not undergone any of the preprocessing steps listed in the ChronoMID paper.<br>Input images for both the control (WildType) and treated (Anabolic) groups have been uploaded at three different time points: at the start of the experiment (W0), at the beginning of the treatment (W5), and at the end of the experiment (W8).Within each mouse-week, the stack of cross-sections ranges from the proximal epiphysis(e.g. 0100.dcm) and extend toward the distal epiphysis (e.g. 1300.dcm).<br>The whole dataset is stored in the University of Sheffield file-store at the link:https://web-unidrive.sheffield.ac.uk/shared/multisim2/WP7/<br>The code used in this study can be found in the following link:https://doi.org/10.15131/shef.data.11798595<br><br>For further information please contact Dr Enrico Dall'Ara: e.dallara@sheffield.ac.uk
DOI: 10.15131/shef.data.11764446.v1
title: Data used in the study "ChronoMID - cross-modal neural networks for 3-D temporal medical imaging data"

description: Link to the data used in:<br>Alexander G. Rakowski, Petar Velickovic, Enrico Dall’Ara, Pietro Liò<br>from:-) Computer Laboratory, University of Cambridge, Cambridge, Cambs, United Kingdom-) Insigneo Institute for in silico medicine, University of Sheffield, Sheffield, SouthYorkshire, United Kingdom<br><i>ChronoMID - cross-modal neural networks for 3-D temporal </i><i>medical imaging data;</i> Plos One 2020.<br>Provided here are a selection of images from the study. The images have been resized to have consistent dimensions, but otherwise have not undergone any of the preprocessing steps listed in the ChronoMID paper.<br>Input images for both the control (WildType) and treated (Anabolic) groups have been uploaded at three different time points: at the start of the experiment (W0), at the beginning of the treatment (W5), and at the end of the experiment (W8).Within each mouse-week, the stack of cross-sections ranges from the proximal epiphysis(e.g. 0100.dcm) and extend toward the distal epiphysis (e.g. 1300.dcm).<br>The whole dataset is stored in the University of Sheffield file-store at the link:https://web-unidrive.sheffield.ac.uk/shared/multisim2/WP7/<br>The code used in this study can be found in the following link:https://doi.org/10.15131/shef.data.11798595<br><br>For further information please contact Dr Enrico Dall'Ara: e.dallara@sheffield.ac.uk
DOI: 10.6084/m9.figshare.11764395
title: Timescales of regional precipitation change

description: Data produced to generate the figures presented in the paper titled "Time-evolving sea surface warming patterns modulate the climate change response of subtropical precipitation over land". All data is saved in netcdf format.<br>Fig1: - pr_response_rcp45_abrupt4xCO2.nc<br>Fig 2 and Fig 3: - pme_timescales_in_abrupt4xCO2.nc- pr_timescales_in_abrupt4xCO2.nc- tas_timescales_in_abrupt4xCO2.nc- ua850_timescales_in_abrupt4xCO2.nc<br>Fig 4: - timescales_warming_rcp45.nc- decomposition_regional_pme_rcp45.nc- decomposition_regional_precipitation_rcp45.nc<br>
DOI: 10.6084/m9.figshare.11764272.v1
title: Data for: Field-testing olfactory ability to understand human olfactory ecology

description: These are the data for the paper referenced above. There are four sheets--two sheets are for data coded as correct, near miss, far miss, and incorrect (one for field and one for lab) and two sheets are for data coded as consistent use of lable (one for field and one for lab).<br>
DOI: 10.6084/m9.figshare.11764185
title: Figure 3 raw data

description: Raw experimental and computational data presented in Figure 3. <br><br><b>Figure 3.</b> Rab5:GDI activation is tuned by free Rab5:GDP abundance. (A) Rab5 cycles between the membrane and solution before and after nucleotide exchange. Top: sCy5-Rab5 molecule counts per frame and collective CF488A-Rab5 activation. Bottom: snapshots of the activation reaction. sCy5- and CF488A-Rab5 are depicted in yellow and cyan, respectively. The sCy5 channel was smoothed before merging to reduce non-specific high frequency noise. Scale bar is 10 µm. (B) Rab5 single molecule trajectories reveal GDP- and GTP-bound proteins on the membrane. Top: 500 tracks of membrane-bound sCy5-Rab5 particles before (GDP) and after (GTP) activation. Bottom: frequency diagram identifies two populations with distinct lifetimes. A monoexponential decay with lifetime τGDP and two-exponential decay with lifetimes τ1GTP and τ2GTP was fitted to grouped data from N = 5 independent experiments, respectively (nGDP = 4829, τGDP = 0.58 ± 0.04 s; nGTP = 3090, τ1GTP = 1.20 ± 0.35 s, τ2GTP = 10.6 ± 3.3 s; errors are 95 % CI). Boxplot with mean lifetimes, **P &lt; 0.01, two-sided Student’s t-test (N = 5). (C) Parameter phase space of the phenomenological model for Rab5 switching, depending on the basal rate of activation (a_0/(a_2 K)) and the strength of positive feedback (a_1/(a_2 K)). Switching is defined as the relative difference in steady-state Rab5 concentration on the membrane relative to the scenario with no positive feedback. Inset: fold activation along the red line in the diagram. Stochasticity was introduced by solving the phenomenological model within a Fokker-Planck framework. See text for parameter definitions. (D) Stoichiometric GDI excess over Rab5 affects delay of Rab5 activation in vitro. Left: solid lines are mean normalized intensities over time, shaded areas correspond to SD (N = 3). Right: corresponding activation Ti and relative maximum rates kmax. (E) Stochastic simulations of the full model for varying initial amounts of GDI excess (0-2000 particle number). Shown are curves from 10 random runs per condition, the mean line from 50 runs is depicted bold. The effective simulation time was scaled to align with experimental results. (F) PRA1 in the membrane enhances Rab5 activation at low GEF concentrations. Solid lines are mean normalized fluorescence intensities, shaded areas correspond to SD (N = 3).<br>
DOI: 10.6084/m9.figshare.11764272.v2
title: Data for: Field-testing olfactory ability to understand human olfactory ecology

description: These are the data for the paper referenced above. There are four sheets--two sheets are for data coded as correct, near miss, far miss, and incorrect (one for field and one for lab) and two sheets are for data coded as consistent use of lable (one for field and one for lab).<br>
DOI: 10.6084/m9.figshare.11764158
title: 6 molecular dynamics simulations of coronavirus 2019-nCoV protease model in complex with different conformations of lopinavir.

description: Molecular dynamics simulations of comparative model of novel coronavirus 2019-nCoV protease Mpro in complex with 6 different conformations based on the Catalophore point-cloud alignment and (re)- docking of lopinavir into the 2019ncov virus protease model. The docking experiment produced 8 clusters of possible conformations, we chose 6 out of 8 conformers and ran an all-atom 300 ps MD at 310 K (=36.85°C).<br>-The two images in the main folder refers to the docked structures before MD simulation.-The file all_centroids.pse contains the frames representing the centroid of the subsequent MD simulation for each docking cluster.-Each archive contains the centroid in PDB format, the starting frame of the simulation in GRO format and the compressed trajectory in XTC format. In the directory "other_files" there are other data generated during the simulation, i.e. heatmap representing the contact frequency between the ligand atoms and the ones belonging to the homology model.
DOI: 10.6084/m9.figshare.11764272.v3
title: Data: Field-testing olfactory ability to understand human olfactory ecology

description: These are the data for the paper referenced above. There are four sheets--two sheets are for data coded as correct, near miss, far miss, and incorrect (one for field and one for lab) and two sheets are for data coded as consistent use of lable (one for field and one for lab).<br>
DOI: 10.6084/m9.figshare.11764158.v1
title: 6 molecular dynamics simulations of coronavirus 2019-nCoV protease model in complex with different conformations of lopinavir.

description: Molecular dynamics simulations of comparative model of novel coronavirus 2019-nCoV protease Mpro in complex with 6 simulations of 6 different conformations based on the Catalophore point-cloud alignment and (re)- docking of lopinavir into the 2019ncov virus protease model. The docking experiment produced 8 clusters of possible conformations, we chose 6 out of 8 conformers and ran an all-atom 300 ps MD at 310 K (=36.85°C).
DOI: 10.6084/m9.figshare.11764146
title: Data collection tools for Impact evaluation of the Steno REACH Certificate Course in Clinical Diabetes Care for health care providers in Malaysia

description: The data collection tools included here are:Quantitative tools: OSCE examination guidelines and scoring rubrics for all the OSCE stations, for both doctors and nurses.Qualitative tools:Interview guides for doctors and nurses, Family Medicine Specialists, and patients. For the doctors, nurses and Family Medicine Specialists, the guides were different for pre- and post-intervention. For patients, the same guide was used.An observation formats was used to observe the clinical practice of doctors and nurses, the same format pre- and post-intervention.Another observation format was used for the assessment of the clinical environment
DOI: 10.6084/m9.figshare.11764146.v2
title: Data collection tools for Impact evaluation of the Steno REACH Certificate Course in Clinical Diabetes Care for health care providers in Malaysia

description: The data collection tools included here are:Quantitative tools: OSCE examination guidelines and scoring rubrics for all the OSCE stations, for both doctors and nurses.Qualitative tools:Interview guides for doctors and nurses, Family Medicine Specialists, and patients. For the doctors, nurses and Family Medicine Specialists, the guides were different for pre- and post-intervention. For patients, the same guide was used.An observation formats was used to observe the clinical practice of doctors and nurses, the same format pre- and post-intervention.Another observation format was used for the assessment of the clinical environment