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Variant call file of DENV2 16681 passage 1 in M3 iPSC cells (open with Microsoft Excel)
Data Types:
  • Sequencing Data
  • Dataset
The Indeterminate Domain (IDD) proteins are a plant specific subclass of C2H2 Zinc Finger transcription factors. Some of these transcription factors play roles in diverse aspects of plant metabolism and development; however the function of most of IDDs is unknown and its molecular evolution has not been explored. Here, Prochetto and Reinheimer reconstructed the evolution of IDDs during plant land conquest. They found that IDDs arose from the common ancestor of Streptophyta. Once in land, IDDs experienced a rapid radiation that accompanied key morphological, physiological and biochemical transitions required in plant terrestrialization. The authors present a solid phylogenetic framework of annotated IDD genes which links genetic and functional knowledge from model to non-model species.
Data Types:
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  • Sequencing Data
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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:
  • Software/Code
  • Sequencing Data
  • Tabular Data
  • Dataset
  • Document
  • Text
Alignments for phylogenetic analyses
Data Types:
  • Sequencing Data
  • Dataset
We simulated the surface evolution for a pre-Nectarian surface unit and found that relative to their size, large complex craters are less destructive to the surrounding terrain than small simple craters. The data is structured as follows: 01_CTEM_Outputs - .dat files in which the craters from the simulations are stored - Python script to convert the .dat files to shapefiles 02_Shapefiles_From_CTEM_Outputs - Shapefiles in which the craters from the simulations are stored - Shapefile of the surrounding area (created manually) 03_CSFD_Measurements - A modified version of CSFD Tools to conduct Cartesian crater size-frequency distribution measurements - .scc files which contain the results from the Traditional Crater Counting and Non-sparseness Correction techniques (for further analysis in Craterstats) 04_Crater_Statistics - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) 05_Crater_Equilibrium - simplified .stat files which contain cumulative number density information - Python script to fit a power law function to the simplified .stat files 06_Geometric_Saturation_Levels - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) - Python script to calculate geometric saturation levels from .stat files - .txt files containing geometric satration levels Data by Orgel et al. (2018) are availiable here: http://www.planet.geo.fu-berlin.de/Orgel_etal_2017_Lunar_basins.zip
Data Types:
  • Other
  • Software/Code
  • Geospatial Data
  • Dataset
  • Document
  • Text
The database, version 26 (first version was available in 2002), contains now 13239 site forms, (most of them with their geographical coordinates), comprising 16695 radiometric data: Conv. 14C and AMS 14C (12922 items), TL (10143 items), OSL (6510 items), ESR, Th/U and AAR (2093 items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All 14C dates are conventional dates BP. This improved version 26 replaces the older version 25. 170 new sites are incorporated and 267 sites have a corrected or an updated content.
Data Types:
  • Software/Code
  • Geospatial Data
  • Tabular Data
  • Dataset
  • Document
We examined the role of employee justice perceptions in explaining the distinct effects of two forms of pay transparency– process versus outcome pay transparency– on counterproductive workplace behavior (CWB). Study 1, a field study of 321 employees, revealed that process pay transparency is inversely related to CWB-O, with this effect mediated by greater procedural justice perceptions. In contrast, among employees perceiving their pay position as being lower than that of referent others, outcome pay transparency is positively associated with both CWB-O and CWB-I, with this effect mediated by reduced distributive justice perceptions. Study 2, using an online simulation-based experiment conducted on 394 employees and assessing actual deception behaviors, replicated and extended these findings. Specifically, when pay allocations were transparent (vs. secretive) and participant's pay was manipulated to be lower than that of teammates, participants reported lower distributive justice perceptions leading to heightened deception behaviors, with this effect mediated by a more negative emotional state. Analyses were done using MPlus 8.4. Files (.dat , and .inp , files are attached) for both CFA (Study 1) and path analyses (Studies 1 and 2). *-alt* files were used for testing alternative models reported in the papaer. R file and .csv file (Study 1) were used to compute alpha and omega values for ordinal indicators. Finally, *-omega* files (Study 2) were used to compute omegas in Study 2.
Data Types:
  • Software/Code
  • Geospatial Data
  • Tabular Data
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  • Document
Among the complex ecosystems and habitats that form the deep sea, submarine canyons and open slope systems are regarded to be potential hot-spots of biodiversity. We assessed spatial and temporal patterns of biodiversity in sediment communities of a NW Mediterranean Canyon and its adjacent open slope (Blanes Canyon) with DNA metabarcoding. We sampled three layers of sediment and four different depths (900-1,750 m) at two seasons. A fragment of the mitochondrial gene cytochrome c oxidase subunit I (COI) was used as marker for such assessments. The final dataset contained a total of 15,318 molecular operational taxonomic units (MOTUs). Metazoa, Stramenopiles and Archaeplastida were the dominant taxa and, within metazoans, Arthropoda, Nematoda and Cnidaria were the most diverse. There was a trend towards decreasing diversity in the first few cm (1 to 5) of the sediment, with only 26.3% of the MOTUs shared across sediment layers. Our results show the presence of heterogeneous communities in the studied area, significantly different between zones, depths and seasons. We compared our results with the ones presented in a previous study, obtained using the v7 region of the 18S rRNA gene in the same samples. There were remarkable differences in the total number of MOTUs, in the most diverse taxa and in MOTU richness. COI recovered a higher number of MOTUs, but more remained unassigned taxonomically. However, broad spatio-temporal patterns elucidated from both datasets coincided, both markers retrieving the same ecological information. Our results showed that COI can be used to accurately characterize the studied communities and constitute a high resolution method to detect ecological shifts. We also noted that COI reference databases for deep-sea organisms have important gaps, and their completeness is essential in order to successfully apply metabarcoding techniques.
Data Types:
  • Sequencing Data
  • Tabular Data
  • Dataset
The dataset contains the Shu-Talas Transboundary Basin shapefiles. The shapefiles are produced using the data from HydroSHEDS project that provides watershed delineations at a global scale. Shu-Talas Basin has two major rivers, Shu and Talas. Their boundary shapefiles are included separately. Very small sub-basins within the Shu-Talas Basin are merged and dissolved.
Data Types:
  • Software/Code
  • Geospatial Data
  • Dataset
In support of the manuscript by Bagley et al. (2020; see below) on quaking aspen phylogeography and ecological niche modeling (ENM), this accession provides 1) the in-house laboratory protocol used to extract DNA from aspen leaf tissues (modified from Strauss Lab); 2) the Supporting Information files for the corresponding manuscript (Bagley et al. 2020); 3) code used to conduct independent runs of the TASSEL-GBSv2 SNP discovery pipeline (Glaubitz et al. 2014) on our final (combined) genotyping-by-sequencing (GBS) dataset; 4) resulting SNP variant files from TASSEL-GBSv2 and final filtered variant call format (VCF) and genotype data files used during our genomic analyses; and 5) unfiltered vs filtered species occurrence data files and computer code used during our ENM analyses of our focal taxon, Populus tremuloides. REFERENCES Bagley, J. C., Heming, N. M., Gutiérrez, E. E., Devisetty, U. K., Mock, K. E., Eckert, A. J., & Strauss, S. H. (2020). Genotyping-by-sequencing and ecological niche modeling illuminate phylogeography, admixture, and Pleistocene range dynamics in quaking aspen (Populus tremuloides). Ecology and Evolution. Glaubitz, J. C., Casstevens, T. M., Lu, F., Harriman, J., Elshire, R. J., Sun, Q., & Buckler, E. S. (2014). TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One, 9(2): e90346
Data Types:
  • Software/Code
  • Geospatial Data
  • Tabular Data
  • Dataset
  • Document
  • Text
  • File Set