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GIS DATASETS: Research_area.zip: the vector map data of the study area; LC08_L1TP_122034_20180417_20180501_01_T1_B3.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B4.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B5.TIF,LC08_L1TP_122034_20180417_20180501_01_T1_B8.TIF:the original image data of four bands; Program DATASETS: py.zip:U-net network file(U-Net.py), evaluation procedure(Analysis.py), cutting program(cut_png.py)
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Overview: 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. Data Used: 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. CAO Methods: 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.
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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.
Data Types:
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By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.
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Hypothesis: Does dogs exhibit different protein profile of seminal plasma and spermatozoa among breeds? What data shows: These data show the proteomic profile and its respectively gene ontology of seminal plasma and sperm cells of four purebred dogs (Golden Retriever n = 3, Bernese Mountain Dog n = 4, Great Dane n = 3, Maremmano-Abruzzese Sheepdog n = 3), with mean ages and standard deviation of 4,0 ± 1,0 years (Golden Retriever), 2,0 ± 1,0 years (Bernese Mountain Dog), 1,4 ± 0,5 years (Great Dane) and 4,0 ± 0,7 years (Maremmano-Abruzzese Sheepdog), kenneled at Sao Paulo State, Brazil. Besides How it was gathered: Entire second fraction and a portion of the third semen fraction were collected into a silicone funnel attached to a graduated plastic tube by manual stimulation of the penis in the presence of a teaser bitch, when possible. The semen was subjectively evaluated at the kennel, and only ejaculate within normal seminal parameters considered for dogs, according to Kustritz et al. (2007), were used in this study. Spermatozoa and seminal plasma were separated by centrifugation and prepared individually for proteomic analysis by ESI Q-Tof mass spectrometer. The gene ontology annotation of the proteins found within the samples was obtained using the UniprotKB website (www.uniprot.org), and considered the molecular function, biological process and cellular component categories. How the data can be interpreted: There are two folders dataset. The "Seminal plasma and sperm cell proteins" folder contain two folders, one with all seminal plasma proteins, and other folder with all sperm cell proteins, which have individual files named by breed for each dog (n=13). The “Gene ontology of seminal plasma and sperm cell proteins” contain three files: Table S1, Table S2, and Table S3. The file Table S1 contain all proteins found in seminal plasma of evaluated dogs and their respective gene ontology. The file Table S2 contain all proteins found in spermatozoa of all dogs evaluated and their respective gene ontology. The file Table S3 contain all common proteins found in seminal plasma and spermatozoa of evaluated dogs and their respective gene ontology. References: Kustritz R. The value of canine semen evaluation for practitioners. Theriogenology 2007;68(3):329-37.
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SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a Z_2 symmetry. With the version 1.2 we announce several new features. First, previous versions were restricted to missing energy signatures and assumed prompt decays within each decay chain. SModelSv1.2 considers the lifetime of each Z_2-odd particle and appropriately takes into account missing energy, heavy stable charged particle and R-hadron signatures. Second, the current version allows for a combination of signal regions in efficiency map results whenever a covariance matrix is available from the experiment. This is an important step towards fully exploiting the constraining power of efficiency map results. Several other improvements increase the user-friendliness, such as the use of wildcards in the selection of experimental results, and a faster database which can be given as a URL. Finally, smodelsTools provides an interactive plots maker to conveniently visualize the results of a model scan.
Data Types:
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By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.
Data Types:
  • Dataset
  • File Set
The wide disparity in adult body size observed both within and among animal taxa has long attracted widespread interest, with several general rules having been proposed to explain trends in body size evolution. Adult body size disparity among the cephalopod mollusks is remarkable, with adult body sizes ranging from a few centimeters to several meters. Some of the smallest cephalopods are found within Pickfordiateuthis, a group comprising four species of squid found in the western Atlantic and tropical eastern Pacific. Pickfordiateuthis pulchella, the type species of the genus, was initially proposed to be closely related to the loliginid squids (Loliginidae), with subsequent descriptions of additional species supporting a placement within Loliginidae. Pickfordiateuthis is remarkable in that all species reach sexual maturity at about one-fifth to one-tenth the size seen in most loliginid species. To date, no phylogenetic analyses have included representatives of Pickfordiateuthis. To infer the phylogenetic position of Pickfordiateuthis and explore its implications for body size evolution, we collected specimens of Pickfordiateuthis pulchella from Brazilian waters and sequenced regions of two loci—the mitochondrial large ribosomal subunit (rrnL a.k.a. 16S) gene and the nuclear gene rhodopsin. Maximum likelihood and Bayesian analyses of these sequences support a placement of Pickfordiateuthis pulchella as sister to a clade comprising the Western Hemisphere loliginid genera Doryteuthis and Lolliguncula. Analyses of body size evolution within Loliginidae suggest that a shift to a smaller body size optimum occurred along the lineage leading to P. pulchella, with some evidence of shifts toward larger sizes in the ancestors of Loligo and Sepioteuthis; these inferences seem to be robust to phylogenetic uncertainty and incomplete taxon sampling. The small size and juvenile-like morphological traits seen in adult Pickfordiateuthis (e.g., sepiolid-like fins and biserial sucker arrangement in the tentacles) may be due to paedomorphosis.
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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)
Data Types:
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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.
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