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Molloy, Erin K.; Warnow, Tandy (2018): NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1424746_V1
This repository includes scripts, datasets, and supplementary materials for the study, "NJMerge: A generic technique for scaling phylogeny estimation methods and its application to species trees", presented at RECOMB-CG 2018. The supplementary figures and tables referenced in the main paper can be found in njmerge-supplementary-materials.pdf. The latest version of NJMerge can be downloaded from Github: https://github.com/ekmolloy/njmerge. ***When downloading datasets, please note that the following errors.*** In README.txt, lines 37 and 38 should read: + fasttree-exon.tre contains lines 1-25, 1-100, or 1-1000 of fasttree-total.tre + fasttree-intron.tre contains lines 26-50, 101-200, or 1001-2000 of fasttree-total.tre Note that the file names (fasttree-exon.tre and fasttree-intron.tre) are swapped. In tools.zip, the compare_trees.py and the compare_tree_lists.py scripts incorrectly refer to the "symmetric difference error rate" as the "Robinson-Foulds error rate". Because the normalized symmetric difference and the normalized Robinson-Foulds distance are equal for binary trees, this does not impact the species tree error rates reported in the study. This could impact the gene tree error rates reported in the study (see data-gene-trees.csv in data.zip), as FastTree-2 returns trees with polytomies whenever 3 or more sequences in the input alignment are identical. Note that the normalized symmetric difference is always greater than or equal to the normalized Robinson-Foulds distance, so the gene tree error rates reported in the study are more conservative. In njmerge-supplementary-materials.pdf, the alpha parameter shown in Supplementary Table S2 is actually the divisor D, which is used to compute alpha for each gene as follows. 1. For each gene, a random value X between 0 and 1 is drawn from a uniform distribution. 2. Alpha is computed as -log(X) / D, where D is 4.2 for exons, 1.0 for UCEs, and 0.4 for introns (as stated in Table S2). Note that because the mean of the uniform distribution (between 0 and 1) is 0.5, the mean alpha value is -log(0.5) / 4.2 = 0.16 for exons, -log(0.5) / 1.0 = 0.69 for UCEs, and -log(0.5) / 0.4 = 1.73 for introns.
phylogenomics; species trees; incomplete lineage sorting; divide-and-conquer
XSEDE-Extreme Science and Engineering Discovery Environment (2018): XSEDE: Allocations Awards for the NSF Cyberinfrastructure Portfolio, 2004-2017. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4817808_V1
The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 to present, with awards continuing through the end of the second XSEDE award in 2021. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation.
allocations; cyberinfrastructure; XSEDE
Molloy, Erin K.; Warnow, Tandy (2019): Data from: TreeMerge: A new method for improving the scalability of species tree estimation methods. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9570561_V1
This repository includes scripts and datasets for the paper, "TreeMerge: A new method for improving the scalability of species tree estimation methods." The latest version of TreeMerge can be downloaded from Github (https://github.com/ekmolloy/treemerge).
divide-and-conquer; statistical consistency; species trees; incomplete lineage sorting; phylogenomics
Miller, Andrew N. (2018): Next-gen sequencing and metadata analyses of Great Lakes fungal data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9320144_V2
The data set consists of Illumina sequences derived from 48 sediment samples, collected in 2015 from Lake Michigan and Lake Superior for the purpose of inventorying the fungal diversity in these two lakes. DNA was extracted from ca. 0.5g of sediment using the MoBio PowerSoil DNA isolation kits following the Earth Microbiome protocol. PCR was completed with the fungal primers ITS1F and fITS7 using the Fluidigm Access Array. The resulting amplicons were sequenced using the Illumina Hi-Seq2500 platform with rapid 2 x 250nt paired-end reads. The enclosed data sets contain the forward read files for both primers, both fixed-header index files, and the associated map files needed to be processed in QIIME. In addition, enclosed are two rarefied OTU files used to evaluate fungal diversity. All decimal latitude and decimal longitude coordinates of our collecting sites are also included. File descriptions: Great_lakes_Map_coordinates.xlsx = coordinates of sample sites QIIME Processing ITS1 region: These are the raw files used to process the ITS1 Illumina reads in QIIME. ***only forward reads were processed GL_ITS1_HW_mapFile_meta.txt = This is the map file used in QIIME. ITS1F_Miller_Fludigm_I1_fixedheader.fastq = Index file from Illumina. Headers were fixed to match the forward reads (R1) file in order to process in QIIME ITS1F_Miller_Fludigm_R1.fastq = Forward Illumina reads for the ITS1 region. QIIME Processing ITS2 region: These are the raw files used to process the ITS2 Illumina reads in QIIME. ***only forward reads were processed GL_ITS2_HW_mapFile_meta.txt = This is the map file used in QIIME. ITS7_Miller_Fludigm_I1_Fixedheaders.fastq = Index file from Illumina. Headers were fixed to match the forward reads (R1) file in order to process in QIIME ITS7_Miller_Fludigm_R1.fastq = Forward Illumina reads for the ITS2 region. Resulting OTU Table and OTU table with taxonomy ITS1 Region wahl_ITS1_R1_otu_table.csv = File contains Representative OTUs based on ITS1 region for all the R1 data and the number of each OTU found in each sample. wahl_ITS1_R1_otu_table_w_tax.csv = File contains Representative OTUs based on ITS1 region for all the R1 and the number of each OTU found in each sample along with taxonomic determination based on the following database: sh_taxonomy_qiime_ver7_97_s_31.01.2016_dev ITS2 Region wahl_ITS2_R1_otu_table.csv = File contains Representative OTUs based on ITS2 region for all the R1 data and the number of each OTU found in each sample. wahl_ITS2_R1_otu_table_w_tax.csv = File contains Representative OTUs based on ITS2 region for all the R1 data and the number of each OTU found in each sample along with taxonomic determination based on the following database: sh_taxonomy_qiime_ver7_97_s_31.01.2016_dev Rarified illumina dataset for each ITS Region ITS1_R1_nosing_rare_5000.csv = Environmental parameters and rarefied OTU dataset for ITS1 region. ITS2_R1_nosing_rare_5000.csv = Environmental parameters and rarefied OTU dataset for ITS2 region. Column headings: #SampleID = code including researcher initials and sequential run number BarcodeSequence = LinkerPrimerSequence = two sequences used CTTGGTCATTTAGAGGAAGTAA or GTGARTCATCGAATCTTTG ReversePrimer = two sequences used GCTGCGTTCTTCATCGATGC or TCCTCCGCTTATTGATATGC run_prefix = initials of run operator Sample = location code, see thesis figures 1 and 2 for mapped locations and Great_lakes_Map_coordinates.xlsx for exact coordinates. DepthGroup = S= shallow (50-100 m), MS=mid-shallow (101-150 m), MD=mid-deep (151-200 m), and D=deep (>200 m)" Depth_Meters = Depth in meters Lake = lake name, Michigan or Superior Nitrogen % Carbon % Date = mm/dd/yyyy pH = acidity, potential of Hydrogen (pH) scale SampleDescription = Sample or control X = sequential run number OTU ID = Operational taxonomic unit ID
Illumina; next-generation sequencing; ITS; fungi
Zhao, Jifu (2019): UIUC Campus Gamma-Ray Radiation Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9119873_V1
This dataset contains the raw nuclear background radiation data collected in the engineering campus of University of Illinois at Urbana-Champaign. It contains three columns, x, y, and counts, which corresponds to longitude, latitude, and radiation count rate (counts per second). In addition to the original background radiation data, there are several separate files that contain the simulated radioactive sources. For more detailed README file, please refer to this documentation: <a href= "https://www.dropbox.com/s/xjhmeog7fvijml7/README.pdf?dl=0">https://www.dropbox.com/s/xjhmeog7fvijml7/README.pdf?dl=0</a>
Fernandez, Roberto; Parker, Gary; Stark, Colin P. (2019): Meltwater Meandering Channels on Ice: Centerlines and Images. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4384362_V1
This dataset includes images and extracted centerlines from experiments looking at the formation and evolution of meltwater meandering channels on ice. The laboratory data includes centimeter- and millimeter-scale rivulets. Dataset also includes an image and corresponding centerlines from the Peterman Ice Island. All centerlines were manually digitized in Matlab but no distributable code was developed for the process. Once digitized, centerlines were smoothed and standardized following methods and routines developed by other authors (Zolezzi and Guneralp, 2016; Guneralp and Rhoads, 2008). Details about the preparation of the centerlines and processing with these methods is included in the dissertation by Fernández (2018) linked to this dataset. "Millimeter scale and Peterman Ice Island centerlines.pdf": This file includes the images of two mm-scale experimetns and the Peterman Ice Island image. Seventeen centerlines were digitized from the former and seven were digitized from the latter. Those centerlines are shown above the images themselves. "Centimeter scale rivulet images.pdf": This file includes images corresponding to all cm-scale centerlines used for the analysis presented in the dissertation by Fernandez (2018). Each image has a short caption indicating the run ID and the time at which it was captured. The images were used to extract centerlines to look at the planform evolution of cm-scale meltwater meandering rivulets on ice. Images include 26 centerlines from four different runs. "Meltwater meandering channel centerlines.xlsx": This spreadsheet contains the centerline data for all fifty centerlines. The workbook includes 51 sheets. The first 50 are related to each one of the channels. The mm scale and Peterman Ice Island ones are identified using the same IDs shown in "Millimeter scale and Peterman Ice Island centerlines.pdf". The cm-scale centerlines are identified by run ID and a number indicating the time in minutes (with t = 0 min being the time at which water started flowing over the ice block). The naming convention is also associated to the images in "Centimeter scale rivulet images.pdf". The last sheet in the workbook includes a summary of the channel widths measured from every image for each centerline. The 50 sheets with the centerline information have four columns each. The titles of the columns are X, Y, S, and C. X,Y are dimensionless coordinates of the centerline. S is dimensionless streamwise coordinate (location along the centerline). C is dimensionless curvature value. All these values were non-dimensionalized with the channel width. See Fernandez (2018), Zolezzi and Guneralp (2016), and Guneralp and Rhoads (2008) for more details regarding the process of smoothing, standardizing and non-dimensionalization of the centerline coordinates.
Meltwater, Meandering, Ice, Supraglacial, Experiments
Clark, Lindsay V.; Dwiyanti, Maria Stefanie; Anzoua, Kossonou G.; Brummer, Joe E.; Ghimire, Bimal Kumar; Głowacka, Katarzyna; Hall, Megan; Heo, Kweon; Jin, Xiaoli; Lipka, Alexander E.; Peng, Junhua; Yamada, Toshihiko; Yoo, Ji Hye; Yu, Chang Yeon; Zhao, Hua; Long, Stephen P.; Sacks, Erik J. (2019): Miscanthus sinensis multi-location trial: phenotypic analysis, genome-wide association, and genomic prediction . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0790815_V3
This dataset contains genotypic and phenotypic data, R scripts, and the results of analysis pertaining to a multi-location field trial of Miscanthus sinensis. Genome-wide association and genomic prediction were performed for biomass yield and 14 yield-component traits across six field trial locations in Asia and North America, using 46,177 single-nucleotide polymorphism (SNP) markers mined from restriction site-associated DNA sequencing (RAD-seq) and 568 M. sinensis accessions. Genomic regions and candidate genes were identified that can be used for breeding improved varieties of M. sinensis, which in turn will be used to generate new M. xgiganteus clones for biomass.
miscanthus; genotyping-by-sequencing (GBS); genome-wide association studies (GWAS); genomic selection
Jones, Todd M.; Benson, Thomas J.; Ward, Michael P. (2019): Flight Ability of Juvenile Songbirds at Fledgling: Examples of Fledgling Drop Tests. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2044905_V1
This data publication provides example video clips related to research on association among flight ability of juvenile songbirds at fledging and juvenile morphological traits (wing emergence, wing length, body condition, mass, and tarsus length. File names reflect the species dropped in each video. These videos are supplemental material for scientific publications by the authors and reflect an example subset of all videos collected form 2017-2018 as part of a larger study on the post-fledging ecology of grassland and shrubland birds in east-Central Illinois, USA. No birds were harmed/injured in the production of these videos and procedures were approved by the Illinois Institutional Animal Care and Use Committee (IACUC), protocol no. 18221. Individuals depicted in the videos have given consent for the videos to be shared (talent/model release form; <a href="https://publicaffairs.illinois.edu/resources/release/">https://publicaffairs.illinois.edu/resources/release/</a>)
songbirds; flight ability; wing development; wing length; wing emergence; nestling development; post-fledging
Ando, Amy; Fraterrigo, Jennifer; Guntenspergen, Glenn; Howlader, Aparna; Mallory, Mindy; Olker, Jennifer; Stickley, Samuel (2019): Spatial Conservation and Investment Portfolios to Manage Climate-Related Risk. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2887291_V1
climate change; conservation; diversification; environmental investments; MPT; porftfolio; risk; uncertainty
Anderson, Nicholas L.; Harmon-Threatt, Alexandra N. (2019): Chronic contact with realistic soil concentrations of imidacloprid affects the mass, immature development speed, and adult longevity of solitary bees. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9033534_V1
Chronic contact exposure to realistic soil concentrations (0, 7.5, 15, and 100 ppb) of the neonicotinoid pesticide imidacloprid had species- and sex-specific effects on bee adult longevity, immature development speed, and mass. This dataset contains a life table tracking the development, mass, and deaths of a single cohort of Osmia lignaria and Megachile rotundata over the course of two summers. Other data files include files created for multi-event survival analysis to analyze the effect on development speed. Detected effects included: decreased adult longevity for female O. lignaria at the highest concentration, a trend for a hormetic effect on female M. rotundata development speed and mass (longest development time and greatest mass in the 15 ppb treatment), and decreased adult longevity and increased development speed at high imidacloprid concentrations as well as a hormetic effect on mass (lowest in the 15 ppb treatment treatment) on male M. rotundata.
neonicotinoid; imidacloprid; bee; habitat restoration;
Makhnenko, Roman; Tarokh, Ali (2019): Experimental data on bulk and unjacketed moduli of porous rocks. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7478121_V2
This dataset is provided to support the statements in Tarokh, A., and R.Y. Makhnenko. 2019. Remarks on the solid and bulk responses of fluid-filled porous rock, Geophysics. The unjacketed bulk modulus is a poroelastic parameter that can be directly measured in a laboratory test under a loading that preserves the difference between the mean stress and pore pressure constant. For a monomineralic rock, the measurement of the unjacketed bulk modulus is ignored because it is assumed to be equal to the bulk modulus of the solid phase. To examine this assumption, we tested porous sandstones (Berea and Dunnville) and limestones (Apulian and Indiana) mainly composed of quartz and calcite, respectively, under the unjacketed condition. The presence of microscale inhomogeneities, in the form of non-connected (occluded) pores, was shown to cause a considerable difference between the unjacketed bulk modulus and the bulk modulus of the solid phase. Furthermore, we found the unjacketed bulk modulus to be independent of the unjacketed pressure and Terzaghi effective pressure and therefore a constant.
Poroelasticity; anisotropic solid skeleton; unjacketed bulk modulus; non-connected porosity
Neumann, Elizabeth; Comi, Troy; Rubakhin, Stanislav; Sweedler, Jonathan (2019): Data for: Lipid heterogeneity between astrocytes and neurons revealed with single cell MALDI MS supervised by immunocytochemical classification. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2-3125702_V1
We have recently created an approach for high throughput single cell measurements using matrix assisted laser desorption / ionization mass spectrometry (MALDI MS) (J Am Soc Mass Spectrom. 2017, 28, 1919-1928. doi: 10.1007/s13361-017-1704-1. Chemphyschem. 2018, 19, 1180-1191. doi: 10.1002/cphc.201701364). While chemical detail is obtained on individual cells, it has not been possible to correlate the chemical information with canonical cell types. Now we combine high-throughput single cell mass spectrometry with immunocytochemistry to determine lipid profiles of two known cell types, astrocytes and neurons from the rodent brain, with the work appearing as “Lipid heterogeneity between astrocytes and neurons revealed with single cell MALDI MS supervised by immunocytochemical classification” (DOI: 10.1002/anie.201812892). Here we provide the data collected for this study. The dataset provides the raw data and script files for the rodent cerebral cells described in the manuscript.
Single cell analysis; mass spectrometry; astrocyte; neuron; lipid analysis
Fernández, Roberto; Parker, Gary; Stark, Colin (2019): Experiments on patterns of alluvial cover and bedrock erosion in a meandering channel. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2-3044828_V1
This dataset includes measurements taken during the experiments on patterns of alluvial cover over bedrock. The dataset includes an hour worth of timelapse images taken every 10s for eight different experimental conditions. It also includes the instantaneous water surface elevations measured with eTapes at a frequency of 10Hz for each experiment. The 'Read me Data.txt' file explains in more detail the contents of the dataset.
bedrock; erosion; alluvial; meandering; alluvial cover; sinuosity; flume; experiments; abrasion;
Imker, Heidi (2019): Funding and Operating Organizations for Long-Lived Molecular Biology Databases. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3993338_V1
The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.
databases; research infrastructure; sustainability; data sharing; molecular biology; bioinformatics; bibliometrics
Lovell, Sarah (2019): Bee visitation for PLOS ONE manuscript. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6066174_V1
The bee visitation data includes the percentage of each bee pollinator group in bee bowls and observed. The data are referenced in the article with the following citation: Bennett, A.B., Lovell, S.T. 2019. Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. Accepted for publication in: PLOS ONE.
Lovell, Sarah (2019): Site attributes for PLOS ONE article. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7869554_V1
Landscape attributes of the nineteen sites as supplemental data for the following article: Bennett, A.B., Lovell, S.T. 2019. Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. Accepted for publication in: PLOS ONE.
Carlstone, Jamie; Kenfield, Ayla Stein; Norman, Michael; Wilkin, John (2019): US books 1931 to 1933 All Parts Transcription from Vendor. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0873454_V1
Vendor transcription of the Catalogue of Copyright Entries, Part 1, Group 1, Books: New Series, Volume 29 for the Year 1932. This file contains all of the entries from the indicated volume.
copyright; Catalogue of Copyright Entries; Copyright Office
Le, Thien; Sy, Aaron; Molloy, Erin K.; Zhang, Qiuyi; Rao, Satish; Warnow, Tandy (2019): Using INC within Divide-and-Conquer Phylogeny Estimation - Datasets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8518809_V1
This repository include datasets that are studied with INC/INC-ML/INC-NJ in the paper `Using INC within Divide-and-Conquer Phylogeny Estimation' that was submitted to AICoB 2019. Each dataset has its own readme.txt that further describes the creation process and other parameters/softwares used in making these datasets. The latest implementation of INC/INC-ML/INC-NJ can be found on https://github.com/steven-le-thien/constraint_inc. Note: there may be files with DS_STORE as extension in the datasets; please ignore these files.
phylogenetics; gene tree estimation; divide-and-conquer; absolute fast converging
Nute, Michael; Yarlagadda, Karthik; Stumpf, Rebecca (2019): PICAN-PI Public Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1678505_V1
This dataset contains all data used in the two studies included in "PICAN-PI..." by Nute, et al, other than the original raw sequences. That includes: 1) Supplementary information for the Manuscript, including all the graphics that were created, 2) 16S Reference Alignment, Phylogeny and Taxonomic Annotation used by SEPP, and 3) Data used in the manuscript as input for the graphics generation (namely, SEPP outputs and sequence multiplicities).
microbiome; data visualization; graphics; phylogenetics; 16S
Portier, Evan; Silver, Whendee; Yang, Wendy H. (2018): Data for: Effects of an invasive perennial forb on gross soil nitrogen cycling and nitrous oxide fluxes. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1324977_V1
This dataset includes data on soil properties, soil N pools, and soil N fluxes presented in the manuscript, "Effects of an invasive perennial forb on gross soil nitrogen cycling and nitrous oxide fluxes," submitted to Ecology for peer-reviewed publication. Please refer to that publication for details about methodologies used to generate these data and for the experimental design.
pepperweed; nitrogen cycling; nitrous oxide; invasive species; Bay Delta
Dong, Xiaoru; Xie, Jingyi; Hoang, Linh (2018): All_Words. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5075871_V1
File Name: AllWords.csv Data Preparation: Xiaoru Dong, Linh Hoang Date of Preparation: 2018-12-12 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: The file contains lists of all words (all features) from the bag-of-words feature extraction. Notes: In order to reproduce the data in this file, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.
Inclusion criteria; Randomized controlled trials; Machine learning; Systematic reviews
Dong, Xiaoru; Xie, Jingyi; Hoang, Linh; Schneider, Jodi (2018): Error_Analysis. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3782968_V1
File Name: Error_Analysis.xslx Data Preparation: Xiaoru Dong Date of Preparation: 2018-12-12 Data Contributions: Xiaoru Dong, Linh Hoang, Jingyi Xie, Jodi Schneider Data Source: The classification prediction results of prediction in testing data set Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews Description: The file contains lists of the wrong and correct prediction of inclusion criteria of Cochrane Systematic Reviews from the testing data set and the length (number of words) of the inclusion criteria. Notes: In order to reproduce the relevant data to this, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.
Inclusion criteria, Randomized controlled trials, Machine learning, Systematic reviews
Xu, Zewei; Wang, Shaowen (2018): A 3DCNN-based method to land cover classification. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0024113_V1
A 3D CNN method to land cover classification using LiDAR and multitemporal imagery
3DCNN; land cover classification; LiDAR; multitemporal imagery
Wang, Yang; Dietrich, Christopher; Zhang, Yalin (2018): NEXUS data file for phylogenetic analysis of Evacanthinae (Hemiptera: Cicadellidae). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8993673_V1
The text file contains the original data used in the phylogenetic analyses of Wang et al. (2017: Scientific Reports 7:45387). The text file is marked up according to the standard NEXUS format commonly used by various phylogenetic analysis software packages. The file will be parsed automatically by a variety of programs that recognize NEXUS as a standard bioinformatics file format. The first six lines of the file identify the file as NEXUS, indicate that the file contains data for 81 taxa (species) and 2905 characters, indicate that the first 2805 characters are DNA sequence and the last 100 are morphological, that the data may be interleaved (with data for one species on multiple rows), that gaps inserted into the DNA sequence alignment are indicated by a dash, and that missing data are indicated by a question mark. The file contains aligned nucleotide sequence data for 5 gene regions and 100 morphological characters. The identity and positions of data partitions are indicated in the mrbayes block of commands for the phylogenetic program MrBayes at the end of the file. The mrbayes block also contains instructions for MrBayes on various non-default settings for that program. These are explained in the original publication. Descriptions of the morphological characters and more details on the species and specimens included in the dataset are provided in the supplementary document included as a separate pdf. The original raw DNA sequence data are available from NCBI GenBank under the accession numbers indicated in the supplementary file.
phylogeny; DNA sequence; morphology; Insecta; Hemiptera; Cicadellidae; leafhopper; evolution; 28S rDNA; wingless; histone H3; cytochrome oxidase I; bayesian analysis
Stein Kenfield, Ayla (2018): ARL IR Metadata Documentation Website Review Data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7323993_V1
Spreadsheet with data about whether or not the indicated institutional repository website provides metadata documentation. See readme file for more information.
institutional repositories; metadata; best practices; metadata documentation