Dataset Search Results

This dataset contains the RegCM4 simulations used in the article " Implementation of dynamic ageing of carbonaceous aerosols in regional climate model RegCM". This dataset was used to investigate the impact of a new aging parameterisation scheme implemented in a regional climate model RegCM4. The dataset contains two sets of simulations: Expt_fix and Expt_dyn. It consists of the seasonal mean and daily mean values of the variables that were used to create the visualizations of this study. The Expt_fix and Expt_dyn dataset contain 34 and 38 NetCDF files, respectively. The CERES_vs_2expts_new.mat file is the comparison between CERES shortwave downward flux at the surface and same model outputs from two experiments for clear sky and all sky conditions. -------------------------------------------------- The following information about the dataset was generated on 2021-01-08 by SUDIPTA GHOSH <b>GENERAL INFORMATION</b> <i>1. Date of data collection (single date, range, approximate date):</i> 2019-01-01 to 2019-12-31 <i>2. Geographic location of data collection:</i> Urbana-Champaign,Illinois, USA <i>3. Information about funding sources that supported the collection of the data:</i> This work is supported by the MoEFCC under the NCAP-COALESCE project [Grant No. 14/10/2014-CC]. The first author acknowledges DST-INSPIRE fellowship [IF150055] and Fulbright-Kalam Climate Doctoral fellowship. N. R. acknowledges funding from NSF AGS-1254428 and DOE grant DE-SC0019192. Department of Science and Technology, Funds for Improvement of Science and Technology infrastructure in universities and higher educational institutions (DST-FIST) grant (SR/FST/ESII-016/2014) are acknowledged for the computing support. <b>DATA & FILE OVERVIEW</b> <i>1. File List:</i> Expt_fix and Expt_dyn datasets contain the analysed seasonal means and daily means of the variables that have been used to create the visualizations of this study. Each of the Expt_fix and Expt_dyn datasets contains 34 and 38 NetCDF files, respectively. <i>2. Relationship between files, if important:</i> NA <i>3. Additional related data collected that was not included in the current data package:</i> No <b>METHODOLOGICAL INFORMATION</b> <i>1. Description of methods used for collection/generation of data: </i> The model RegCM4 code is freely available online from <a href="http://gforge.ictp.it/gf/project/regcm/">http://gforge.ictp.it/gf/project/regcm/</a>. The anthropogenic aerosol emissions considered for the simulations are taken from IIASA inventory. The data used can be easily accessed online <a href="http://clima-dods.ictp.it/regcm4/">http://clima-dods.ictp.it/regcm4/</a> website. TRMM observed precipitation data can be assessed from <a href="https://giovanni.gsfc.nasa.gov/giovanni/">https://giovanni.gsfc.nasa.gov/giovanni/</a> website. CRU temperature data is available at <a href="https://crudata.uea.ac.uk/cru/data/hrg/">https://crudata.uea.ac.uk/cru/data/hrg/</a>. CERES satellite surface shortwave downward fluxes are available at <a href="https://ceres.larc.nasa.gov/data/">https://ceres.larc.nasa.gov/data/</a> website. Input files for the RegCM4 model are archived in <a href="http://clima-dods.ictp.it/regcm4/">http://clima-dods.ictp.it/regcm4/</a> website. This dataset contains the RegCM4 simulations used in the article " Implementation of dynamic ageing of carbonaceous aerosols in regional climate model RegCM ". Two sets of simulations: Expt_fix and Expt_dyn consists of the output data . This dataset only contains the analysed seasonal mean and daily mean of the variables that have been used to create the visualizations of this study. Each of Expt_fix and Expt_dyn contains 34 and 38 NetCDF files respectively. This dataset was used to investigate the impact of a new aging parameterisation scheme implemented in a regional climate model RegCM4. <i>2. Methods for processing the data:</i> Seasonal Mean and daily average values were extracted from 6-hourly model output. <i>3. Instrument- or software-specific information needed to interpret the data:</i> CDO-1.7.1, Grads-2.0.a9, Matlab2016b <i>4. Standards and calibration information, if appropriate:</i> NA <i>5. Environmental/experimental conditions:</i> NA <i>6. Describe any quality-assurance procedures performed on the data:</i> NA <i>7. People involved with sample collection, processing, analysis and/or submission:</i> Sudipta Ghosh, Nicole Riemer, Graziano Giuliani, Filippo Giorgi, Dilip Ganguly, Sagnik Dey <b>DATA-SPECIFIC INFORMATION FOR: Expt_fix_data.tar.gz</b> <i>1. Number of variables:</i> 29 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Mass concentration (Kg m-3) of BC, BC_HB, BC_HL, OC, OC_HB, OC_HL; Columnar burden (mg m-2)] of BC, BC_HL, BC_HB, OC; Dry deposition flux (mg m-2 day-1) of BC_HB, BC_HL, OC_HB, OC_HL; Wet deposition flux due washout (mg m-2 day-1) of BC_HB, BC_HL, OC_HB, OC_HL; Wet deposition flux due to rainout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; AOD (unit less), precipitation (Kg m-2 s-1), temperature (K) , v-wind (m s-1), u-wind (m s-1), Surface shortwave downward flux (W m-2), Shortwave radiative forcing at the surface and top of atmosphere (W m-2) <b>DATA-SPECIFIC INFORMATION FOR: Expt_dyn_data.tar.gz</b> <i>1. Number of variables:</i> 30 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Mass concentration (Kg m-3) of BC, BC_HB, BC_HL, OC, OC_HB, OC_HL; Columnar burden (mg m-2)] of BC, BC_HL, BC_HB, OC; Dry deposition flux (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; Wet deposition flux due washout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; Wet deposition flux due to rainout (mg m-2 day-1) of BC_HB, BC_HL OC_HB, OC_HL; AOD (unit less); precipitation (Kg m-2 s-1); temperature (K); v-wind (m s-1); u-wind (m s-1); Surface shortwave downward flux (W m-2); Shortwave radiative forcing at the surface and top of atmosphere (W m-2); ageingscale (s-1) <b>DATA-SPECIFIC INFORMATION FOR: CERES_vs_2expts_new.mat</b> <i>1. Number of variables:</i> 12 <i>2. Number of cases/rows:</i> NA <i>3. Variable List:</i> Surface shortwave downward flux for clear sky (W/m-2) for CERES, Expt_fix, Expt_dyn (for winter JF and monsoon JJAS seasons); Surface shortwave downward flux for all sky conditions (W/m-2) for CERES, Expt_fix, Expt_dyn (for winter JF and monsoon JJAS seasons). <b>NOTE:</b> The following information applies for all three (3) files: <i> Missing data codes:</i> NA <i>Specialized formats or other abbreviations used:</i> NA

This geodatabase serves two purposes: 1) to provide State of Illinois agencies with a fast resource for the preparation of maps and figures that require the use of shape or line files from federal agencies, the State of Illinois, or the City of Chicago, and 2) as a start for social scientists interested in exploring how geographic information systems (whether this is data visualization or geographically weighted regression) can bring new meaning to the interpretation of their data. All layer files included are relevant to the State of Illinois. Sources for this geodatabase include the U.S. Census Bureau, U.S. Geological Survey, City of Chicago, Chicago Public Schools, Chicago Transit Authority, Regional Transportation Authority, and Bureau of Transportation Statistics.

This dataset includes neuronal development of the Caenorhabditis elegans dauer and adult.

In a set of field studies across four years, the effect of self-shading on photosynthetic performance in lower canopy sorghum leaves was studied at sites in Champaign County, IL. Photosynthetic parameters in upper and lower canopy leaves, carbon assimilation, electron transport, stomatal conductance, and activity of three C4-specific photosynthetic enzymes, were compared within a genetically diverse range of accessions varying widely in canopy architecture and thereby in the degree of self-shading. Accessions with erect leaves and high light transmission through the canopy are henceforth referred to as ‘erectophile’ and those with low leaf erectness, ‘planophile’. In the final year of the study, bundle sheath leakiness in erectophile and planophile accessions was also compared.

Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

This data set is a matrix of values. The element in the row "i" and the column "j" denotes the influence of hexagonal pyramidal distribution at node "i" on the node "j". The size of the matrix is 16641x16641. This matrix corresponds to a 129x129 grid. Influence coefficient matrix on a smaller grid can be obtained by appropriately choosing the elements from the bigger matrix.

This dataset contains model output from the Community Earth System Model, Version 1 (CESM1; Hurrell et al., 2013) and variables from the European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis v5 (ERA5; Hersbach et al., 2020). These data were used for analysis in “The location of large-scale soil moisture anomalies affects moisture transport and precipitation over southeastern South America”, published in Geophysical Research Letters. Acknowledgments: This work was supported by NSF Award AGS-1852709. We acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF. We thank Dr. Haiyan Teng for providing guidance on setting up the CESM experiments and offering valuable advice. References: Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999–2049. https://doi.org/10.1002/qj.3803 Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A Framework for Collaborative Research. Bull. Amer. Meteor. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1

This repository includes scripts and datasets for the paper, "Polynomial-Time Statistical Estimation of Species Trees under Gene Duplication and Loss."

This repository includes a simulated dataset and related scripts used for the paper "Moss: Accurate Single-Nucleotide Variant Calling from Multiple Bulk DNA Tumor Samples".

Supplemental data sets for the Manuscript entitled "Contribution of fungal and invertebrate communities to mass loss and wood depolymerization in tropical terrestrial and aquatic habitats"

Data inputs, and scripts for the analysis detailed in Bradshaw et al, published in PlosONE 2020.

This dataset include data pulled from the World Bank 2009, the World Values Survey wave 6, Transparency International from 2009. The data were used to measure perceptions of expertise from individuals in nations that are recipients of development aid as measured by the World Bank.

This dataset includes images and annotated counts for 150 airborne pollen samples from the Center for Tropical Forest Science 50 ha forest dynamics plot on Barro Colorado Island, Panama. Samples were collected once a year from April 1994 to June 2010.

This page provides the data for SuperFine, DACTAL, and BeeTLe publications. - Swenson, M. Shel, et al. "SuperFine: fast and accurate supertree estimation." Systematic biology 61.2 (2012): 214. - Nguyen, Nam, Siavash Mirarab, and Tandy Warnow. "MRL and SuperFine+ MRL: new supertree methods." Algorithms for Molecular Biology 7 (2012): 1-13. - Neves, Diogo Telmo, et al. "Parallelizing superfine." Proceedings of the 27th Annual ACM Symposium on Applied Computing. 2012. - Nelesen, Serita, et al. "DACTAL: divide-and-conquer trees (almost) without alignments." Bioinformatics 28.12 (2012): i274-i282. - Liu, Kevin, and Tandy Warnow. "Treelength optimization for phylogeny estimation." PLoS One 7.3 (2012): e33104.

This dynamic photosynthesis model of soybean canopy is developed by Yu Wang (yuwangcn@illinois.edu), IGB, University of Illinois. If you want to know more details, please check the following publication Yu Wang, Steven J. Burgess, Elsa de Becker, Stephen P. Long. Photosynthesis in the fleeting shadows: An overlooked opportunity for increasing crop productivity? The Plant Journal.

Data files associated with the assembly of mitochondrial minicircles from five species of parasitic lice. This includes data from four species in the genus Columbicola and from the human louse (Pediculus humanus). The files include FASTA sequences for all five species, reference sequences for read mapping approaches, resulting contigs produced by various assembly approaches, and alignments of human louse minicircles mapped to published sequences of the same species.

Airglow images and Meteor radar data used in the paper "Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign".

This is the 5 states 5000 cells synthetic expression file we used for validation of SimiC, a single cell gene regulatory network inference method with similarity constraints. Ground truth GRNs are stored in Numpy array format, and expression profiles of all states combined are stored in Pandas DataFrame in format of Pickle files.

This dataset contains the protein sequences and trees used to compare Non-Ribosomal Peptide Synthetase (NRPS) condensation domains in the AMB gene cluster and was used to create figure S1 in Rojas et al. 2015. Instead of having to collect representative sequences independently, this set of condensation domain sequences may serve as a quick reference set for coarse classification of condensation domains.

BAM files for evolved strains from migration rate selection experiments conducted in low viscosity (0.2% w/v) agar plates containing M63 minimal medium with 1mM of mannose, melibiose, N-acetylglucosamine or galactose

The dataset includes the data used in the study of Classical Topological Order in the Kinetics of Artificial Spin Ice. This includes the photoemission electron microscopy intensity measurement of artificial spin ice at different temperatures as a function of time. The data includes the raw data, the metadata, and the data cookbook. Please refer to the data cookbook for more information. Note: vertex_population.xlsx file in the meta_data_code folder can be disregarded.

This is the experimental data of tetris artificial spin ice. The islands are made of Permalloy materials with size of 170 nm by 470 nm by 2.5 nm. The systems are measured at a temperature where the islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the dataset can be found in the file Note.txt and Tetris_data_list.xlsx Note: 2 files name bl11_teris600_033 and bl11_tetris600_2_135 are not recorded in the excel sheet because they are corrupted during the measurement. Any data that is not recorded in the excel sheet is either corrupted or of low quality. From files *_028 to *_049, tetris is spelled with “t” while in the raw data folder without “t”. This is a typo. Throughout the dataset, tetris and teris are supposed to have the same meaning.

Results generated using SharpTNI on data collected from the 2014 Ebola outbreak in Sierra Leone.

The data in this directory corresponds to: Skinner, R.K., Dietrich, C.H., Walden, K.K.O., Gordon, E., Sweet, A.D., Podsiadlowski, L., Petersen, M., Simon, C., Takiya, D.M., and Johnson, K.P. Phylogenomics of Auchenorrhyncha (Insecta: Hemiptera) using Transcriptomes: Examining Controversial Relationships via Degeneracy Coding and Interrogation of Gene Conflict. Systematic Entomology. Correspondance should be directed to: Rachel K. Skinner, rskinn2@illinois.edu If you use these data, please cite our paper in Systematic Entomology. The following files can be found in this dataset: Amino_acid_concatenated_alignment.phy: the amino acid alignment used in this analysis in phylip format. Amino_acid_raxml_partitions.txt (for reference only): the partitions for the amino acid alignment, but a partitioned amino acid analysis was not performed in this study. Amino_acid_concatenated_tree.newick: the best maximum likelihood tree with bootstrap values in newick format. ASTRAL_input_gene_trees.tre: the concatenated gene tree input file for ASTRAL README_pie_charts.md: explains the the scripts and data needed to recreate the pie charts figure from our paper. There is also another Corresponds to the following files: ASTRAL_species_tree_EN_only.newick: the species tree with only effective number (EN) annotation ASTRAL_species_tree_pp1_only.newick: the species tree with only the posterior probability 1 (main topology) annotation ASTRAL_species_tree_q1_only.newick: the species tree with only the quartet scores for the main topology (q1) ASTRAL_species_tree_q2_only.newick: the species tree with only the quartet scores for the first alternative topology (q2) ASTRAL_species_tree_q3_only.newick: the species tree with only the quartet scores for the second alternative topology (q3) print_node_key_files.py: script needed to create the following files: node_keys.key: text file with node IDs and topologies complete_q_scores.key: text file with node IDs multiplied q scores EN_node_vals.key: text file with node IDs and EN values create_pie_charts_tree.py: script needed to visualize the tree with pie charts, pp1, and EN values plotted at nodes ASTRAL_species_tree_full_annotation.newick: the species tree with full annotation from the ASTRAL analysis. NOTE: It may be more useful to examine individual value files if you want to visualize the tree, e.g., in figtree, since the full annotations are extensive and can make viewing difficult. Complete_NT_concatenated_alignment.phy: the nucleotide alignment that includes unmodified third codon positions. The alignment is in phylip format. Complete_NT_raxml_partitions.txt: the raxml-style partition file of the nucleotide partitions Complete_NT_concatenated_tree.newick: the best maximum likelihood tree from the concatenated complete analysis NT with bootstrap values in newick format Complete_NT_partitioned_tree.newick: the best maximum likelihood tree from the partitioned complete NT analysis with bootstrap values in newick format Degeneracy_coded_nt_concatenated_alignment.phy: the degeneracy coded nucleotide alignment in phylip format Degeneracy_coded_nt_raxml_partitions.txt: the raxml-style partition file for the degeneracy coded nucleotide alignment Degeneracy_coded_nt_concatenated_tree.newick: the best maximum likelihood tree from the degeneracy-coded concatenated analysis with bootstrap values in newick format Degeneracy_coded_nt_partitioned_tree.newick: the best maximum likelihood tree from the degeneracy-coded partitioned analysis with bootstrap values in newick format count_ingroup_taxa.py: script that counts the number of ingroup and/or outgroup taxa present in an alignment

These are the alignments of transcriptome data used for the analysis of members of Heteroptera. This dataset is analyzed in "Deep instability in the phylogenetic backbone of Heteroptera is only partly overcome by transcriptome-based phylogenomics" published in Insect Systematics and Diversity.