Normalised wound area showing migration of NIH3T3 cells upon exosome treatment. Migration rate showing enhanced migration of NIH3T3 cells upon exosome treatment. MTT assay of HaCaT cells on PUAO and PUAO-CPO scaffolds. MTT assay of NIH3T3 cells on PUAO and PUAO-CPO scaffolds. MTT assay of ADSCs cells on PUAO and PUAO-CPO scaffolds
Contributors:Mohammed Abdullah Hamad Alharbi, Shota Akioka, Hoang Anh Tuan, Shinji Hirai, Wataru Shoji
These files contained lab notes on the preparation of hot-pressed plant-based biopolymers and, raw, filtered and analyzed data on bending properties, thermal and structural analysis.
Contributors:Jonas Klos, Tim Krieger, Sven Stöwhase
This file contains codes necessary to replicate the results in the paper „Measuring Intra-generational Redistribution in PAYG Pension Schemes“ by Jonas Klos, Tim Krieger, and Sven Stöwhase.
Contributors:Franziska Schroter, Bianca Günther, Petra Jansen
The data shows the effects of a sadness induction on the Rubber Hand Illusion. Our experiment was composed of three experimental groups. One group saw neutral pictures in the beginning of each trial, one group saw subliminally presented sad pictures and one group saw supraliminally presented sad pictures. Following the picture presentation, the Rubber Hand Illusion task was performed. Before and after the stimulation, the proprioceptive estimation of the left index finger was taken and a questionnaire on the subjective illusion strength (Botvinick & Cohen, 1998) was surveyed after the post-proprioception measurement. We performed the picture task and the rubber hand illusion task 4 times, each time applying a different stroking style. Stroking was carried out either synchronously (SY) or asynchronously (AS) combined with either slow (3cm/s) (SL) or fast (30cm/s) (FA) stroking speed. At the baseline and after each picture task, participants completed the SAM scale (Bradley & Lang, 1994). In the end, demographical information was surveyed, as well as a questionnaire on dissociative symptoms (FDS; Freyberger et al., 1999).
This study uses a set of criteria to examine cold air outbreaks (CAOs) across the globe from 1979 – 2018 and to determine how CAOs have changed over the last 40 years. We found CAOs occur most frequently in the Northern Hemisphere, with as many as 8 CAO days per year in North America and Eurasia. CAOs were found to have decreased in size, intensity, frequency, and duration across much of the globe, with the largest decreases in Alaska, Canada, and the North Atlantic, while an increase in CAOs was observed in Eastern Europe, Central Eurasia, and the Southern Ocean. Early and late winter CAOs have also become much less frequent in most regions.
Two-meter temperature (T2m) data was acquired from the NCEP/NCAR (NNR) climate reanalysis dataset (National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) and the recently released ERA5 reanalysis data set from the European Center for Medium-Range Weather Forecasts (ECMWF). ERA5 T2m was acquired at a 1 degree spatial resolution on an hourly timescale and converted to daily mean T2m while NNR daily mean T2m was acquired at a T62 gaussian grid (192 longitude and 94 latitude) spatial resolution from 1979 - 2018.
Three criteria for a CAO were designed to capture the most extreme CAOs while being flexible enough to capture the entire evolution of the event.
1.) Magnitude: The magnitude criterion requires the daily mean temperature to be at or below the 2.5th percentile threshold of deseasonalized 2-meter temperature (T2m). The daily mean T2m must also be at or below 20 degrees Celsius with a departure from the climatological mean of at least -2 degrees Celsius.
2.) Spatial Extent: The daily spatial extent, which is a summation of all contiguous grid points that meet the magnitude criteria, must be at least 1,000,000 km2.
3.) Duration: The duration criterion requires the magnitude criterion for the entire CAO be met for at least five consecutive days and begins on the first day in which the spatial extent criterion is met and ends on the last day the spatial extent criterion is met.
How to use and interpret data:
There are 3 files:
1.) and excel file of all CAOs for both the NNR and ERA5 (separate tabs). Because the ERA5 data is the primary data set used in this study it has two additional columns of data, one for the region of the CAO and one for the hemisphere of the CAO.
2.) A .mat file (MATLAB) of all the ERA5 CAO data. The column headers are as follows: [1. daily data for each CAO event, 2. onset date, 3. duration, 4. Mean z-score 5. mean z-score per gridpoint, 6. total duration per gridpoint 7. daily z-score per gridpoint 8. temperature anomaly each day, 9. Region 10. hemisphere]
3.) A similar .mat file, but for the NNR CAOs. Differences: columns 4 and 5 and 11 in the NNR file are not in the ERA5 file (shift headers). These were used in calculations but omitted from ERA5 file for size restraints.
Codes to produce the data in the figures in the main paper. These codes utilise the theory established in the methods and Supplemental material. Codes are written in Python (Jupyter) and Wolfram Mathematica notebooks.
Objective: To engage in a priority-setting exercise with both clinicians and consumers to determine systematic reviews of highest priority to update.
Study Design & Setting: The US Satellite of the Cochrane Pregnancy & Childbirth Group (US-PCG) narrowed a list of over 600 review titles due for updating down to 97 review titles based on US relevance. The US-PCG then used the Delphi method to explore consensus on which titles to prioritize for updating. In Round 1, participants self-identified as a clinician/researcher or consumer, and then ranked titles into “high”, “medium”, and “low” priority groups. In Round 2, participants were given Round 1 results and asked to rank their top 5 titles. Results were analyzed within and between groups.
Contributors:Martin Bitomský, Pavla Mládková, Robin Pakeman, Martin Duchoslav
Data from: Bitomský M., Mládková P., Pakeman RJ, & Duchoslav M. (2020). Clade composition of a plant community indicates its phylogenetic diversity. Ecology and Evolution. doi: 10.1002/ece3.6170
Data summarises results from the case studies and simulations presented in our paper. In addition, we provide an R script for calculation of proposed phylogenetic diversity metrics (the clade indices).
Brief description of each file:
1. Grasslands_DNA_markers_info.xls - Accession numbers of all DNA markers used for phylogeny inference in grasslands
2. Grasslands_DNA_alignment_BEFORE_GBlocks.fasta - DNA alignment matrix before utilisation of the GBlocks tool
3. Grasslands_DNA_alignment_AFTER_GBlocks.fasta - DNA alignment matrix after utilisation of the GBlocks tool
4. Grasslands_BEAST_file.xml - BEAST .xml file submitted to the CIPRES portal (www.phylo.org)
5. Grasslands_tree.txt - Dated MCC tree, grasslands (newick format)
6. Grasslands_tree.nex - Dated MCC tree, grasslands (nexus format)
7. Phyto-database_pruned_tree.txt - Pruned dated tree from the super tree of European flora (Durka & Michalski 2012, Ecology), phytosociological database (newick format)
8. Plot_data.slx - plot data of all case studies + species lists
9. Simulation_results.txt - Summary of R2 values (phylogeny-based metric ~ the clade index) for simulated phylogenies and community matrices (manipulated: phylogenetic scale, species pool size and species richness range)
10. Bitomsky2020EE_R_script_indices.R - An R script for computation of the clade indices (with notes and examples)
Contributors:Eefje Poppelaars, Johannes Klackl, Belinda Pletzer, Eva Jonas
Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2020). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat."
Hypotheses and analyses were preregistered: Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2018). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat. Open Science Framework. https://osf.io/8gchf/register/565fb3678c5e4a66b5582f67.
Description of the dataset:
A dataset of 37 men and 30 women (tested in the luteal phase of their menstrual cycle) participated in a public speaking task to induce social-evaluative threat. Responses of multiple stress systems were measured (sympathetic and parasympathetic nervous system activity, self-reported affect, and hypothalamic–pituitary–adrenal axis activity), as well as personality traits (e.g. trait social anxiety), and EEG delta-beta cross-frequency coupling (e.g., frontal and parietal amplitude-amplitude correlation and phase-amplitude coupling).
Description of analyses files:
- File 'README.txt' contains the description of the files (metadata).
- File 'SET_CFC_MatlabOutput.xlsx' contains the delta-beta coupling data, calculated using MATLAB scripts from https://github.com/ESPoppelaars/Cross-frequency-coupling.
- File 'SETData.sav' contains the raw stress and personality data, taken from https://doi.org/10.17632/7vj8r76s6f.
- Files 'SET_CFC.outl.del.RData' contains the complete dataset with missing values and outliers deleted.
- File 'Codebook_SET_CFC.outl.del.csv' contains a description of all variables in the 'SET_CFC.outl.del.RData' file (metadata).
- Files 'SET_CFC.outl.del.imp.RData' and 'SET_CFC.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper.
- File 'LSA_HSA_brief.RData' contains data to use as informed priors for the Bayesian analyses, calculated from data published at https://doi.org/10.3758/s13415-018-0603-7.
- File 'Codebook_LSA_HSA_brief.csv' contains a description of all variables in the 'LSA_HSA_brief.RData' file (metadata).
- File '01_CalculationOfData.R' is an R analysis script that imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file.
- File '02_AnalysisOfImputedData.R' is an R analysis script that calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R', as well as the 'BF_t.R' file as taken from https://doi.org/10.17045/sthlmuni.4981154.v3.