Displaying Dataset 201 - 225 of 321 in total

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U.S. National Science Foundation (NSF) (83)
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2020 (103)
2019 (74)
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CC0 (185)
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custom (3)
published: 2019-02-22
 
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.
keywords: bedrock; erosion; alluvial; meandering; alluvial cover; sinuosity; flume; experiments; abrasion;
published: 2019-02-19
 
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.
keywords: databases; research infrastructure; sustainability; data sharing; molecular biology; bioinformatics; bibliometrics
published: 2019-02-02
 
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.
published: 2019-02-02
 
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.
published: 2019-01-07
 
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.
keywords: copyright; Catalogue of Copyright Entries; Copyright Office
published: 2019-01-27
 
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.
keywords: phylogenetics; gene tree estimation; divide-and-conquer; absolute fast converging
published: 2019-02-07
 
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).
keywords: microbiome; data visualization; graphics; phylogenetics; 16S
published: 2018-08-16
 
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.
keywords: pepperweed; nitrogen cycling; nitrous oxide; invasive species; Bay Delta
published: 2018-12-20
 
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.
keywords: Inclusion criteria; Randomized controlled trials; Machine learning; Systematic reviews
published: 2018-12-20
 
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.
keywords: Inclusion criteria, Randomized controlled trials, Machine learning, Systematic reviews
published: 2018-12-13
 
A 3D CNN method to land cover classification using LiDAR and multitemporal imagery
keywords: 3DCNN; land cover classification; LiDAR; multitemporal imagery
published: 2018-12-04
 
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.
keywords: phylogeny; DNA sequence; morphology; Insecta; Hemiptera; Cicadellidae; leafhopper; evolution; 28S rDNA; wingless; histone H3; cytochrome oxidase I; bayesian analysis
published: 2018-12-14
 
Spreadsheet with data about whether or not the indicated institutional repository website provides metadata documentation. See readme file for more information.
keywords: institutional repositories; metadata; best practices; metadata documentation
published: 2018-12-06
 
The text file contains the original DNA sequence data used in the phylogenetic analyses of Krishnankutty et al. (2016: Systematic Entomology 41: 580–595). 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 file contains five separate data blocks, one for each character partition (28S, histone H3, 12S, indels, and morphology) for 53 taxa (species). Gaps inserted into the DNA sequence alignment are indicated by a dash, and missing data are indicated by a question mark. The separate "indels1" block includes 40 indels (insertions/deletions) from the 28S sequence alignment re-coded using the modified complex indel coding scheme, as described in the "Materials and methods" of the original publication. The DIMENSIONS statements near the beginning of each block indicate the numbers of taxa (NTax) and characters (NChar). The file contains aligned nucleotide sequence data for 3 gene regions and 40 morphological characters. The file is configured for use with the maximum likelihood-based phylogenetic program GARLI but can also be parsed by any other bioinformatics software that supports the NEXUS format. 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 supporting pdf file. More details on individual analyses are provided in the original publication.
keywords: phylogeny; DNA sequence; morphology; Insecta; Hemiptera; Cicadellidae; leafhopper; evolution; 28S rDNA; histone H3; 12S mtDNA; maximum likelihood
published: 2018-12-20
 
This dataset contains data used to generate figures and tables in the corresponding paper.
keywords: Black carbon; Emission Inventory; Observations; Climate change, Diesel engine, Coal burning
published: 2018-12-20
 
File Name: WordsSelectedByManualAnalysis.csv Data Preparation: Xiaoru Dong, Linh Hoang Date of Preparation: 2018-12-14 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: this file contains the list of 407 informative words reselected from the 1655 words by manual analysis. In particular, from the 1655 words that we got from information gain feature selection, we then manually read and eliminated the domain specific words. The remaining words then were selected into the "Manual Analysis Words" as the results. Notes: Even though the list of words in this file was selected manually. However, 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.
keywords: Inclusion criteria; Randomized controlled trials; Machine learning; Systematic reviews
published: 2018-12-20
 
File Name: WordsSelectedByInformationGain.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 a list of 1655 informative words selected by applying information gain feature selection strategy. Information gain is one of the methods commonly used for feature selection, which tells us how many bits of information the presence of the word are helpful for us to predict the classes, and can be computed in a specific formula [Jurafsky D, Martin JH. Speech and language processing. London: Pearson; 2014 Dec 30].We ran Information Gain feature selection on Weka -- a machine learning tool. 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.
keywords: Inclusion criteria; Randomized controlled trials; Machine learning; Systematic reviews
published: 2018-10-17
 
This is the dataset used in the Ecological Applications publication of the same name. This dataset consists of the following files: Internal.Community.Data.txt Regional.Community.Data.txt Site.Attributes.txt Year.Of.Final.Bio.Monitoring.txt Internal.Community.Data.txt is a site and plot by species matrix. Column labeled SITE consists of site IDs. Column labeled Plot consists of Plot numbers. All other columns represent species relative abundances per plot. Regional.Community.Data.txt is a site by species matrix of relative abundances. Column labeled site consists of site IDs. All other columns represent species relative abundances per site. Site.attributes.txt is a matrix of site attributes. Column labeled SITE consists of site IDs. Column labeled Long represents longitude in decimal degrees. Column labeled Lat represents latitude in decimal degrees. Column labeled Richness represents species richness of sites calculated from Regional Community Data. Column labeled NAT_COMP_REST represents designation as a randomly selected natural wetland (NAT), compensation wetland (COMP) or reference quality natural wetland (REF). Column labeled HQ_LQ_COMP represents designation as high quality (HQ), low quality (LQ) or compensation wetland (COMP). Column labeled SAMPLING_YEAR_INTERNAL represents year data used for analysis of internal β-diversity was gathered. Column labeled SAMPLING_YEAR_REGIONAL represents year data used for analysis of regional β-diversity was gathered. Column labeled TRANSECT_LENGTH represents length in meters of initial sampling transect. INAI_GRADE represents Illinois Natural Areas Inventory grades assigned to each site. Grades range from A for highest quality natural areas to E for lowest quality natural areas. Year.Of.Final.Bio.Monitoring.txt is a table representing years of final monitoring of compensation wetlands as mandated by the US Army Corps of Engineers. Column labeled Site consists of site IDs. Column labeled YR_FIN_BIO_MON consists of years of final monitoring. Entries of N/A represent dates that were unable to be located. More information about this dataset: Interested parties can request data from the Critical Trends Assessment Program, which was the source for data on naturally occurring wetlands in this study. More information on the program and data requests can be obtained by visiting the program webpage. Critical Trends Assessment Program, Illinois Natural History Survey. http://wwx.inhs.illinois.edu/research/ctap/
keywords: biodiversity; wetlands; wetland mitigation; biotic homogenization; beta diversity
published: 2018-11-21
 
This set of scripts accompanies the manuscript describing the R package polyRAD, which uses DNA sequence read depth to estimate allele dosage in diploids and polyploids. Using several high-confidence SNP datasets from various species, allelic read depth from a typical RAD-seq dataset was simulated, then genotypes were estimated with polyRAD and other software and compared to the true genotypes, yielding error estimates.
keywords: R programming language; genotyping-by-sequencing (GBS); restriction site-associated DNA sequencing (RAD-seq); polyploidy; single nucleotide polymorphism (SNP); Bayesian genotype calling; simulation
published: 2018-10-24
 
This dataset was compiled between 2010 and 2011 from data published in the scientific literature from articles evaluating the influence of cropping systems and soil management practices on soil organic Carbon. We used the Thomas Reuter Web of Science database and by reviewed the reference sections of key peer-reviewed articles. Articles included in the database presented results from field sites within the continental United States.
keywords: Cropping systems; soil management; soil organic carbon; soil quality.
published: 2016-08-16
 
This archive contains all the alignments and trees used in the HIPPI paper [1]. The pfam.tar archive contains the PFAM families used to build the HMMs and BLAST databases. The file structure is: ./X/Y/initial.fasttree ./X/Y/initial.fasta where X is a Pfam family, Y is the cross-fold set (0, 1, 2, or 3). Inside the folder are two files, initial.fasta which is the Pfam reference alignment with 1/4 of the seed alignment removed and initial.fasttree, the FastTree-2 ML tree estimated on the initial.fasta. The query.tar archive contains the query sequences for each cross-fold set. The associated query sequences for a cross-fold Y is labeled as query.Y.Z.fas, where Z is the fragment length (1, 0.5, or 0.25). The query files are found in the splits directory. [1] Nguyen, Nam-Phuong D, Mike Nute, Siavash Mirarab, and Tandy Warnow. (2016) HIPPI: Highly Accurate Protein Family Classification with Ensembles of HMMs. To appear in BMC Genomics.
keywords: HIPPI dataset; ensembles of profile Hidden Markov models; Pfam
published: 2018-12-13
 
The dataset contains a complete example (inputs, outputs, codes, intermediate results, visualization webpage) of executing Height Above Nearest Drainage HAND workflow with CyberGIS-Jupyter.
keywords: cybergis; hydrology; Jupyter
published: 2016-08-02
 
These data are the result of a multi-step process aimed at enriching BIBFRAME RDF with linked data. The process takes in an initial MARC XML file, transforms it to BIBFRAME RDF/XML, and then four separate python files corresponding to the BIBFRAME 1.0 model (Work, Instance, Annotation, and Authority) are run over the BIBFRAME RDF/XML output. The input and outputs of each step are included in this data set. Input file types include the CSV; MARC XML; and Master RDF/XML Files. The CSV contain bibliographic identifiers to e-books. From CSVs a set of MARC XML are generated. The MARC XML are utilized to produce the Master RDF file set. The major outputs of the enrichment code produce BIBFRAME linked data as Annotation RDF, Instance RDF, Work RDF, and Authority RDF.
keywords: BIBFRAME; Schema.org; linked data; discovery; MARC; MARCXML; RDF
published: 2017-08-11
 
Enclosed in this dataset are transport data of kagome connected artificial spin ice networks composed of permalloy nanowires. The data herein are reproductions of the data seen in Appendix B of the dissertation titled "Magnetotransport of Connected Artificial Spin Ice". Field sweeps with the magnetic field applied in-plane were performed in 5 degree increments for armchair orientation kagome artificial spin ice and zigzag orientation kagome artificial spin ice.
keywords: Magnetotransport; artificial spin ice; nanowires
published: 2016-08-18
 
Copyright Review Management System renewals by year, data from Table 2 of the article "How Large is the ‘Public Domain’? A comparative Analysis of Ringer’s 1961 Copyright Renewal Study and HathiTrust CRMS Data."
keywords: copyright; copyright renewals; HathiTrust