University Library, University of Illinois at Urbana-Champaign

The dataset shows all poison frogs (superfamily Dendrobatoidea) in private U.S. collections during 1990–2020. For each species and color morph, there is a date of arrival, the way it arrived in U.S. collections, and detailed notes related to its presence in the pet trade.

This dataset contains sequencing data obtained from Illumina MiSeq device to prove the concept of the proposed 2DDNA framework. Please refer to README.txt for detailed description of each file.

Data from an a field experiment at El Velo, Chiriqui, Republic of Panama. Data contain information about functional traits of seedlings growing in different treatments including type of forest, nitrogen addition and organic matter.

Shared dataset consists of 16S sequencing data of microbial communities. Each community is composed of heterotrophic bacteria derived from one of two soil samples and the model algae Chlamydomonas reinhardtii. Each comunity was placed in a materially closed environment with an initial supply of carbon in the media and subjected to light-dark cycles. The closed microbial ecosystems (CES) survived via carbon cycling. Each CES was subjected to rounds of dilution, after which the community was sequenced (data provided here). The shared dataset allowed us to conclude that CES consistently self-assembled to cycle carbon (data not provided) via conserved metabolic capabilites (data not provided) dispite differences in taxonomic composition (data provided). --------------------------- Naming convention: [soil sample = A or B][CES replicate = 1,2,3, or 4]_[round number = 1,2,3,or 4]_[reverse read = R or forward read = F]_filt.fastq Example -- A1_r1_F_filt.fastq means soil sample A, CES replicate 1, end of round1, forward read

Dataset associated with Merrill et al. ECE-2021-05-00793.R1 submission: Early life patterns of growth are linked to levels of phenotypic trait covariance and post-fledging mortality across avian species. Excel CSV files with all of the data used in analyses and file with descriptions of each column.

This dataset contains re-estimated gene trees from the ASTRAL-II [1] simulated datasets. The re-estimated variants of the datasets are called MC6H and MC11H -- they are derived from the MC6 and MC11 conditions from the original data (the MC6 and MC11 names are given by ASTRID [2]). The uploaded files contain the sequence alignments (half-length their original alignments), and the re-estimated species trees using FastTree2. Note: - "mc6h.tar.gz" and "mc11h.tar.gz" contain the sequence alignments and the re-estimated gene trees for the two conditions - the sequence alignments are in the format "all-genes.phylip.splitted.[i].half" where i means that this alignment is for the i-th alignment of the original dataset, but truncating the alignment halving its length - "g1000.trees" under each replicate contains the newline-separated re-estimated gene trees. The gene trees were estimated from the above described alignments using FastTree2 (version 2.1.11) command "FastTree -nt -gtr" [1]: Mirarab, S., & Warnow, T. (2015). ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics, 31(12), i44-i52. [2]: Vachaspati, P., & Warnow, T. (2015). ASTRID: accurate species trees from internode distances. BMC genomics, 16(10), 1-13.

This data set contains survey results from a 2021 survey of University of Illinois University Library employees conducted as part of the Becoming A Trans Inclusive Library Project to evaluate the awareness of University of Illinois faculty, staff, and student employees regarding transgender identities, and to assess the professional development needs of library employees to better serve trans and gender non-conforming patrons. The survey instrument is available in the IDEALS repository: http://hdl.handle.net/2142/110080.

This data set contains survey results from a 2021 survey of University of Illinois University Library patrons who identify as transgender or gender non-conforming conducted as part of the Becoming a Trans Inclusive Library Project to assess the experiences of transgender patrons seeking information and services in the University Library. Survey instruments are available in the IDEALS repository: http://hdl.handle.net/2142/110081.

This dataset contains all the data for the results section in the study presented in the paper entitled "Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry mode" submitted to Geoscientific Model Development (GMD). In this paper, two sets of simulations were run to test CAMP with this results included here. This consists of (1) box model inputs and outputs presented in Section 4.2 for modal, binned and particle-resolved simulations to compare the application of identical chemical mechanisms to different aerosol representations and (2) the 3D Eulerian output presented in Section 4.3.

This dataset includes the source data for Figures 1-4 and supplementary figures 1-10 for the manuscript "Kinetic and structural mechanism for DNA unwinding by a non-hexameric helicase".

Bigheaded carp were collected from the Illinois and Des Plaines Rivers, parts of the Illinois Waterway, from May to November 2018. A total of 93 fish were collected during sampling for a study comprised of 40 females, 41 males, and 12 unsexed fish. GC/MS metabolite profiling analysis detected 180 compounds. Livers from carp at the leading edge had differences in energy use and metabolism, and suppression of protective mechanisms relative to downstream fish; differences were consistent across time. This body of work provides evidence that water quality is linked to carp movement in the Illinois River. As water quality in this region continues to improve, consideration of this impact on carp spread is essential to protect the Great Lakes.

This dataset contains daily and hourly temperature measurements in twenty different bat box designs deployed in central Indiana, USA from May to September 2018. Daily and hourly environmental data (temperature, solar radiation, wind speed and direction) are also included for days and hours sampled. Bat box temperature data were reclassified to cool (</= 30°C), permissive (30.1–39.9°C), and stressful (>/= 40°C) categories according to known temperature tolerances of temperate-zone bats.

Atomic oxygen data from SCIAMACHY, for the MLT, 2002-2012, averaged for 26, 14 day periods, beginning January 1.

Atomic oxygen densities in the MLT, averaged for 2002-2018 for 26, 14 day periods, beginning January 1.

Information on the location, dimensions, time of treefall or death, decay state, wood nutrient, wood pH and wood density data, and soil moisture, slope, distance from forest edge and soil nutrient data associated with the publication "Interspecific wood trait variation predicts decreased carbon residence time in changing forests" authored by Sierra Perez, Jennifer Fraterrigo, and James Dalling. ** <b>Note:</b> Blank cells indicate that no data were collected.

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.

Drainage network analysis is fundamental to understanding the characteristics of surface hydrology. Based on elevation data, drainage network analysis is often used to extract key hydrological features like drainage networks and streamlines. Limited by raster-based data models, conventional drainage network algorithms typically allow water to flow in 4 or 8 directions (surrounding grids) from a raster grid. To resolve this limitation, this paper describes a new vector-based method for drainage network analysis that allows water to flow in any direction around each location. The method is enabled by rapid advances in Light Detection and Ranging (LiDAR) remote sensing and high-performance computing. The drainage network analysis is conducted using a high-density point cloud instead of Digital Elevation Models (DEMs) at coarse resolutions. Our computational experiments show that the vector-based method can better capture water flows without limiting the number of directions due to imprecise DEMs. Our case study applies the method to Rowan County watershed, North Carolina in the US. After comparing the drainage networks and streamlines detected with corresponding reference data from US Geological Survey generated from the Geonet software, we find that the new method performs well in capturing the characteristics of water flows on landscape surfaces in order to form an accurate drainage network. This dataset contains all the code, notebooks, datasets used in the study conducted for the research publication titled " A Vector-Based Method for Drainage Network Analysis Based on LiDAR Data ". ## What's Inside A quick explanation of the components * `A Vector Approach to Drainage Network Analysis Based on LiDAR Data.ipynb` is a notebook for finding the drainage network based on LiDAR data *`Picture1.png` is a picture representing the pseudocode of our new algorithm * HPC` folder contains codes for running the algorithm with sbatch in HPC ** `execute.sh` is a bash script file that use sbatch to conduct large scale analysis for the algorithm ** `run.sh` is a bash script file that calls the script file `execute.sh` for large scale calculation for the algorithm ** `run.py` includes the codes implemented for the algorithm * `Rowan Creek Data` includes data that are used in the study ** `3_1.las` and `3_2.las ` are the LiDAR data files that is used in our analysis presented in the paper. Users may use this data file to reproduce our results and may replace it with their own LiDAR file to run this method over different areas ** `reference` folder includes reference data from USGS *** `reference_3_1.tif` and `reference_3_2.tif` are reference data for the drainage system analysis retrieved from USGS.

This data set describes temperature, dissolved oxygen, and secchi depth in 1-m interval profiles in the deepest point in 10 Illinois reservoirs between the years 1995 and 2016.

This dataset contains the ClonalKinetic dataset that was used in SimiC and its intermediate results for comparison. The Detail description can be found in the text file 'clonalKinetics_Example_data_description.txt' and 'ClonalKinetics_filtered.DF_data_description.txt'. The required input data for SimiC contains: 1. ClonalKinetics_filtered.clustAssign.txt => cluster assignment for each cell. 2. ClonalKinetics_filtered.DF.pickle => filtered scRNAseq matrix. 3. ClonalKinetics_filtered.TFs.pickle => list of driver genes. The results after running SimiC contains: 1. ClonalKinetics_filtered_L10.01_L20.01_Ws.pickle => inferred GRNs for each cluster 2. ClonalKinetics_filtered_L10.01_L20.01_AUCs.pickle => regulon activity scores for each cell and each driver gene. <b>NOTE:</b> “ClonalKinetics_filtered.rds” file which is mentioned in “ClonalKinetics_filtered.DF_data_description.txt” is an intermediate file and the authors have put all the processed in the pickle/txt file as described in the filtered data text.

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

We studied vegetation metric robustness to environmental (season, interannual, and regional) and methodological (observer) variables, as well as adequate sample size for vegetation metrics across four regions of the United States.

All of the files in this dataset pertain to the evaluation of a novel statistic, Hind/He, for distinguishing Mendelian loci from paralogs. They are derived from a RAD-seq genotyping dataset of diploid and tetraploid Miscanthus sacchariflorus.

This repository includes datasets for the paper "Re-evaluating Deep Neural Networks for Phylogeny Estimation: The issue of taxon sampling" accepted for RECOMB2021 and submitted to Journal of Computational Biology. Each zipped file contains a README.

Dataset associated with Jones and Ward BEAS-D-21-00106R2 submission: Parasitic cowbird development up to fledging and subsequent post-fledging survival reflect life history variation found across host species. Excel CSV files and .inp file with data used in nest survival and Brown-headed Cowbird post-fledging analyses and file with descriptions of each column. The CSV file is setup for logistic exposure models in SAS or R and the .inp file is setup to be uploaded into program MARK for multi-state recaptures only analysis. Species included in the analyses: American Robin, Blue Grosbeak, Brown Thrasher, Blue-winged Warbler, Carolina Chickadee, Chipping Sparrow, Common Yellowthroat, Dickcissel, Eastern Bluebird, Eastern Phoebe, Eastern Towhee, Field Sparrow, Gray Catbird, House Wren, Indigo Bunting, Northern Cardinal, Red-winged Blackbird, Tree Swallow, Yellow-breasted Chat, and Yellow Warbler.

In 2020, early-season extreme precipitation events occurred following the planting of Sorghum bicolor (L.) Moench and Zea mays L. in central Illinois that caused ponding. Following the first rainfall event 50m transects were established to assess the waterlogging effects on seedling emergence and crop yields. Soil moisture, emergence, stem and tiller count, LAI, and yield were measured at various points in the season along these transects.