Displaying datasets 526 - 550 of 633 in total

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published: 2018-06-05
 
A complete building coverage area dataset (i.e. area occupied by building structures, excluding other built surfaces such as roads, parking lots, and public parks) at the level of census block groups for the contiguous United States (CONUS). The dataset was assembled based on an ensemble prediction of nonlinear hierarchical models to account for spatial heterogeneities in the distribution of built surfaces across different urban communities. Percentage of impervious land and housing density were used as predictors of the estimated area of buildings and cross-validation results showed that the product estimated area represented by buildings with a mean error of 0.049 %.
keywords: Building Coverage Area; Urban Geography; Regional; Sustainability; US Census Block Groups; CONUS Data
published: 2018-12-31
 
Sixty undergraduate STEM lecture classes were observed across 14 departments at the University of Illinois Urbana-Champaign in 2015 and 2016. We selected the classes to observe using purposive sampling techniques with the objectives of (1) collecting classroom observations that were representative of the STEM courses offered; (2) conducting observations on non-test, typical class days; and (3) comparing these classroom observations using the Class Observation Protocol for Undergraduate STEM (COPUS) to record the presence and frequency of active learning practices utilized by Community of Practice (CoP) and non-CoP instructors. Decimal values are the result of combined observations. All COPUS codes listed are from Smith (2013) "The Classroom Observation Protocol for Undergraduate STEM (COPUS): A New Instrument to Characterize STEM Classroom Practices" paper. For more information on the data collection process, see "Evidence that communities of practice are associated with active learning in large STEM lectures" by Tomkin et. al. (2019) in the International Journal of STEM Education.
keywords: COPUS, Community of Practice
published: 2019-05-07
 
Data set of trophic cascade in mesocosms experiments for zooplankton (biomass and body size) and phytoplankton (chlorophyll a concentration) caused by Bluegill as well as zooplankton production in those same treatment groups. Zooplankton were collected by tube sampler and phytoplankton were collected through grab samples.
keywords: Trophic cascades; size-selective predation; compensatory mechanisms; biomanipulation; invasive fish; Daphnia; Moina
published: 2019-05-16
 
The associated data sets include information on stable isotopes from organic matter sources in high elevation lakes, the percentage of production assimilated from the different sources of organic matter, and the relationship between different metrics for trophic position and environmental variables.
keywords: Stable isotopes; macroinvertebrate production; trophic position
published: 2019-05-22
 
This is the experimental data of isolated nanomagnet islands with or without the presence of large nanomagnet islands. The small 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 small islands are fluctuating around room temperature. The data is recorded as photoemission electron microscopy intensity. More details about the data can be found in the note.txt and Spe_2016.xlsx file. Note: The raw data folders are stored in five volumes during the compression. All five volumes are needed in order to recover the original folder.
keywords: artificial spin ice; magnetism
published: 2019-05-20
 
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.
keywords: artificial spin ice
published: 2019-04-05
 
File Name: Inclusion_Criteria_Annotation.csv Data Preparation: Xiaoru Dong Date of Preparation: 2019-04-04 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 inclusion criteria of Cochrane Systematic Reviews and the manual annotation results. 5420 inclusion criteria were annotated, out of 7158 inclusion criteria available. Annotations are either "Only RCTs" or "Others". There are 2 columns in the file: - "Inclusion Criteria": Content of inclusion criteria of Cochrane Systematic Reviews. - "Only RCTs": Manual Annotation results. In which, "x" means the inclusion criteria is classified as "Only RCTs". Blank means that the inclusion criteria is classified as "Others". Notes: 1. "RCT" stands for Randomized Controlled Trial, which, in definition, is "a work that reports on a clinical trial that involves at least one test treatment and one control treatment, concurrent enrollment and follow-up of the test- and control-treated groups, and in which the treatments to be administered are selected by a random process, such as the use of a random-numbers table." [Randomized Controlled Trial publication type definition from https://www.nlm.nih.gov/mesh/pubtypes.html]. 2. 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. 3. This datafile (V2) is a updated version of the datafile published at https://doi.org/10.13012/B2IDB-5958960_V1 with some minor spelling mistakes in the data fixed.
keywords: Inclusion criteri; Randomized controlled trials; Machine learning; Systematic reviews
published: 2018-07-29
 
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.
keywords: phylogenomics; species trees; incomplete lineage sorting; divide-and-conquer
published: 2019-03-05
 
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>
keywords: Nuclear Radiation
published: 2019-03-25
 
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.
keywords: miscanthus; genotyping-by-sequencing (GBS); genome-wide association studies (GWAS); genomic selection
published: 2019-03-06
 
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.
keywords: neonicotinoid; imidacloprid; bee; habitat restoration;
published: 2019-02-26
 
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.
keywords: Single cell analysis; mass spectrometry; astrocyte; neuron; lipid analysis
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: 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: 2018-12-04
 
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
 
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