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published: 2020-12-31
This dataset contains the amino acid and nucleotide alignments corresponding to the phylogenetic analyses of South et al. 2020 in Systematic Entomology. This dataset also includes the gene trees that were used as input for coalescent analysis in ASTRAL.
keywords: Plecoptera; stoneflies; phylogeny; insects
published: 2020-11-18
These data obtained from the peer-reviewed literature and a public database depict the geographic expansion of the black-legged tick (Ixodes scapularis) and human cases of Lyme disease in the midwestern U.S. <b><i>Note</b></i>: There was an omission from the first version (V1) of the data set that required us to update the data. Specifically, we failed to include the data from the article "Caporale DA, Johnson CM, Millard BJ. 2005 Presence of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) in Southern Kettle Moraine State Forest, Wisconsin, and characterization of strain W97F51. J. Med. Entomol. 42, 457–472". In the second version (V2) of the data, this omission is corrected.
keywords: Lyme disease; Borrelia burgdorferi; Ixodes scapularis; black-legged tick
published: 2020-11-18
This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. Please see the github for the most up-to-date data after the revision process: https://github.com/dopplerchase/Chase_et_al_2021_NN Authors: Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar Corresponding author: Randy J. Chase (randyjc2@illinois.edu) Here we have the data used in the manuscript. Please email me if you have specific questions about units etc. 1) DDA/GMM database of scattering properties: base_df_DDA.csv This is the combined dataset from the following papers: Leinonen & Moisseev, 2015; Leinonen & Szyrmer, 2015; Lu et al., 2016; Kuo et al., 2016; Eriksson et al., 2018. The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. The first column is just an index column. 2) Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc This was the result of combining the PSDs and DDA/GMM particles randomly to build the training and test dataset. 3) Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb This is the notebook used to train the neural network. 4)Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. You will need this to run the retrieval. 5) Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. You will need these to scale your data if you wish to run the retrieval. 6) <b>New in this version</b> - Example notebook of how to run the trained neural network on Ku- Ka- band observations. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynb 7) <b>New in this version</b> - APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc The data for the analysis on the observations are not provided here because of the size of the radar data. Please see the GHRC website (<a href="https://ghrc.nsstc.nasa.gov/home/">https://ghrc.nsstc.nasa.gov/home/</a>) if you wish to download the radar and in-situ data or contact me. We can coordinate transferring the exact datafiles used. The GPM-DPR data are avail. here: <a href="http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05">http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05</a>
published: 2020-11-14
Dataset includes temperature data (local average April daily temperatures), first egg dates and reproductive output of Prothonotary Warblers breeding in southernmost Illinois, USA. Also included are arrival dates for warblers returning to breeding grounds from wintering grounds, and global temperature anomaly data for comparison with local temperatures. These data were used in the manuscript entitled "Warmer April Temperatures on Breeding Grounds Promote Earlier Nesting in a Long-Distance Migratory Bird, the Prothonotary Warbler" published in Frontiers in Ecology and Evolution. A rich text file is included with explanations of each variable in the dataset.
keywords: first egg dates; global warming; local temperature effects; long-distance migratory bird; prothonotary warbler; protonotaria citrea; reproductive output
published: 2020-11-06
This data contains bam files and transcripts in the simulated instances generated for the paper 'JUMPER: Discontinuous Transcript Assembly in SARS-CoV-2' submitted for RECOMB 2021. The folder 'bam' contained the simulated bam files aligned using STAR wile the reads were generated using the method polyester Note: in the readme file, close to the end of the document, please ignore this sentence: 'Those files can be opened by using [name of software].'
keywords: transcript assembly; SARS-CoV-2; discontinuous transcription; coronaviruses
published: 2020-11-05
This version 2 dataset contains 34 files in total with one (1) additional file, called "Culture-dependent Isolate table with taxonomic determination and sequence data.csv". The remaining files (33) are identical to version 1. The following is the information about the new file and its variables: <b>Culture-dependent Isolate table with taxonomic determination and sequence data.csv</b>: Culture table with assigned taxonomy from NCBI. Single direction sequence for each isolate is include if one could be obtained. Sequence is derived from ITS1F-ITS4 PCR amplicons, with Sanger sequencing in one direction using ITS5. The files contains 20 variables with explanation as below: IsolateNumber : unique number identify each isolate cultured Time: season in which the sample was collected Location: the specific name of the location Habitat: type of habitat : either stream or peatland State: state in the USA in which the specific location is located Incubation_pH ID: pH of the medium during isolation of fungal cultures Genus: phylogenetic genus of the fungal isolates (determined by sequence similarity) Sequence_quality: base call quality of the entire sequence used for blast analysis, if known %_coverage: sequence coverage reported from GenBank %_ID: sequence similarity reported from GenBank Life_style : ecological life style if known Phylum: phylogenetic phylum as indicated by Index Fungorum Subphylum: phylogenetic subphylum as indicated by Index Fungorum Class: phylogenetic class as indicated by Index Fungorum Subclass: phylogenetic subclass as indicated by Index Fungorum Order: phylogenetic order as indicated by Index Fungorum Family: phylogenetic Family as indicated by Index Fungorum ITS5_Sequence: single direction sequence used for sequence similarity match using blastn. Primer ITS5 Fasta: sequence with nomenclature in a fasta format for easy cut and paste into phylogenetic software Note: blank cells mean no data is available or unknown.
keywords: ITS1 forward reads; Illumina; peatlands; streams; bogs; fens
published: 2020-07-15
This repository includes scripts and datasets for Chapter 6 of my PhD dissertation, " Supertree-like methods for genome-scale species tree estimation," that had not been published previously. This chapter is based on the article: Molloy, E.K. and Warnow, T. "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Bioinformatics, In press. https://doi.org/10.1093/bioinformatics/btaa444. The results presented in my PhD dissertation differ from those in the Bioinformatics article, because I re-estimated species trees using FastMulRF and MulRF on the same datasets in the original repository (https://doi.org/10.13012/B2IDB-5721322_V1). To re-estimate species trees, (1) a seed was specified when running MulRF, and (2) a different script (specifically preprocess_multrees_v3.py from https://github.com/ekmolloy/fastmulrfs/releases/tag/v1.2.0) was used for preprocessing gene trees (which were then given as input to MulRF and FastMulRFS). Note that this preprocessing script is a re-implementation of the original algorithm for improved speed (a bug fix also was implemented). Finally, it was brought to my attention that the simulation in the Bioinformatics article differs from prior studies, because I scaled the species tree by 10 generations per year (instead of 0.9 years per generation, which is ~1.1 generations per year). I re-simulated datasets (true-trees-with-one-gen-per-year-psize-10000000.tar.gz and true-trees-with-one-gen-per-year-psize-50000000.tar.gz) using 0.9 years per generation to quantify the impact of this parameter change (see my PhD dissertation or the supplementary materials of Bioinformatics article for discussion).
keywords: Species tree estimation; gene duplication and loss; statistical consistency; MulRF, FastRFS
published: 2020-08-01
This data set shows how density effects have an important influence on mixing at a small river confluence. The data consist of results of simulations using a detached eddy simulation model.
keywords: confluence; flow dynamics; density effects
published: 2020-08-25
The Allan Lab has published a Fluidigm pipeline online. This is the url: https://github.com/HPCBio/allan-fluidigm-pipeline. This url includes a tutorial for running the pipeline. However it does not have test datasets yet. This tarball hosted at the Illinois Data Bank is the dataset that completes the github tutorial. It includes inputs (custom database of tick pathogens and fluidigm raw reads) and output files (tables of samples with taxonomic classifications).
keywords: custom database of tick pathogens; fluidigm pipeline; fluidigm paired reads; fluidigm tutorial
published: 2020-08-31
This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard". The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.
keywords: Social Cost of Carbon; Social Cost of Nitrogen; Cost-Benefit Analysis; Indirect Land-Use Change
published: 2020-10-11
This dataset contains the publication record of 6429 computer science researchers collected from the Microsoft Academic dataset provided through their Knowledge Service API (http://bit.ly/microsoft-data).
published: 2020-11-01
A 30 year record of the vegetation in sample plots in a woodland in the Chicago area. The changes in these plots over time show how ecological restoration can yield dramatic results.
keywords: woodland; ecological restoration; floristic quality; vegetation; plant ecology; ecological management
published: 2020-10-30
Supporting information for "Urinary Phthalate Metabolite Concentrations and Hot Flashes in Pre- and Perimenopausal Women from the Midlife Women’s Health Study." This file contains tables of the results of stratified analyses of the associations of hot flash outcomes with urinary phthalates metabolites by menopause status, race/ethnicity, body mass index, and depressive status. This file also contains supplementary HPLC methods for the analysis of phthalate metabolites.
keywords: Hot flashes; menopause; phthalates; women
published: 2020-10-27
The data file contains a list of included studies with their detailed metadata, taken from Cochrane reviews which were used in a project associated with the manuscript "Evaluation of an automated probabilistic RCT Tagger applied to published Cochrane reviews".
keywords: Cochrane reviews; automation; randomized controlled trial; RCT; systematic review
published: 2020-10-27
The data file contains detailed information of the Cochrane reviews that were used in a project associated with the manuscript (working title) "Evaluation of an automated probabilistic RCT Tagger applied to published Cochrane reviews".
keywords: Cochrane reviews; systematic reviews; randomized control trial; RCT; automation
published: 2020-08-22
We are releasing the tracing dataset of four microservice benchmarks deployed on our dedicated Kubernetes cluster consisting of 15 heterogeneous nodes. The dataset is not sampled and is from selected types of requests in each benchmark, i.e., compose-posts in the social network application, compose-reviews in the media service application, book-rooms in the hotel reservation application, and reserve-tickets in the train ticket booking application. The four microservice applications come from [DeathStarBench](https://github.com/delimitrou/DeathStarBench) and [Train-Ticket](https://github.com/FudanSELab/train-ticket). The performance anomaly injector is from [FIRM](https://gitlab.engr.illinois.edu/DEPEND/firm.git). The dataset was preprocessed from the raw data generated in FIRM's tracing system. The dataset is separated by on which microservice component is the performance anomaly located (as the file name suggests). Each dataset is in CSV format and fields are separated by commas. Each line consists of the tracing ID and the duration (in 10^(-3) ms) of each component. Execution paths are specified in `execution_paths.txt` in each directory.
keywords: Microservices; Tracing; Performance
published: 2020-10-16
Video footage of an Eastern Box Turtle (Terrapene carolina carolina) partially predating a Field Sparrow nest (Spizella pusilla) at 0845 h on the 31 of May 2020. Please note that the date on the video footage is incorrect due to user error, but the time is correct.
keywords: nest predation; turtle; songbird; nest camera; Terrapene carolina carolina; Spizella pusilla;
published: 2020-10-15
This dataset consists of various input data that are used in the GAMS model. All the data are in the format of .inc which can be read within GAMS or Notepad. Main data sources include: acreage data (acre), crop budget data ($/acre), crop yield data (e.g. bushel/acre), Soil carbon sequestration data (KgCO2/ha/yr). Model details can be found in the "Assessing the Additional Carbon Savings with Biofuel" and GAMS model package. ## File Description (1) GAMS Model.zip: This includes all the input files and scripts for running the model (2) Table*.csv: These files include the data from the tables in the manuscript (3) Figure2_3_4.csv: This contains the data used to create the figures in the manuscript (4) BaselineResults.csv: This includes a summary of the model results. (5) SensitivityResults_*.csv: Model results from the various sensitivity analyses performed (6) LUC_emission.csv: land use change emissions by crop reporting district for changes of pasturelands to annual crops.
keywords: Biogenic carbon intensity; Corn ethanol; Economic model; Dynamic optimization; Anticipated baseline approach; Life cycle carbon intenisty
published: 2020-10-14
Data on permanent plots at Fortuna and the Panama Canal Watershed, Republic of Panama, containing counts and percent of trees with one or more multiple stems >10cm diameter, with and without palms. Accompanying environmental data includes elevation, precipitation, soil type and soil chemical variables (pH, total N, NO3, NO4, resin P, mehlich Ca, K and Mg.
keywords: multiple stems; resprouting; Panama Canal Watershed; Fortuna Forest Reserve
published: 2020-10-13
Data in this spreadsheet presents basic information on Cahokia, Mound 72 shell artifacts. This includes taxonomic identifications, provenience, and bead measurements. There are five tabs: 1. Raw data; 2. Disk bead measurements; 3. Columella bead measurements; 4. Data on cups and pendants; and, 5. Information on whole shell beads.
keywords: Cahokia; Mound 72; Lightning whelk; Bead crafting
published: 2020-10-01
Raw gas exchange data for photosynthetic induction in 6 rice accession flag leaves. Photosynthetic induction and point measurements were made at ambient [CO2]. Two accessions (AUS 278 and IR64) were selected to screen in greater detail in which photosynthetic induction was measured at six [CO2].
published: 2020-09-25
This repository contains the datasets and corresponding results for the paper "MAGUS: Multiple Sequence Alignment using Graph Clustering". The Datasets.zip archive contains the ROSE, balibase, Gutell, and RNASim datasets used in our experiments. The Results.zip archive contains the outputs of running our methods against these datasets. Datasets used: ROSE: 10 simulated nucleotide model conditions from the SATe paper, each with 20 replicates, and with 1000 sequences per replicate. The ROSE datasets were originally taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/sate-i">https://sites.google.com/eng.ucsd.edu/datasets/alignment/sate-i</a> RNASim: This is a collection of simulated nucleotide datasets that were generated under a model of evolution that reflects selection due to RNA structural constraints. We sampled 20 subsets of 1000 sequences each, as well as 10 subsets of 10000 each, by randomly sampling from the original million-sequence RNASim dataset. Gutell: 16S.M, 16S.3, 16S.T, 16S.B.ALL: Four biological nucleotide datasets from the Comparative Ribosomal Website (CRW) with cleaned reference alignments from SATe. Since PASTA is restricted to datasets without sequence length heterogeneity, these were modified to remove sequences that deviate by more than 20% from the median length. The scrubbed datasets range from 740 to 24,246 sequences. The pre-screened 16S datasets were taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/16s23s">https://sites.google.com/eng.ucsd.edu/datasets/alignment/16s23s</a> BAliBASE: We use eight BAliBASE amino acid datasets used in the PASTA paper. As above, we remove outlier sequences, which leaves us with sizes ranging from 195 to 732 sequences. The pre-screened Balibase datasets were taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp">https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp</a>
published: 2020-09-27
This dataset contains R codes used to produce the figures submitted in the manuscript titled "Understanding the multifaceted geospatial software ecosystem: a survey approach". The raw survey data used to populate these charts cannot be shared due to the survey consent agreement.
keywords: R; figures; geospatial software